A woman at 39 weeks reports reduced fetal movement. She gets a non-stress test. Twenty minutes of tracing, a few accelerations, no decelerations. One clinician calls it reactive and sends her home. Another looks at the same strip and wants another twenty minutes. Neither is wrong, because there is no measurement being made. There is only a reading.

The non-stress test asks whether the fetal heart rate accelerates.

Reactive means two accelerations in twenty minutes.

That definition sounds objective.

The act of applying it is not.

When Blackwell and colleagues gave 154 fetal heart rate segments to three maternal-fetal medicine specialists and asked them to assign NICHD categories, interobserver reliability was moderate at kappa 0.45. For category III, the tracings we treat as the emergency, agreement was kappa 0.0. Not poor. Zero. The disagreement came down to whether variability was absent or minimal. These were subspecialists, not residents.

The finding is not an outlier. When six clinicians applied the 2015 FIGO guidelines to 151 tracings, proportions of agreement for overall classification ran from 0.54 to 0.67, and experience made no difference. That last detail deserves more attention than it gets. If more years on labor and delivery do not improve agreement, the variability is not a training problem. It is built into the task.

Most of this literature is intrapartum, because that is where the lawsuits are.

The antepartum reliability literature is thinner, which is itself part of the indictment. We have never seriously measured the reproducibility of a test we order millions of times a year.

An objective alternative has existed since the 1980s. Geoffrey Dawes and Christopher Redman built a computerized analysis at Oxford that returns a binary answer, criteria of normality met or not met, derived from a database of well over 100,000 traces and their outcomes. Its central output is short-term variation, the millisecond-level beat-to-beat difference that no eye can compute. TRUFFLE used a short-term variation of 3.5 milliseconds as an intervention trigger in early-onset growth restriction. Bhide and colleagues then examined 14,025 computerized assessments and found criteria unmet in roughly one of every sixteen.

Stillbirth was almost nine times more frequent in that group, odds ratio 8.78, 95% confidence interval 4.28 to 18.02. Exclude the cases with low short-term variation and the signal holds, odds ratio 7.62.

So here is the state of the evidence. We have a subjective test with documented agreement problems in the hands of subspecialists, and an objective test with a strong prognostic signal across 14,025 pregnancies.

The obvious next step is a trial. Now look at what the profession actually did.

The Cochrane review of antenatal cardiotocography includes six trials and 2,105 women in total. The comparison that matters, computerized versus visual interpretation, rests on two studies and 469 women. It showed a reduction in perinatal mortality, 0.9 percent versus 4.2 percent, risk ratio 0.20 with a confidence interval of 0.04 to 0.88 that nearly touches one. A 2021 systematic review found three randomized trials, 497 women, and a single antenatal stillbirth across all of them.

Four hundred sixty-nine women. That is the entire randomized evidence base for whether objective interpretation of the fetal heart rate saves babies.

For comparison, the ARRIVE trial randomized 6,106 low-risk nulliparous women to answer a question about the timing of induction, and the field changed its practice within two years. We found the money, the sites, and the will for that. We have never found them for this.

The consequence lands on the patient. A woman sent home after a reactive non-stress test believes a measurement was made.

What actually happened is that one clinician looked at a strip and formed an impression another clinician might not have shared. She was never told that. It is not in any consent conversation I have ever heard.

Conclusion

The algorithm is not the problem. Dawes-Redman received FDA clearance in March 2025, so in the United States the regulatory excuse is gone, and what remains is a purchasing decision and a research agenda nobody has demanded. The honest reading is this.

We built the backbone of antenatal surveillance out of a subjective judgment.

We have known for at least fifteen years that experienced subspecialists disagree about it, including on the category we call an emergency. And we never ran the trial that would tell us whether the objective version does better.

That is not a failure of technology or of regulators.

It is our failure.

A profession that can randomize six thousand women to settle a question about induction timing can randomize enough women to settle this one.

If you order non-stress tests, ask what your agreement rate with your partner would be on the last ten you read.

Then ask why nobody has ever measured it. ObGyn Intelligence is free, and it stays independent because paid subscribers keep it that way.

References

1. Blackwell SC, Grobman WA, Antoniewicz L, Hutchinson M, Gyamfi Bannerman C. Interobserver and intraobserver reliability of the NICHD 3-Tier Fetal Heart Rate Interpretation System. Am J Obstet Gynecol. 2011;205(4):378.e1-5. doi:10.1016/j.ajog.2011.06.086

2. Rei M, Tavares S, Pinto P, Machado AP, Monteiro S, Costa A, et al. Interobserver agreement in CTG interpretation using the 2015 FIGO guidelines for intrapartum fetal monitoring. Eur J Obstet Gynecol Reprod Biol. 2016;205:27-31. doi:10.1016/j.ejogrb.2016.08.017

3. Bhide A, Meroni A, Frick A, Thilaganathan B. The significance of meeting Dawes-Redman criteria in computerised antenatal fetal heart rate assessment. BJOG. 2024;131(2):207-212. doi:10.1111/1471-0528.17464

4. Grivell RM, Alfirevic Z, Gyte GML, Devane D. Antenatal cardiotocography for fetal assessment. Cochrane Database Syst Rev. 2015;(9):CD007863. doi:10.1002/14651858.CD007863.pub4 [issue number pending RefVerify]

5. Baker H, Pilarski N, Hodgetts-Morton VA, Morris RK. Comparison of visual and computerised antenatal cardiotocography in the prevention of perinatal morbidity and mortality. A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2021. doi:10.1016/j.ejogrb.2021.05.048 [volume and pages pending RefVerify]

6. Lees CC, Marlow N, van Wassenaer-Leemhuis A, Arabin B, Bilardo CM, Brezinka C, et al. 2 year neurodevelopmental and intermediate perinatal outcomes in infants with very preterm fetal growth restriction (TRUFFLE): a randomised trial. Lancet. 2015;385(9983):2162-72. [pending RefVerify]

7. Grobman WA, Rice MM, Reddy UM, Tita ATN, Silver RM, Mallett G, et al. Labor induction versus expectant management in low-risk nulliparous women. N Engl J Med. 2018;379(6):513-523. doi:10.1056/NEJMoa1800566 [pending RefVerify]

8. Huntleigh Healthcare. FDA 510(k) clearance granted for Dawes-Redman CTG Analysis. Press release, 24 March 2025.

Most clinicians will never see the difference. They don’t even go to “settings” or add skills.

Here is how to set it up.

Subscribe now

Claude Opus 5.5 for Clinicians, Part 1 of 2

A two-part series. Part 1: what changed and how to set it up. Part 2: how to prompt it.

Claude Opus 5.5 Is Here. Most Clinicians Will Use It Like a Search Box.

On September 22, Anthropic released Claude Opus 5.5, its newest AI model (1). By the company’s account, it is a major step up from the model it replaces. I expect most clinicians will never notice.

Here is why. In my experience, most doctors and nurses use AI the way they use a search engine. One question. One answer. Close the tab. I suspect most have never opened Settings. Give that user a better model and she gets a slightly better first answer, and nothing else. It is like buying a new ultrasound machine and never leaving the default preset.

This post covers what actually changed in Opus 5.5 and the five things to set up before you use it. Part 2 covers how to prompt it.

Before your first question: ten minutes of setup

1. Before you type a single question into Opus 5.5, spend ten minutes on setup. Most users never do this, and it is the step that matters most.

2. First, open Settings: on the web, click your initials in the lower left corner and choose Settings. Find the field called “Instructions for Claude” (some versions call it personal preferences). Write five or six sentences about who you are and how you want answers: your profession, your setting, the level of detail you expect, and your ground rules, such as “give risks as absolute numbers” and “never invent a reference.” Whatever you write there applies to every conversation from then on (4).

   ObGyn Intelligence: The Evidence of Women’s Health is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

3. Second, close Settings and click the model name next to the send button. Make sure Opus 5.5 is selected, and check that the effort level is at its default (3). Only then ask your first question. You would not start a shift without checking the monitors are working and the orders are in. The same logic applies here: set it up once, and every answer after that starts from the right place.

What changed on September 22

Anthropic says Opus 5.5 performs at the level of its more expensive top-tier model, Claude Fable 5.1, on most work. It costs about 40% less to run than the previous Opus 5 and writes its answers more than 30% faster (1). Speed is nice. For clinicians, three other claims matter more.

The first is accuracy with sources. In an internal test, Anthropic asked Opus 5.5, Fable 5.1, and Opus 5 to write reports on a company’s performance using only information they could find. An automated grader checked every figure and every quote against the sources. A single invented number or quote meant failure. Opus 5.5 passed in 16 of 18 attempts. Neither of the other two models passed a single time (1). If you have ever caught an AI inventing a reference, you know why this matters.

The second is writing. Anthropic reports that Opus 5.5 writes more clearly, puts the most important information first, uses less jargon, and follows the writing rules you give it (1). Remember that last point. It changes how you should set it up.

The third is honesty. On Anthropic’s internal behavioral audit, a battery of close to 2,000 test scenarios, Opus 5.5 is the company’s strongest model on most measures of honesty (1).

Share

Now read the fine print

Every number above comes from Anthropic or from early testers it chose to quote. That is not independent validation. To its credit, Anthropic says so itself. It writes that at this level of capability, benchmark scores have become a less reliable guide to real-world differences. It also reports that the model often seems to suspect it is being tested, which limits what any test can show (1).

And look again at 16 of 18. That means 2 of 18 reports, about 1 in 9, still failed. That is a better model, not an infallible one. Every dose, every figure, and every reference still gets checked against the primary source. That rule did not change on September 22.

Why the upgrade is wasted on one question

Anthropic has also studied how people use Claude.

Earlier this year it compared users who had been on the platform for six months or more with newer users (2). The experienced users worked back and forth with Claude more often, instead of handing off a task and walking away. They brought it harder, more work-related problems. And they succeeded more often: after accounting for the task, model, language, and country, about 4 more successful conversations out of every 100 (2).

Subscribe now

Anthropic notes the obvious caveat: early adopters may simply be more technical (2). But the authors also found a pattern consistent with learning by doing. You do not need six months to get there. You need five habits.

1. Make sure you are actually using Opus 5.5

You have to use the paid version.

Next to the send button is the name of the model you are using. Click it to see your choices (3). Depending on your plan, you may not see every model. Check before you start. Do not assume the app picked the most capable model for you. In Anthropic’s own usage study earlier this year, the default model was Sonnet, not Opus (2).

2. Set the effort, then leave it alone

In the same menu is a setting called Effort. It controls how much Claude thinks before it answers. Higher effort gives more thorough answers, but they take longer and use up your usage limit faster (3). Each model marks its recommended level as the default (3). For Opus 5.5, the default is medium, and Anthropic reports that Opus 5.5 at medium beats its competitors at their highest settings on several tests (1).

My advice: leave it at medium for everyday questions. Raise it for genuinely hard work, such as reviewing a long guideline or a complicated case summary. One early tester, an investment firm, reported that at a higher setting Opus 5.5 noticed an error in the tester’s own instructions and corrected for it (1). That is the kind of catch you want on a difficult review. Do not raise effort just to feel safer. A longer wait does not make a simple answer more correct.

3. Tell it who you are, once

Opus 5.5 follows the writing rules you give it (1). So give it rules. On the web, click your initials in the lower left corner, choose Settings, and find “Instructions for Claude.” What you write there applies to all your conversations (4). Some versions of the app call this field personal preferences.

Think of it as the orientation you would give a new fellow on her first day. Adapt this one:

I am a labor and delivery nurse in a US teaching hospital. Answer at a clinical level. Give risks as absolute numbers, such as 3 in 1,000. Say clearly when the evidence is weak or when you are not sure. Never invent a reference; if you cannot verify one, say so. Write in short paragraphs, not bullet points.

Five minutes, once. Every future answer starts from there.

Share

4. The first answer is the headline, not the chart

Opus 5.5 answers more briefly than its predecessor and leads with the conclusion. One early tester measured its answers as 40% less wordy, with no loss in accuracy (1). That is good news for a busy clinician. It also means the depth now lives in the follow-up questions. Stop after one question and you get only the headline.

• Treat Claude the way you treat a consultant on the phone: ask, listen, then push.

• What assumptions did you make in that answer?

• What would change your recommendation?

• How strong is the evidence for each point: trial, observational data, or expert opinion?

• Now rewrite it for a patient at a seventh-grade level.

Stay in the same chat while you work on the same problem. Claude sees everything said earlier in that conversation.

A new chat starts from a blank page.

5. Give it the source, and give it a home

The accuracy test above rewarded working from sources. Do the same. Do not ask Opus 5.5 what a guideline says from memory. Attach the guideline and ask it to answer from the document, and to show you where.

Answer only from the attached guideline. For each point, tell me the section it comes from. If the guideline does not address something, say so instead of filling the gap.

That turns “trust me” into “check me.” If you return to the same topic again and again, create a Project. A Project holds its own files and its own instructions, and those instructions apply only to chats inside it (4). A resident preparing grand rounds on preterm labor could keep the key guidelines and her outline in one Project, with instructions about her audience. Every new chat in that Project starts with that context already loaded.

Two safety notes

1. First, do not upload identifiable patient information to a personal AI account. If your institution has an approved, contracted tool, use that one and follow its rules.

2. Second, Opus 5.5 ships with new safeguards around biology research. Anthropic states that everyday health and educational questions are unaffected (5).

Conclusion

Opus 5.5 is a real improvement, and Anthropic’s own testing suggests it invents facts less often than any Claude model before it. But a better model does not fix a bad habit. The clinician who asks one question and closes the tab will get a slightly better one-line answer and miss almost everything else. Pick the right model. Set the effort. Tell it who you are. Ask the second and third question. Give it the source. None of this is technical. It is the discipline we already expect at the bedside: know your source, ask the follow-up, and never accept an answer because it sounds confident.

This week, open Settings and write five sentences about who you are and how you want answers. Part 2 covers how to prompt Opus 5.5: what to delete from your old prompts, what to add, and prompts you can copy. Subscribe to ObGyn Intelligence so you do not miss it.

References

1. Anthropic. Introducing Claude Opus 5.5 [Internet]. San Francisco (CA): Anthropic; 2026 Sep 22 [cited 2026 Sep 27]. Available from: https://www.anthropic.com/claude-opus-5-5

2. Massenkoff M, Lyubich E, McCrory P, Appel R, Heller R. Anthropic Economic Index report: learning curves [Internet]. San Francisco (CA): Anthropic; 2026 Mar 24 [cited 2026 Sep 27]. Available from: https://www.anthropic.com/research/economic-index-march-2026-report

3. Anthropic. Change the model, effort, and thinking settings [Internet]. San Francisco (CA): Anthropic; [cited 2026 Sep 27]. Available from: https://support.claude.com/en/articles/8664678-change-the-model-effort-and-thinking-settings

4. Anthropic. Understanding Claude’s personalization features [Internet]. San Francisco (CA): Anthropic; [cited 2026 Sep 27]. Available from: https://support.claude.com/en/articles/10185728-understanding-claude-s-personalization-features

5. Anthropic. Prompting Claude Opus 5.5 [Internet]. San Francisco (CA): Anthropic; 2026 [cited 2026 Sep 27]. Available from: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5-5

The head delivers.

Then it stops.

The shoulders do not follow.

The room changes instantly.

Someone announces shoulder dystocia.

The legs are flexed.

Suprapubic pressure is applied.

Additional maneuvers follow.

Seconds become very long.

Eventually the baby is delivered.

Then one arm does not move normally.

A brachial plexus injury is diagnosed.

Months later, a question appears:

Who pulled too hard?

Sometimes that is the wrong question.

Shoulder Dystocia Is an Emergency, Not a Diagnosis We Can Reliably Predict

Read more

On September 17, the subject line read: “Video: Nurse Attacked; Doc’s High-Speed Arrest; Fake Ob/Gyn Pleads Guilty.”

Subscribe now

I have read MedPage for years.

Lately it felt different.

More arrests.

More killings.

More doctors in handcuffs.

But a feeling is not data. So I counted.

What I measured

MedPage sends a newsletter called “Exclusives & Features” twice a week. The Thursday edition is a roundup of three legal stories. I went through every Thursday edition still in my inbox, from 2023 to this week. That came to 54 issues and 150 headline items. I sorted each item into one of five groups: a clinician accused or convicted of a crime, violence against a clinician, a malpractice suit or other civil case, a licensing action, and everything else.

Share

In August 2023, crime and violence made up 1 in 3 items. Half were malpractice suits and verdicts. By 2025, crime and violence had risen to 6 in 10. This year they sit at about half. Malpractice news, the part a practicing clinician can actually learn from, fell to about 1 in 4.

Two more things stood out. About half of the criminal items describe an accusation, an arrest or an investigation, not a conviction.

Some headlines end in a question mark, such as “Doc Hit Dementia Patient?” A question mark is not a verdict. It reads like one.

The Monday edition changed too.

From April to September 2025, none of its 23 lead stories was a medical oddity. Most were policy reporting.

From June to September 2026, 13 of 17 were oddities, with headlines like “Eyeball Tattoo Goes Sideways.”

One finding cuts the other way, and it belongs here. MedPage’s Weekly Review, the editors’ pick of the week’s best stories, featured these crime roundups a handful of times in 2024 and 2025. In 38 issues this year, it featured none. MedPage is not pushing crime onto its front page.

My data have limits. This is my inbox, not MedPage’s full archive, and some months are missing. The 2023 sample is small. I read headlines, not the articles, and I coded them alone. The direction is clear. The exact percentages are not precise.

The best case for the column

Accountability journalism matters. Impostors who pose as doctors, clinicians who harm patients, licensing boards that fail to act: the profession needs to know. Violence against nurses is a real workplace danger. Silence protects the wrong people.

I agree with every word.

That is exactly why this column fails.

Why it fails

A professional news service has a different job than a tabloid. Coverage of a colleague’s wrongdoing earns its place when it teaches. How did the impostor get hired? Why did the board miss the warning signs? What did the hospital change after the attack? A three-item list of arrests teaches none of that. “Doc Accused of Luggage Theft” does not make anyone a better doctor.

Journalists have their own code of ethics. It tells reporters to “avoid pandering to lurid curiosity, even if others do.” It asks them to weigh a suspect’s right to a fair trial and to think hard before naming someone who has not been charged. It asks them to update stories as facts change (1). Charges get dropped. Cases get dismissed. My sample includes both. A one-line accusation stays online long after the case ends.

In our work, beneficence means seeking the greatest good for the person who trusts us. A news service for clinicians owes its readers the same thing: stories that make them better at their jobs. Spectacle does the opposite. It spends clinicians’ limited attention on the worst colleague in the country, not on the lesson that might protect their own patients.

Conclusion

MedPage’s flagship original-reporting newsletter has drifted from accountability journalism toward spectacle.

It names clinicians before adjudication, emphasizes lurid detail, and offers no systemic lesson.

I do not know why the mix changed, and I will not guess. I do know what a professional news service owes its readers. Report a crime when it exposes a system failure, and explain the failure. Wait for charges before naming anyone. Follow up when charges are dropped. And give Thursday back to the malpractice verdicts that actually teach.

Check your own inbox this Thursday and count the arrests. Then share this with a colleague who still calls it must-read.

If careful counting is what you want from medical news, subscribe to ObGyn Intelligence.

References

1. Society of Professional Journalists. SPJ Code of Ethics [Internet]. Society of Professional Journalists; 2014 [cited 2026 Sep 25]. Available from: https://www.spj.org/spj-code-of-ethics/

Data source: author’s analysis of MedPage Today newsletter emails received August 2023 to September 2026 (Thursday “Exclusives & Features” roundups, Monday editions, and Weekly Review issues). Headline items coded by a single reviewer.

A digital rectal examination (DRE) is not recommended as a routine component of prenatal care in an asymptomatic patient. Pregnancy itself is not a contraindication. A gentle, consented DRE can be appropriate when a specific anorectal or pelvic question is likely to change management. I found no direct human evidence that a properly performed DRE causes miscarriage, preterm birth, rupture of membranes, placental bleeding, or fetal injury. The case against routine use is therefore lack of demonstrated benefit plus discomfort and small local risks, not proven pregnancy harm.

1. What procedure is being discussed?

A DRE means insertion of a lubricated, gloved finger through the anus to assess the anal canal, distal rectum, stool, tenderness, masses, and sometimes sphincter tone. It is not the same as:

• a digital vaginal examination to assess the cervix or labor;

• a rectovaginal examination, with one finger in the vagina and one in the rectum; or

• a vaginal-rectal swab for group B streptococcus (GBS), which uses a swab rather than a finger examination.

2. Is it recommended during pregnancy?

Routine prenatal screening: No

Current prenatal-care guidance specifies the components of the initial assessment but does not recommend routine DRE in asymptomatic pregnant patients. Even routine bimanual pelvic examination has limited screening value and is generally reserved for a clinical indication.[1,2] Absence from a guideline is not proof of harm, but it means routine DRE lacks an established prenatal screening purpose.

Symptom-directed evaluation: Sometimes

A DRE may be reasonable for rectal bleeding, suspected rectal mass, selected anorectal pain, fecal impaction, suspected abscess, or assessment of sphincter function when the result could affect management. A pregnancy-specific review of lower gastrointestinal bleeding recommends perineal inspection and DRE because these may identify hemorrhoids, fissure, or a rectal mass. It also notes that fissure-related spasm may make DRE poorly tolerated.[3] This is expert-review guidance rather than trial evidence of outcome benefit.

Abdominal pain or suspected appendicitis: Not routinely

A systematic review and meta-analysis of 19 studies involving 7,511 patients found poor diagnostic performance for appendicitis: pooled sensitivity 0.49 and specificity 0.61.[4] The studies were not pregnancy-specific. The findings support omitting routine DRE for suspected appendicitis unless there is another specific rectal indication.

After vaginal birth: A separate, recommended indication

After delivery, the patient is postpartum rather than still pregnant. RCOG guidance recommends systematic examination, including DRE, when assessing perineal trauma, especially before suturing, because occult obstetric anal sphincter injury or rectal mucosal injury may otherwise be missed.[5] This postpartum indication should not be used to justify routine antepartum DRE.

GBS screening: Recommended swab, not DRE

ACOG recommends universal vaginal-rectal culture at 36 0/7 to 37 6/7 weeks in most pregnancies.[6] The rectum is sampled with a swab. This is not a digital rectal examination.

3. Is there evidence that DRE is harmful in pregnancy?

Direct evidence is notably sparse. I found no randomized trial, cohort study, systematic review, or major guideline showing that a properly performed DRE during pregnancy increases miscarriage, preterm labor, preterm birth, membrane rupture, fetal injury, or neonatal infection. Therefore, a categorical claim that DRE is dangerous to the pregnancy is not evidence-based.

Known or plausible harms are mainly local and patient-centered:

• transient discomfort or pain;

• minor bleeding or abrasion, especially with hemorrhoids, fissures, inflamed tissue, or anticoagulation;

• rare local tissue injury; and

• loss of dignity, distress, or retraumatization if consent, explanation, privacy, or trauma-informed technique is inadequate.

General clinical references list infection or bacteremia as possible complications,[8] but pregnancy-specific incidence data were not identified. The risk appears theoretical or rare in immunocompetent patients when gloves, lubrication, and gentle technique are used. Avoidance in severe neutropenia or profound immunosuppression is often advised, but the supporting evidence is limited and not pregnancy-specific.

4. Important caution: do not confuse rectal with vaginal examination

Digital vaginal cervical examination can provoke severe hemorrhage when placenta previa is present and should be avoided until previa has been excluded in a patient with later-pregnancy bleeding. That established warning concerns entry through the vagina and manipulation near the cervix and placenta. It should not be automatically transferred to a gentle DRE. One Canadian placenta previa guideline advises avoidance of vaginal or anal examination in this setting.[7] This is a condition-specific precaution, not evidence that DRE is generally harmful in pregnancy.

5. Practical standard

• Clinical context

• Position

• Reason

• Asymptomatic prenatal care

• Do not perform routinely

• No established screening benefit.

• Rectal bleeding, mass, impaction, selected anorectal symptoms

• Consider if findings will change care

• Potential diagnostic value; begin with history and inspection.

• Suspected appendicitis or nonspecific abdominal pain

Usually omit

• Poor diagnostic accuracy; use appropriate imaging and clinical evaluation.

• Placenta previa or unexplained later-pregnancy bleeding

• Avoid pelvic manipulation pending assessment

• A vaginal exam is clearly hazardous; some guidance also cautions against anal examination.

• After vaginal birth

• Offer as part of perineal-trauma assessment

• May detect occult sphincter or rectal injury.

Before any DRE, the clinician should explain the exact diagnostic question, obtain explicit consent, offer a chaperone according to policy and patient preference (or even better, have a chaperone in the room with all intimate examinations), use gloves and generous lubrication, stop if the patient asks or pain is disproportionate, and avoid the examination when the same information can be obtained less invasively.

6. Evidence judgment

Best-supported conclusion: Do not perform DRE merely because a patient is pregnant or as an automatic part of a prenatal examination. Do perform it selectively when there is a defensible clinical indication and the expected information outweighs discomfort and small local risks.

“Not routinely indicated” is accurate.

“Contraindicated in pregnancy” or “known to cause miscarriage or preterm birth” is not supported by the evidence located.

References

American College of Obstetricians and Gynecologists. Tailored Prenatal Care Delivery for Pregnant Individuals. Clinical Consensus. 2025. ACOG prenatal consensus

Ramírez SI, et al. Prenatal Care: An Evidence-Based Approach. Am Fam Physician. 2023;108. AAFP prenatal review

Story L, Rafique S, Samadi N, Mawdsley J, Singh B, Banerjee A. Lower gastrointestinal bleeding in pregnancy: differential diagnosis, assessment and management. Obstet Med. 2021;14(3):129-134. doi:10.1177/1753495X20948300. Full text

Takada T, Nishiwaki H, Yamamoto Y, et al. The Role of Digital Rectal Examination for Diagnosis of Acute Appendicitis: A Systematic Review and Meta-Analysis. PLoS One. 2015;10(9):e0136996. doi:10.1371/journal.pone.0136996. PLOS One article

Royal College of Obstetricians and Gynaecologists. The Management of Third- and Fourth-Degree Perineal Tears. Green-top Guideline No. 29. 2015. RCOG guideline

American College of Obstetricians and Gynecologists. Prevention of Group B Streptococcal Early-Onset Disease in Newborns. Committee Opinion No. 797. 2020. ACOG GBS guidance

Jain V, Bos H, Bujold E. Guideline No. 402: Diagnosis and Management of Placenta Previa. J Obstet Gynaecol Can. 2020;42(7):906-917.e1. doi:10.1016/j.jogc.2019.07.019. JOGC guideline

Herrero JAV, et al. Rectal Exam. StatPearls. Updated 2023. NCBI Bookshelf

Limitations: There is little pregnancy-specific comparative research on DRE harms. Much of the safety assessment relies on absence of reported obstetric harm, anatomy, general DRE complication literature, and condition-specific clinical guidance. Absence of evidence is not proof of zero risk.

Focused evidence review | 8 September 2026

A Swedish study published this week followed more than 3.6 million people born between 1987 and 2021. Of them, 975 had a recorded congenital infection in the group often called TORCH:

toxoplasmosis,

syphilis,

rubella,

cytomegalovirus (CMV), or

herpes simplex.

Children with a recorded infection were more likely to receive a diagnosis of intellectual disability or autism than children without one. The association remained when researchers compared siblings in the same family.[1]

That finding matters. But it does not mean that an ordinary fever during pregnancy causes autism. The researchers studied diagnosed congenital infections, meaning infections recorded in the child, rather than every infection a pregnant woman might have had. These were rare events, and the strongest finding was for intellectual disability.

In the main analysis, the rate of an autism diagnosis was about three times higher among children with a recorded congenital TORCH infection.

The association was stronger for autism with intellectual disability than for autism without it.

Yet the authors estimated that the recorded infections accounted for only about 0.034% of autism diagnoses in their study population.[1] A large increase in risk within a small group can coexist with a very small contribution to autism overall.

What the study can, and cannot, tell us

This is a strong observational study. Its size, long follow-up, and sibling comparisons make the results more persuasive than a simple comparison of unrelated children. The findings support what clinicians have long known: some infections that reach a fetus can cause serious harm to the developing brain.

Sibling comparisons cannot, however, prove that infection caused every later diagnosis. The study relied on clinical records rather than uniform infection testing of every newborn. It could not fully assess infections that were never diagnosed, identify exactly when each infection occurred, or tell us how much current prevention or treatment would change the measured outcomes.[1] In an accompanying editorial, pediatric infectious disease specialist Mark Schleiss also raised questions about how consistently congenital infections were established across the long study period.[2]

The practical lesson is therefore narrower, and more useful, than the headline “infection causes autism.” Prevent infections we can prevent, detect those we can test for, and treat those we can treat. These steps are justified by the known harms of congenital infection, regardless of whether they prevent a measurable number of autism diagnoses.

What you can do during pregnancy

1. Reduce exposure to CMV, especially if you care for young children.

CMV is common and often causes no symptoms. Young children can carry it in saliva and urine. The Centers for Disease Control and Prevention (CDC) advises pregnant people to avoid sharing food, cups, utensils, or pacifiers with a child, and to wash their hands with soap and water after changing diapers or helping a child use the toilet. These are practical precautions, not a reason to avoid caring for or showing affection to your child. No precaution removes all risk.

There is currently no CMV vaccine available for routine use. Routine blood screening for CMV during pregnancy is not recommended by the American College of Obstetricians and Gynecologists (ACOG). If you have a suspected infection or an ultrasound finding that raises concern, ask your obstetric clinician about an individualized evaluation. A positive CMV antibody result needs careful interpretation; it does not by itself establish that a fetus is infected.

2. Prevent toxoplasmosis through food and soil precautions.

Cook meat thoroughly using a food thermometer. Wash produce, hands, knives, and cutting boards after contact with raw meat or soil. If possible, ask someone else to change the cat litter. If you must do it yourself, use gloves, change it daily, and wash your hands afterward. You do not need to give up your cat.

3. Make sure syphilis testing has been done, and repeat it when indicated.

Syphilis may have no obvious symptoms, but it can be passed to the fetus. In the United States, CDC guidance calls for testing at the first prenatal visit. Repeat testing at 28 weeks and at delivery is recommended for pregnant women in communities with high syphilis rates or with risk of acquiring syphilis during pregnancy. If a test is positive, prompt treatment and partner evaluation matter. Ask whether your testing is current, especially if you have a new exposure after your first test.

4. Check your rubella status.

The measles, mumps, and rubella (MMR) vaccine protects against rubella, but it is not given during pregnancy. Prenatal care includes checking for rubella immunity. If you are not immune, discuss vaccination after delivery. Contact your clinician promptly if you are exposed to someone with rubella while pregnant. Before a future pregnancy, vaccination offers the chance to prevent this infection.

5. Tell your obstetric clinician about genital herpes or a possible new exposure.

This matters most when genital herpes is first acquired late in pregnancy, because prevention of neonatal herpes depends in part on avoiding a new infection and on planning care at delivery. If you or your partner has genital or oral herpes, discuss the specific precautions that apply to you. Report new genital sores, pain, or burning promptly, including when labor begins. People with a known history of genital herpes may be offered antiviral medication near the end of pregnancy.

These steps should be part of good prenatal care, not a test of whether an individual woman can keep every infection away. Clinicians and health systems are responsible for explaining risks clearly, providing timely testing, interpreting results correctly, and making treatment accessible.

The Swedish study adds evidence that rare, diagnosed congenital infections are linked to substantial risks for affected children. It does not explain most autism. Its most useful message for pregnancy today is concrete: take CMV hygiene seriously, practice food safety, check immunizations, complete recommended infection screening, and seek care promptly when an exposure or new symptom needs evaluation.

References

1. Sjöqvist H, Dalman C, Mataix-Cols D, Gardner RM, Karlsson H. Congenital TORCH infections and neurodevelopmental outcomes. JAMA Pediatr. Published online September 21, 2026. doi:10.1001/jamapediatrics.2026.4229

2. Schleiss MR. TORCH infections in pregnancy and autism spectrum disorders. JAMA Pediatr. Published online September 21, 2026. doi:10.1001/jamapediatrics.2026.4468

Biology does not.

A woman delivers.

Her baby is healthy.

She walks out of the hospital carrying a car seat, discharge papers, medications, and instructions.

Everyone feels that the dangerous part is over.

Sometimes it has barely begun.

Delivery Is an Event. Postpartum Is a Process.

The United States recorded 649 maternal deaths under the National Center for Health Statistics definition in 2024, a maternal mortality rate of 17.9 per 100,000 live births. Importantly, that definition captures maternal deaths during pregnancy or within 42 days after pregnancy and therefore does not capture the entire year of pregnancy-associated risk.

Clinical risk does not disappear at discharge.

Hypertension can worsen.

Infection can evolve.

Venous thromboembolism can occur.

Cardiomyopathy may first become clinically apparent.

Bleeding can recur.

Surgical wounds can deteriorate.

Psychiatric illness can become life-threatening.

A national claims-based cohort followed nearly 460,000 deliveries for one year. Women who experienced severe maternal morbidity during delivery remained at increased risk of readmission throughout that entire year. Sepsis and hypertensive disorders were among the major reasons for subsequent readmission.

That makes postpartum care an essential part of obstetric safety, not an epilogue.

The Dangerous Handoff Is Often to Nobody

Inside the hospital, responsibility is visible.

There is an obstetrician.

A nurse.

An anesthesia team.

A charge nurse.

A blood bank.

An operating room.

Then the patient goes home.

Who owns her worsening blood pressure at 9 PM three days later?

Who owns the wound photograph she sends through the portal?

Who owns the headache mentioned to an answering service?

Who sees the emergency department note from another hospital?

Who makes sure the patient actually receives the follow-up that was recommended?

This is where postpartum care can become fragmented.

The patient has left the building, but she has not left the disease process.

Read more

That sentence appears in too many serious adverse-event reviews.

The nurse was worried.

The resident was worried.

Anesthesia knew.

The attending had been called.

The tracing was deteriorating.

The patient’s condition was changing.

Everyone possessed part of the information.

But nobody owned the escalation.

Share

Communication Is Not the Same as Escalation

Clinicians communicate constantly.

“FYI.”

“Doctor aware.”

“I sent a message.”

“I told the resident.”

“I mentioned it during sign-out.”

Information moved.

But escalation requires more.

Escalation means that information reaches someone with the authority and ability to make the next decision, and that the communication produces a defined response.

That distinction matters enormously in obstetrics.

Labor and Delivery Is a Team Sport Played at High Speed

Read more

A physician asks an AI model a clinical question.

The response comes quickly, and it’s easy to understand, well-structured, and sounds really convincing. That’s exactly why what happens next is so important.

The most dangerous habit in clinical AI may be treating the first response as an answer rather than as the beginning of an inquiry. A polished response feels finished. In medicine, however, the important question is not whether an answer sounds finished. It is whether the reasoning is supported, the necessary facts are present, and the conclusion survives verification.

The First Output Is a Draft, Not a Verdict

While large language models can provide incredibly helpful responses, it’s essential to remember that even the best prompts can’t turn them into flawless medical experts. By crafting specific, context-aware, and goal-oriented prompts, we can certainly improve the relevance of their outputs. Iteration also plays a key role in refining these prompts. However, at the end of the day, a well-designed prompt is not a substitute for human medical expertise. It’s crucial to approach language models with a clear understanding of their limitations, especially in high-stakes fields like medicine.

Consider the prompt: “Should I deliver this patient?”

Even with some medical facts, the question still asks for a decision.

A better instruction is: “Identify the clinical factors that determine whether delivery should be considered. Separate established indications from factors requiring additional information. Do not make a patient-specific recommendation when decision-critical data are missing.”

That changes the task.

You are no longer asking the model to pronounce judgment. You are asking it to expose the structure of the decision.

Make the Model Identify What Could Change Its Answer

After the first response, ask three questions: What information are you missing? Which claims require external verification? What findings would change your conclusion?

Then verify the important claims independently.

When you’re trying to figure out the right answer, start by checking the guidelines if that’s what it’s based on. If it mentions a specific study, take a closer look at that study to understand its findings. If a certain drug dose is recommended, make sure to verify that dose by looking it up in a trusted source. And if the model suggests that a particular finding means intervention is needed, don’t just take its word for it - check if that’s really supported and if it actually applies to the patient in question. This way, you can ensure that the decisions you make are well-informed and tailored to the individual patient’s needs. It’s all about being thorough and careful in your approach, considering all the relevant information before making a move.

The purpose is not to distrust everything AI says. That would make the tool nearly useless. The purpose is calibrated trust.

Do Not Prompt Until It Agrees With You

If AI disagrees with us, we can keep prompting until it produces the answer we wanted.

That is not refinement.

It is confirmation bias with a keyboard.

Ask instead: “Give me the strongest evidence-based argument against your current conclusion.” Then ask: “What evidence or additional information would distinguish between the two positions?” Now the model becomes a tool for testing reasoning rather than validating it.

Clinical AI Competence

Fluency is not evidence. Confidence is not calibration. Detail is not accuracy. The physician remains responsible for deciding which parts of an AI response deserve trust and which require verification.

Don’t just ask AI for a simple answer. Instead, ask it to help you identify the decision that needs to be made, point out what information is missing, challenge the conclusions you’ve drawn, and show you what needs to be verified. The whole point of using AI in clinical settings is not to replace human thinking, but to make our thought process more robust and less prone to errors. By working together with AI, we can make sure our thinking is more accurate and reliable. This means we need to use AI as a tool to help us think more critically, not just to give us easy answers.

TRY THIS PROMPT:

“Analyze this clinical question without giving an immediate final recommendation. First list the decision-critical facts, then important missing information. Identify the evidence or guidelines governing the decision, give the strongest reasonable interpretation and alternative, label claims requiring verification, and state what additional information would most change the assessment.”

A Practical Clinical Example

Imagine asking an AI model about a patient at 35 weeks with hypertension. The model may quickly recommend delivery, expectant management, or additional evaluation depending on the details it notices. The clinically competent response is not to choose the most authoritative-sounding paragraph. It is to make the model reveal the variables driving the recommendation.

Ask it to construct a decision table. One column should contain the facts already known. A second should contain facts that remain unknown. A third should explain how each unknown could change management. A fourth should identify the guideline or evidence that would need verification. This makes the reasoning inspectable.

This approach is helpful because it assists the clinician in understanding the decision-making process, rather than making the actual decision. It’s like having a tool that can quickly show you the different parts of a decision, so you can see what’s important and what might be missing. For example, it might remind you to consider a specific lab result, symptom, or medication issue that’s relevant to the case. It can also help you identify potential weaknesses in your decision-making, such as assuming something is true when it’s actually unknown. By using this approach, clinicians can make more informed decisions and provide better care for their patients. It’s not about relying on the model to make the decision, but rather using it as a way to organize your thoughts and make sure you’re considering all the relevant factors.

That distinction is central. An AI response can be useful even when you do not accept its conclusion. Sometimes its greatest value is showing you which questions must be answered before anyone should reach a conclusion.

The Responsibility Does Not Move

Here’s a rewritten version of the input in a more human-like tone, mimicking the style and vocabulary of the provided human reference paragraphs: “It’s essential to consider a crucial ethical aspect that can be easily overlooked. When a clinician utilizes artificial intelligence, the responsibility for making clinical decisions doesn’t simply vanish into the technology. The physician remains accountable for upholding their duties, including competence, evidence-based reasoning, effective communication, and thorough documentation. In other words, the clinician’s obligations persist, even when AI is involved in the decision-making process.”

Just because we have AI doing some tasks, it doesn’t mean doctors have to redo everything on their own. Medicine has always used tools to help with decisions, like labs, consultants, and calculators. What’s important is that we check the work in a way that makes sense for how important the decision is. If the decision is really important and could have big consequences if it’s wrong, then we need to double-check it more carefully. This is what we call proportional verification - the more serious the potential mistake, the more thoroughly we need to verify the results.

An AI-generated suggestion about the wording of a teaching slide does not require the same scrutiny as an AI-generated recommendation about timing of delivery. Clinical AI competence includes recognizing that difference.

So, the real question isn’t about whether AI can handle a task, but rather about how sure we need to be that it’s done right. This is something that requires a professional to decide, not just a matter of tweaking some technical settings. It’s about figuring out how much confidence and verification are needed for a particular task, and that’s a judgment call that needs to be made by someone with expertise.

Closing Thought

AI will become more capable, and that makes disciplined use more important, not less. The clinician who knows how to interrogate an answer, identify missing information, demand counterarguments, and verify decisive claims will be safer than the clinician who simply has access to the best model. Access is becoming common. Competence is the differentiator.

The ultrasound looked reassuring.

The bleeding stopped.

Two years later, she bled again.

Someone wrote:

“Previously evaluated.”

Six months after that:

endometrial cancer.

The first evaluation may have been entirely appropriate.

The mistake may have been believing that it was valid forever.

Subscribe now

Postmenopausal Bleeding Is a Symptom, Not a Diagnosis

Most women with postmenopausal bleeding will not have endometrial cancer.

That matters.

Medicine should not turn every episode of bleeding into catastrophe.

But postmenopausal bleeding is clinically important because endometrial cancer frequently presents with bleeding.

Evaluation therefore aims to identify who requires endometrial assessment while avoiding unnecessary invasive procedures in everyone.

Read more

The Society for Maternal-Fetal Medicine published a new statement this month. It deals with a hard problem: how to care for women with dangerous preg…

Read more

I have been looking at a document published in January 2026 by Ariadne Labs, the patient safety center jointly run by Brigham and Women’s Hospital and the Harvard T.H. Chan School of Public Health. It is called the Operating Room Crisis Checklists. It covers 17 emergencies — cardiac arrest, OB hemorrhage, malignant hyperthermia, failed airway, anaphylaxis, fire — and a debriefing protocol. The content is good. The evidence base is solid. The drug doses are correct. The differential diagnoses are thoughtful.

On the cover, in bold orange type, is this instruction:

>> Do not remove book from this room <<

Seven words. And in those seven words, the entire problem with how medicine uses checklists in 2026 is perfectly summarized.

It is a book. It must stay in the room. Someone has to find it, open it to the right page, and read it aloud during a crisis. This is the state of the art from one of the most respected patient safety organizations in the world, published three months ago.

Here is a “Shoulder Delivery Phase Timer”
This checklist is available 24/7. Unmovable. Accessible to everyone. Use it for every vaginal births and it will safe lives. I promise. And at the very least it will improve communications.

Or even better, install a display in each room (I just bout a 50 inch display for under $300).

Every L&D room needs a MOM — a Maternal Obstetric Monitor — a 50-inch display on the wall driven by the smartphone in every clinician's pocket. Pilots have had this for 30 years. Mothers deserve it too.

Where This Started

On October 30, 1935, a Boeing Model 299 lifted off a runway in Dayton, Ohio. It was the most advanced bomber ever built — so capable that a test pilot with thousands of flight hours forgot to release the elevator lock before takeoff. The plane climbed briefly, stalled, and crashed. Two crew members died.

Boeing’s engineers did not blame the pilot. They created a piece of paper. A short list of items to be checked, out loud, before each phase of flight. Not because pilots were incompetent. Because the aircraft had become too complex for any human memory to manage reliably alone.

That piece of paper became the foundation of modern aviation safety. The Boeing B-17 went on to fly 291,000 combat missions in World War II. The checklist was not a sign of failure. It was an acknowledgment of human limits.

How Aviation Evolved

What began as a laminated card grew into a system. By the 1970s, commercial aviation had developed crew resource management — a structured approach where every crew member has a defined role in running checklists. Challenge and response. Every item called, every response spoken aloud, every step logged.

Today, pilots do not hold a laminated card or open a book. They use an Electronic Flight Bag — an iPad running aviation software that tracks checklist completion in real time, records which items were confirmed and when, flags skipped steps, and updates automatically when procedures change. If a hydraulic system fails mid-flight, the abnormal checklist for that exact failure appears on the screen. The pilot does not flip through binders. The right checklist finds the pilot.

In 90 years, aviation went from a handwritten card after a crash to an integrated digital system embedded in every aircraft’s operating environment. The goal never changed: make sure nothing important is forgotten. The delivery mechanism changed completely.

Medicine’s Late Arrival

It took medicine another 70 years to pay serious attention. In 2001, Peter Pronovost at Johns Hopkins developed a five-item checklist for central line insertion in the ICU. The items were not complicated. Wash hands, use full sterile barrier, clean the skin with chlorhexidine, avoid the femoral vein, remove unnecessary catheters. Every physician knew these things. And yet, implementing the checklist reduced central line infection rates in Michigan ICUs by 66 percent over 18 months. More than 1,500 lives saved. [1]

The World Health Organization’s Surgical Safety Checklist followed — 19 items, three phases of surgery. A randomized trial across eight hospitals on four continents showed a 36 percent reduction in major complications and a 47 percent reduction in deaths. [2]

Obstetrics adopted the surgical timeout for cesarean delivery. Labor and delivery units developed hemorrhage bundles and sepsis protocols. The Safe Childbirth Checklist targeted birth attendants in low-resource settings. The evidence was clear. Checklists save lives.

But medicine made the same mistake aviation made in 1935: it stopped at the piece of paper. Or in 2026, the book.

The Book in the Room

Look at what the Ariadne Labs OR Crisis Checklists document is asking operating room teams to do. During a cardiac arrest, someone must find the book, open to checklist 04 or 05 depending on the rhythm, designate a checklist reader, and have that person read each step aloud while the team manages the patient. During OB hemorrhage, checklist 15. During malignant hyperthermia — a rare, rapidly fatal reaction — checklist 13, after calling the MH hotline at 1-800-644-9737, a number that is printed in the book because without the book you would not have it.

The checklist content itself is excellent. The OB hemorrhage checklist correctly identifies the four T’s — tone, trauma, tissue, thrombin. Drug doses are specific and current. The debriefing protocol, using the WATER framework, is thoughtful. Ariadne Labs has done real work here.

But the delivery mechanism is a physical book that must not leave the room. In an emergency, someone must find it. Someone must hold it. Someone must be designated to read it. If the book is in the wrong place, or the team skips the designation step because things are moving fast, the checklist does not happen.

This is not a criticism of Ariadne Labs. This is a description of where medicine is. The book is the standard. Most operating rooms and delivery suites do not have anything better.

The Timeout as Theater

The surgical timeout has the same problem. Someone calls it. The team pauses. Someone reads from a laminated sheet or a whiteboard. Patient name, procedure, site, allergies, anticipated blood loss. The team confirms. Then the surgery begins.

How often is this done correctly? How often are all items actually addressed? How often does a team member raise a concern? Studies suggest: not reliably. A 2012 observational study found that surgical timeouts were completed fully less than 50 percent of the time in real practice, even in institutions reporting high compliance. [3] Nobody logs whether the timeout was done. Nobody knows if an item was skipped. There is no record. The timeout either happened or it did not, and most of the time no one can say which.

A laminated sheet does not create accountability. A dry-erase whiteboard in a labor and delivery room can display a patient’s GBS status and gestational age. It cannot alert the team when blood pressure crosses a critical threshold. It cannot flag that a patient’s hemorrhage risk changed because her labor required oxytocin augmentation. It cannot be read from the nursing station at 2 a.m. It cannot hand off to the incoming night team. It is a static snapshot in a dynamic, high-risk environment. That gap is where errors live.

What Aviation Is Doing Right Now

Commercial aviation carries 4.5 billion passengers per year. The fatal accident rate for large commercial aircraft is approximately 0.07 per million flights. [4] This did not happen because pilots became better people. It happened because the system was redesigned around the assumption that humans make errors, and the system’s job is to catch those errors before they become crashes.

The Electronic Flight Bag does not replace pilot judgment. It handles memory so the pilot can handle thinking. The checklist is embedded in the workflow, time-stamped, logged, and integrated with the aircraft’s actual systems. There is no book that must not leave the room. The right checklist appears on the screen when it is needed, triggered by the situation itself.

Medicine is nowhere near this. Most hospitals do not have systems that automatically escalate when a patient’s early warning score crosses a threshold. Most labor and delivery units do not have integrated digital checklists that record completion and hand off between shifts. Most obstetric teams still rely on memory, verbal communication, a whiteboard, and a book that must stay in the room.

What This Costs

The Joint Commission reviewed 1,000 maternal deaths and found that in the majority of cases, warning signs were either not recognized or not acted upon in time. [5] This is exactly the failure a well-designed checklist system is built to prevent. Hemorrhage is the leading cause of preventable maternal death worldwide. California’s Maternal Quality Care Collaborative showed that implementing a structured hemorrhage bundle reduced severe maternal morbidity by 20 percent. [6] That bundle is, at its core, a checklist.

The tools exist. The evidence exists. The gap is the delivery mechanism. A checklist that requires someone to find a book during a hemorrhage is not a safety system. It is a good intention dressed as one.

What 2026 Should Look Like

Electronic health records in most major hospitals already capture the data needed to drive dynamic, context-sensitive checklists. A patient whose hemorrhage risk score crosses a threshold should trigger an automatic display of the hemorrhage readiness checklist on every screen in the room. A second-stage labor duration alert should prompt the provider to document a decision. A sepsis bundle should activate when vital sign criteria are met, not when someone remembers to order it.

The OR Crisis Checklists that Ariadne Labs published in January 2026 should exist as an integrated application in every operating room’s display system, triggered by the documented clinical event, logged when completed, time-stamped for each step, and automatically handed off to the next team. Not a book. Not a laminated card. Not a whiteboard. A system.

This is not futuristic. Aviation has been doing it since the 1990s. The technology exists in medicine. What is missing is the will to accept that a book with a warning not to leave the room is not a safety system — it is 1935 technology with a Harvard logo on the cover.

A better checklist: Always available. To everybody. FREE:

Example: The Shoulder Delivery Phase Timer. Use it. Every Vaginal delivery.

My Take

I have been in delivery rooms for 50 years. I have watched checklists get introduced, celebrated, and quietly ignored when the unit got busy. I have seen timeouts that lasted 15 seconds and documented nothing. I have seen whiteboards with information that was never updated.

The Ariadne Labs OR Crisis Checklists are well-made. The OB hemorrhage checklist, the cardiac arrest checklists, the debriefing protocol — the content is right.

The people who wrote this document care about patient safety.

I do not question that.

What I question is the delivery mechanism. The book. The instruction not to remove it from the room. That instruction exists because if someone takes it out, it will not be there when it is needed. Which tells you exactly how the system works: it depends on a physical object being in the right place at the right moment, held by the right person, in the middle of a crisis.

Pilots figured out that this was not good enough. They kept improving the delivery mechanism until the checklist was part of the aircraft itself. Medicine should do the same. The Boeing engineers in 1935 had the right idea. It has now been 91 years. The book is not the answer.

Today, every pilot on every commercial flight runs checklists on an iPad. Every one of us has a smartphone in our pocket with more computing power than the Apollo mission. And in our operating rooms and delivery suites, we are still telling people not to remove the book from the room."

References

1. Pronovost P, Needham D, Berenholtz S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med. 2006;355(26):2725-2732.

2. Haynes AB, Weiser TG, Berry WR, et al. A surgical safety checklist to reduce morbidity and mortality in a global population. N Engl J Med. 2009;360(5):491-499.

3. Vats A, Vincent CA, Nagpal K, et al. Practical challenges of introducing WHO surgical checklist: UK pilot experience. BMJ. 2010;340:b5433.

4. International Air Transport Association. Safety Report 2023. Montreal: IATA; 2024.

5. The Joint Commission. Sentinel Event Alert: Preventing maternal death. Issue 44. 2010.

6. Main EK, Goffman D, Scavone BM, et al. National partnership for maternal safety: consensus bundle on obstetric hemorrhage. Obstet Gynecol. 2015;126(1):155-162.

We are not biologically alone. Trillions of microorganisms inhabit the gastrointestinal tract, skin, oral cavity, and other body sites. Collectively, these microbial communities interact with metabolism, immunity, inflammation, and host physiology. During pregnancy and early infancy, the microbiome is especially interesting because this is a period of rapid biological development and microbial colonization.

The infant microbiome does not appear fully formed at birth. It develops dynamically during the first years of life and is influenced by multiple exposures, including gestational age, antibiotics, diet, breastfeeding, environmental exposures, and mode of delivery. Studies have repeatedly found differences between infants born vaginally and those born by cesarean delivery, particularly early in infancy, although the biological mechanisms and long-term clinical importance of many of these differences remain incompletely understood.1-4

That scientific uncertainty creates an important obligation: microbiome observations should not be converted into clinical claims before the evidence supports them.

And that brings us to home birth.

The microbiome argument for home birth

Supporters of planned home birth have proposed many reasons why giving birth at home might be preferable to giving birth in a hospital. Some concern autonomy, comfort, continuity of care, fewer obstetric interventions, or dissatisfaction with hospital maternity care. Those are legitimate subjects for discussion.

But another argument is biological.

Could babies born at home acquire a more favorable microbiota because they avoid the hospital environment?

It is an attractive hypothesis.

It is also, at present, unsupported.

A new 2026 report in The Lancet Microbe directly addresses this question.

Stojanov and colleagues begin by acknowledging the claim plainly:

“Supporters of home delivery suggest that neonatal microbiota associated with home birth might be healthier than that associated with hospital birth.”

The investigators had previously studied neonatal bacterial colonization in babies born at home and in hospitals. Their 2022 report found no difference in initial bacterial colonization across multiple body sites according to place of birth.

But perhaps differences emerged later.

So they went back.

What happened three years later?

The investigators reassessed 20 mother-child pairs from the original cohort. Eleven children had been born at home and nine in a hospital. Importantly, all had been delivered vaginally, which reduces the obvious confounding that would occur if one group contained substantially more cesarean deliveries.

At age 3 to 4 years, researchers sampled four sites: the armpit, forearm, stool, and saliva, using 16S rRNA gene profiling.

The result was remarkably consistent.

They found no significant difference in microbiota composition at any of the four sites between children born at home and those born in hospitals.

The beta-diversity analyses were nonsignificant for every sampled site:

• armpit: p=0.18

• forearm: p=0.13

• stool: p=0.42

• saliva: p=0.25.

There was an especially interesting additional observation.

Children born at home had been breastfed substantially longer, a median of 2.5 years compared with 0.9 years among hospital-born children. Yet even with that striking difference in feeding history, the investigators found no corresponding microbiota signature at follow-up.

By approximately age 3, the children’s microbial communities were also compositionally similar to those of their mothers at the corresponding anatomical sites. The authors concluded that anatomical location, rather than age, was driving community structure.

Their conclusion is appropriately restrained:

Differences attributable to place of birth could not be identified either during the neonatal period or at 3 to 4 years of age. They further conclude that birth setting alone should not be considered a determinant of long-term microbiota composition or assumed to protect children against allergy.

What this study does NOT prove

This is important.

The study included only 20 mother-child pairs. It used 16S rRNA profiling rather than deeper metagenomic or functional analyses. A study this small cannot demonstrate biological equivalence, exclude subtle microbial differences, or determine whether uncommon microbiome-mediated clinical outcomes differ between birth settings.

The authors appropriately call for larger longitudinal cohorts.

So we should not overstate the finding.

The correct conclusion is not:

Home and hospital birth can never affect the microbiome.

The evidence does not establish that.

The correct conclusion is:

There is currently no demonstrated microbiota advantage of home birth that should be used as a clinical argument for choosing home birth.

That distinction matters.

The larger problem: searching for biological justifications for home birth

The home-birth debate should be based on clinically important outcomes, not speculative surrogate benefits.

Planned home birth is often defended because it is associated with fewer interventions. That observation deserves serious consideration. But fewer interventions and greater maternal satisfaction cannot simply be assumed to compensate for increased neonatal risk.

In the United States, the American College of Obstetricians and Gynecologists states that planned home birth is associated with fewer maternal interventions but with a more than twofold increased risk of perinatal death and approximately threefold increased risk of neonatal seizures or serious neurologic dysfunction. ACOG therefore considers hospitals and accredited birth centers the safest birth settings, while recognizing the pregnant patient’s right to make an informed choice.5 (ACOG)

When a birth setting may increase the risk of a catastrophic neonatal outcome, even if the absolute risk is small, proponents have a responsibility to distinguish demonstrated benefits from hypothesized ones.

A “healthier microbiome” sounds compelling. It invokes nature, immunity, maternal-infant biology, and an increasingly fashionable area of science.

But biological plausibility is not evidence of clinical benefit.

Microbiome science should not become microbiome mythology

We have seen this problem before.

The microbiome is extraordinarily easy to oversell because almost any environmental exposure can be associated with differences in microbial composition. Finding a difference does not establish that the difference is beneficial. Finding an association with disease does not establish causation. And demonstrating a microbial difference does not establish that deliberately changing the exposure that produced it will improve health.

In the home-birth question, we have even less.

The investigators looked for differences and did not identify them, first in neonates and now again several years later.

That does not end microbiome research on birth environments.

It should end, at least for now, the claim that a healthier childhood microbiome is an evidence-based reason to give birth at home.

Birth setting should be judged by outcomes that matter

The appropriate comparison between home and hospital birth is not which setting feels more natural or which theoretical microbial exposure sounds more physiologic.

The relevant questions are clinical:

• Does the mother survive?

• Does the baby survive?

• Are serious maternal complications reduced or increased?

• Are neonatal seizures, hypoxic-ischemic injury, infection, and other major morbidity reduced or increased?

• Can an unexpected emergency be recognized and treated quickly enough?

• What maternal benefits are gained, and what neonatal risks are accepted in exchange?

Those are patient-important outcomes.

Microbiome composition is interesting science. Until it is connected convincingly to meaningful clinical outcomes, it remains primarily a biomarker and mechanistic research question, not a reason to choose a potentially less safe place of birth.

The new Lancet Microbe report does not prove that birth environment has zero effect on every component or function of the microbiome.

It tells us something more clinically useful:

After looking in the neonatal period and again 3 to 4 years later, investigators have found no evidence that home birth produces the supposedly healthier microbiota sometimes invoked in its defense.

Home birth should therefore stand or fall on its demonstrated maternal and neonatal outcomes.

The microbiome does not rescue the argument.

References

1. Reyman M, et al. Impact of delivery mode-associated gut microbiota dynamics on health in the first year of life. Nat Commun. 2019;10:4997.

2. Shao Y, et al. Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. Nature. 2019;574:117-121.

3. Bokulich NA, et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci Transl Med. 2016;8:343ra82.

4. Fehr K, et al. Breastfeeding, birth mode, and other determinants of the infant gut microbiome. Microbiome. 2020;8:131.

5. American College of Obstetricians and Gynecologists. Planned Home Birth. Committee Opinion No. 697. Obstet Gynecol. 2017;129:e117-e122.

6. Hutton EK, Reitsma A, Simioni J, Brunton G, Kaufman K. Perinatal or neonatal mortality among women who intend at the onset of labour to give birth at home compared with women of low obstetrical risk who intend to give birth in hospital: a systematic review and meta-analyses. EClinicalMedicine. 2019;14:59-70.

7. Stojanov M, Savoy F, Baud D. Home or hospital birth: a 3-year microbiota follow-up. Lancet Microbe. 2026. doi:10.1016/j.lanmic.2026.101526.

At 11:40 p.m. on April 14, 1912, the Titanic hit an iceberg. She didn’t go under until 2:20 a.m. That’s two hours and forty minutes in which roughly 2,200 people lived between the impact and the catastrophe, and for most of that time the majority of them didn’t believe anything serious had happened.

You can see why. The lights stayed on. The cabins were warm. The deck felt level. A steward telling you to put on a life vest and climb into a small open boat, then be lowered sixty feet to the freezing Atlantic, sounded like the irrational option. The ship was the safe place. Everyone knew that.

So the early lifeboats went down half empty.

Lifeboat No. 1 had room for 40 and reportedly left with 12. Lifeboat No. 6 had room for 65 and carried about two dozen. By the time the bow was visibly under water and disbelief turned into panic, the last conventional lifeboat had already been lowered, at about 2:05. Fifteen minutes later there was no ship.

The people on board didn’t fail to notice danger. They spent the only time in which noticing could still have changed anything.

Medicine is in that interval now

AI has already hit the profession. It’s changing how patients get information, how we search the literature, how notes get written, how differentials get generated, and how decisions get explained to the people they affect.

I’ve been writing about this for a while. We published the first paper on AI in ObGyn in the American Journal of Obstetrics & Gynecology more than three years ago, and again a few months ago, and more than ten articles in between. I also spend a good part of my week vibecoding clinical tools with it, which is not something I expected to be doing in my seventies.

And yet a large share of physicians, obstetricians and gynecologists very much included, behave as if nothing structural has changed. They warn about the dangers of AI and stop there. They dismiss it as unreliable. They find one hallucination and treat it as if it settles the whole question. Some ridicule the colleagues who are learning to use it. Most assume that thirty years of training will keep being enough without learning how this particular tool works, and they’re waiting for a hospital, a professional society, a regulator or a medical school to tell them what to do.

The exam rooms are full. The ORs are running. The lights are on. It feels safe to stay where we are.

Our patients aren’t waiting for us

Patients are already using AI to read their lab results, their ultrasound and pathology reports, their medication lists and the treatment plan we handed them. Pregnant women are asking it about induction, cesarean, prenatal testing, fetal anomalies, vaccines and birth plans, usually the night before the visit.

Some arrive with information that’s wrong or dangerously oversimplified. Others arrive with sharper questions than we’re used to: a paper from last month, a management option we hadn’t mentioned, a risk estimate we hadn’t calculated. Both kinds of patient need a clinician who can tell which is which.

That’s the actual problem. Patients are using these systems while many clinicians can’t judge the output’s accuracy, can’t see what it left out, can’t correct it, and don’t use it themselves. A physician who refuses to learn AI isn’t protecting anyone from AI. He’s handing interpretation to the patient, the algorithm, a Facebook group, or whichever commercial product gives the most confident answer.

Our professional organizations haven’t helped. There’s no clear AI competency curriculum. There are the usual panels and slide decks at the annual meeting, where few people learn much, and then everyone goes home.

Clinical authority used to rest partly on the fact that the physician controlled access to medical information. That’s over. What’s left is the ability to evaluate information more critically than the patient can, apply it to this patient in front of you, say plainly where the uncertainty is, and explain why you’re recommending what you’re recommending.

Competence isn’t trust

I’m not asking anyone to accept AI output at face value, let a chatbot diagnose patients on its own, or replace the exam and the relationship with a screen. Competence means knowing enough to use the tool safely and to recognize when it shouldn’t be used at all.

In practice that means knowing what generative AI can and can’t do, how persuasive its fabrications are, how to check a claim against the primary source, how the answer changes when the prompt is missing clinical context, where bias enters through training data, how confidentiality gets breached, how automation bias erodes your own judgment, where AI helps (documentation, patient education, evidence retrieval, translation), when a human has to review, and who’s accountable for the decision at the end. The last one has an easy answer. You are.

Nobody needs to become a programmer. Every physician needs to be able to supervise a tool that’s already influencing their patients and their practice.

Why obstetrics can’t afford to wait

Obstetrics is unusually exposed to confident, incomplete advice. Decisions are time-sensitive, risk estimates depend heavily on which population you’re in, the evidence is often thin, and every recommendation lands on two patients at once.

A generic AI answer on induction at 39 weeks, TOLAC, fetal growth restriction, aneuploidy screening, home birth or a medication in pregnancy can sound authoritative and still omit the one detail that decides whether it applies. If our patients are reading those answers, we have to be able to spot false certainty, missing contraindications, outdated guidance and invented references. We also have to learn where the tool helps us: finding evidence faster, preparing individualized counseling, checking whether a patient understood, surfacing the question nobody thought to ask.

Refusal isn’t a safety strategy.

The obligation has changed

Medicine has absorbed disruptive technology before. What’s different this time is speed, access, and the fact that patients adopted it first. AI didn’t need hospital installation or a department chair’s approval. It’s on every phone in the waiting room.

Whether AI enters clinical medicine stopped being a question a while ago. The open questions are whether clinicians will use it competently, whether institutions will teach it, and whether professional organizations will set standards before bad habits harden. Clinical AI competence belongs in medical school, residency, maintenance of certification, credentialing and patient-safety programs, and the teaching should cover how to interrogate and challenge the tool, not just how to operate it.

AI isn’t replacing clinical judgment. It’s changing what competent clinical judgment consists of.

The Titanic story isn’t about AI sinking medicine.

It’s about what people do with two hours and forty minutes when the lights are still on.

The worst outcome would be to wait until AI’s influence can no longer be ignored and then start preparing in a panic, lowering boats in the dark.

The iceberg has already been struck.

If you are an academic physician, try this experiment.

Upload a manuscript to a capable AI model and type:

“Review this paper for me.”

You may get an impressive response.

You may also get three pages of minor complaints, stylistic suggestions, speculative concerns, and methodological objections that sound sophisticated but do not materially affect the paper.

The problem is not necessarily the AI.

The problem may be your prompt.

We would never invite a human reviewer to evaluate a manuscript without telling them what we need. Is this an editorial triage? A statistical review? A methodological review? A search for fatal flaws? A review for publication? Are we interested in grammar, or only problems that could change the conclusions?

Yet we routinely give AI four words:

“Review this for me.”

Then we judge the model by what comes back.

That is not AI competence.

Tell AI what matters

For most manuscript reviews, I do not want twenty minor criticisms.

I want to know:

Is there anything seriously wrong with this paper?

I want the model to distinguish between a problem that threatens the validity of the conclusions and something that merely could have been done differently.

So instead of:

Review this paper for me.

try this:

Act as a rigorous peer reviewer for a high-quality medical journal. Review the attached manuscript for MAJOR problems only.

Focus on errors or limitations that could materially affect the validity, interpretation, reproducibility, or clinical implications of the study.

Specifically examine:

1. Study design and whether it can answer the stated research question.

2. Selection bias, misclassification, confounding, and other important sources of bias.

3. Statistical methods and whether the analyses support the conclusions.

4. Discrepancies between the data presented and the authors’ claims.

5. Unsupported causal language or conclusions that go beyond the data.

6. Missing analyses or information that could materially change interpretation.

7. Internal inconsistencies among the abstract, text, tables, figures, and conclusions.

8. Clinically important limitations that the authors have overlooked or understated.

Subscribe now

Do not list minor stylistic issues, preferences, or changes that would not materially affect the paper.

For every major concern:

• identify exactly where the problem occurs;

• explain why it matters;

• distinguish a demonstrated error from a possible concern;

• state what evidence in the manuscript supports your criticism; and

• explain what would be required to correct or resolve it.

Do not invent missing information. If something cannot be determined from the manuscript, say so explicitly.

End with:

Overall assessment: Are there major problems that materially weaken the paper?

Recommendation: Acceptable as written, minor revision, major revision, or not suitable for publication, with a brief explanation.

That is a very different assignment.

Share

But a better prompt does not make AI right

This is the part of AI competence that is often forgotten.

A sophisticated prompt can produce a sophisticated-looking error.

AI can misunderstand the study design. It can criticize an analysis that is actually appropriate. It can overlook an important confounder. It can claim that information is missing when it is sitting in Table 3.

And, unless constrained and checked, it can fabricate references or confidently attribute claims to papers that do not support them.

So the output is not the peer review.

It is a candidate analysis for the reviewer to interrogate.

For every important criticism, ask:

Where exactly is the evidence for this?

Then go back to the manuscript.

If the criticism depends on an external reference, open the original reference.

If the model says the statistics are wrong, verify the statistical argument.

If it says the authors have contradicted themselves, compare the passages yourself.

The clinician or scientist remains responsible for the final judgment.

This is Clinical AI Competence

Prompt engineering is useful.

But clinical AI competence is larger than prompt engineering.

It means knowing what to ask, what to trust, what to verify, and when not to accept the machine’s answer.

That distinction will become increasingly important as AI gets better.

The danger is not merely that AI will produce obviously bad answers.

The more interesting problem is the opposite:

AI will increasingly produce answers that are so persuasive that we may forget to check whether they are true.

That is why physicians and scientists need more than access to AI.

They need competence in using it.

And sometimes that competence begins with something as simple as replacing:

“Review this paper for me.”

with a much better question.

Clinical AI Competence #1

This is the first in a continuing ObGyn Intelligence series on using AI effectively and safely in clinical medicine, research, education, and academic work.

Subscribe to ObGyn Intelligence for the next Clinical AI Competence lesson.

The form is complete.

Her name is correct.

The physician signed it.

The risks are listed.

The date and time are present.

Legally and administratively, everything may look excellent.

There is only one problem.

The patient may still not understand what she agreed to.

That is not a paperwork problem.

It is an informed consent problem.

Consent and Informed Consent Are Not the Same Thing

A signature proves remarkably little.

It proves that a person signed something.

It does not prove that she understood the probability of success.

It does not prove that she understood the alternatives.

It does not prove that she understood the consequences of declining treatment.

It does not prove that the clinician explained the information in a way she could understand.

ACOG’s guidance is clear that valid informed consent requires more than disclosure. The patient must receive adequate, accurate, understandable information and be able to understand and reason through it while making an intentional and voluntary choice. [1]

That is a much higher standard than obtaining a signature.

Obstetrics Makes Consent Especially Difficult

Consent becomes harder when decisions occur during labor.

Read more

What was announced

On September 10, 2026, Olivia and Tom Walton committed an initial $100 million over five years to Healthy Moms Healthy Babies America (HMHBA), a program housed within Heartland Forward’s Maternal and Child Health Center for Policy and Practice, with Neel Shah as president and Robin Reck as executive director.

The stated goal is to cut the US maternal mortality rate in half within five years.

The money is described as catalytic capital for state matching grants, “high-impact partnerships,” and infrastructure, with four pillars:

1. prenatal care (virtual care, community health workers, doulas, midwives),

2. “protecting maternity units as more units close,”

3. a first year of family support (postpartum home visits, mental health screening, paid leave), and

4. better data (2,3).

On closures the language is careful.

The press release says hospitals “are closing obstetric units at an accelerating pace,” and the Call to Action says “hundreds of U.S. hospitals, disproportionately in rural communities, have closed maternity wards,” attributing this to liability cost and workforce shortage and stating that families “must travel farther,” disrupting continuity of care. Neither document states that closures caused the rise in maternal deaths; the causal inference is left to the reader, and the press coverage has supplied it (2,3). On midwives, the Call to Action cites the Lancet Global Health modelling estimate that midwifery could avert 41% of maternal deaths, and on doulas it cites a 52.9% reduction in cesarean odds and 57.5% reduction in postpartum depression or anxiety (3). Each of these claims is examined below against the primary literature.

The baseline matters for a “halve it” target. NCHS reports 649 maternal deaths in 2024, a rate of 17.9 per 100,000 live births, not significantly different from 18.6 in 2023; the non-Hispanic Black rate was 44.8, the non-Hispanic White rate 14.2, the Hispanic rate 12.1, and the rate at age 40 and over 62.3 (1). HMHBA’s own website uses a pooled 2019–2023 figure of 23.5, which includes the pandemic years. Halving 17.9 means roughly 9 per 100,000 by 2031; halving 23.5 means roughly 12, a number the country is arguably within measurement error of already. Which baseline is chosen will determine whether the program is later judged a success.

Claim 1: obstetric unit closures drive maternal mortality

The closures are real and well counted. Among 4,964 short-term acute-care hospitals, the share without obstetric services rose from 35.2% in 2010 to 42.4% in 2022; 537 hospitals lost obstetric care (238 rural, 299 urban) and 138 added it (5). Kozhimannil’s original rural analysis found that 179 rural counties lost hospital obstetric services between 2004 and 2014 (4). March of Dimes counts 96 labor and delivery closures across 35 states from January 2024 to May 2026, 58% of them the county’s sole birthing facility, and estimates 1,113 counties (34.6%) as maternity care deserts (6). None of this is in dispute.

What is in dispute is the outcome. No quasi-experimental study of closures in the United States has demonstrated an effect on maternal death. The closure literature measures place of birth, travel time, prenatal visit counts, preterm birth and, less often, maternal morbidity, and the results are summarized in the table below.

The only positive severe morbidity signal comes from New Jersey, a largely urban and suburban setting (10). The most rigorous rural analysis, covering 605 closures over three decades, found maternal morbidity slightly better after closure, which the authors attribute to women delivering at higher-volume, higher-quality hospitals (7). That interpretation is consistent with the volume literature: among low-risk women, delivery at rural hospitals with 10 to 110 births a year carried an adjusted SMM risk ratio of 1.65 compared with rural hospitals above 460 births (14). Keeping the smallest units open is therefore not self-evidently protective; it may trade one risk for another.

The mortality signal that advocacy documents cite comes from cross-sectional comparisons of places, not from closures. Atwani and colleagues, using county-level data on 14.8 million births from 2018 to 2021, found a maternal mortality rate of 32.25 per 100,000 in desert counties versus 23.62 in full-access counties (adjusted incidence rate ratio 1.36, 95% CI 1.21–1.54), with no difference for low- or moderate-access counties (12). In Louisiana, desert residence carried an adjusted relative risk of 3.37 for pregnancy-related mortality on 112 deaths (13). Driving distance to the delivery hospital in Pennsylvania was associated with a composite of transfusion, ICU admission and hysterectomy (aRR 1.53 at 80 km), but the authors themselves ask whether distance is a cause or “simply a marker of social deprivation” (11). Desert counties are poorer, sicker, more rural and more often in the South; the same counties had high maternal mortality before their units closed.

The honest summary is this: closures worsen access, lengthen travel by about 14 to 25 minutes on average, push a small fraction of births out of hospital, and in rural counties may modestly increase preterm birth (4,6). There is no evidence that they have contributed measurably to the national maternal mortality rate, and the best-designed study could not detect an effect because maternal death in closure counties is too rare to study. Any statement that closures are “responsible for” US maternal mortality outruns the data. The more defensible claim is that they are a threat to timely care in specific communities, which is a legitimate reason to fund rural obstetric readiness and transfer systems, but not a mechanism that will halve the national rate.

Claim 2: more midwives and doulas will reduce maternal deaths

This claim rests on three literatures, none of which measures maternal mortality in a high-income country.

The 2024 Cochrane review of midwife continuity models pooled 17 randomized trials with 18,533 women in Australia, Canada, China, Ireland and the United Kingdom (no US trial). It found moderate-certainty increases in spontaneous vaginal birth (RR 1.05) and reductions in cesarean (RR 0.91) and regional analgesia; the 2016 review’s apparent reductions in preterm birth and fetal loss did not persist in the update (preterm RR 0.95, 95% CI 0.78–1.16; neonatal death RR 0.85, 0.43–1.71, low certainty). Maternal death was a prespecified outcome: no deaths occurred in the three trials reporting it, so the effect is unestimable rather than null (15). The 2017 Cochrane review of continuous labor support (27 trials, 15,858 women) found a cesarean RR of 0.75 and better birth experience; maternal mortality does not appear among its outcomes (16).

The US doula evidence is observational Medicaid claims work. The Minnesota comparison behind the original cost argument found cesarean 22.3% versus 31.5% (17). The eClinicalMedicine study cited by HMHBA compared 298 propensity-matched pairs across three states and reported cesarean OR 0.47 and postpartum depression or anxiety OR 0.43; it did not report maternal death or severe morbidity (18). The 2024 follow-up on 722 matched pairs found lower cesarean and preterm rates and more postpartum visits, but no difference in emergency visits, admissions, depression or severe maternal morbidity within 30 days, and the authors state they “did not evaluate maternal mortality, because we lacked statistical power” (19). A 2026 systematic review of midwifery and doula care specifically for Black women located 16 studies and concluded the literature “remains severely limited”; none reported mortality or SMM (58). Both the Elevance studies were authored by the insurer that funds the benefit, and propensity matching cannot remove the selection of motivated, engaged women into doula programs.

The “41% of maternal deaths” figure is a Lives Saved Tool model of 88 low- and middle-income countries that together account for 98% of the world’s maternal deaths. It estimates what would happen if midwife-deliverable interventions (uterotonics, antibiotics, antihypertensives, skilled attendance) were scaled from current low coverage; the authors describe the results as “indicative and directional rather than exact.” No high-income country is modelled, and the interventions being credited are already nearly universal in US hospitals (20). Citing this number for the United States is a category error.

Ecological comparisons are the remaining support. The Commonwealth Fund notes the United States has about 4 midwives and 12 obstetrician-gynecologists per 1,000 live births against 61 and 13 in the United Kingdom and 81 and 5 in Sweden (23). Vedam’s state integration index correlated with vaginal birth, breastfeeding and neonatal outcomes, not maternal mortality (21). But the country with the most midwife-led system in Europe, the United Kingdom, reports a maternal mortality rate of 12.80 per 100,000 maternities for 2022–2024, described by MBRRACE as roughly 20% higher than in 2009–2011 and rising (25). The Netherlands, the archetype of community midwifery, had a confidential-enquiry rate of 12.1 in 1993–2005 that fell to 6.2 in 2006–2018 while home birth declined and hospital care was reorganized (26). Nothing in either series attributes change in mortality to midwifery workforce size.

Midwives and doulas have a solid evidence base for what they do: fewer cesareans, fewer instrumental births, less regional analgesia, better experience, and, for doulas in Medicaid populations, plausibly fewer preterm births. These are worthwhile ends and they may reduce the morbidity that follows surgery.

But a program that promises to halve maternal deaths and then invests in workforce categories whose trials have never recorded a maternal death is promising an outcome the intervention has not been shown to deliver.

The one caveat on the other side is that a US planned home birth attended by a midwife carries a roughly threefold higher neonatal mortality than hospital birth (22); “more midwives” is only safe as “more midwives in integrated hospital and birth-center systems.”

The European comparison: how much of the gap is real

The claim that US mortality is “more than double” or “three times” the European rate rests on comparing the US vital-statistics number with OECD civil-registration counts for Europe. Both sides of that comparison are biased, in opposite directions.

On the European side, Euro-Peristat declines to publish routine maternal mortality at all because certificate data are “unreliable.” The eight-country enhanced-surveillance study in the BMJ found that vital statistics “underestimated maternal mortality by 36% or more in all countries except Denmark,” and produced rates per 100,000 live births of 2.7 in Norway, 3.4 in Denmark, 4.3 in Finland, 5.3 in the Netherlands, 6.3 in Italy, 8.0 in France, 9.6 in the United Kingdom and 10.9 in Slovakia (24). Germany, which has no confidential enquiry, reports 3.5 through Destatis, but a certificate-by-certificate review in Berlin found 7.8 to 9.1, “over double the official estimate” (27). The Dutch audit figure of 6.2 is more than double the OECD figure of 2.8 used in the Commonwealth Fund exhibit (23,26).

On the US side, the pregnancy checkbox inflates the count. NCHS’s own linkage found that in 2014 and 2016 more than half of certificates with a positive checkbox had no pregnancy-related hospitalization, and that if the checkbox were ignored entirely the 2018 rate would have been 8.7 rather than 17.4 (28). Rossen and colleagues attributed 9.6 deaths per 100,000 to the checkbox and found no significant trend once it was removed (29). Joseph and colleagues showed that when pregnancy must appear among the recorded causes of death rather than only on the checkbox, the rate is 10.2 in 1999–2002 and 10.4 in 2018–2021, with no increase; their bias-corrected sensitivity estimate is about 16 to 17 in both periods (30). Declercq and Thoma found that for the same 42-day window, NCHS runs 44% to 63% above the CDC Pregnancy Mortality Surveillance System (31). The rebuttal from Janevic, Declercq and Howell is that the checkbox also has false negatives: 62% of decedents with a pregnancy hospitalization in the prior year were not flagged, and New York’s review committee identified 386 potential deaths of which only 129 were identifiable from certificates alone (32). Both positions have merit; the debate is about the size of the correction, not whether one is needed.

A like-for-like comparison therefore places the United States at roughly 16 to 18 per 100,000 against 13 in the United Kingdom, 8 to 9 in France and Germany, 6 in the Netherlands and 3 to 4 in Scandinavia. The gap is real, but it is a factor of 1.3 to 3, not 5 to 10, and the “two-thirds lower” figure that HMHBA and others use is an artefact of comparing a checkbox-inflated numerator with under-ascertained European ones. One consequence for the Waltons’ target deserves saying plainly: if NCHS ever adopts Joseph’s multiple-cause criterion, the national rate will “halve” on paper without a single death prevented, and if states finish building enhanced surveillance, the rate will rise. A program that promises a 50% fall measured by a statistic this unstable has set itself a target that measurement changes can meet or defeat.

What is not an artefact is the Black rate. At 44.8 per 100,000 in 2024, it exceeds every European figure under any method, and the composition of US deaths differs from Europe’s. In the 36-state review committee data for 2017–2019, mental health conditions accounted for 22.7% of pregnancy-related deaths, hemorrhage 13.7%, cardiac conditions 12.8%, infection 9.2%, thrombotic embolism 8.7% and cardiomyopathy 8.5%, with 84% judged preventable and 30% occurring between 43 and 365 days (33). France and the United Kingdom are dominated by cardiac disease, thrombosis and suicide; hemorrhage and infection deaths of the kind that safety bundles address have largely been driven out. Outside the WHO definition altogether, US pregnancy-associated homicide (3.62 per 100,000 live births, exceeding every leading obstetric cause) and overdose (11.85 per 100,000 in 2020, rising to 13.0 by 2023) have no European counterpart at that scale (34,35,36). The US excess is real, but a substantial part of it is not obstetric.

What reduces maternal mortality among Black women

The disparity is not explained by education or income. In the 2007–2016 pregnancy mortality data, Black women with a college degree or higher had a ratio of 40.2 per 100,000 against 7.8 for White women with the same education, a 5.2-fold difference, and higher than White women who had not finished high school (25.0) (37). The ratio widens with age, from 1.5 under 20 to 4.3 at 30–34, consistent with cumulative physiological burden. The leading causes among Black women are cardiac and coronary conditions (15.9%), cardiomyopathy (13.9%), thrombotic embolism (11.9%) and hypertensive disorders (9.9%); more than half of cardiomyopathy deaths in the 32-state review were among non-Hispanic Black women, and more than three quarters were judged preventable (33,41).

A large part of the disparity is located in which hospital a woman delivers in. In New York City, 65% of White deliveries but 23% of Black deliveries occurred in hospitals in the lowest SMM tertile, and Howell estimated that site of delivery accounted for up to 47.7% of the Black-White SMM gap (38). Within the same hospital, the adjusted odds of SMM for Black women remained 1.52, independent of insurance (39). Nationally, among 73.9 million delivery hospitalizations, the failure-to-rescue rate after SMM was 1.79 times higher for Black women (40). The disparity is therefore three things at once: worse hospitals, worse care within hospitals, and worse rescue when things go wrong. Each is a hospital-quality problem, which is the one domain in which we have interventions with measured effects.

The following table ranks interventions by the strength of evidence for reducing mortality or severe morbidity specifically among Black women.

Two cautions belong beside this table. First, California’s overall rate halved to about 7.0 per 100,000 by 2013, on par with Western Europe (44), but its Black pregnancy-related mortality was 56.2 per 100,000 in 2014–2016 and 49.7 in 2019–2021 against 14.0 for White women, and the state’s own reviewers reported that the disparity widened between 2008 and 2016 (45). Hemorrhage bundles narrowed the SMM gap; they did not close the mortality gap, because the deaths of Black women in California are increasingly cardiovascular, and the bundle does not treat cardiomyopathy. Second, a 2026 scoping review found only 16 peer-reviewed intervention studies aimed at maternal disparities in the entire US literature and described them as “limited” in rigor (57). No intervention has yet demonstrated a reduction in Black maternal mortality in a controlled analysis. What has been demonstrated, repeatedly, is that when hospitals measure SMM by race and run standardized hemorrhage and hypertension protocols, Black women benefit disproportionately, because they were disproportionately harmed by the absence of those protocols.

The implication for priorities follows from the cause distribution. Cardiac disease, cardiomyopathy, hypertension and embolism together account for roughly half of pregnancy-related deaths among Black women, and 30% of all deaths occur after six weeks, when the woman has left obstetric care (33). The interventions with the best mechanistic fit are the ones that treat those conditions: universal postpartum blood pressure surveillance with remote monitoring, treatment of chronic hypertension from the first trimester (50), cardio-obstetric pathways for women with cardiomyopathy risk factors, structured postpartum follow-up in the first year with insurance to pay for it, and objective blood-loss measurement with escalation protocols. Review committees also judge more than 90% of preeclampsia and mental-health deaths preventable (60), which argues for the same postpartum year of contact HMHBA proposes, but delivered by clinicians who can prescribe an antihypertensive, not only by a visitor who can recognize a warning sign.

Does the plan address access to physicians and specialists?

No.

The press release does not mention obstetricians, maternal-fetal medicine, cardiologists, anesthesiologists, hospital quality, safety bundles or perinatal quality collaboratives; its workforce language is “growing the maternal health workforce with an emphasis on training midwives” and scaling “integrated care teams, including midwives and doulas” (2).

The Call to Action goes further in the opposite direction. It describes prenatal care as having “traditionally centered on obstetricians as the primary, and often sole, providers of care” and proposes “shifting from siloed OB-GYN care to a broader suite of maternal health providers” so that services “become more accessible.”

It acknowledges that “cardiovascular disease is the leading cause of maternal death, yet prenatal care often focuses narrowly on pregnancy-specific conditions,” but proposes no mechanism for getting a woman with cardiomyopathy risk to a cardiologist.

Physician supply, subspecialist consultation, levels of maternal care, transfer protocols and hospital quality measurement do not appear (3). The plan is, in effect, a substitution plan: fewer encounters with physicians, more with midwives, doulas, community health workers and telehealth.

That is a defensible strategy for low-risk women and a poorly matched one for the women who die.

The evidence on specialist access is thinner than one would like, but what exists points one way. Sullivan and colleagues showed in 2005 that an increase of five maternal-fetal medicine specialists per 10,000 live births was associated with a 27% reduction in state maternal mortality (RR 0.73, 95% CI 0.58–0.93) after adjustment for poverty, education, race and age (61). Kawakita and colleagues replicated this with 2018–2021 data: states in the highest tertile of MFM density had an adjusted incidence rate ratio of 0.70 (0.58–0.85) for maternal mortality and 0.83 (0.71–0.98) for pregnancy-related mortality compared with the lowest, although the middle tertile showed no effect, so the relationship is not a clean gradient (62). An HRSA analysis found the South had the lowest availability of nearly every relevant occupation, obstetrician-gynecologists included, and the highest mortality, while explicitly disclaiming causation (63). These are state-level ecological studies with the usual limits, and none adjusts for hospital quality, but they are the only mortality data we have on any workforce category, and they concern subspecialists, not midwives.

Regionalization is the operational form of specialist access, and here the evidence is mixed in an instructive way. In Massachusetts, women with high-risk conditions who delivered at a hospital without the resources their condition required had an adjusted odds ratio of 3.34 (2.24–4.96) for severe maternal morbidity (64), and in a national sample the excess SMM risk of high-comorbidity women was 9.55-fold at low-acuity hospitals versus 6.50-fold at high-acuity ones (65). But hospital level as such is not protective: in four states, SMM rose with level of care and level I hospitals had marginally lower adjusted risk than level IV (66), and Georgia found no difference in maternal outcomes by service level among high-risk women (67). Risk-appropriate matching helps; a level designation on the door does not. Rural access to the right level is poor: only 27.5% of higher-risk rural women who needed level IV care delivered at such a hospital (68). Twenty-four-hour in-house obstetric coverage has no mortality data, and its SMM signal is one unadjusted survey (69,70). Cardio-obstetric teams reduce 30-day readmission (RR 0.29) but the pooled maternal mortality estimate is null on six small studies with approximated controls (71). MFM telehealth in Arkansas raised consultation rates without outcome data (72).

The most direct evidence that specialist involvement matters for death comes from the confidential enquiries HMHBA says it admires. MBRRACE-UK judged that improvements in care may have made a difference to the outcome in 45% of deaths in 2020–2022 and 2021–2023 and 61% in 2022–2024; in the cardiovascular chapter the figure was 38%, with named failures such as “she was not seen by a cardiologist until 32 weeks’ gestation” and a woman with severe postpartum hypertension who “was not reviewed by a senior doctor prior to her discharge” (73,74). In France, care was suboptimal in 66% of maternal deaths in 2016–2018 and 59.7% were preventable; for cardiovascular deaths the enquiry’s recommendations are multidisciplinary follow-up, repeated risk assessment and “early referral to an expert centre (expert cardiologists, obstetricians, anaesthetists and intensive care)” (75,76). These are expert judgments, not effect estimates, but they come from midwife-led systems, and what they keep finding is not too few midwives. It is delayed senior and specialist review.

For Black women the point is sharper. Their leading causes are cardiac disease, cardiomyopathy, embolism and hypertension (33,41); a substitution model that reduces physician contact in pregnancy and the postpartum year moves in the wrong direction for exactly those conditions.

Nothing in the HMHBA documents proposes to get more Black women to hospitals in the lowest SMM tertile, which is where roughly half of the SMM disparity lives (38), or to a cardiologist before 32 weeks. A plan that names cardiovascular disease as the leading cause and then omits cardiologists has a gap at its center.

Would universal coverage make a big difference?

Coverage is the strongest single policy lever in the evidence base, and it is also the one whose limits are best documented. The two facts need to be held together.

On the first, the only causal-design estimate of a mortality effect is Eliason’s difference-in-differences across 612 state-years, which associated Medicaid expansion with 7.01 fewer maternal deaths per 100,000 live births overall and 16.27 fewer among Black women (53). That is a large effect, about a third of a non-expansion-state rate, and it has not been replicated; it uses death-certificate data across the checkbox transition and the Black subgroup estimate has visible outliers. Delivery-hospitalization SMM studies with stronger designs are null or small: a synthetic-control analysis of 12 million deliveries in 26 states found a 0.02 percentage point change in SMM after expansion (77), a Health Economics event study found “little evidence” of morbidity change (78), and New York’s 2014 expansion produced a 0.42 percentage point relative improvement in the SMM trend for low-income women (79). The coverage-to-utilization chain is robust: expansion reduced early postpartum hospitalizations by 17% (80), the pandemic continuous-enrollment rule raised 12-month postpartum coverage from 59% to 91% (81), and Colorado’s postpartum extension increased treatment of perinatal mood disorders by 20.5 percentage points (82). No study has yet linked the 12-month extension to mortality; the review-committee lag makes that impossible before about 2027 (54).

The reason coverage would help is timing. Thirty percent of pregnancy-related deaths occur 43 to 365 days after delivery, after the historical 60-day Medicaid cutoff, and another 23% between 7 and 42 days (33). In 2015–2018, 26.8% of women with prenatal Medicaid were uninsured before pregnancy and 21.9% were uninsured two to six months after it (83); continuous perinatal coverage was 75.3% among White women and 55.4% among Black women (84). Medicaid pays for roughly two thirds of Black births. Those are the women, and the months, in which cardiomyopathy, postpartum hypertension, overdose and suicide kill. Much of that counterfactual, however, is already spent: 40 states and the District of Columbia have expanded Medicaid and nearly every state now covers twelve months postpartum. The marginal step to universal coverage mainly reaches the roughly eight million uninsured women of reproductive age concentrated in the Southern non-expansion states, which are also the states with the highest Black pregnancy-related mortality.

On the second fact, every universally insured system that has been examined still carries a large Black or African-origin excess, and the table below is the cleanest natural experiment available on what coverage alone does.

Two US findings deserve emphasis. In the TRICARE system, where every woman has the same coverage and most deliver in the same military hospitals, the Black excess in non-transfusion SMM is 68%; the one outcome where the military system appears to have equalized risk is preeclampsia-related SMM, plausibly because military hospitals apply hypertension protocols uniformly (85,89). And in the ACA dependent-coverage study, the reduction in SMM accrued to non-Hispanic White women (aOR 0.89) and not to others, a caution that coverage gains do not flow automatically to the women who need them most (90).

A defensible estimate is that universal coverage would remove on the order of 10% to 25% of the US rate, perhaps two to four deaths per 100,000, concentrated in late-postpartum, cardiovascular, mental-health and chronic-disease deaths in the South, and would narrow the absolute Black-White gap by more than that because Black women are disproportionately uninsured and in non-expansion states. It would not bring the United States to European rates, and the relative Black-White ratio would most likely remain in the 1.5 to 3 range seen in every universal system, because the remainder of the gap lives in which hospital a woman delivers in, what happens to her inside it, her chronic disease burden, and factors that operate identically under the NHS and under TRICARE. Universal coverage is necessary; the countries that have it and still run 12 to 13 per 100,000 show that it is not sufficient. HMHBA’s plan, to its credit, does address coverage: it treats Medicaid as the financing vehicle for 40% of births and supports the postpartum extension (3). It does not address what the coverage buys, which is the specialist and hospital-quality problem above.

Assessment

HMHBA has chosen a target, halving maternal mortality in five years, that is measured by a statistic whose level is disputed by a factor of two and whose year-to-year changes since 2022 have not been statistically significant. It has chosen mechanisms, protecting rural units and expanding midwives and doulas, that improve access and the experience of birth but have never been shown to reduce maternal death in a high-income country, and it has justified them with numbers that do not transfer (a low-income-country model for midwifery, cross-sectional desert associations for closures, an insurer’s propensity-matched claims study for doulas). The comparison with Europe that motivates the program overstates the gap by comparing an inflated numerator with under-counted ones.

The omission that matters most is the one raised by the two new sections. The plan names cardiovascular disease as the leading cause of death and then proposes a workforce with fewer physicians in it; it never mentions maternal-fetal medicine, cardiology, anesthesia, hospital quality or levels of care, which are the only workforce and system variables with any mortality signal in the US literature and the recurring failures named by the European enquiries it cites as models. Universal coverage, which the plan supports through Medicaid, is the single policy with the strongest claim to a mortality effect, but the natural experiments of TRICARE, Kaiser, the NHS and France show that it narrows the Black excess without removing it. None of this means the money will be wasted. The evidence points clearly at where it would do the most: paying every delivering hospital to run hemorrhage and hypertension bundles with race-stratified outcome reporting, funding remote postpartum blood pressure programs and cardio-obstetric follow-up, supporting states to keep women insured for a year, building the enhanced surveillance that HMHBA says it wants (and accepting that the measured rate may rise when the counting improves), and funding rural transfer and readiness systems rather than propping up units delivering fewer than 100 births a year. Doulas and midwives belong in that system for the outcomes they have been shown to change. If the program’s own scorecard is the NCHS rate, it should say now which baseline it is using and how it will separate real change from measurement change; otherwise the result in 2031 will be unfalsifiable, and both success and failure will be claimed.

References

1. Hoyert DL. Maternal mortality rates in the United States, 2024. NCHS Health E-Stats. 2026 Mar;(113):1–7.

2. Healthy Moms Healthy Babies America. Olivia and Tom Walton commit an initial $100 million to cut U.S. maternal deaths in half [press release]. GlobeNewswire; 2026 Sep 10.

3. Healthy Moms Healthy Babies America. Maternal health call to action. Bentonville (AR): Heartland Forward; 2026. Available from: hmhba.org/maternal-health-call-to-action.pdf

4. Kozhimannil KB, Hung P, Henning-Smith C, Casey MM, Prasad S. Association between loss of hospital-based obstetric services and birth outcomes in rural counties in the United States. JAMA. 2018;319(12):1239–47. doi:10.1001/jama.2018.1830

5. Kozhimannil KB, Interrante JD, Carroll C, Sheffield EC, Fritz AH, McGregor AJ, et al. Obstetric care access declined in rural and urban hospitals across US states, 2010–22. Health Aff (Millwood). 2025;44(7):806–11. doi:10.1377/hlthaff.2024.01552

6. Stoneburner A, Chestnut JF, Lucas R, Jones EE, DeMaria AL. Nowhere to go: maternity care deserts across the U.S. Report No. 5. Arlington (VA): March of Dimes; 2026.

7. Fischer S, Royer H, White C. Health care centralization: the health impacts of obstetric unit closures in the United States. Am Econ J Appl Econ. 2024;16(3):113–41. doi:10.1257/app.20220341

8. Chatterji P, Ho CY, Wu X. Obstetric unit closures and racial/ethnic disparity in health. NBER Working Paper 30986. Cambridge (MA): National Bureau of Economic Research; 2023. [Journal publication not verified]

9. Battaglia E. The effect of hospital maternity ward closures on maternal and infant health. Am J Health Econ. 2025;11(2):201–46. doi:10.1086/727738

10. McGregor AJ, Hung P, Garman D, Amutah-Onukagha N, Cooper JA. Obstetrical unit closures and racial and ethnic differences in severe maternal morbidity in the state of New Jersey. Am J Obstet Gynecol MFM. 2021;3(6):100480. doi:10.1016/j.ajogmf.2021.100480

11. Minion SC, Krans EE, Brooks MM, Mendez DD, Haggerty CL. Association of driving distance to maternity hospitals and maternal and perinatal outcomes. Obstet Gynecol. 2022;140(5):812–9. doi:10.1097/AOG.0000000000004960

12. Atwani R, Robbins L, Saade G, Kawakita T. Association of maternity care deserts with maternal and pregnancy-related mortality. Obstet Gynecol. 2025;146(2):181–8. doi:10.1097/AOG.0000000000005976

13. Wallace M, Dyer L, Felker-Kantor E, Benno J, Vilda D, Harville E, et al. Maternity care deserts and pregnancy-associated mortality in Louisiana. Womens Health Issues. 2021;31(2):122–9. doi:10.1016/j.whi.2020.09.004

14. Kozhimannil KB, Leonard SA, Handley SC, Passarella M, Main EK, Lorch SA, et al. Obstetric volume and severe maternal morbidity among low-risk and higher-risk patients giving birth at rural and urban US hospitals. JAMA Health Forum. 2023;4(6):e232110. doi:10.1001/jamahealthforum.2023.2110

15. Sandall J, Fernandez Turienzo C, Devane D, Soltani H, Gillespie P, Gates S, et al. Midwife continuity of care models versus other models of care for childbearing women. Cochrane Database Syst Rev. 2024;4(4):CD004667. doi:10.1002/14651858.CD004667.pub6

16. Bohren MA, Hofmeyr GJ, Sakala C, Fukuzawa RK, Cuthbert A. Continuous support for women during childbirth. Cochrane Database Syst Rev. 2017;7(7):CD003766. doi:10.1002/14651858.CD003766.pub6

17. Kozhimannil KB, Hardeman RR, Attanasio LB, Blauer-Peterson C, O’Brien M. Doula care, birth outcomes, and costs among Medicaid beneficiaries. Am J Public Health. 2013;103(4):e113–21. doi:10.2105/AJPH.2012.301201

18. Falconi AM, Bromfield SG, Tang T, Malloy D, Blanco D, Disciglio S, et al. Doula care across the maternity care continuum and impact on maternal health: evaluation of doula programs across three states using propensity score matching. EClinicalMedicine. 2022;50:101531. doi:10.1016/j.eclinm.2022.101531

19. Falconi AM, Ramirez L, Cobb R, Levin C, Nguyen M, Inglis T. Role of doulas in improving maternal health and health equity among Medicaid enrollees, 2014–2023. Am J Public Health. 2024;114(11):1275–85. doi:10.2105/AJPH.2024.307805 [author list verified via Europe PMC; confirm on PubMed]

20. Nove A, Friberg IK, de Bernis L, McConville F, Moran AC, Najjemba M, et al. Potential impact of midwives in preventing and reducing maternal and neonatal mortality and stillbirths: a Lives Saved Tool modelling study. Lancet Glob Health. 2021;9(1):e24–32. doi:10.1016/S2214-109X(20)30397-1

21. Vedam S, Stoll K, MacDorman M, Declercq E, Cramer R, Cheyney M, et al. Mapping integration of midwives across the United States: impact on access, equity, and outcomes. PLoS One. 2018;13(2):e0192523. doi:10.1371/journal.pone.0192523

22. Grünebaum A, McCullough LB, Orosz B, Chervenak FA. Neonatal mortality in the United States is related to location of birth (hospital versus home) rather than the type of birth attendant. Am J Obstet Gynecol. 2020;223(2):254.e1–8. doi:10.1016/j.ajog.2020.01.045

23. Gunja MZ, Gumas ED, Masitha R, Zephyrin LC. Insights into the U.S. maternal mortality crisis: an international comparison. New York: Commonwealth Fund; 2024 Jun 4.

24. Diguisto C, Saucedo M, Kallianidis A, Bloemenkamp K, Bødker B, Buoncristiano M, et al. Maternal mortality in eight European countries with enhanced surveillance systems: descriptive population based study. BMJ. 2022;379:e070621. doi:10.1136/bmj-2022-070621

25. MBRRACE-UK. Data brief: maternal mortality 2022–24. Oxford: National Perinatal Epidemiology Unit; 2026 Sep. [Author list not verified]

26. Kallianidis AF, Schutte JM, Schuringa LEM, Beenakkers ICM, Bloemenkamp KWM, Braams-Lisman BAM, et al. Confidential enquiry into maternal deaths in the Netherlands, 2006–2018. Acta Obstet Gynecol Scand. 2022;101(4):441–9. doi:10.1111/aogs.14312

27. Callaghan J, Dudenhausen J, Paulson L, Hellmeyer L, Vetter K, Ziegert M, et al. Analysis of maternal mortality in Berlin, Germany – discrepancy between reported maternal mortality and comprehensive death certificate exploration. J Perinat Med. 2024;52(4):375–84. doi:10.1515/jpm-2023-0403

28. Hoyert DL, Miniño AM. Maternal mortality in the United States: changes in coding, publication, and data release, 2018. Natl Vital Stat Rep. 2020;69(2):1–18.

29. Rossen LM, Womack LS, Hoyert DL, Anderson RN, Uddin SFG. The impact of the pregnancy checkbox and misclassification on maternal mortality trends in the United States, 1999–2017. Vital Health Stat 3. 2020;(44). [Page range not verified]

30. Joseph KS, Lisonkova S, Boutin A, Muraca GM, Razaz N, John S, et al. Maternal mortality in the United States: are the high and rising rates due to changes in obstetrical factors, maternal medical conditions, or maternal mortality surveillance? Am J Obstet Gynecol. 2024;230(4):440.e1–13. doi:10.1016/j.ajog.2023.12.038

31. Declercq E, Thoma M. Measuring US maternal mortality. JAMA. 2023;330(18):1731–2. doi:10.1001/jama.2023.19945

32. Janevic T, Declercq E, Howell EA. Data have consequences—centring equity in the maternal mortality surveillance debate. Paediatr Perinat Epidemiol. 2025;39(4):311–3. doi:10.1111/ppe.13138

33. Trost SL, Beauregard J, Njie F, Berry J, Harvey A, Goodman DA, et al. Pregnancy-related deaths: data from maternal mortality review committees in 36 US states, 2017–2019. Atlanta (GA): Centers for Disease Control and Prevention; 2022. [Author list beyond first three not verified]

34. Wallace M, Gillispie-Bell V, Cruz K, Davis K, Vilda D. Homicide during pregnancy and the postpartum period in the United States, 2018–2019. Obstet Gynecol. 2021;138(5):762–9. doi:10.1097/AOG.0000000000004567

35. Bruzelius E, Martins SS. US trends in drug overdose mortality among pregnant and postpartum persons, 2017–2020. JAMA. 2022;328(21):2159–61. doi:10.1001/jama.2022.17045 [issue/pages not verified]

36. Drew LB, Goldman-Mellor S, Joachim G, Gemmill A, Margerison CE. Pregnancy-associated drug overdose mortality in the United States, 2018–2023. Obstet Gynecol. 2026 Jul 30 [Epub ahead of print]. doi:10.1097/AOG.0000000000006389

37. Petersen EE, Davis NL, Goodman D, Cox S, Syverson C, Seed K, et al. Racial/ethnic disparities in pregnancy-related deaths — United States, 2007–2016. MMWR Morb Mortal Wkly Rep. 2019;68(35):762–5. doi:10.15585/mmwr.mm6835a3

38. Howell EA, Egorova NN, Balbierz A, Zeitlin J, Hebert PL. Site of delivery contribution to black-white severe maternal morbidity disparity. Am J Obstet Gynecol. 2016;215(2):143–52. doi:10.1016/j.ajog.2016.05.007

39. Howell EA, Egorova NN, Janevic T, Brodman M, Balbierz A, Zeitlin J, et al. Race and ethnicity, medical insurance, and within-hospital severe maternal morbidity disparities. Obstet Gynecol. 2020;135(2):285–93. doi:10.1097/AOG.0000000000003667

40. Guglielminotti J, Wong CA, Friedman AM, Li G. Racial and ethnic disparities in death associated with severe maternal morbidity in the United States: failure to rescue. Obstet Gynecol. 2021;137(5):791–800. doi:10.1097/AOG.0000000000004362

41. Briller J, Trost SL, Busacker A, Joseph NT, Davis NL, Petersen EE, et al. Pregnancy-related mortality due to cardiovascular conditions: maternal mortality review committees in 32 U.S. states, 2017 to 2019. JACC Adv. 2024;3(12):101382. doi:10.1016/j.jacadv.2024.101382

42. Main EK, Cape V, Abreo A, Vasher J, Woods A, Carpenter A, et al. Reduction of severe maternal morbidity from hemorrhage using a state perinatal quality collaborative. Am J Obstet Gynecol. 2017;216(3):298.e1–11. doi:10.1016/j.ajog.2017.01.017

43. Main EK, Chang SC, Dhurjati R, Cape V, Profit J, Gould JB. Reduction in racial disparities in severe maternal morbidity from hemorrhage in a large-scale quality improvement collaborative. Am J Obstet Gynecol. 2020;223(1):123.e1–14. PMID 31978432. [DOI not captured]

44. Main EK, Markow C, Gould J. Addressing maternal mortality and morbidity in California through public-private partnerships. Health Aff (Millwood). 2018;37(9):1484–93. doi:10.1377/hlthaff.2018.0463

45. California Department of Public Health. California Pregnancy Mortality Surveillance System: pregnancy-related deaths, 2019–2021 [fact sheet]. Sacramento (CA): CDPH; 2025. See also: California Pregnancy-Associated Mortality Review, pregnancy-related deaths 2008–2016.

46. Gillispie-Bell V, Wallace K. Improving and maintaining reductions in severe maternal morbidity through a statewide perinatal quality collaborative. Obstet Gynecol. 2026 [Epub ahead of print]. doi:10.1097/AOG.0000000000006269. PMID 41886758.

47. Borders A, Keenan-Devlin L, Oh EH, Young D, Grobman W, Lee King P. Reducing severe maternal morbidity for birthing persons with severe hypertension through a statewide quality improvement initiative. Am J Obstet Gynecol. 2024 Apr 30 [Epub ahead of print]. PMID 38697335. [Volume/pages not verified]

48. Davidson C, Denning S, Thorp K, Tyer-Viola L, Belfort M, Sangi-Haghpeykar H, et al. Examining the effect of quality improvement initiatives on decreasing racial disparities in maternal morbidity. BMJ Qual Saf. 2022;31(9):670–8. doi:10.1136/bmjqs-2021-014225

49. Kern-Goldberger AR, Hirshberg A, James A, Levine LD, Howell E, Harbuck E, et al. Trends in severe maternal morbidity following an institutional team goal strategy for disparity reduction. Am J Obstet Gynecol MFM. 2024:101529. doi:10.1016/j.ajogmf.2024.101529

50. Tita AT, Szychowski JM, Boggess K, Dugoff L, Sibai B, Lawrence K, et al. Treatment for mild chronic hypertension during pregnancy. N Engl J Med. 2022;386(19):1781–92. doi:10.1056/NEJMoa2201295

51. Arkerson BJ, Finneran MM, Harris SR, Schnorr J, McElwee ER, Demosthenes L, et al. Remote monitoring compared with in-office surveillance of blood pressure in patients with pregnancy-related hypertension: a randomized controlled trial. Obstet Gynecol. 2023;142(4):855–61. doi:10.1097/AOG.0000000000005327 [title as retrieved; confirm]

52. Hirshberg A, Zhu Y, Smith-McLallen A, Srinivas SK. Association of a remote blood pressure monitoring program with postpartum adverse outcomes. Obstet Gynecol. 2023;141(6):1163–70. doi:10.1097/AOG.0000000000005197

53. Eliason EL. Adoption of Medicaid expansion is associated with lower maternal mortality. Womens Health Issues. 2020;30(3):147–52. doi:10.1016/j.whi.2020.01.005

54. Eliason EL, Steenland MW, Gourevitch RA. Extended pregnancy Medicaid during COVID-19 and enrollment and health care use in the postpartum year. Milbank Q. 2026;104(2):488–507. doi:10.1111/1468-0009.70079

55. Greenwood BN, Hardeman RR, Huang L, Sojourner A. Physician–patient racial concordance and disparities in birthing mortality for newborns. Proc Natl Acad Sci U S A. 2020;117(35):21194–200. doi:10.1073/pnas.1913405117

56. Borjas GJ, VerBruggen R. Physician–patient racial concordance and newborn mortality. Proc Natl Acad Sci U S A. 2024;121(39):e2409264121. doi:10.1073/pnas.2409264121

57. Nantwi AK, Janevic T. Intervention research to reduce disparities in severe maternal morbidity and mortality in the United States: a scoping review. Obstet Gynecol. 2026;147(6):817. doi:10.1097/AOG.0000000000006305 [page range not verified]

58. Gray A, Thomas Z, Robinson L, Ashley-McNamee RR, Cornelius A, Louis ER, et al. Midwifery care and doula support for Black women in the United States: a systematic review. J Racial Ethn Health Disparities. 2026 [Epub ahead of print]. doi:10.1007/s40615-026-03121-9

59. McConnell MA, Rokicki S, Ayers S, Allouch F, Perreault N, Gourevitch RA, et al. Effect of an intensive nurse home visiting program on adverse birth outcomes in a Medicaid-eligible population: a randomized clinical trial. JAMA. 2022;328(1):27–37. doi:10.1001/jama.2022.9703

60. Qian J, Wolfson C, Kramer B, Creanga AA. Insights from preventability assessments across 42 state and city maternal mortality reviews in the United States. Am J Obstet Gynecol. 2025;232(4):394.e1–10. doi:10.1016/j.ajog.2024.08.030

61. Sullivan SA, Hill EG, Newman RB, Menard MK. Maternal-fetal medicine specialist density is inversely associated with maternal mortality ratios. Am J Obstet Gynecol. 2005;193(3 Pt 2):1083–8. doi:10.1016/j.ajog.2005.05.085

62. Kawakita T, Atwani R, Hayasaka M, Robbins L, Saade G. The association between maternal-fetal medicine physician density and pregnancy outcomes. Am J Perinatol. 2026;43(9):1121–6. doi:10.1055/a-2717-3951

63. Snyder JE, Stahl AL, Streeter RA, Washko MM. Regional variations in maternal mortality and health workforce availability in the United States. Ann Intern Med. 2020;173(11 Suppl):S45–54. doi:10.7326/M19-3254

64. Osei-Poku GK, Prentice JC, Easter SR, Diop H. Delivery at an inadequate level of maternal care is associated with severe maternal morbidity. Am J Obstet Gynecol. 2024;231(5):546.e1–20. doi:10.1016/j.ajog.2024.02.308

65. Clapp MA, James KE, Kaimal AJ. The effect of hospital acuity on severe maternal morbidity in high-risk patients. Am J Obstet Gynecol. 2018;219(1):111.e1–7. doi:10.1016/j.ajog.2018.04.015

66. Handley SC, Passarella M, Radack J, Amuzie O, Hsu JY, Srinivas SK, et al. Maternal levels of care and association with severe maternal morbidity during birth hospitalizations. PLoS One. 2026;21(7):e0353016. doi:10.1371/journal.pone.0353016

67. Vanderlaan J, Rochat R, Williams B, Dunlop A, Shapiro SE. Associations between hospital maternal service level and delivery outcomes. Womens Health Issues. 2019;29(3):252–8. doi:10.1016/j.whi.2019.02.004

68. Handley SC, Formanowski B, Passarella M, Thorsen ML, Interrante JD, Busse CE, et al. Risk-appropriate childbirth care among higher-risk pregnant rural residents. JAMA Health Forum. 2025;6(11):e254241. PMID 41269701. [DOI not captured]

69. Srinivas SK, Small DS, Macheras M, Hsu JY, Caldwell D, Lorch S. Evaluating the impact of the laborist model of obstetric care on maternal and neonatal outcomes. Am J Obstet Gynecol. 2016;215(6):770.e1–9. doi:10.1016/j.ajog.2016.08.007

70. Torbenson VE, Tatsis V, Bradley SL, Butler J, Kjerulff L, McLaughlin GB, et al. Use of obstetric and gynecologic hospitalists is associated with decreased severe maternal morbidity in the United States. J Patient Saf. 2023;19(3):202–10. doi:10.1097/PTS.0000000000001102

71. Sebastian SA, Atwal H, Ayyalu T, Gulati M. Impact of cardio-obstetrics care on maternal outcomes in pregnant women with heart disease: a systematic review and meta-analysis. Curr Probl Cardiol. 2025;50(12):103190. doi:10.1016/j.cpcardiol.2025.103190

72. Magann EF, Bronstein J, McKelvey SS, Wendel P, Smith DM, Lowery CL. Evolving trends in maternal fetal medicine referrals in a rural state using telemedicine. Arch Gynecol Obstet. 2012;286(6):1383–92. PMID 22821508.

73. MBRRACE-UK. Saving lives, improving mothers’ care: state of the nation report 2025 — lessons learned to inform maternity care from the UK and Ireland confidential enquiries into maternal deaths and morbidity 2021–23. Oxford: National Perinatal Epidemiology Unit; 2025. [Editor list not verified]

74. MBRRACE-UK. Saving lives, improving mothers’ care: state of the nation report 2026 — surveillance of maternal deaths 2022–24. Oxford: National Perinatal Epidemiology Unit; 2026. [Editor list not verified]

75. Deneux-Tharaux C, Saucedo M; Comité National d’Experts sur la Mortalité Maternelle. Mortalité maternelle, adéquation des soins et évitabilité, France 2016–2018. Gynecol Obstet Fertil Senol. 2024;52(4):201–9. doi:10.1016/j.gofs.2024.02.021

76. Bruyère M, Morau E, Verspyck E; Comité National d’Experts sur la Mortalité Maternelle. Mortalité maternelle par maladies cardiovasculaires en France 2016–2018. Gynecol Obstet Fertil Senol. 2024;52(4):221–30. PMID 38373486.

77. Guglielminotti J, Daw JR, Friedman AM, Samari G, Li G. Medicaid expansion and severe maternal morbidity during delivery hospitalizations. Obstet Gynecol. 2026 Aug 27 [Epub ahead of print]. doi:10.1097/AOG.0000000000006415

78. Chatterji P, Glenn H, Markowitz S, Montez JK. Affordable Care Act Medicaid expansions and maternal morbidity. Health Econ. 2023;32(10):2334–52. doi:10.1002/hec.4724

79. Guglielminotti J, Landau R, Li G. The 2014 New York State Medicaid expansion and severe maternal morbidity during delivery hospitalizations. Anesth Analg. 2021;133(2):340–8. doi:10.1213/ANE.0000000000005371

80. Steenland MW, Wherry LR. Medicaid expansion led to reductions in postpartum hospitalizations. Health Aff (Millwood). 2023;42(1). doi:10.1377/hlthaff.2022.00819 [pages not verified]

81. Gordon SH, Chen L, DeLew N, Sommers BD. COVID-19 Medicaid continuous enrollment provision yielded gains in postpartum continuity of coverage. Health Aff (Millwood). 2024;43(3):336–43. doi:10.1377/hlthaff.2023.00580

82. Gordon SH, Lee S, Steenland MW, Deen N, Feinberg E. Extended postpartum Medicaid in Colorado associated with increased treatment for perinatal mood and anxiety disorders. Health Aff (Millwood). 2024;43(4):523–31. doi:10.1377/hlthaff.2023.01441

83. Johnston EM, McMorrow S, Alvarez Caraveo C, Dubay L. Post-ACA, more than one-third of women with prenatal Medicaid remained uninsured before or after pregnancy. Health Aff (Millwood). 2021;40(4):571–8. doi:10.1377/hlthaff.2020.01678

84. Daw JR, Kolenic GE, Dalton VK, Zivin K, Winkelman TNA, Kozhimannil KB, et al. Racial and ethnic disparities in perinatal insurance coverage. Obstet Gynecol. 2020;135(4):917–24. doi:10.1097/AOG.0000000000003728

85. Fuller S, Kuenstler MR, Snipes MA, Miller MJ, Lutgendorf MA. Obstetrical health care inequities in a universally insured health care system. AJOG Glob Rep. 2023;3(3):100256. doi:10.1016/j.xagr.2023.100256

86. Oakley LP, Li X, Tartof SY, Wilkes-Grundy M, Fassett MJ, Lawrence JM. Racial disparities in severe maternal morbidity in an integrated health care system, Southern California, 2008–2017. Womens Health Issues. 2023;33(3):280–8. doi:10.1016/j.whi.2023.01.001

87. Saucedo M, Deneux-Tharaux C; Comité National d’Experts sur la Mortalité Maternelle. Mortalité maternelle en France, 2016–2018, fréquence, causes et profil des femmes. Gynecol Obstet Fertil Senol. 2024;52(4):185–200. doi:10.1016/j.gofs.2024.02.020

88. Urquia ML, Glazier RH, Mortensen L, Nybo-Andersen AM, Small R, Davey MA, et al. Severe maternal morbidity associated with maternal birthplace in three high-immigration settings. Eur J Public Health. 2015;25(4):620–5. doi:10.1093/eurpub/cku230

89. Lundeberg K, Tindal R, Grob PC, Hamilton JL, Gonzalez-Brown VM, Keyser EA. Military healthcare system mitigates racial disparities for severe maternal morbidity from preeclampsia. AJOG Glob Rep. 2023;3(3):100215. doi:10.1016/j.xagr.2023.100215

90. Guglielminotti J, Li G, Daw JR, Friedman AM, Samari G. Reduced odds of severe maternal morbidity associated with the US Affordable Care Act dependent coverage provision. Am J Obstet Gynecol MFM. 2025;7(6):101668. doi:10.1016/j.ajogmf.2025.101668

The Clinical Event: Shoulder Dystocia and Injury

The delivery proceeds normally until the final stage of labor. The fetal head is delivered, but immediately retracts against the perineum—a phenomenon known clinically as the “turtle sign.” This signals shoulder dystocia, an obstetrical emergency where the baby’s anterior shoulder becomes impacted behind the mother’s pubic bone. The obstetrician and nursing team must act instantly to prevent fetal asphyxia. Standard protocols, such as the McRoberts maneuver (flexing the mother’s legs) and suprapubic pressure, are initiated to free the shoulder.

However, in this scenario, the impaction is severe. If excessive lateral traction (pulling) is applied to the fetal head while the shoulder remains stuck, the nerves of the brachial plexus—which run from the spine down the arm—are stretched beyond their limit. This results in an injury ranging from neuropraxia (stretch) to avulsion (tearing from the spine), causing Brachial Plexus Injury (BPI), also known as Erb’s palsy. The newborn is delivered with a limp arm, internally rotated and adducted, facing potentially lifelong paralysis or sensory deficits.

The Lifelong Reality: Expenses and Experience

For the child and family, the diagnosis initiates a lifetime of physical, emotional, and financial challenges. The physical journey often begins with nerve graft or transfer surgeries in infancy to restore basic function, followed by secondary procedures like tendon transfers or osteotomies in childhood to address muscle imbalances and bone deformities. Daily life is dominated by physiotherapy, occupational therapy, and the use of splints or electrical stimulation units to prevent contractures.

The financial burden is staggering. In severe cases, lifetime costs for medical care, adaptive equipment, and loss of earning capacity can exceed $1 million to $5 million. Parents often reduce work hours to manage therapy schedules, compounding the economic strain. Psychosocially, the toll is equally heavy. Children may face bullying due to their limb difference, struggle with two-handed tasks like tying shoes or cutting food, and experience exclusion from contact sports. As adults, they often contend with chronic pain, early-onset arthritis, and career limitations in manual fields. The psychological impact includes body image anxiety and higher rates of depression, while parents frequently suffer from guilt and PTSD related to the traumatic birth.

With the diagnosis of a permanent brachial plexus injury, the family’s journey transitions from the delivery room to the legal arena, where they must navigate complex systems to secure their child’s future. While the obstetrical definition of shoulder dystocia remains constant across borders, the mechanism for seeking redress differs profoundly based on the jurisdiction. In some nations, the process is a combat of experts in high-stakes litigation; in others, it is a streamlined administrative assessment of “avoidability” or a consumer dispute. The following sections explore the legal steps, financial risks, and compensation realities of bringing a malpractice case in nine major regions—highlighting how geography dictates the burden of proof, the cost of justice, and the ultimate value placed on the injury.

Subscribe now

Read more

A colleague is giving a talk called “The Future of Baby Making.”

I would like to hear the one called “The Present.”

Here is the present.

In 2022, fertility clinics in the United States ran 435,426 treatment cycles and delivered 98,289 babies. That is about 2.6% of all babies born in this country. In 2023 there were 3,596,017 births. So roughly 3.5 million babies were made the ordinary way, at home, with no clinic, no laboratory, and no invoice.

That 2.6% gets the conferences. It gets the investment money. It gets the word “future.”

The other 97.4% got this. The share of pregnant women starting prenatal care in the first three months fell to 76.1% in 2023. The share getting no prenatal care at all rose by 5%. The preterm birth rate sat at 10.41% and has barely moved in twenty years. Those are not small problems. They are the main event.

So why does the small group get called the future?

Look at how the money moves.

Embryo selection, the long menu of laboratory add-ons, and surrogacy arrangements are paid for directly, priced per cycle and per journey, and marketed to people who are frightened and hopeful at the same time. Ordinary prenatal care is bundled into one flat fee, and close to half of all births in this country are paid for by Medicaid. One of those is a product. The other is a line item.

SURROGACY IS THE CLEAREST EXAMPLE

Every surrogacy arrangement runs through a fertility clinic. There is no other way to do it. Eggs have to be retrieved. An embryo has to be made in a laboratory. That embryo has to be transferred into another woman’s uterus. Often donor eggs are used, which means a second retrieval and a second set of fees.

Stop and notice what that means. Each arrangement generates laboratory work whether or not the intended parents have any fertility problem at all. A healthy couple who cannot carry a pregnancy, or chooses not to, still produces a full cycle of clinic revenue. Gay couples, single men, and women born without a uterus or who have lost one are entering this market in growing numbers. At this scale, that market did not exist twenty years ago. It is one of the fastest growing parts of reproductive medicine, and it grows for reasons that have nothing to do with infertility.

Then look at the price. Agencies advertise a full surrogacy journey in the United States at roughly $150,000 to $220,000. These are marketing figures, not registry data, and no one publishes an authoritative national average. But the structure is consistent across every agency that posts its prices. The agency takes something like $35,000 to $55,000 for matching and case management. Lawyers take $10,000 to $25,000. The clinic takes its cycle fees. Escrow companies, insurance brokers, and specialty lenders each take a slice. The carrier is usually paid somewhere between $60,000 and $95,000.

She is also the only person in the arrangement who can end up in an intensive care unit.

That is the part I keep coming back to. The gestational carrier is the patient in the way that matters. She is the pregnant one. She carries the risk of preeclampsia, of hemorrhage, of an unplanned cesarean, of a placenta that will not separate, of a hysterectomy she did not plan for. Everyone else in the arrangement carries financial risk. She carries the medical risk in her body.

Now look at who pays for her protection.

Her lawyer is frequently paid for by the people on the other side of the contract.

Her psychological screening is arranged by an agency that gets paid only when she is matched.

Her medical care is directed by a clinic retained by the intended parents.

Many ordinary health plans exclude carrier pregnancies, so a separate policy has to be bought, and the gap is discovered after she has already signed.

When money moves around a pregnant woman from every direction except toward her own independent counsel, the profession should say so out loud instead of calling it a journey.

I want to be careful here. Many carriers do this knowingly, want to do it, and describe it afterward as one of the best things they have done.

Many agencies screen carefully and behave decently. I am not calling surrogacy wrong, and I am not calling the people who work in it bad. I am saying that informed consent means the person taking the physical risk gets her own advisors, her own doctor, and her own honest numbers about what pregnancy can do to a body. That standard is not met by a contract drafted for someone else’s benefit.

And when the American price is too high, the business travels. There are all-inclusive programs advertised overseas at a fraction of the US figure, in countries with far less oversight of how carriers are recruited, where they live during the pregnancy, what care they receive, and what happens to them if something goes wrong. That is not the future of baby making. That is outsourcing the risk to women with fewer options.

I am not saying anyone is acting in bad faith. I am saying that incentives decide which questions get asked, which talks get booked, and which parts of human reproduction get to call themselves the future.

THE EVIDENCE UNDERNEATH THE FUTURE IS THINNER THAN THE MARKETING

A great deal of what is sold as tomorrow has already been tested and did not deliver. Testing embryos for chromosome number, and testing the lining of the uterus to pick a transfer day, were both studied in randomized trials. Neither raised the chance of taking a baby home for the average patient who started treatment. That is the number a woman thinks she is buying. She is often quoted a different one, usually a success rate calculated on a smaller and healthier group than the one she belongs to.

Now the part nobody books a keynote on.

Most fertility treatment is a workaround for problems we failed to prevent earlier. Women are starting families later, and eggs age on a schedule no clinic can renegotiate. Untreated pelvic infections damage tubes. Weight and metabolic disease affect ovulation. And in a large share of couples, sperm is part of the problem, yet the man is often tested last, or not at all.

None of that is glamorous. None of it can be billed as a journey. All of it is where the real gains are.

WHAT THIS MEANS IF YOU ARE TRYING TO GET PREGNANT

Most couples do not need a laboratory.

They need better timing, an honest look at both partners, and a few unglamorous things done early.

1. Know your cycle. The fertile window is short, and most people place it wrong. The Fertility and Conception Calendar walks you through it: https://tools.obmd.com/fertility-calculator

2. Start folic acid before you conceive, not after the test is positive. The neural tube closes in the first month, often before a woman knows she is pregnant.

3. Test the man early, not after a year of testing only the woman. A semen analysis is cheap, fast, and changes the plan when it is abnormal. The male fertility tools are here: https://tools.obmd.com/male-fertility-tools

4. Deal with what can be dealt with. Smoking, alcohol, weight, thyroid disease, diabetes, and medications that should be changed before pregnancy.

Know when to ask for help. Twelve months of trying without success is the usual threshold. Six months if you are 35 or older. Sooner if your periods are irregular or absent, or if there are known problems such as tubal or testicular disease.

The full preconception set is here: https://tools.obmd.com/preconception

CONCLUSION

I have no quarrel with in vitro fertilization. It is one of the genuine achievements of my professional lifetime, and for some couples it is the only road. I have no quarrel with a woman who chooses to carry a pregnancy for someone else, either. My quarrel is with the framing, and with what the framing hides.

When we call the laboratory “the future of baby making,” we tell 97 out of every 100 families that their pregnancy is the past. It is NOT. Their pregnancy is the NOW and the THEN.

We let ourselves off the hook for the things that would help far more people: earlier evaluation, honest counseling, prenatal care that actually reaches women, and prevention that starts before anyone is trying.

And we dress a fast growing commercial arrangement in the language of miracles, while the woman doing the physical work of it is the one person in the room without her own paid advocate.

A field that only gets excited about the expensive 2.6% is not looking at reproduction. It is looking at its own invoice.

Ask what the future holds for the 97.4%. If the room goes quiet, that is the talk worth giving.

If this was useful, subscribe. It is free, and so are the tools.

REFERENCES

1. Centers for Disease Control and Prevention. ART surveillance: 2022 national summary. Atlanta (GA): CDC; 2024. Available from: https://www.cdc.gov/art/php/surveillance/index.html

2. National Center for Health Statistics. Births in the United States, 2023. NCHS Data Brief No. 507. Hyattsville (MD): NCHS; 2024. Available from: https://www.cdc.gov/nchs/products/databriefs/db507.htm

3. Munné S, Kaplan B, Frattarelli JL, et al. Preimplantation genetic testing for aneuploidy versus morphology as selection criteria for single frozen-thawed embryo transfer in good-prognosis patients: a multicenter randomized clinical trial. Fertil Steril. 2019;112(6):1071-1079.e7. [PENDING RefVerify]

4. Doyle N, Jahandideh S, Hill MJ, et al. Effect of timing by endometrial receptivity testing vs standard timing of frozen embryo transfer on live birth in patients undergoing in vitro fertilization: a randomized clinical trial. JAMA. 2022;328(21):2117-2125. [PENDING RefVerify]

Note on the surrogacy figures: all dollar ranges in this piece are taken from surrogacy agency and fertility clinic marketing pages, not from peer-reviewed or registry sources. No authoritative national average exists, and published estimates differ because they include different services. Sources consulted include Hatch (hatch.us), Circle Surrogacy (circlesurrogacy.com), SurrogateFirst (surrogatefirst.com), and Egg Donor and Surrogacy Institute (eggdonorandsurrogacy.com), accessed September 2026.

Imagine reviewing a manuscript in your specialty.

The question matters. The methods appear reasonable. You examine the analysis, challenge the conclusions, and recommend revisions. The authors respond. The paper is accepted.

Buried in its bibliography is a study that does not exist.

The citation looks entirely ordinary. Recognizable journal. Plausible title. Familiar author names. Nothing about its appearance tells you that the evidence it supposedly represents is missing.

Your review may have improved the manuscript substantially. Yet something more basic escaped scrutiny: whether part of its supporting literature was real.

That possibility is now the subject of a troubling research report.

And it leads me to a position that journals need to take seriously: AI assistance must become part of the infrastructure of peer review.

This is my proposed standard, not a claim that every available AI tool has been proved effective.

Subscribe now

In their 2026 arXiv preprint, Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences, first author Mark Russinovich and colleagues audited accepted papers from major AI and security conferences. Their definition covered nonexistent works and substantial author mismatches, excluding routine bibliographic discrepancies. Approximately one in twenty NeurIPS and USENIX Security papers in 2025 contained at least two likely hallucinated academic references. Reference-level rates were generally below 1%.1

The authors interpreted this as evidence that peer review alone does not reliably enforce citation integrity. Their verification pipeline combined bibliographic databases with AI-assisted web searching.1

The limitations matter. This is a preprint, not an established estimate for medical journals. The audit does not establish that AI generated each problematic reference, and citation identity checks do not establish whether a real source supports a claim.1

My conclusion is that journals should make systematic verification an explicit part of review.

Expertise cannot substitute for retrieval.

An experienced reviewer may recognize an invented landmark trial immediately. But a plausible reference to an unfamiliar paper presents a different problem.

You cannot establish that a paper exists by knowing a great deal about its subject. You must retrieve it.

You cannot establish that it supports a sentence by recognizing the journal. You must examine what it actually reports.

Share

You cannot establish that its findings apply to the population under discussion by reading its title. You must compare the study with the claim.

A reviewer can perform these checks for individual references. The unrealistic expectation is that every reviewer will perform them exhaustively, for every submission, while also evaluating design, statistics, interpretation, originality, and clinical relevance.

No individual reviewer can serve as an exhaustive verification system for the scientific literature at publishing scale.

Telling reviewers to “be more careful” does not solve that design problem.

Consider a hypothetical manuscript with 80 references. Allow just three minutes to locate and check each one. That is four hours before assessing whether the sources support the statements attached to them. The arithmetic is illustrative, but the workload is real in principle: every additional layer of verification consumes time.

The response should be to build a process that makes those checks routine.

The objection that “AI hallucinates” strengthens the case for verification.

It is a valid objection to treating a chatbot’s answer as evidence.

Ask an AI system whether a citation is real, accept its confident answer, and you may simply add another unsupported assertion to the chain.

A useful verification system must show its work in an inspectable form: the retrieved record, the matching publication, the relevant passage, and any unresolved discrepancy.

The distinction matters. A model’s recollection is not a bibliographic record. Agreement between two models is not independent confirmation. A functioning link does not establish that the linked paper supports the manuscript’s claim.

AI assistance should help retrieve, compare, and flag. Its conclusions must remain open to inspection.

Straightforward checks should use conventional software and authoritative databases wherever possible. AI adds a potential layer of assistance when references are incomplete, wording differs, or a claim needs comparison with source text. Those more interpretive functions require separate validation.

The goal is a documented chain from assertion to evidence.

A real reference can still support a false narrative.

Imagine a manuscript stating that an intervention “prevents complications.”

The reference exists. The authors and journal are correct. The DOI works.

But the cited study measured a laboratory marker rather than complications. Or it enrolled a different population. Or its observational design supports an association while the manuscript asserts a causal effect.

These are hypothetical examples of the questions a review process should ask. Passing a citation-existence check would leave every one of them unresolved.

That is why I would require two distinct layers: bibliographic verification for every reference, followed by source comparison for the claims that carry the paper’s argument.

AI can assist with that comparison by placing the manuscript’s statement beside the relevant source passage and identifying possible discrepancies. The reviewer then assesses whether the discrepancy matters.

A system that presents the evidence for scrutiny is much more useful than one that merely produces another polished review.

Journals should provide the tools and own the process.

My proposed standard would require journals to:

• Check every reference against retrievable bibliographic records.

• Provide reviewers with an AI-assisted evidence audit for the manuscript’s central claims.

• Distinguish confirmed errors from unresolved searches and interpretive concerns.

• Require human assessment before an automated flag influences an editorial decision.

• Validate the system and monitor both missed errors and false accusations.

An unsuccessful search should be labeled “unresolved.” Calling it “fabricated” requires stronger evidence. Authors must have a fair opportunity to supply the source or correct the record.

Confidentiality also belongs in the design. ICMJE requires reviewers to follow journal AI policies or obtain permission, preserve manuscript confidentiality, disclose AI use, and ensure that the resulting content is appropriate and valid.2

Journals should therefore provide approved, secure systems. Reviewers should not have to improvise access to the tools the journal expects them to use.

The responsibility remains human. The assistance should be systematic.

Authors remain responsible for their references and claims. Reviewers remain responsible for their judgments. Editors remain responsible for publication decisions.

Adding AI does not transfer any of those obligations.

It can, however, give those people a more complete set of questions to investigate and a clearer record of what has actually been checked.

The conference audit does not prove that AI-assisted review improves clinical outcomes or catches every false claim. Those outcomes need evaluation. It does expose a verification gap that deserves a concrete response.

My position is that journals must build and evaluate AI-assisted verification as a standard part of review, with human oversight and traceable evidence. They should define its tasks narrowly enough to test its performance and expand its role only when the results justify doing so.

“Peer reviewed” should never be mistaken for “every claim verified.”

But we should be working to close that gap.

AI hallucinations make the need harder to ignore. A fabricated reference can look perfectly scholarly. Human expertise cannot make an unread source real.

Reviewers need tools that help them discover what deserves closer inspection.

Journals must provide them.

References

1. Russinovich M, Siva Kumar RS, Salem A. Phantom references: hallucinated citations that survive peer review at top-tier conferences. arXiv [Preprint]. 2026 Jul 1. arXiv:2607.00738. Full text.

2. International Committee of Medical Journal Editors. Use of AI by reviewers. In: Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals [Internet]. [cited 2026 Sep 8]. Recommendations.