Rationing by Race

Singh and Venkataramani ask whether resource scarcity causes discriminatory rationing of health care by patient race, with patient death as the starkest possible outcome of biased allocation decisions. They examine 107,221 inpatient admissions from 2015 to 2018 at two large urban academic teaching hospitals (each with over 500 beds) in a Southeastern U.S. city with a sizable Black population. Black patients accounted for 60% of admissions, were on average younger (52 vs. 59 years), more likely to be female (65% vs. 50%), and had similar comorbidity burdens and baseline in-hospital death rates (approximately 2% for both groups), but waited over two hours longer on average for an inpatient bed and were 27% less likely to be admitted to the ICU.

The authors exploit quasi-exogenous hour-to-hour variation in hospital capacity strain — measured as the share of inpatient beds occupied at the hour of a patient’s arrival — which clinical and qualitative literature establishes is difficult to predict even day-to-day. Capacity strain is coded in hospital-specific deciles (beds filled ranged from 69–78% in decile 1 to 91–95% in decile 10). The core regression interacts patient race with strain decile, controlling for hospital-specific hour-of-day, day-of-week, month-of-year, and year fixed effects; physician-of-record fixed effects; and a rich vector of patient characteristics including Elixhauser comorbidity indices, insurance status, and vital signs. Identification rests on the assumption that strain at the hour of arrival is conditionally independent of unobserved patient characteristics correlated with race — an assumption validated through balance tests on demographics, comorbidities, vital signs, machine-learning-derived admission themes, and selective discharge patterns.

The main finding is that in-hospital mortality rises for Black patients but not for White patients as hospitals approach capacity. At the tenth decile of strain, Black patients face a mortality rate 0.7 percentage points higher than White patients — a 47.6% relative increase over the 1.47% White mortality rate at the same decile. A pooled difference-in-differences estimate implies that approximately 15% of Black patient deaths at high strain (decile 10) would not have occurred had Black patients faced the same strain-mortality relationship as White patients (coefficient 0.0052, p = 0.025). This pattern is concentrated among patients with the greatest ex ante medical need as measured by above-median Elixhauser mortality index scores (a score with AUC of 0.92 for predicting in-hospital mortality) and, in qualitatively similar but less precisely estimated form, by abnormal vital signs at arrival.

The authors identify wait time for an inpatient bed as the primary mechanism. At all levels of capacity strain, high-need Black patients wait longer than low-need White patients — a pattern the authors characterize as a striking inversion of any need-based allocation principle. Racial disparities in wait times widen further at the highest decile of strain, exactly mirroring the mortality pattern. As an additional, more suggestive mechanism, the authors analyze free-text clinical documentation (the Reason for Admission field) using descriptive text features (time to completion, character count, average word length), sentiment analysis (subjectivity and polarity scores via TextBlob), and adjective counts. Documentation for Black patients exhibits features consistent with lower provider effort at all strain levels — shorter notes, less time deferred to completion — and subjectivity of notes and adjective counts diverge further by race at the highest strain decile, with White patients receiving increasingly detailed and descriptive notes as strain rises.

The findings are robust across sparse models (age, gender, hospital fixed effects only) through fully saturated specifications (DRG fixed effects, interactions of all controls with race and strain), and to replacing Elixhauser index composites with their 31 individual comorbidity components. The authors explicitly scope their findings to a pre-COVID-19 period (2015–2018), while noting that pandemic-era record capacity strain and racial disparities in health outcomes suggest de facto race-based rationing may have been far more severe during COVID-19.

Q: What is the central research question and why is the health care setting chosen? A: The paper asks whether increasing resource scarcity causes discriminatory rationing on the basis of race in consequential, high-stakes real-world decisions. Health care is chosen because it is high-stakes (patient death is the outcome), has a long documented history of racial discrimination at both provider and system levels, and offers uniquely detailed time-stamped electronic health record data that enables identification from hour-to-hour variation in capacity strain — a finer temporal resolution than most prior work.

Q: How is hospital capacity strain measured and what is the identifying variation? A: Strain is measured as the total number of patients occupying inpatient beds at the specific hour of a patient’s arrival, converted into hospital-specific deciles. The first decile corresponds to 69–78% of beds filled and the tenth decile to 91–95%. The identifying variation is residual hour-to-hour fluctuation in this measure after removing hospital-specific hour-of-day, day-of-week, month-of-year, and year fixed effects, which absorbs all predictable capacity patterns. Clinical and qualitative evidence establishes that even day-to-day strain is difficult to anticipate, making hour-to-hour residual variation plausibly as-if random.

Q: What are the main mortality findings, and how large are the racial disparities at peak strain? A: At the tenth decile of capacity strain, Black patients face a mortality rate 0.7 percentage points higher than White patients, representing a 47.6% relative increase over the 1.47% White mortality rate at that decile. The pooled difference-in-differences estimate (comparing decile 10 to deciles 1–9) implies that approximately 15% of Black patient deaths at high strain would not have occurred if Black patients had the same strain-mortality relationship as White patients (coefficient 0.0052, p = 0.025). White patient mortality does not increase at high strain; if anything, small (imprecisely estimated) decreases appear at deciles 7–9.

Q: Which patients drive the racial mortality disparity? A: The disparity is concentrated among patients with above-median Elixhauser mortality index scores — the ex ante sickest patients. The Elixhauser Mortality Index has a predictive AUC of 0.92 for in-hospital mortality. At decile 10, high-need Black patients experience a sharp increase in mortality not seen for high-need White patients or for low-need Black patients. Qualitatively similar but less precisely estimated results appear when acute need is measured by abnormal vital signs at arrival, with the difference that the triple interaction (race × strain × high-need vitals) is not statistically significant, consistent with vital signs being noisier proxies for severity than the Elixhauser indices.

Q: How do the authors validate the identifying assumption that strain is conditionally independent of patient composition by race? A: They document five types of supporting evidence: (i) the distribution of Black and White patients across hours of arrival and across strain deciles is nearly identical; (ii) regressions of patient demographics, all five Elixhauser comorbidity measures, and five vital signs abnormalities on race × strain interactions show no significant differential selection by race at different strain levels; (iii) machine-learning (Latent Dirichlet Allocation) topic themes from free-text admission notes change similarly by strain for Black and White patients; (iv) there is no evidence of selective discharge to hospice care by race and strain, with point estimates running counter to the hypothesis; and (v) strain is computed at time of arrival to the hospital rather than time of admission to an inpatient bed, preserving exogeneity.

Q: What is the primary identified mechanism for the mortality finding? A: Wait time for an inpatient bed is the primary mechanism. Black patients experience greater increases in wait times as strain rises compared to White patients, with the clearest divergence at decile 10 — exactly mirroring the mortality pattern. More strikingly, at every decile of strain (including decile 1, when beds are most abundant), high-need Black patients wait longer for a bed than low-need White patients, implying that the disparity is not solely a product of logistical constraints but reflects ingrained factors in clinical protocols, likely including implicit or explicit provider bias.

Q: What does the wait time evidence reveal about the role of medical need vs. race in allocation decisions? A: At lower strain levels, low-need patients appropriately wait longer than high-need patients. However, at higher strain levels (deciles 8–10) this need-based gap almost entirely disappears, while the racial gap in wait times persists. The gap between high-need Black and low-need White patients is larger than the gap between high-need and low-need patients of the same race, meaning race is a stronger predictor of wait times than medical need. This pattern is consistent with the paper’s conceptual framework in which increasing strain reduces providers’ ability to accurately assess medical need while increasing the weight assigned to racial identity.

Q: How is provider effort measured and what are the findings? A: Provider effort is inferred from features of free-text Reason for Admission documentation: time to completion, character count, average word length, TextBlob subjectivity and polarity scores, and adjective counts. Across all strain levels, Black patients’ documentation exhibits features consistent with lower effort — shorter completion times (providers less likely to defer documentation for clinical tasks), shorter notes with fewer characters and shorter words. At the highest strain decile, subjectivity scores for Black patients’ notes increase relative to White patients’ (driven by both rising Black and falling White subjectivity), and White patients receive more adjectives as strain rises while Black patients’ adjective counts do not increase. Polarity scores remain stable by race and strain.

Q: What do the documentation patterns suggest about compensatory behavior by providers? A: The authors speculate that providers may anticipate reduced care quality at high strain and compensate by becoming more conscientious with White patients — writing longer, more detailed, more descriptive notes as strain increases, and potentially exerting greater care effort correlated with these documentation improvements. This protective compensatory behavior appears substantially less pronounced or absent for Black patients, which the authors suggest may translate into the small imprecisely estimated decrease in White patient mortality at higher strain deciles. They explicitly characterize this interpretation as speculative and requiring further investigation.

Q: How robust are the main mortality findings to specification choices? A: The mortality findings hold across: (i) sparse models with only age, gender, and hospital/year fixed effects; (ii) linear probability and logistic models; (iii) models with DRG fixed effects to compare within-diagnosis; (iv) models interacting all control variables with patient race and strain; (v) models replacing the Elixhauser composite index with its 31 individual comorbidity components; and (vi) models additionally controlling for five individual abnormal vital sign indicators. Results are substantively unchanged across all these specifications.

Q: What additional care intensity measures are examined and what do they show? A: The authors also examine ICU admission, ICU length of stay, total inpatient length of stay, and inpatient charges. They find no strain-related racial disparities on these margins. However, they note that unconditionally (across all strain levels), Black patients receive fewer resources on average — they are 27% less likely to be admitted to the ICU. The authors treat these care intensity measures as harder to interpret because both over- and under-provision can harm patients, and thus view them as less informative for their research question.

Q: What conceptual framework guides the empirical predictions? A: The framework models providers as assessing perceived medical need Nij(t) = Ni × exp(−γ × S(t)), where the parameter γ captures the diminishing ability to accurately assess true need as strain S(t) rises. Simultaneously, the racial weight Rij(t) = Ri × φ(S(t)) increases with strain through the parameter φ(S(t)). When γ = 0 and φ = 0, allocation is race-neutral and need-based. When both parameters are positive, increasing strain simultaneously degrades need assessment and amplifies reliance on racial identity in allocation decisions — the paper’s core prediction, which is confirmed empirically.

Q: How do the findings relate to the COVID-19 pandemic? A: The data predate COVID-19 (2015–2018). The authors argue that pandemic conditions — record hospital capacity strain (especially in hospitals serving Black patients), extreme provider burnout, and documented racial disparities in health access — suggest race-based rationing may have been considerably more severe during COVID-19. The paper also contextualizes its findings within the pandemic-era debate over whether explicit race-based triage protocols were ethical or legal, arguing that de facto rationing by race appears to occur in ordinary care settings under typical stressors irrespective of that normative debate.

Q: What policy interventions do the authors suggest? A: The authors propose: increasing provider awareness of implicit biases; developing new algorithms to improve triage decisions for high-mortality-risk patients who might otherwise be overlooked; correcting existing care algorithms with documented racial bias; building provider peer networks to reduce biased treatment decisions; supporting patient self-advocacy; improving capacity prediction systems (as spurred by COVID-19); and creating load-shifting protocols and inter-hospital transfer networks to prevent resources from being stretched beyond capacity during high-strain periods.

Capacity strain: The state of a hospital when a high share of inpatient beds are occupied, measured here at the hour of patient arrival as hospital-specific deciles of bed occupancy (ranging from 69–78% full at decile 1 to 91–95% full at decile 10); the paper’s primary measure of resource scarcity.

Rationing by race: The paper’s term for the phenomenon whereby, as resource scarcity deepens, allocation decisions increasingly reflect patient racial identity rather than medical need — a form of discriminatory rationing that the authors distinguish from explicit (de jure) race-based triage and document as de facto practice.

Perceived need (N*): In the paper’s conceptual framework, the provider’s assessment of a patient’s medical need, which deviates from true need Ni by the factor exp(−γ × S(t)) as strain S(t) increases; captures the provider team’s diminishing ability or willingness to accurately assess true medical need under cognitive and resource constraints.

Racial weight (R*): The weight assigned to a patient’s racial identity in allocation decisions, modeled as Ri × φ(S(t)), where the function φ is increasing in capacity strain; represents the potential for discrimination — from implicit bias, algorithmic bias, reduced patient advocacy, or provider-patient social distance — to intensify as strain rises.

Wait time inversion: The condition, documented throughout the paper, where high-need Black patients wait longer for an inpatient bed than low-need White patients at every decile of capacity strain, including decile 1 when resources are most abundant — inverting the normative principle that greater medical need should yield faster access to care.

Elixhauser Mortality Index: A widely validated composite score of patient comorbid conditions used to predict in-hospital mortality (AUC = 0.92); used in this paper as the primary measure of chronic medical need, with patients split at the median into relatively sick (above median) and relatively healthy (below median) groups.

Provider effort (inferred): An unobserved construct inferred in this paper from features of free-text clinical documentation in the Reason for Admission field, including time to note completion, character count, average word length, TextBlob subjectivity and polarity scores, and adjective counts; features argued to reflect how much attention, detail, and care a provider invested in documenting — and by extension, in assessing — a patient’s condition.