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. Author manuscript; available in PMC: 2026 Apr 26.
Published in final edited form as: Health Aff (Millwood). 2026 Feb;45(2):154–163. doi: 10.1377/hlthaff.2025.00847

Changes in medical debt and bankruptcy after acute traumatic injuries

John W Scott 1,2, Nora V Becker 3,4, Mark R Hemmila 4,5, Nicholas Kunnath 5, John Z Ayanian 3,4, A Mark Fendrick 3,4,6, Andrew M Ryan 7, Justin B Dimick 4,5
PMCID: PMC13109849  NIHMSID: NIHMS2161602  PMID: 41628389

Abstract

Despite expanded insurance coverage following the Affordable Care Act, medical debt remains a significant burden for millions of Americans, particularly after acute medical events such as traumatic injuries. We evaluated the financial impact of hospitalization for acute traumatic injury using data from a statewide trauma registry linked to consumer credit reports from 2019–2021. Using a stacked difference-in-differences event study design, we compared financial outcomes for 12,823 injured patients versus 25,195 not-yet-injured matched controls. At 18 months post-injury, the proportion of patients with medical debt in collections increased by +5.2 percentage points (a 24% relative increase compared to pre-injury baseline), and the mean medical debt (including patients with no debt) rose by $290 (76% relative increase). Post-injury changes in bankruptcy filings peaked at +3.2 per 1,000 individuals (6% relative increase) at 15 months post-injury. Financial hardship disproportionately affected uninsured, younger, lower-income, and privately-insured patients, while those with Medicare and Medicaid experienced minimal change. These findings highlight persistent financial vulnerabilities, even among privately insured patients, and underscore the need for policy enhancements that strengthen protections against the financial consequences of unanticipated acute medical events.

INTRODUCTION

The US healthcare system excels at providing life-saving emergency care but often fails to protect patients from the financial burdens of medical debt and bankruptcy.1,2 Nearly one in five adults has medical debt,3 and many personal bankruptcies are related to injury or illness.4–6 Over the past 15 years, policies expanding health insurance have aimed to reduce the financial risks of major acute illness, particularly since the 2010 Affordable Care Act (ACA). Insurance expansion policies such as extending family coverage to young adults and expanding Medicaid eligibility have been shown to reduce both medical debt7–12 and bankruptcy filings.6,10,12–14

However, the financial consequences of major acute illness after insurance expansion remain poorly described. Prior to the ACA, three longitudinal studies assessed the financial impact of hospitalizations and injuries.15–17 All found increases in medical debt, with mixed bankruptcy findings.15–17 It is not known whether these risks have persisted since the insurance expansion policies from the last decade, or whether rising healthcare costs and cost-sharing continue to leave patients vulnerable to financial hardship after major acute illness.

In this paper, we examined whether traumatic injuries requiring hospitalization lead to increased medical debt and bankruptcy in the current policy era. We used a unique dataset linking a statewide trauma registry with longitudinal consumer credit reports, allowing us to track financial outcomes before and after hospitalization. Traumatic injury represents an acute, unplanned health shock that cuts across demographics and insurance types, often requiring costly emergency operations, intensive care, and prolonged rehabilitation, and resulting in work disruption, which can lead to substantial financial vulnerabilities. By comparing injured patients with similar individuals who were not hospitalized during the same period, we identified which patient populations remain vulnerable to economic hardship despite broad insurance coverage gains.

METHODS

Study Design

We evaluated the association between injury-related hospital admission and patient-level financial outcomes. Using a stacked differences-in-differences event study design,18–20 we analyzed quarterly credit data to compare outcomes before and after hospitalization against concurrent data from a comparison cohort who did not experience an injury. This study involved secondary use of existing data sources and was approved by the institutional review board at the University of Michigan (HUM00041947).

Data and Study Population

We used data from the Michigan Trauma Quality Improvement Program (MTQIP)— a statewide collaborative quality initiative including all 35 Level 1 and Level 2 trauma centers in the state of Michigan.21–23 The MTQIP registry contains more than 250 variables on demographics, injury severity, and treatment intensity, abstracted from medical records using the National Trauma Data Standards. (Data definitions are in the appendix.)24 In 2022, MTQIP linked registry data to 4.5 years of quarterly credit reports (January 2018-April 2022) from a major US consumer credit bureau. A limited-use, deidentified dataset was made available to our research team.

Our injured cohort consisted of adults aged ≥18 years who had an injury-related hospitalization between January 1, 2019, and June 30, 2020, corresponding to at least four pre-injury observations and six post-injury observations in the credit data. Patients were excluded if their hospital discharge disposition was death or hospice and if they lacked eleven continuous quarters of peri-injury credit report outcomes data (see appendix exhibit A1 for cohort derivation).24 We also excluded patients whose credit reports indicated death within two years post-injury. To account for broad economic effects unrelated to an injury-related hospitalization during the study period (2018–2022), we used pre-injury credit data from a comparison cohort of trauma registry patients whose injury-related hospitalization occurred after the six-quarter post-injury window of the relevant injured cohort, as explained in section V of the online appendix.24

Study Outcomes

The primary outcome was mean medical debt in collections from patients’ quarterly credit reports. Medical debt in collections is defined as bills reported to credit agencies by third-party debt collectors, typically appearing 6 months after services, and excluding credit card or payment-plan balances..25 The secondary outcome was the number of public bankruptcies listed on quarterly credit reports. These bankruptcy filings typically remain on credit reports for 7–10 years, meaning our baseline bankruptcy measure reflects cumulative filings over multiple years.26 Together, these two outcomes capture distinct dimensions of financial hardship: medical debt measures direct unpaid medical bills, while bankruptcy represents comprehensive deterioration of financial stability following acute illness.

Statistical Analysis

We used a weighted, stacked difference-in-differences18–20 event study design to evaluate the association between unplanned hospitalization and changes in economic outcomes. Our panel dataset included repeated quarterly observations for each patient. For our injured cohort, we used four quarters of pre-injury and six quarters of post-injury data (quarters −4 to +6), with the quarter of hospitalization defined as quarter 0.

A key challenge in evaluating the financial impact of traumatic injury is controlling for broader economic trends unrelated to injury. Because we did not have economic outcomes data on never injured patients, we constructed a comparison cohort from other trauma registry patients whose injuries occurred after the observation window. Their pre-injury financial data overlapped with the calendar time used to observe the injured cohort but were not influenced by injury during that period. This design allowed us to compare similar individuals—drawn from the same registry—whose outcomes were either affected by a recent injury (our injured cohort) or not yet affected by injury (our comparison cohort).

We used a stacked difference-in-differences event study model to estimate the treatment effect at each quarter before and after injury (the mean change between a given quarter and the quarter of injury).18–20 Our injured cohort spans six exposure periods (Q1 2019–Q2 2020), so we created separate “stacks” for each, aligning injured and comparison cohort data to the same event time (anchored at event time zero). Each stack included all patients hospitalized for injury in a given quarter, and a comparison cohort of not-yet-injured individuals contributing pre-injury economic outcomes anchored at that same period. All comparison patients had at least six quarters of pre-injury data through Q3 2020. We weighted our regression to adjust for those individuals in the comparison cohort who appear in more than one stack.20 We applied corrective sample weights that ensure the estimated changes in financial outcomes for each stack are weighted by their share of the injured sample, with injured patients receiving a weight of 1 and control patients weighted proportionally across stacks, following Wing et al.20 Further details, including a visual explanation of the stacked difference in differences event study model, explanation of model weights, and the primary model equation, are available in the online appendix.24

Standard errors were clustered at the patient level. We ran our models for the entire cohort and then stratified according to demographic groups relevant to medical debt and bankruptcy: age, sex, estimated household income, and health insurance coverage.

We tested for differential pre-injury trends using trauma–by–time interaction terms in weighted linear regressions with trauma center fixed effects; results showed no significant differences across outcomes, supporting the parallel trends assumption (appendix exhibit A6).24 We also performed a robustness check for our main outcome models by incorporating patient-level fixed effects (appendix exhibit A10).24 All analyses were conducted with Stata/MP, version 18.0 (StataCorp; College Station, TX).

Limitations

Our findings must be interpreted considering the study’s limitations. First, this is a single-state analysis and may not be representative of other states with different demographics and debt collection practices. However, our findings align with national data showing 18% of US adults have medical debt in collections.3 Second, our primary outcome is medical debt reported to credit agencies and may underestimate actual total medical debt (i.e. medical expenses paid through credit cards, personal loans, retirement withdrawals, structured payment plans). Although our data were obtained prior to recent action by both credit bureaus and the federal government to limit the ways that medical debt is included in credit reports,27 patients may have more medical debt than is reported on credit reports. Third, our 18-month follow-up may understate longer-term debt accrual or delayed bankruptcies since medical bills often arrive months after discharge, meaning the full financial impact may not be apparent until more than a year after injury. Fourth, portions of our post-injury follow-up period overlapped with the Covid-19 pandemic, during which both medical debt and bankruptcy rates temporarily decreased due to stimulus payments and various economic policy interventions.28,29 Since our stacked difference-in-differences design accounts for secular trends common to both the treatment and comparison groups, this would only introduce bias if it impacted the two groups differently (e.g. lower-than typical elective healthcare spending during the pandemic among the comparison cohort). Reassuringly, analysis of our comparison cohort showed no significant changes in trends in our outcomes before versus after Covid-19 onset (appendix exhibits A7–A8).24 Fifth, because the health insurance variable is assigned at the time of hospital discharge, those patients who were uninsured at time of injury but obtained retroactive Medicaid coverage (up to three months in Michigan) during admission are indistinguishable from patients enrolled in Medicaid at the time of injury. Estimates suggest as many as 40% to 70% of uninsured trauma patients may gain Medicaid in this manner.30,31 Lastly, missing data that led to exclusion may not have been missing at random, possibly leading to under-representation of male, non-Hispanic Black, or publicly insured patients (appendix exhibit A9).24

RESULTS:

Our injured patient cohort consisted of 12,823 adults, while our comparison cohort included pre-injury data from 25,195 unique individuals, weighted to account for 83,520 observations. Demographic characteristics of both cohorts were similar (appendix exhibit A2).24 For the injured cohort, 49.6% were male, 46.1% were under age 65, 82.2% were non-Hispanic White, 25.0% had an estimated annual income below $40,000, 23.7% were privately insured, and 2.3% had no health insurance coverage at hospital discharge. Additional clinical details are shown in appendix exhibit A2.24 We found no evidence of differential pre-injury time trends between injured and control cohorts as interaction terms comparing linear slopes were minimal and statistically insignificant across outcomes (appendix exhibit A6).24

Changes in Medical Debt After Injury

Exhibit 1 shows that the quarterly unconditional mean medical debt in collections increased at 9 months post-injury and persisted through 18 months. The lack of change for the first two quarters after hospitalization is consistent with the typical six-month waiting period for past-due medical debt to be sent to collections.25 Before injury, 21.5% of injured patients had medical debt in collections with an unconditional mean medical debt (i.e. including those with zero debt) of $384 (exhibit 2). Our difference-in-differences estimates revealed that at 18 months after an injury-related hospitalization, the proportion of patients with medical debt increased by 5.2 percentage-points (95% confidence interval (CI): 4.4–5.9, p<0.001), a relative increase of 24%. At 18 months after injury, the unconditional mean medical debt in collections increased by $290 (95% CI: $222-$358, p<0.001), a relative increase of 76%. Among patients with non-zero medical debt, the median was $675 at baseline and $812 at 18 months post-injury (exhibits A4–A5).24 At 18 months post-injury, one-in-ten indebted patients carried a medical debt balance in collections in excess of $4,480 (appendix exhibit A5).24

EXHIBIT 1. Quarterly Trends in Medical Debt, Among Injured Patients versus Comparison Cohort in Michigan, 2019–2021.

EXHIBIT 1

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Source/Notes: Source: Michigan Trauma Quality Improvement Project Clinical Trauma Registry, 2019–2021. Notes: The top two lines show weighted quarterly means with 95% confidence intervals (I bars) for medical debt in collections, calculated for injured patients (dark blue) and the comparison cohort of uninjured patients (light blue). The bottom gold line represents the quarterly, weighted marginal estimates from the stacked difference-in-differences analysis that represent the mean change in medical debt in collections among injured patients compared to event time 0, with 95% confidence intervals (I bars). Horizontal black dotted line represents no change from baseline at time 0. Vertical red dashed lines represent time of index hospital admission.

EXHIBIT 2.

Post-Injury Changes in Medical Debt among Injured Patients in Michigan Relative to Comparison Cohort, 2019–2021

Share with any medical debt in collections (%) Mean unconditional medical debt in collections ($)a
Baselineb 18 months
after injury		 Baselineb 18 months
after injury
Patient Cohort Mean Percentage Among Injured Patients Difference-in-Differences Estimate Mean Medical Debt Among Injured Patients Difference-in-Differences Estimate
All Patients 21.4 +5.2 **** $384 $290 ****
Sex
 Male 24.8 +7.2 **** $478 $421 ****
 Female 18.0 +3.2 **** $288 $160 ****
Age group (in years)
 18–39 36.9 +10.8 **** $666 $767 ****
 40–64 30.3 +7.1 **** $649 $414 ****
 65–79 16.4 +3.5 **** $249 $137 ****
 80 and older 6.5 +2.4 **** $50 $34 ****
Race and ethnicity
 Non-Hispanic Black 37.7 +9.4 **** $545 $523 ***
 Hispanic 35.1 +10.1 *** $1,267 $-37
 Other 20.5 +6.0 ** $293 $358 *
 Non-Hispanic White 18.4 +4.5 **** $340 $261 ****
Health insurance
 Medicaid coverage 43.1 +3.1 * $993 $303
 No insurance coverage 39.7 +16.5 **** $719 $1,734 ****
 Other insurance coverage 28.3 +10.4 **** $493 $488 ****
 Private health insurance 22.3 +7.4 **** $414 $449 ****
 Medicare coverage 14.0 +2.6 **** $212 $89 ****
Estimated income
 Less than $40,000 45.6 +9.0 **** $960 $578 ****
 $40,000 to $100,000 16.5 +4.8 **** $241 $254 ****
 More than $100,000 3.6 +1.1 ** $42 $40 **
Baseline comorbidities
 Three or more 23.5 +4.0 **** $467 $223 ****
 One or two 21.0 +6.3 **** $351 $358 ****
 None 16.7 +5.5 **** $247 $272 ****
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Source/Notes: Source: Michigan Trauma Quality Improvement Project Clinical Trauma Registry, 2018–2021. Notes: The values represent the coefficients from the stacked difference-in-difference event study model for post-injury quarter number six.

a

This unconditional mean includes patients with zero medical debt in collections.

b

Baseline values are from the quarter of the index hospital admission. Statistical Significance:

*

p < 0.10,

**

p < 0.05,

***

p < 0.01,

****

p < 0.001.

Changes in Bankruptcy Filings After Injury

Exhibit 3 shows that the quarterly number of bankruptcy filings per 1,000 individuals increased at 9 months post-injury and peaked at 15 months. This increase is notable as it represents new bankruptcies occurring over a relatively short follow-up period compared to the multi-year accumulation reflected in the baseline rate.26 Before injury, the mean number of bankruptcy filings per 1,000 individuals was 56.5 (exhibit 4). Our difference-in-differences estimates revealed that the post-injury increase in bankruptcy filings peaked at +3.2 bankruptcy filings per 1,000 individuals (95% CI: 0.4–5.9) at 15 months post-injury, a relative increase of 6% (exhibit 4).

EXHIBIT 3. Quarterly Trends in Bankruptcies Among Injured Patients in Michigan relative to Comparison Cohort, 2019–2021.

EXHIBIT 3

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Source/Notes: Source: Michigan Trauma Quality Improvement Project Clinical Trauma Registry, 2019–2021. Notes: The top two lines show weighted quarterly means and 95% confidence intervals (I bars) for bankruptcies per 1,000 individuals, calculated for injured patients (dark blue) and the comparison cohort of uninjured patients (light blue). The bottom gold line represents the quarterly, weighted marginal estimates from the stacked difference-in-differences analysis that represent the mean change in bankruptcies per 1,000 individuals among injured patients compared to event time 0, with 95% confidence intervals (I bars). Horizontal black dotted line represents no change from baseline at time 0. Vertical red dashed lines represent time of injury admission.

EXHIBIT 4.

Post-Injury Changes in Bankruptcy Filings among Injured Patients in Michigan Relative to Comparison Cohort, 2019–2021

Number of bankruptcy filings (per 1,000 individuals)
Baselinea 9 months
after injury		 12 months
after injury		 15 months
after injury		 18 months after injury
Patient Cohort Unadjusted Mean Difference-in-Differences Estimateb Difference-in-Differences Estimateb Difference-in-Differences Estimateb Difference-in-Differences Estimateb
All Patients 55.1 +2.4 ** +2.9 ** +3.2 ** +2.3
Sex
 Male 56.8 +2.0 +0.9 +1.0 −0.3
 Female 53.4 +2.8 * +4.8 *** +5.2 *** +4.8 **
Age group (in years)
 18–64 75.5 +4.1 ** +4.8 ** +4.8 * +2.9
 65 and older 37.7 +1.0 +1.4 +1.7 +1.6
Race and ethnicity
 Non-Hispanic Black 79.6 −3.4 −0.2 −0.4 +1.5
 Non-Hispanic White 51.9 +2.7 ** +2.7 ** +3.0 ** +1.8
 Hispanic 43.3 +8.7 +10.2 +7.2 +4.1
 Other 37.9 +16.4 * +18.3 * +19.5 * +16.8
Health insurance
 Private Health Insurance 71.6 +7.9 *** +8.0 *** +7.4 ** +6.9 *
 Medicare coverage 40.7 +0.8 +1.4 +1.3 +0.8
 Other insurance coverage 65.0 +0.3 +0.3 +2.6 +0.6
 No insurance 58.6 +6.9 +12.7 +19.6 +17.1
 Medicaid coverage 71.6 +3.1 +4.8 +3.6 +1.1
Estimated income
 Less than $100,000 64.0 +2.2 * +2.9 ** +3.2 * +2.1
 More than $100,000 11.5 +3.7 ** +3.1 * +3.6 * +3.6 *
Baseline comorbidities
 None 51.2 +1.2 +0.6 −0.1 −1.0
 One or more 55.8 +2.6 ** +3.3 ** +3.7 ** +2.7
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Source/Notes: Source: Michigan Trauma Quality Improvement Project Clinical Trauma Registry, 2019–2021. Notes:

a

Baseline values are from the quarter of the index admission and these values represent cumulative filing over seven to ten years.

b

These represent the coefficients from the stacked difference-in-difference event study model for post-injury quarters three through six. Statistical Significance:

*

p < 0.10,

**

p < 0.05,

***

p < 0.01,

****

p < 0.001.

Subgroup Analyses

Stratified analyses of medical debt in collections among policy-relevant demographic subgroups are shown in exhibit 2 and appendix exhibit A3.24 The largest post-injury increases in medical debt were seen among male patients, younger patients, patients with lower estimated income, and patients without health insurance at hospital discharge. Across nearly all subgroups, we observed significant increases in both the share of individuals with medical debt in collections and the unconditional mean amount of that debt (exhibit 2). When stratified by insurance type, the smallest changes in medical debt were among those enrolled in Medicare and Medicaid, the latter of whom experienced no statistically significant increase in either outcome (p=0.09).

Unlike medical debt, post-injury increases in bankruptcy filings were less widespread in stratified analyses, possibly due to smaller sample sizes in stratified cohorts and a relatively rare outcome (exhibit 4). Significant increases in bankruptcy filings were seen among female patients, younger patients, non-Hispanic white patients, patients with estimated incomes both above and below $100,000, and patients with baseline comorbidities. No significant increases were seen among male patients, patients over age 65, and patients with no baseline comorbidities. The only health insurance cohort with a significant increase in bankruptcy filings was privately insured patients. Some smaller subgroups saw large but non-significant increases (e.g., +19.4 per 1,000 among uninsured patients, 95% CI: −14.2 to 52.2, p=0.26).

DISCUSSION:

Our longitudinal evaluation of medical debt and bankruptcy before and after an acute traumatic injury demonstrates that many remain vulnerable to financial hardship after an acute health shock in the post-ACA era, despite 98% of the patients in our study having health insurance. Our study has three principal findings. First, we found a significant increase in the proportion of injured patients with medical debt in collections, and in the amount of medical debt in collections, that increased throughout the 18-month follow-up period. Second, we found a significant increase in the number of bankruptcy filings per 1,000 patients. Third, and perhaps most notably, subgroup analyses by insurance type revealed that many privately insured patients experienced substantial financial hardship, underscoring the limited financial protection offered by private insurance, in contrast to the relative protection observed among patients enrolled in Medicaid or Medicare.

Although 98% of our cohort had health insurance coverage, we identified significant and sustained increases in medical debt after injury. Approximately one-in-five patients held medical debt at baseline, with an unconditional mean balance of just under $400. These numbers align with national analyses showing that 18% of individuals in the US have medical debt in collections, with an unconditional mean balance of $429.3 Our event-study model, which adjusted for secular trends using a stacked difference-in-differences approach, found that injury-related hospitalizations were associated with a 5.2-percentage-point increase at 18 months post-injury, and a nearly $300 increase in medical debt over this period. The temporal pattern of financial impact is notable, with medical debt increases persisting throughout our 18-month follow-up period, suggesting that the full financial consequences of a major health shock may not be apparent for nearly two years after the initial event. These numbers align with pre-ACA work by Dobkin et al. showing medical debt increases of $127 at 12 months and $271 at 48 months after hospitalization among 18–64 year olds with health insurance.17 These findings also align with a recent analysis of working-age, commercially insured trauma patients, which similarly found significant increases in medical debt after injury, though this was a cross-sectional study without longitudinal pre- and post-injury data and it lacked a comparison group.32 Together, these findings suggest that despite the population-level gains in health insurance coverage over the past decade, protection against medical debt after an acute health shock has not improved for many patients.

We also observed a significant increase in bankruptcy after injury, with an estimated increase of approximately 3.2 bankruptcies per 1,000 patients at 15-month post-injury. Given that bankruptcy filings remain on credit reports for 7–10 years,26 this increase reflects new financial distress during a relatively brief post-injury period. This estimate is similar to pre-ACA analyses by Dobkin et al. and Morrison et al., which found health shocks such as hospital admission or traumatic brain injuries increased bankruptcies by approximately 4 per 1,000 individuals.16,17 While evaluation of the ACA’s dependent coverage provision and multiple evaluations of Medicaid expansion have all found these insurance expansion policies to be associated with reductions in bankruptcy filings,6,7,10,13 our findings suggest that many patients, particularly those with private insurance, remain at risk for bankruptcy after major acute illness. It is notable that filing for bankruptcy requires both financial resources and legal navigation capabilities, and thus bankruptcy patterns may reflect the complex intersection of having inadequate insurance financial risk protection, sufficient assets worth protecting, and the means to file. The pattern of increased bankruptcies among privately insured patients alongside substantial medical debt increases suggests inadequate financial protection despite coverage.

The differential risk of poor post-injury financial outcomes provides insight into policy interventions and opportunities. Across both outcomes, patients under age 65, patients without health insurance, low-income patients, and privately insured patients experienced the most financial hardship. For patients without health insurance, a traumatic injury can be financially devastating. In contrast, public insurance appears to provide better protection from financial hardship after acute injuries. Medicare enrollees experienced only small increases in medical debt and no change in bankruptcy. Additionally, low-income patients under age 65 covered by Medicaid experienced no statistically significant increase in medical debt or bankruptcy after injury. These findings align with prior work demonstrating a reduction in financial hardship associated with gaining Medicare or Medicaid coverage.6,9–13,33–35 The superior financial protection offered by public insurance programs highlights critical gaps in private insurance which leaves many patients underinsured.32,36,37

Privately insured patients were at risk of both medical debt and bankruptcy after injury, likely from multiple factors. First, many private health insurance plans have high out-of-pocket spending limits38—with average annual deductibles exceeding $1,700 for individual coverage in employer plans and over $2,500 in high-deductible health plans.39 These costs disproportionately burden lower-income patients facing expensive health shocks like traumatic injuries. Second, privately insured patients may encounter substantial expenses from out-of-network care that is common in emergency settings, particularly for ambulance transport, air medical evacuation, and specialists involved in trauma care.40–42 Third, these individuals often face income disruption through employment lapses and other financial hardship following injury,43–45 potentially leading to insurance coverage gaps.46 Policies that could improve financial protection for insured patients after acute health events include: provision of first-dollar coverage for at least some healthcare services not subject to overuse (e.g. major traumatic injury), prohibiting surprise billing for emergency care, setting income-based limits on deductibles and out-of-pocket costs, and further decoupling health insurance from employment.

CONCLUSION

This study shows that despite expanded insurance coverage over the past decade, financial hardship following traumatic injury remains substantial, with sustained increases in medical debt and bankruptcy filings. These burdens were especially high among younger adults, low-income individuals, uninsured individuals, and those with private insurance, highlighting that coverage alone does not ensure financial protection. The hardship experienced by privately insured patients contrasts sharply with the stronger protection provided by Medicare and Medicaid, suggesting that features of public insurance could inform improvements to private plans. Our findings reveal the inadequacy of current financial protections. Proposed policy changes, such as the 2025 Federal Budget Reconciliation Bill, threaten to substantially worsen these vulnerabilities by eliminating coverage for over 10 million Americans and increasing Medicaid cost-sharing requirements.47 Instead, transformative policies are needed that strengthen financial protections across all insurance types, moving beyond coverage expansion to ensure comprehensive affordability for all patients facing unavoidable medical emergencies.

Supplementary Material

Online Appendix

Funding and Conflict of Interest Statement:

JWS – Dr Scott reported grants from the Agency for Healthcare Research and Quality (K08 HS28672), which funded the conduct of the study, as well as funding from the National Institutes of Health (R01 DK137466) outside the submitted work.

NVB – Dr. Becker received grant funding from the Agency for Healthcare Research and Quality (K08 HS028817) related to this work. Dr. Becker has also received prior funding unrelated to this work from the Michigan Department of Health and Human Services and the Michigan Value Collaborative (MVC), a Collaborative Quality Initiative under Blue Cross Blue Shield of Michigan’s (BCBSM) Value Partnerships Program.

MRH – Dr. Hemmila receives grant funding from Blue Cross Blue Shield of Michigan and Blue Care Network (a nonprofit mutual company) and the Michigan Department of Health and Human Services for support of the Michigan Trauma Quality Improvement Program related to the submitted work. Dr. Hemmila receives grant funding from General Motors Corporation, Subaru Corporation, Toyota North America, and the Insurance Institute for Highway Safety for support of the International Center for Automotive Medicine, outside of the submitted work. Dr. Hemmila receives grant funding from the Uniformed Services University of the Health Sciences and the Henry M. Jackson Foundation for the Advancement of Military Medicine for support of an evidence-based assessment of combat wound infection management, outside of the submitted work. Dr. Hemmila receives grant funding from the Michigan Department of Health and Human Services and NIH-NIDA (1R01DA058640) for support of the Overdose Prevention Engagement Network (OPEN), outside of the submitted work.

NK – None

JZA – Dr. Ayanian receives grant funding from the National Institute on Aging, Michigan Department of Health and Human Services, and the Centers for Medicare and Medicaid Services, unrelated to this work.

AMF – Dr Fendrick reported being a consultant for AbbVie, CareFirst BlueCross BlueShield, Centivo, Community Oncology Alliance, Covered California, EmblemHealth, EBRI, Elektra Health, Exact Sciences, GRAIL, Harvard University, Health at Scale Technologies, Hopewell Fund, Johnson & Johnson, Medtronic, MedZed, Merck, Mother Goose Health, Phathom Pharmaceuticals, Proton Intelligence, RA Capital, Sempre Health, the state of Minnesota, US Department of Defense, Virginia Center for Health Innovation, Washington Health Benefit Exchange, Wellth, Yale New Haven Health System, and Zansors; receiving research support from the Agency for Healthcare Research and Quality, Gary and Mary West Health Policy Center, Arnold Ventures, National Pharmaceutical Council, Patient-Centered Outcomes Research Institute, Pharmaceutical Research and Manufacturers of America, Robert Wood Johnson Foundation, state of Michigan, and Centers for Medicare & Medicaid Services; serving as co–editor in chief of the American Journal of Managed Care; and being a partner in VBIDHealth, LLC.

AMR – Dr Ryan reported receiving grants from Arnold Ventures and the National Institutes of Health (NIH) outside the submitted work.

JBD – Dr. Dimick is co-founder and equity owner in ArborMetrix, Inc, a software and analytics company focused on healthcare quality and efficiency, outside of the submitted work.

Disclaimer Statement:

Support for MTQIP and OPEN is provided by Blue Cross and Blue Shield of Michigan and Blue Care Network as part of the BCBSM Value Partnerships program. Although Blue Cross Blue Shield of Michigan and MTQIP/ OPEN work collaboratively, the opinions, beliefs and viewpoints expressed by the author do not necessarily reflect the opinions, beliefs and viewpoints of BCBSM or any of its employees. Microsoft Word was used for grammar, spelling, and syntax editing, and Microsoft Copilot with commercial data protection (built upon the GPT-4 large language model) was used for grammar, syntax editing, and word count management during final manuscript preparation. No language models were used to generate novel content. The first author (JWS) takes full responsibility for the accuracy, integrity, and originality of the submitted work.

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