American Hospital Association Research Articles

Navigating geographical disparities: access to obstetric hospitals in maternity care deserts and across the United States

BackgroundAccess to maternity care in the U.S. remains inequitable, impacting over two million women in maternity care “deserts." Living in these areas, exacerbated by hospital closures and workforce shortages, heightens the risks of pregnancy-related complications, particularly in rural regions. This study investigates travel distances and time to obstetric hospitals, emphasizing disparities faced by those in maternity care deserts and rural areas, while also exploring variances across races and ethnicities.MethodsThe research adopted a retrospective secondary data analysis, utilizing the American Hospital Association and Centers for Medicaid and Medicare Provider of Services Files to classify obstetric hospitals. The study population included census tract estimates of birthing individuals sourced from the U.S. Census Bureau's 2017-2021 American Community Survey. Using ArcGIS Pro Network Analyst, drive time and distance calculations to the nearest obstetric hospital were conducted. Furthermore, Hot Spot Analysis was employed to identify areas displaying significant spatial clusters of high and low travel distances.ResultsThe mean travel distance and time to the nearest obstetric facility was 8.3 miles and 14.1 minutes. The mean travel distance for maternity care deserts and rural counties was 28.1 and 17.3 miles, respectively. While birthing people living in rural maternity care deserts had the highest average travel distance overall (33.4 miles), those living in urban maternity care deserts also experienced inequities in travel distance (25.0 miles). States with hotspots indicating significantly higher travel distances included: Montana, North Dakota, South Dakota, and Nebraska. Census tracts where the predominant race is American Indian/Alaska Native (AIAN) had the highest travel distance and time compared to those of all other predominant races/ethnicities.ConclusionsOur study revealed significant disparities in obstetric hospital access, especially affecting birthing individuals in maternity care deserts, rural counties, and communities predominantly composed of AIAN individuals, resulting in extended travel distances and times. To rectify these inequities, sustained investment in the obstetric workforce and implementation of innovative programs are imperative, specifically targeting improved access in maternity care deserts as a priority area within healthcare policy and practice.

US Hospital Service Availability and New 340B Program Participation

The US 340B Drug Pricing Program enables eligible hospitals to receive substantial discounts on outpatient drugs to improve hospitals' financial sustainability and maintain access to care for patients who have low income and/or are uninsured. However, it is unclear whether hospitals use program savings to subsidize access as intended. To evaluate whether the 340B program is associated with improvements in access to hospital-based services and to test whether the association varies by hospital ownership. Difference-in-differences and cohort analysis from 2010 to 2019. Never and newly participating 340B general, acute, nonfederal hospitals in the US using data from the American Hospital Association's Annual Survey of Hospitals merged with hospital and market characteristics. Data were analyzed from January 1, 2023, to January 31, 2024. New enrollment in 340B between 2012 and 2018. Total number of unprofitable service lines, ie, substance use, psychiatric (inpatient and outpatient), burn clinic, and obstetrics services; and profitable services, ie, cardiac surgery and orthopedic, oncologic, neurologic, and neonatal intensive services. The study sample comprised a total of 2152 hospitals, 1074 newly participating and 1078 not participating in the 340B program. Participating hospitals were more likely than nonparticipating hospitals to be critical access and teaching hospitals, have higher Medicaid shares, and be located in rural areas and in Medicaid expansion states. At public hospitals, participation in the 340B program was associated with a significant increase in total unprofitable services (0.21; 95% CI, 0.04 to 0.38; P = .02) and marginal increases in substance use (5.4 percentage points [pp]; 95% CI, -0.8 pp to 11.6 pp; P = .09) and inpatient psychiatric (6.5 pp; 95% CI, -0.7 pp to 13.7 pp; P = .09) services. Among nonprofit hospitals, there was no significant association between 340B and service offerings (profitable and unprofitable) except for an increase in oncologic services (2.5 pp; 95% CI, 0.0 pp to 5.0 pp; P = .05). The finding of the cohort study indicate that participation in the 340B program was associated with an increase in unprofitable services among newly participating public hospitals. Nonprofit hospitals were largely unaffected. These findings suggest that public hospitals responded to 340B savings by improving patient access, whereas nonprofits did not. This heterogeneous response should be considered when evaluating the eligibility criteria for the 340B program and how it affects social welfare.

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

Association of Hospitals' Experience with Bundled Payment for Care Improvement Model with the Diffusion of Acute Hospital Care at Home.

To examine whether hospitals' experience in a prior payment model incentivizing care coordination is associated with their decision to adopt a new payment program for a care delivery innovation. Data were sourced from Medicare fee-for-service claims in 2017, the list of participants in Bundled Payment for Care Improvement initiatives (BPCI and BPCI-Advanced), the list of hospitals approved for Acute Hospital Care at Home (AHCaH) between November 2020 and August 2022, and the American Hospital Association Survey. Retrospective cohort study. Hospitals' adoption of AHCaH was measured as a function of hospitals' BPCI experiences. Hospitals' BPCI experiences were categorized into five mutually exclusive groups: (1) direct BPCI participation, (2) indirect participation through physician group practices (PGPs) after dropout, (3) indirect participation through PGPs only, (4) dropout only, and (5) no BPCI exposure. All data are derived from pre-existing sources. General acute hospitals eligible for both BPCI initiatives and AHCaH are included. Of 3248 hospitals included in the sample, 7% adopted AHCaH as of August 2022. Hospitals with direct BPCI experience had the highest adoption rate (17.7%), followed by those with indirect participation through BPCI physicians after dropout (11.8%), while those with no exposure to BPCI were least likely to participate (3.2%). Hospitals that adopted AHCaH were more likely to be located in communities where more peer hospitals participated in the program (median 10.8% vs. 0%). After controlling for covariates, the association of the adoption of AHCaH with indirect participation through physicians after dropout was as strong as with early BPCI adopter hospitals (average marginal effect: 5.9 vs. 6.2 pp, p < 0.05), but the other categories were not. Hospitals that participated in the bundled payment model either directly or indirectly PGPs were more likely to adopt a care delivery innovation requiring similar competence in the next period.

Telemedicine critical care availability and outcomes among mechanically ventilated patients

PurposeTelemedicine Critical Care (TCC) improves adherence to evidence based protocols associated with improved mortality among patients receiving invasive mechanical ventilation (IMV). We sought to evaluate the relationship between hospital availability of TCC and outcomes among patients receiving IMV. Materials and methodsWe performed a cross-sectional study of 66,522 adults who received IMV for non-postoperative acute respiratory failure at 318 non-federal hospitals in New York, Massachusetts, Maryland, and Florida in 2018. Hospital-level TCC availability was ascertained from the 2018 American Hospital Association Annual Survey. The primary outcome was in-hospital mortality. Secondary outcomes included the composite of tracheostomy or reintubation and duration of IMV. We used two-level hierarchical multivariable regression models to investigate the association between TCC availability and outcomes. Results20,270 (30.5%) patients were admitted into 89 TCC-available hospitals. There was no difference between TCC and non-TCC-available hospitals in mortality (odds ratio [OR] 0.94, 99% confidence interval [CI] 0.84–1.05), composite of tracheostomy or reintubation (OR 0.95 [0.82–1.11], or duration of IMV (OR 0.95 [0.83–1.09]). There was no difference in outcomes among the subgroup of patients with acute respiratory distress syndrome. ConclusionsHospital TCC availability was not associated with improved outcomes among patients receiving IMV.

What happens to rural hospitals during a ransomware attack? Evidence from Medicare data.

Hospitals are increasingly the target of cybersecurity threats, including ransomware attacks. Little is known about how ransomware attacks affect care at rural hospitals. We used data on hospital ransomware attacks from the Tracking Healthcare Ransomware Events and Traits database, linked to American Hospital Association survey data and Medicare fee-for-service (FFS) claims data from 2016 to 2021. We measured Medicare FFS volume and revenue in the inpatient, outpatient, and emergency room setting-at the hospital-week level. We then conducted a stacked event study analysis, comparing hospital volume and revenue at ransomware-attacked and nonattacked hospitals before and after attacks. Ransomware attacks severely disrupted hospital operations-with comparable effects observed at rural versus urban hospitals. During the first week of the attack, inpatient admissions volume fell by 14.7% at rural hospitals (P=.04) and 16.9% at urban hospitals (P=.01)-recovering to preattack levels within 2-3 weeks. Outpatient visits fell by 35.3% at rural hospitals (P<.01) and 22.0% at urban hospitals (P=.03) during the first week. Emergency room visits fell by 10.0% at rural hospitals (P=.04) and 19.3% at urban hospitals (P=.01). Travel time and distance to the closest nonattacked hospital was 4-7 times greater for rural ransomware-attacked hospitals than for urban ransomware-attacked hospitals. Ransomware attacks disrupted hospital operations in rural and urban areas. Disruptions of similar magnitudes may be more detrimental in rural areas, given the greater distances patients must travel to receive care and the outsized impact that lost revenue may have on rural hospital finances.

The Role of Hospital Characteristics in Clinical and Quality Outcomes for Gastrointestinal Bleeding in a National Cohort.

There is substantial variability in patient outcomes for gastrointestinal bleeding (GIB) across hospitals. This study aimed to identify hospital factors associated with GIB outcomes. This was a retrospective cohort study of Medicare fee-for-service beneficiaries hospitalized for GIB from 2016-2018. These data were merged with the American Hospital Association Annual Survey data to incorporate hospital characteristics. We used generalized linear mixed effects models to estimate the effect of hospital level characteristics on patient outcomes after adjusting for patient risk factors including anticoagulant and antiplatelet use, recent GIB, and comorbidities. The primary outcome was 30-day mortality and secondary outcomes included length of stay (LOS), and a composite outcome of 30-day readmission or mortality. Factors associated with improved GIB 30d mortality included large hospital size (defined as beds>400, OR: 0.93 (0.90, 0.97)), greater case volume (OR 0.97 (0.96, 0.98)) increased residents and nurse staffing (OR: 0.88 (0.83, 0.94)), and blood donor center designation (OR: 0.93 (0.88, 0.99)). Patients treated at a hospital with multiple advanced capabilities, such as availability of advanced endoscopy, advanced ICU capabilities (both a medical-surgical ICU and cardiac ICU), blood donor center, and liver transplant center, had a 22% reduction in 30-day mortality risk, compared to those hospitalized in a hospital with none of these services (OR: 0.78 (0.68, 0.91)). However, LOS increased with additional services. Patients hospitalized for GIB at hospitals with multiple advanced specialized capabilities have lower mortality but longer lengths of stay. Further research should examine the processes of care linked to these services that contribute to improved mortality in GIB.

Validation of a measure of hospital maternal level of care for the United States

BackgroundLack of a validated assessment of maternal risk-appropriate care for use in population data has prevented the existing literature from quantifying the benefit of maternal risk-appropriate care. The objective of this study was to develop a measure of hospital maternal levels of care based on the resources available at the hospital, using existing data available to researchers.MethodsThis was a secondary data analysis. The sample was abstracted from the American Hospital Association Annual Survey Database for 2018. Eligibility was limited to short-term acute general hospitals that reported providing maternity services as measured by hospital reporting of an obstetric service level, obstetric services, or birthing rooms. We aligned variables in the database with the ACOG criteria for each maternal level of care, then built models that used the variables to measure the maternal level of care. In each iteration, the distribution of hospitals was compared to the distribution in the CDC Levels of Care Assessment Tool Validation Pilot, assessing agreement with the Wilson Score for proportions for each level of care. Results were compared to hospital self-report in the database and measurement reported with another published method.ResultsThe sample included 2,351 hospitals. AHA variables were available to measure resources that align with ACOG Levels 1, 2, and 3. Overall, 1219 (51.9%) of hospitals reported resources aligned with Maternal Level One, 816 (34.7%) aligned with maternal level two, and 202 (8.6%) aligned with maternal level Three. This method overestimates the prevalence of hospitals with maternal level one compared to the CDC measurement of 36.1% (Mean 52.9%; 95% CI47.2%—58.7%), and likely includes hospitals that would not qualify as level one if all resources required by the ACOG guidelines could be assessed. This method underestimates the prevalence of hospitals with maternal critical care services (Level 3 or 4) compared to CDC measure of 12.1% (Mean 8.1%; 95%CI 6.2% – 10.0%) but is an improvement over hospital self-report (24.7%) and a prior published method (32.3%).ConclusionsThis method of measuring maternal level of care allows researchers to investigate the value of perinatal regionalization, risk-appropriate care, and hospital differences among the three levels of care. This study identified potential changes to the American Hospital Association Annual Survey that would improve identification of maternal levels of care for research.

REPORTED PRICES FOR HIGH VOLUME HAND SURGERY IN THE ERA OF PRICE TRANSPARENCY: IMPLICATIONS FOR FUTURE POLICY ITERATIONS.

In 2021, the United States enacted a law requiring hospitals to report prices for healthcare services. Across several healthcare services, poor compliance and wide variation in pricing was found. This study aims to investigate variation in reporting and listed prices by hospital features for high-volume hand surgeries including Carpal Tunnel release, Trigger Finger Release, De Quervain Tenosynovitis Release, and Carpometacarpal Arthroplasty. The Turquoise Health price transparency database was used to obtain listed prices and linked to hospital characteristics from the 2021 Annual American Hospital Association Survey. This study used descriptive statistics and generalized linear regression. The analytic cohort included 2,652 hospitals from across the US. The highest rate of price reporting was in the Midwest (52%, n=836) and lowest in the South (39%, n=925). Compared to commercial insurers, ($3,609, 95% CI: $3,414 to $3,805) public insurance rates were significantly lower (Medicare: $1,588, 95% CI: $1,484 to $1,693, adjusted difference = -$2,021, p<0.001, Medicaid: $1,403, (95% CI: $1,194 to $1,612, adjusted difference = -$2,206, p<0.001). Listed rates for self-pay patients were not statistically different from commercial rates. Although pricing for high volume elective hand surgeries is frequently reported, a high proportion of hospitals do not report prices. These data highlight the need for future transparency policy to include pricing for high-volume hand surgery to give patients the ability to make financially informed choices. These results are a valuable aid for surgeons and patients to promote financially conscious decisions.

Associations of U.S. hospital closure (2007-2018) with area socioeconomic disadvantage and racial/ethnic composition

PurposeTo examine whether hospital closure is associated with high levels of area socioeconomic disadvantage and racial/ethnic minority composition. MethodsPooled cross-sectional analysis (2007–2018) of 6467 U.S. hospitals from the American Hospital Association’s Annual Survey, comparing hospital population characteristics of closed hospitals to all remaining open hospitals. We used multilevel mixed-effects logistic regression models to assess closure as a function of population characteristics, including area deprivation index ([ADI], a composite measure of socioeconomic disadvantage), racial/ethnic composition, and rural classification, nesting hospitals within hospital service areas (HSAs) and hospital referral regions. Secondary analyses examined public or private hospital type. ResultsOverall, 326 (5.0%) of 6467 U.S. hospitals closed during the study period. In multivariable models, hospitals in HSAs with a higher burden of socioeconomic disadvantage (per 10% above median ADI ZIP codes, AOR 1.05; 95% CI, 1.01–1.09) and Black Non-Hispanic composition (highest quartile, AOR 4.03; 95% CI, 2.62–6.21) had higher odds of closure. We did not observe disparities in closure by Hispanic/Latino composition or rurality. Disparities persisted for Black Non-Hispanic communities, even among HSAs with the lowest burden of disadvantage. ConclusionsDisproportionate hospital closure in communities with higher socioeconomic disadvantage and Black racial composition raises concerns about unequal loss of healthcare resources in the U.S.

Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area.

We examined the geographic distribution and sociodemographic and economic characteristics of chronic disease prevalence in the US. Understanding disease prevalence and its impact on communities is crucial for effective public health interventions. Data came from the American Community Survey, the American Hospital Association Survey, and the Centers for Disease Control and Prevention's PLACES. We used quartile thresholds for 10 chronic diseases to assess chronic disease prevalence by Zip Code Tabulation Areas (ZCTAs). ZCTAs were scored from 0 to 20 based on their chronic disease prevalence quartile. Three prevalence categories were established: least prevalent (score ≤6), moderately prevalent (score 7-13), and highest prevalence (score ≥14). Community characteristics were compared across categories and spatial analyses to identify clusters of ZCTAs with high disease prevalence. Our study showed a high prevalence of chronic disease in the southeastern region of the US. Populations in ZCTAs with the highest prevalence showed significantly greater socioeconomic disadvantages (ie, lower household income, lower home value, lower educational attainment, and higher uninsured rates) and barriers to health care access (lower percentage of car ownership and longer travel distances to hospital-based intensive care units, emergency departments, federally qualified health centers, and pharmacies) compared with ZCTAs with the lowest prevalence. Socioeconomic disparities and health care access should be addressed in communities with high chronic disease prevalence. Carefully directed resource allocation and interventions are necessary to reduce the effects of chronic disease on these communities. Policy makers and clinicians should prioritize efforts to reduce chronic disease prevalence and improve the overall health and well-being of affected communities throughout the US.

Teaching and Safety-Net Hospital Penalization in the Hospital-Acquired Condition Reduction Program

The Hospital-Acquired Condition Reduction Program (HACRP) evaluates acute care hospitals on the occurrence of patient safety events and health care-associated infections. Since its implementation, several studies have raised concerns about the overpenalization of teaching and safety-net hospitals, and although several changes in the program's methodology have been applied in the last few years, whether these changes reversed the overpenalization of teaching and safety-net hospitals is unknown. To determine hospital characteristics associated with HACRP penalization and penalization reversal. This retrospective cross-sectional study assessed data from 3117 acute care hospitals participating in the HACRP. The HACRP penalization and hospital characteristics were obtained from Hospital Compare (2020 and 2021), the Inpatient Prospective Payment System impact file (2020), and the American Hospital Association annual survey (2018). Hospital characteristics, including safety-net status and teaching intensity (no teaching and very minor, minor, major, and very major teaching levels). The primary outcome was HACRP penalization (ie, hospitals that fell within the worst quartile of the program's performance). Multivariable models initially included all covariates, and then backward stepwise variable selection was used. Of 3117 hospitals that participated in HACRP in 2020, 779 (25.0%) were safety-net hospitals and 1090 (35.0%) were teaching institutions. In total, 771 hospitals (24.7%) were penalized. The HACRP penalization was associated with safety-net status (odds ratio [OR], 1.41 [95% CI, 1.16-1.71]) and very major teaching intensity (OR, 1.94 [95% CI, 1.15-3.28]). In addition, non-federal government hospitals were more likely to be penalized than for-profit hospitals (OR, 1.62 [95% CI, 1.23-2.14]), as were level I trauma centers (OR, 2.05 [95% CI, 1.43-2.96]) and hospitals located in the New England region (OR, 1.65 [95% CI, 1.12-2.43]). Safety-net hospitals with major teaching levels were twice as likely to be penalized as non-safety-net nonteaching hospitals (OR, 2.15 [95% CI, 1.14-4.03]). Furthermore, safety-net hospitals penalized in 2020 were less likely (OR, 0.64 [95% CI, 0.43-0.96]) to revert their HACRP penalization status in 2021. Findings from this cross-sectional study indicated that teaching and safety-net hospital status continued to be associated with overpenalization in the HACRP despite recent changes in its methodology. Most of these hospitals were also less likely to revert their penalization status. A reevaluation of the program methodology is needed to avoid depleting resources of hospitals caring for underserved populations.

Abstract WP108: Predictive Accuracy of Multivariable Models for 30-day and 1-year All Cause Readmission After Stroke Using Different Combinations of Registry, Hospital and Claims Based Data Sources

Introduction: Hospital readmissions are often used as an indicator of quality of care. However, identifying patients at risk of readmission after stroke is challenging and predictive models have historically not performed well, in part because they often rely on single data sources. Data linkage might offer a solution. Methods: We probabilistically linked data from the Michigan’s Get With The Guidelines Stroke registry and Michigan Value Collaborative multipayer claims database from Medicare and Blue Cross Blue Shield beneficiaries discharged alive following acute stroke (ICD-10 I61-I63) between 2016-2020. The registry dataset included 64 variables covering demographics, stroke presentation, medical history, procedures, and complications. The hospital dataset included 20 variables from the American Hospital Association’s database. The claims dataset included payer and 79 HCC comorbidity codes. Using combinations of the 3 data sources, we examined the performance of multivariable LASSO logistic regression models to predict all cause readmission at 30-days and 1-year post discharge. We generated hospital-specific testing and training models and reported the mean model discrimination (AUC) of all combinations of the 3 datasets. Results: Of 19,382 linked stroke discharges, 2,724 (14.1%) and 8,169 (42.2%) were readmitted within 30-days and 1-year, respectively. For 30-day readmission, the model based on only registry data produced the best performance (M1, Table). However, for prediction of 1-year readmission, the combination of registry and claims data produced the best performing model (M13, Table). Hospital level characteristics did not have any significant impact on prediction accuracy. Conclusions: Clinical registry data was the best data source for predicting 30-day readmission. However, claims based data were additive when predicting readmission within 1-year, probably because HCC codes add information about total comorbidity burden.

Mortality Among the Dementia Population in Not-For-Profit Hospitals with Better Nursing Resources

ABSTRACT The dementia population has higher rates of mortality during hospital stays than those without dementia. The aim of this study is to examine the relationship between ownership status (i.e. for-profit vs. not-for-profit) and nursing resources (i.e. nurse work environment, nurse-to-patient staffing, and nurse education) on 30-day mortality among post-surgical older adults with dementia. A cross-sectional analysis of linked American Hospital Association, Medicare claims, and nurse survey data was conducted using multi-level logistic regression models. We examined these models to assess the relationship between ownership status and 30-day mortality after adjusting patient and hospital characteristics. We also analyzed the relationship between the hospital ownership status and the 30-day mortality, after considering the three nursing resources. Older adults with dementia who received care in hospitals with not-for-profit status were less likely to die within 30 days of admission following surgery compared to those treated in hospitals with for-profit hospital status (i.e. odds ratio 0.82, 95% confidence interval 0.73–0.92, p = <.001). In addition, the odds ratios estimating the association between ownership and mortality were similar across the different models of the three nursing resources with and without those controls (i.e. 0.88 vs. 0.83 vs. 0.82). Surgical patients with dementia had better outcomes when cared for in not-for-profit hospitals, particularly with greater levels of nurse education and nurse staffing. The relationship between profit status and mortality was partly explained by the lower levels of nurse staffing and education in for-profit vs. not-for-profit hospitals.

Hospital Capabilities Associated With Behavioral Health Integration Within Emergency Departments.

To identify hospital capabilities associated with behavioral health (BH) processes in emergency departments (EDs). Six hundred two hospital responses to the 2017/2018 National Survey of Healthcare Organizations and Systems were linked to 2017 American Hospital Association Annual Survey data. Separate multivariable regressions estimated how hospital capabilities (the use of quality improvement methods, approaches to disseminate best patient-care practices, barriers to using care delivery innovations, and inpatient beds for psychiatric or substance use) were associated with each of 4 ED-based BH processes: mental health and substance use disorder screening, team-based approaches to BH, telepsychiatry, and direct referrals to community-based BH clinicians. Models controlled for hospital structural characteristics and area-level socioeconomic factors. Most hospitals screened for BH conditions and provided direct referrals to community-based BH clinicians. Approximately half of the hospitals used a team approach to BH. A minority had implemented telepsychiatry. Each additional process used to disseminate best patient-care practices was associated with more screening for BH conditions (an increase of 4.07 points on the screening index, P <0.01) and greater likelihood of using a team approach to BH [4.41 percentage point ( P <0.01) increase]. Hospitals reporting more barriers to the use of care delivery innovations reported less screening and use of a team approach [a decrease of 0.15 points on the screening index ( P <0.01) and 0.28 percentage points reduction in likelihood of team approach use ( P <0.001) for 1-point increase in the barrier index]. Research and interventions focused on removing innovation barriers or adding processes to disseminate best practices offer a path to accelerate BH integration in hospital EDs.

Racial and ethnic disparities in emergency department transfers to public hospitals.

To examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences. ED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010-2019); American Hospital Association Annual Survey (2009-2018). Logistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non-ECSCs. We reported marginal effects at the means. We examined 1,265,588 adult ED patients transferred from 187 hospitals. Black patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5-1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were - 0.6 pp to -1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer. Large population-level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.

The Revival of US Hospital Care, 2004-2019.

Between 2004 and 2019, the US hospital industry reversed the 21-year decline in its share of national health spending. To measure and explain changes in hospital utilization, cost, charges, and inpatient case mix. Principal sources were the American Hospital Association annual survey, the National Inpatient Sample, and the Healthcare Cost Reporting Information System. The study included all US community hospitals (n=5141 in 2019). We used factor decomposition to separate the impacts of population, utilization, unit cost, and charge markups on the growth in cost and charges for inpatient and outpatient care nationwide and for each state. For unit cost, we separated the impacts of input price inflation and treatment intensity. To measure the inpatient case mix, we applied an all-patient diagnosis-related groups algorithm. Between 2004 and 2019, charges more than tripled to $4.11 trillion. The cost more than doubled to $911 billion. For inpatient care, discharges fell 5%, discharges per person fell 15%, cost per discharge increased 88%, and charge markups rose 43%. For outpatient care, visits rose 36%, visits per person rose 21%, cost per visit rose 119%, and charge markups rose 52%. Treatment intensity increased by 33% per discharge and 55% per visit. Nationwide, the inpatient case mix increased by 34%, reflecting sicker patients and better clinical documentation. We quantified 3 important trends: rapid growth in outpatient visits, increased treatment intensity, and sustained increases in markups. Increased treatment intensity was the largest factor behind $491 billion in hospital cost growth between 2004 and 2019.

Challenges and dynamics of public health reporting and data exchange during COVID-19: insights from US hospitals

Abstract The US health care response during the early stages of the COVID-19 pandemic unveiled challenges in public health reporting systems and electronic clinical data exchange. Using data from the 2020 and 2022 American Hospital Association information technology supplement surveys, this study examined US hospitals’ experiences in public health reporting, accessing clinical data from external providers for COVID-19 patient care, and their success in reporting vaccine-related adverse events to relevant state and federal agencies. Results showcase significant disparities in reporting practices across government levels due to inconsistent requirements. Although many hospitals leaned toward automated data transmission, a substantial portion continued to depend on manual processes. Pertaining to electronic clinical data, while entities like large commercial laboratories outperformed others, a considerable number were sluggish in delivering critical information. Moreover, a small percentage of hospitals reported challenges in recording vaccine-related adverse events, emphasizing the need for transparent reporting systems. The study underscores the necessity for standardized reporting protocols, explicit directives, and a pivot from manual to automated processes. Tackling these challenges is pivotal for ensuring prompt and reliable data, bolstering future public health responses, and rejuvenating public trust in health institutions.

Alternative Payment Models and Patient-Reported Quality of Preparation for Discharge: A Retrospective Longitudinal Study

Preparing patients for posthospital care may improve readmission risk. Alternative payment models (APMs) incent hospitals to reduce readmissions by tying payment to outcomes. The impact of APMs on preparation for discharge is not well understood. We assessed whether patient-reported preparation for posthospital care was associated with reduced readmissions, and whether APM participation was associated with improved preparation for posthospital care. We used mixed-effects regression on retrospective (2013–2017) observational data for 2685 U.S. hospitals. We measured patient-reported preparation for posthospital care using the 3-Item Care Transition Measure and readmission using 30-day all-cause risk-adjusted readmissions from Hospital Compare. Participation in accountable care organizations (ACOs), Medical Homes, and Medicare's Bundled Payments for Care Improvement program was obtained from Medicare, the American Hospital Association's Annual Survey, and Leavitt Partner's ACO database. We found that APMs are not associated with improved preparation for posthospital care, even though it was associated with reduced readmissions (Marginal Effect: −0.012 percentage points). This may be because hospitals are not investing in patient engagement. This study has limited insight into causality and reduced generalizability among smaller, rural, and non-teaching hospitals.

Playing by the Rules? Tracking U.S. Hospitals' Responses to Federal Price Transparency Regulation.

As of January 1, 2021, the Centers for Medicare & Medicaid Services requires most U.S. hospitals to publish pricing information on their website to help consumers make decisions regarding services and to transform negotiations with health insurers. For this study, we evaluated changes in hospitals' compliance with the federal price transparency rule after the first year of enactment, during which the Centers for Medicare & Medicaid Services increased the penalty for noncompliance. Using a nationally representative random sample of 470 hospitals, we assessed compliance with both parts of the hospital transparency rule (publishing a machine-readable price database and a consumer shopping tool) in the first quarter of 2022 and compared its baseline level in the first quarter of 2021. Using data from the American Hospital Association and Clarivate, we next assessed how compliance varied by hospital factors (ownership, number of beds, system membership, teaching status, type of electronic health record system), market factors (hospital and insurer market concentration), and the estimated change in penalty for noncompliance. By early 2022, 46% of hospitals had posted both machine-readable and consumer-shoppable data, an increase of 24% from the prior year. Almost 9 in 10 hospitals had complied with the consumer-shoppable data requirement by early 2022. Larger hospitals and public hospitals had lower probabilities of baseline compliance with the machine-readable and consumer-shoppable requirements, respectively, although public hospitals were significantly more likely to become compliant with the consumer-shoppable requirement by 2022. Higher hospital market concentration was also associated with higher baseline compliance for both the machine-readable and consumer-shoppable requirements. Furthermore, our analyses found that hospitals with certain electronic health record systems were more likely to comply with the consumer-shoppable requirement in 2021 and became increasingly compliant with the machine-readable requirement in 2022. Finally, we found that hospitals with a larger estimated penalty were more likely to become compliant with the machine-readable requirement. Longitudinal analyses of compliance with the federal price transparency rule are valuable for monitoring changes in hospitals' behavior and assessing whether compliance changes vary systematically for specific types of hospitals and/or market structures. Our results suggest a trend toward increased hospital compliance between 2021 and 2022. Although hospitals perceive the consumer-shopping tools as being the most impactful, the value of this information depends on whether it is comprehensible and comparable across hospitals. The new price transparency rule has facilitated the creation of new data that have the potential to significantly alter the competitive landscape for hospitals and may require hospital leaders to consider how their organizational strategies change concerning their engagement with payers and patients. Finally, greater price transparency is likely to bolster national policy discussions related to price variation, affordability, and the role of regulation in healthcare markets.