The Gunman in the Room
The system, the power asymmetry, and why this is engineering logic applied to a medical crisis, not ideology.
There is a trillion dollars flowing toward maternal health innovation in the United States right now. Blood pressure monitors. Remote monitoring platforms. Prenatal apps. Postpartum check-in tools. AI-powered risk stratification. The investment is real, the technology is often excellent, and almost none of it is going to solve the problem.
I want to explain why. Not as an argument against those companies — I genuinely want them to succeed. I want to live in a world where maternal health technology is the undisputed frontier. But we cannot get there yet. Understanding why requires being willing to follow the data to a conclusion that most people in this space have not been willing to say out loud.
What the Data Actually Says
Black women in the United States die in childbirth at 3.2 times the rate of White women. [1] That statistic is widely cited. What is less widely discussed is what it does and does not tell us.
The instinct is to reach for explanations that feel solvable: insurance coverage, access to care, prenatal visits, geography, poverty. These are real factors. They matter at the margins. But here is the data point that forecloses every one of those explanations.
A 2023 study in The Lancet Regional Health analyzed 13 million live births and found that Black mothers in the least vulnerable counties — the counties with the best access, the best resources, the most support — remained at higher risk of maternal mortality than White mothers in the most vulnerable counties. [2] The racial gap was not explained by county-level resources. It persisted regardless of where the woman lived.
The CDC’s own data is equally direct. Among college-educated Black women, the pregnancy-related mortality ratio is 5.2 times that of college-educated White women — and 1.6 times that of White women without a high school diploma. [3] More education makes White women safer. It does not make Black women safe. A 2022 NBER study linking birth records to IRS income data found that maternal mortality rates among the highest-income Black women equaled those of low-income White women. Babies born to Black women in the top 10% of earners had more risk factors than those born to the poorest White mothers — in the same hospitals. [4]
Serena Williams. Beyoncé. Both described having to fight for their lives in medical settings despite every credential, every resource, every advocate money could provide.
This is not anecdote. It is confirmation of what the data already shows: wealth cannot buy out of this disparity. Education cannot either. That is what structural racism looks like.
The Mechanism
This is not a claim about bad intentions. It is a claim about what the data shows happens in clinical settings when the patient is Black.
In 2016, researchers published a study in PNAS documenting that 50% of White medical students and residents endorsed at least one false belief about biological differences between Black and White people — that Black people’s skin is thicker, that their blood coagulates more quickly, that they feel less pain. Those who endorsed these beliefs rated Black patients’ pain as lower and made less accurate treatment recommendations. [5] Half of the people entering our medical workforce are carrying demonstrably false beliefs about Black bodies into clinical encounters.
The consequences downstream are measurable. Black patients presenting with extremity fractures receive analgesics at a rate of 57% versus 74% for White patients — a 66% greater risk of receiving nothing at all. [6] A meta-analysis of 14 emergency department studies found Black patients were 40% less likely to receive analgesia for equivalent pain. [7] A decade’s worth of implicit association test research found that 31 of 37 studies detected pro-White or anti-Black bias among healthcare providers — and every study that examined real clinical encounters found that bias correlated with poorer communication. [8]
For obstetric settings: a 2023 KFF survey found that 22% of Black women who had been pregnant in the last decade reported being refused pain medication they believed they needed. [9] Twenty-two percent.
Now add the power asymmetry. A 2024 study published in Science — using 1.5 million quasi-random physician-patient assignments in military emergency departments, where military rank provides an exogenous measure of patient power — found that high-power patients who outranked their physician received 3.6% more diagnostic effort and resources, and had a 15% lower likelihood of hospital admission in the following 30 days. [10] When a high-power patient entered the room, low-power patients received less attention simultaneously. The racial dimension was direct: White physicians consistently invested less effort in Black patients. Black patients who outranked their White physician received more effort — but still less than equivalent White patients.
This is the mechanism. Patient power — perceived social status, authority, the visible weight of consequence for dismissing this person — changes what a physician does. Not always consciously. Often below the level of deliberate decision-making. But measurably, causally, with life-or-death downstream effects.
The Firefighter
In a modern American medical system, an intraabdominal hemorrhage progressing over 12 hours to a patient’s death should be impossible. Protocols exist. Tools exist. The intervention is known. Multiple layers of protection are built specifically to prevent this outcome. And yet it happens. Women die of complications that medicine knows how to treat. They die with the solution available, down the hall, unused.
The CDC’s Maternal Mortality Review Committees analyzed 1,018 pregnancy-related deaths across 36 states. Eighty-four percent were judged preventable [11] — meaning reviewers determined there was at least some chance of averting them through reasonable changes in patient, community, provider, facility, or system factors. Eighty-four percent.
This is not a knowledge gap. Medicine knows what kills women in childbirth and how to stop it. The E-MOTIVE trial, published in the New England Journal of Medicine in 2023, enrolled 210,132 women across 80 hospitals and demonstrated that a standardized early-detection-and-treatment bundle for postpartum hemorrhage — activated within 15 minutes of detection, administered concurrently — reduced severe hemorrhage outcomes by 60%. Detection rates in intervention hospitals rose from 51% to 93%. Treatment adherence rose from 19% to 91%. [12] The intervention worked because it made the protocol unavoidable. It removed the human judgment about whether this patient’s hemorrhage was urgent enough to act on immediately.
Think about what that means. In control hospitals, 49% of hemorrhages were going undetected. Treatment bundles were being followed only 19% of the time. [12] Not because the knowledge was absent. Because someone in the room was deciding — consciously or not — whether to deploy what was in their hands.
Think about a firefighter standing in front of a burning house. He has a hose. He has training. He knows exactly what to do. And he refuses to use the hose. He stands there and watches the house burn.
That is what sending a woman home with the worst headache of her life looks like. The brain scan exists. The neurologist is down the hall. The protocol is unambiguous. And she went home. Not because medicine failed. Because a human being inside a system with every resource available made a calculation — conscious or not — that her urgency was negotiable.
The Three Delays Model, developed by Thaddeus and Maine in 1994 and applied to over 2,800 studies since, identifies three failure points between an obstetric complication and a preventable death: delay in deciding to seek care, delay in reaching an adequate facility, and delay in receiving adequate care once there. [13] A 2025 analysis presented at the American Public Health Association annual meeting interviewed 56 women of color who survived maternal near-misses in the United States. The Third Delay — failure to receive adequate care at the facility — was the dominant theme, documented 64 times in qualitative coding versus 17 times for the first delay. [14]
These women reached the hospital on time. The hospital did not meet them there.
A PLOS ONE systematic review titled “Why Are Women Dying When They Reach Hospital on Time?” documented the same pattern globally: facility-level failures, not access failures, account for a significant share of preventable maternal deaths. [15] Women are arriving. The system is failing them after they arrive.
This is not a monitoring problem. The woman dying today is not the woman who missed a prenatal appointment or couldn’t get a blood pressure reading. The woman dying today came to the emergency room and was sent away. She is the one the system saw and dismissed. No app catches that. No remote monitor prevents it. The intervention that was needed was not technological. It was the presence of someone in that room with the institutional authority to say: you will do your job, or there will be consequences.
The Power Asymmetry Is the Problem
I want to be precise about what I mean by power asymmetry, because it is not a theoretical concept. It is a clinical variable with measurable effects on outcomes.
The research is now conclusive that patient social status shapes physician effort. Patients perceived as higher-status receive more thorough diagnostic workups, more information, more communication, more treatment adherence. Patients perceived as lower-status receive less of all of these — not because of insurance differences or access differences, but because the physician in the room is unconsciously calibrating their effort to the perceived weight of consequence for failing this particular patient.
For Black women, that calibration runs against them at every level. False beliefs about their physiology. Implicit biases that rate their pain lower. A historical context in which their suffering has been structurally deprioritized. And a physical environment — because 35% of US counties are now maternity care deserts with no birthing facility and no obstetric clinician, and because 117 rural labor and delivery units have closed since 2020 at a rate of two per month — in which the nearest hospital may be 30 or 50 miles away, staffed by clinicians spread thin across patients they cannot adequately serve. [17] [18]
The 2024 March of Dimes Nowhere to Go report found that more than 2.3 million women of reproductive age live in maternity care deserts, and that women in these counties face a 13% higher risk of preterm birth. [17]
The infrastructure is collapsing around patients whose survival already depended on everything going right inside it.
Power asymmetry is not a moral concern in the abstract. It is a clinical variable that, when uncorrected, produces preventable death. That is what Black maternal mortality is. That is the data.
What This Means for the Field
I want to be precise about something: this argument is not a critique of the companies building maternal health technology. It is a critique of the sequencing.
Imagine you are about to perform surgery, and there is someone with a weapon in the room. You can have the best hands in the world. The right tools. The right team. The right protocol. None of it activates until the gunman is gone. You do not work around him. You do not upgrade the surgical suite. You get him out. Then the medicine can do its job.
Blood pressure monitoring matters. Postpartum check-ins matter. Remote access to care matters. I want all of it to succeed. But it will not reach its potential until the power asymmetry is addressed directly.
You cannot optimize a system whose fundamental operating logic excludes the patients you are trying to serve. Closing the infrastructure gap without closing the power gap means building better technology to serve a system that will continue to fail the patients most at risk.
Think of it another way: imagine a Black man being pulled over. His rights are being violated. He is alone against the full institutional weight of the state. What does he need in that moment? Not an essay. Not a policy proposal. He needs a lawyer to materialize at his window and say, clearly, with the full weight of consequence: if you do not stop, there will be legal action. That presence — institutional, credentialed, ready — is what changes the calculus of the person holding the power.
That is what Diosa Ara is. In the delivery room. At the moment the system looks at a Black woman and decides, consciously or not, that her urgency is negotiable. We are the coordinated deployment of clinical and institutional authority — OB team leadership, MFM consultation, legal advocacy, doulas — at the precise moment the power asymmetry is most likely to be lethal.
The doula research is instructive here. A Cochrane meta-analysis of 27 randomized controlled trials found that continuous support during childbirth — having a knowledgeable person present who is neither hospital staff nor family — reduced cesarean rates by 25%, reduced negative birth experiences by 31%, and reduced low Apgar scores by 38%. [19] A 2024 study in the American Journal of Public Health found that Medicaid-enrolled women with doula care showed a 47% lower risk of cesarean delivery and a 29% lower risk of preterm birth — and crucially, Black and White doula recipients showed no significant differences in these outcomes. [20] The racial disparity largely disappeared. Not because the medicine changed. Because the power dynamic in the room changed.
This is proof of concept. Inserting coordinated institutional presence into a clinical encounter changes what happens to the patient. We are building that at the emergency level — at the moment of crisis, when the stakes are highest, when the gap between what medicine can do and what it does for Black women is widest.
Preventable Means Preventable
The CDC finding deserves to be stated plainly. Eighty-four percent of the women who died did not have to die. [11] The tools to save them existed. The knowledge existed. The protocols existed. What was missing in each case was either the detection system to trigger a response, the adherence to protocols already in place, or — most often — the power rebalancing in the room that would have forced the system to take this patient’s urgency seriously.
These are not random failures. They are predictable, documented, patterned failures that fall disproportionately on Black women, regardless of where they live, how much money they have, how many degrees they hold, or what hospital they deliver in.
The solution is not upstream and it is not technological. It is human, institutional, and immediate. It is the presence in the room that cannot be ignored. It is the rebalancing of the equation at the moment it matters most.
That is what we are building. Not to compete with the field. To make the field’s work possible.
The gunman has to come out first.
This essay is the second piece in a series. Each piece that follows takes one argument made here and builds it out fully.
What It Costs - On Power, Structural Racism, and the Real Price of Building While Black and Female in Medicine
The Gunman in the Room — the system, the power asymmetry, and why this is engineering logic applied to a medical crisis, not ideology. [You are here]
The Tax — what the research actually calls what you have been carrying, and why precision about the cost changes what you can do about it.
The Well — capacity accounting, feedback as dominance behavior, and why protecting your bandwidth is the most mission-critical decision you make.
The Conversion — where Diosa Ara came from, what productive resilience actually is, and why the spiral is not the enemy.
Why I Can Say This — radical transparency as competitive strategy, the legibility gap, and what becomes possible when you stop protecting a position you were never going to be protected in.
If this landed with, the next one will too.
References
MacDorman MF, Thoma M, Declercq E, Howell EA. Racial and ethnic disparities in maternal mortality in the United States using enhanced vital records, 2016–2017. American Journal of Public Health. 2021;111(9):1673–1681. DOI: 10.2105/AJPH.2021.306375.
Valerio VC, Downey J, Sgaier SK, Callaghan WM, Hammer B, Smittenaar P. Black-White disparities in maternal vulnerability and adverse pregnancy outcomes: an ecological population study in the United States, 2014–2018. Lancet Regional Health – Americas. 2023;20:100456. DOI: 10.1016/j.lana.2022.100456.
Petersen EE, Davis NL, Goodman D, et al. Racial/ethnic disparities in pregnancy-related deaths — United States, 2007–2016. MMWR Morbidity and Mortality Weekly Report. 2019;68(35):762–765. DOI: 10.15585/mmwr.mm6835a3.
Kennedy-Moulton K, Miller S, Persson P, Rossin-Slater M, Wherry L, Aldana G. Maternal and infant health inequality: new evidence from linked administrative data. NBER Working Paper No. 30693. National Bureau of Economic Research, 2022.
Hoffman KM, Trawalter S, Axt JR, Oliver MN. Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proceedings of the National Academy of Sciences. 2016;113(16):4296–4301. DOI: 10.1073/pnas.1516047113.
Todd KH, Deaton C, D’Adamo AP, Goe L. Ethnicity and analgesic practice. Annals of Emergency Medicine. 2000;35(1):11–16. DOI: 10.1016/S0196-0644(00)70099-0.
Lee P, Le Saux M, Siegel R, et al. Racial and ethnic disparities in the management of acute pain in US emergency departments: meta-analysis and systematic review. American Journal of Emergency Medicine. 2019;37(9):1770–1777. DOI: 10.1016/j.ajem.2019.06.014.
Maina IW, Belton TD, Ginzberg S, Singh A, Johnson TJ. A decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test. Social Science & Medicine. 2018;199:219–229. DOI: 10.1016/j.socscimed.2017.05.009.
KFF. Survey on experiences of Black women during pregnancy and childbirth. Kaiser Family Foundation, 2023.
Schwab SD, Singh M. How power shapes behavior: evidence from physicians. Science. 2024;384(6697):802–808. DOI: 10.1126/science.adl3835.
Trost SL, Beauregard J, Njie F, Chandra G, Harvey A, Berry J, Goodman DA. Pregnancy-related deaths: data from maternal mortality review committees in 36 US states, 2017–2019. Centers for Disease Control and Prevention, 2022.
Gallos I, Devall A, Martin J, et al. Randomized trial of early detection and treatment of postpartum hemorrhage. New England Journal of Medicine. 2023;389(1):11–21. DOI: 10.1056/NEJMoa2303966.
Thaddeus S, Maine D. Too far to walk: maternal mortality in context. Social Science & Medicine. 1994;38(8):1091–1110. DOI: 10.1016/0277-9536(94)90226-7.
Hernandez-Spalding K, et al. Application of the three delays model on maternal near misses among women of color in the United States. APHA Annual Meeting Abstract. American Public Health Association, 2025.
Knight HE, Self A, Kennedy SH. Why are women dying when they reach hospital on time? A systematic review of the “third delay.” PLOS ONE. 2013;8(6):e63846. DOI: 10.1371/journal.pone.0063846.
Howell EA. Reducing disparities in severe maternal morbidity and mortality. Clinical Obstetrics and Gynecology. 2018;61(2):387–399. DOI: 10.1097/GRF.0000000000000349.
Stoneburner A, Lucas R, Fontenot J, Brigance C, Jones E, DeMaria AL. Nowhere to go: maternity care deserts across the US. March of Dimes, 2024.
Miller H. Stopping the loss of rural maternity care. Center for Healthcare Quality and Payment Reform, 2025.
Bohren MA, Hofmeyr GJ, Sakala C, Fukuzawa RK, Cuthbert A. Continuous support for women during childbirth. Cochrane Database of Systematic Reviews. 2017;(7):CD003766. DOI: 10.1002/14651858.CD003766.pub6.
Crear-Perry J, et al. Role of doulas in improving maternal health and health equity among Medicaid enrollees, 2014–2023. American Journal of Public Health. 2024;114(11). DOI: 10.2105/AJPH.2024.307805.
Dr. Yamicia Connor, MD, PhD, MPH
Founder & CEO, Diosa Ara | Creator & Editor-in-Chief, The Labora Collective
The Labora Collective publishes at the intersection of clinical care, policy, and innovation — because only 10% of your health outcomes come from the exam room. The other 90% is what we cover.
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