Hospital Association Annual Survey Database Research Articles

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.

Screening for Health-Related Social Needs and Collaboration With External Partners Among US Hospitals

In recent years, hospitals and health systems have reported increasing rates of screening for patients' individual and community social needs, but few studies have explored the national landscape of screening and interventions directed at addressing health-related social needs (HRSNs) and social determinants of health (SDOH). To evaluate the associations of hospital characteristics and area-level socioeconomic indicators to quantify the presence and intensity of hospitals' screening practices, interventions, and collaborative external partnerships that seek to measure and ameliorate patients' HRSNs and SDOH. This cross-sectional study used national data from the American Hospital Association Annual Survey Database for fiscal year 2020. General-service, acute-care, nonfederal hospitals were included in the study's final sample, representing nationally diverse hospital settings. Data were analyzed from July 2022 to February 2023. Organizational characteristics and area-level socioeconomic indicators. The outcomes of interest were hospital-reported patient screening of and strategies to address 8 HRSNs and 14 external partnership types to address SDOH. Composite scores for screening practices and external partnership types were calculated, and ordinary least-square regression analyses tested associations of organizational characteristics with outcome measures. Of 2858 US hospital respondents (response rate, 67.0%), most hospitals (79.2%; 95% CI, 77.7%-80.7%) reported screening patients for at least 1 HRSN, with food insecurity or hunger needs (66.1%; 95% CI, 64.3%-67.8%) and interpersonal violence (66.4%; 95% CI, 64.7%-68.1%) being the most commonly screened social needs. Most hospitals (79.4%; 95% CI, 66.3%-69.7%) reported having strategies and programs to address patients' HRSNs; notably, most hospitals (52.8%; 95% CI, 51.0%-54.5%) had interventions for transportation barriers. Hospitals reported a mean of 4.03 (95% CI, 3.85-4.20) external partnership types to address SDOH and 5.69 (5.50-5.88) partnership types to address HRSNs, with local or state public health departments and health care practitioners outside of the health system being the most common. Hospitals with accountable care contracts (ACCs) and bundled payment programs (BPPs) reported higher screening practices (ACC: β = 1.03; SE = 0.13; BPP: β = 0.72; SE = 0.14), interventions (ACC: β = 1.45; SE = 0.12; BPP: β = 0.61; SE = 0.13), and external partnership types to address HRSNs (ACC: β = 2.07; SE = 0.23; BPP: β = 1.47; SE = 0.24) and SDOH (ACC: β = 2.64; SE = 0.20; BPP: β = 1.57; SE = 0.21). Compared with nonteaching, government-owned, and for-profit hospitals, teaching and nonprofit hospitals were also more likely to report more HRSN-directed activities. Patterns based on geographic and area-level socioeconomic indicators did not emerge. This cross-sectional study found that most US hospitals were screening patients for multiple HRSNs. Active participation in value-based care, teaching hospital status, and nonprofit status were the characteristics most consistently associated with greater overall screening activities and number of related partnership types. These results support previously posited associations about which types of hospitals were leading screening uptake and reinforce understanding of the role of hospital incentives in supporting health equity efforts.

Assessing the impact of health information exchange on hospital data breach risk

ObjectiveWidespread electronic health information exchange (HIE) across hospitals remains an important policy goal for reducing costs and improving the quality of care. Meanwhile, cybersecurity incidents are a growing threat to hospitals. The relationship between the electronic sharing of health information and cybersecurity incidents is not well understood. The objective of this study was to empirically examine the impact of hospitals’ HIE engagement on their data breach risk. Materials and MethodsA balanced panel dataset included 4,936 US community hospitals spanning the period 2010–2017, which was assembled by linking the American Hospital Association annual survey database and the Information Technology (IT) supplement, and the Department of Health and Human Services reports of health data breaches. The relationship between HIE engagement and hospital data breaches was modeled using a difference-in-differences specification controlling for time-varying hospital characteristics. ResultsThe percentage of hospitals electronically exchanging information has more than tripled (from 18% to 68%) from 2010 to 2017. Hospital data breaches increased concurrently, largely due to the rise in hacking and unauthorized access. HIE engagement was associated with a 0.672 percentage point increase in the probability of an IT breach three years after the engagement. Hospitals actively engaging in a health information organization and exchanging data with outside providers were associated with a higher risk of IT related breaches in the long run; however, hospitals actively engaging in HIE and exchanging data with inside providers were not associated with any significant risk of IT related breaches. DiscussionOver time, the increasing amount and complexity of patient information being exchanged can create challenges for cybersecurity if data protection is not up to date. Additionally, data security depends on the weakest link of HIE, and providers with fewer resources for data governance and infrastructure are more vulnerable to data breaches. ConclusionMoving toward widespread health information exchange has important cybersecurity implications that can significantly impact both patients and healthcare organizations.

Were hospitals with sustained high performance more successful at reducing mortality during the pandemic's second wave?

In 2019, the COVID-19 pandemic emerged. Variation in COVID-19 patient outcomes between hospitals was later reported. This study aims to determine whether sustainers-hospitals with sustained high performance on Hospital Value-Based Purchasing Total Performance Score (HVBP-TPS)-more effectively responded to the pandemic and therefore had better patient outcomes. We calculated hospital-specific risk-standardized event rates using deidentified patient-level data from the UnitedHealth Group Clinical Discovery Database. HVBP-TPS from 2016 to 2019 were obtained from Centers for Medicare & Medicaid Services. Hospital characteristics were obtained from the American Hospital Association Annual Survey Database (2019), and county-level predictors were obtained from the Area Health Resource File. We use a repeated-measures regression model assuming an AR(1) type correlation structure to test whether sustainers had lower mortality rates than nonsustainers during the first wave (spring 2020) and the second wave (October to December 2020) of the pandemic. Sustainers did not have significantly lower COVID-19 mortality rates during the first wave of the pandemic, but they had lower COVID-19 mortality rates during the second wave compared to nonsustainers. Larger hospitals, teaching hospitals, and hospitals with higher occupancy rates had higher mortality rates. During the first wave of the pandemic, mortality rates did not differ between sustainers and nonsustainers. However, sustainers had lower mortality rates than nonsustainers in the second wave, most likely because of their knowledge management capabilities and existing structures and resources that enable them to develop new processes and routines to care for patients in times of crisis. Therefore, a consistently high level of performance over the years on HVBP-TPS is associated with high levels of performance on COVID-19 patient outcomes. Investing in identifying the knowledge, processes, and resources that foster the dynamic capabilities needed to achieve superior performance in HVBP might enable hospitals to utilize these capabilities to adapt more effectively to future changes and uncertainty.

The Rise Of Cross-Market Hospital Systems And Their Market Power In The US.

Although hospital consolidation within markets has been well documented, consolidation across markets has not, even though economic theory predicts-and evidence is emerging-that cross-market hospital systems raise prices by exerting market power across markets when negotiating with common customers (primarily insurers). This study analyzes hospital systems using the American Hospital Association Annual Survey Database and defines hospital geographic markets as commuting zones that link workers to places of employment. The share of community hospitals in the US that were part of hospital systems increased from 10percent in 1970 to 67percent in 2019, resulting in 3,436 hospitals within 368 systems in 2019. Of these systems, 216 (59percent) owned hospitals in multiple commuting zones, in part because 55percent of the 1,500 hospitals targeted for a merger or acquisition between 2010 and 2019 were located in a different commuting zone than the acquirer. Based on market-power differences among hospitals in systems, the number of systems in urban commuting zones that could potentially exert enhanced cross-market power increased from thirty-seven systems in 2009 to fifty-seven systems in 2019, an increase of 54percent. The increase in cross-market hospital systems warrants concern and scrutiny because of the potential anticompetitive impact of hospital systems exerting market power across markets in negotiations with common customers.

The Hospital Value-Based Purchasing Program: Do hospitalists improve health care value.

As healthcare organizations examine the associated benefits of employing a larger hospitalist workforce, there is a need to better understand the association with patients' quality, experience, and efficiency. However, there is a lack of information regarding how hospital use of hospitalists over time influences hospital scoring on quality programs, such as the Center for Medicare and Medicaid Services (CMS) Hospital Inpatient Value-Based Purchasing (HVBP) Program. This study examines the association between hospitalist staffing between 2014 and 2019 and HVBP scores. We used a cross-sectional panel study design. Total Performance Score (TPS) and its domains were obtained from CMS from 2014 to 2019 and merged with the American Hospital AssociationAnnual Survey Database. We utilized random-effects multivariable panel regression models and zero-inflated negative binomial regression to examine the association between the hospitalist-staffing ratio and the HVBP Program. All models were adjusted for hospital characteristics. A total of 2126 hospitals were included in the study. The average ratio of hospitalists per staffed bed was 0.06, with a standard deviation of 0.15. This study suggests that hospitals that employ a higher percentage of hospitalists see improvement in their overall TPS (β = 5.40; p < .001), Patient Experience (β = 2.49; p <.05), andEfficiency (incidence-rate ratio= 1.41; p < .001) domain. However, the Clinical Care domain was no different in organizations employing more hospitalists. There are benefits associated with TPS, Patient Experience, and Efficiency from employing hospitalists. Managers should seek opportunities to leverage hospitalists' expertise in providing care, particularly in improving care processes.

ACA Medicaid expansion reduced disparities in use of high-volume hospitals for pancreatic surgery

BackgroundEarly evaluation of the Affordable Care Act’s Medicaid expansion demonstrated persistent disparities among Medicaid beneficiaries in use of high-volume hospitals for pancreatic surgery. Longer-term effects of expansion remain unknown. This study evaluated the impact of expansion on the use of high-volume hospitals for pancreatic surgery among Medicaid and uninsured patients. MethodsState inpatient databases (2012-2017), the American Hospital Association Annual Survey Database, and the Area Resource File from the Health Resources and Services Administration, were used to examine 8,264 non-elderly adults who underwent pancreatic surgery in nine expansion and two non-expansion states. High-volume hospitals were defined as performing 20 or more resections/year. Linear probability triple differences models measured pre- and post-Affordable Care Act utilization rates of pancreatic surgery at high-volume hospitals among Medicaid and uninsured patients versus privately insured patients in expansion versus non-expansion states. ResultsThe Affordable Care Act’s expansion was associated with increased rates of utilization of high-volume hospitals for pancreatic surgery by Medicaid and uninsured patients (48% vs 55.4%, P = .047) relative to privately insured patients in expansion states (triple difference estimate +11.7%, P = .022). A pre-Affordable Care Act gap in use of high-volume hospitals among Medicaid and uninsured patients in expansion states versus non-expansion states (48% vs 77%, P < .0001) was reduced by 15.1% (P = .001) post Affordable Care Act. A pre Affordable Care Act gap between expansion versus non-expansion states was larger for Medicaid and uninsured patients relative to privately insured patients by 24.9% (P < .0001) and was reduced by 11.7% (P = .022) post Affordable Care Act. Rates among privately insured patients remained unchanged. ConclusionMedicaid expansion was associated with greater utilization of high-volume hospitals for pancreatic surgery among Medicaid and uninsured patients. These findings are informative to non-expansion states considering expansion. Future studies should target understanding referral mechanism post-expansion.

Characteristics of U.S. Acute Care Hospitals That Have Implemented Telemedicine Critical Care.

Telemedicine critical care is associated with improved efficiency, quality, and cost-effectiveness. As of 2010, fewer than 5% of U.S. hospitals had telemedicine critical care, and fewer than 10% of ICU beds were covered. We evaluated recent telemedicine critical care implementation and bed coverage rates in the United States and compared characteristics of hospitals with and without telemedicine critical care. Cross-sectional study of 2018 American Hospital Association Annual Survey Database. U.S. hospitals. None. None. We obtained data regarding telemedicine critical care implementation, ICU capability (defined as ≥ 1 ICU bed), other hospital characteristics, and the Herfindahl-Hirschman Index, a measure of ICU market competition based on hospital referral regions. Among 4,396 hospitals (response rate 71%), 788 (17.9%) had telemedicine critical care, providing potential coverage to 27,624 (28% of total) ICU beds. Among 306 hospital referral regions, 197 (64%) had a respondent hospital with telemedicine critical care. Telemedicine critical care implementation was associated with being a nonprofit (odds ratio, 7.75; 95% CI, 5.18-11.58) or public (odds ratio, 4.16 [2.57-6.73]) compared with for-profit hospital; membership in a health system (odds ratio, 3.83 [2.89-5.08]; stroke telemedicine presence (odds ratio, 6.87 [5.35-8.81]); ICU capability (odds ratio, 1.68 [1.25-2.26]); and more competitive ICU markets (odds ratio per 1,000-point decrease in Herfindahl-Hirschman Index 1.11 [1.01-1.22]). Notably, rural critical access hospitals had lower odds of telemedicine critical care implementation (odds ratio, 0.49 [0.34-0.70]). Teaching status, geographic region, and rurality were not associated with telemedicine critical care implementation. About one fifth of respondent hospitals had telemedicine critical care by 2018, providing potential coverage of nearly one third of reported ICU beds. This represents a substantial increase in telemedicine critical care implementation over the last decade. Future expansion to include more rural hospitals that could benefit most may be aided by addressing hospital financial and market barriers to telemedicine critical care implementation.

Hospital Adoption of Electronic Decision Support Tools for Preeclampsia Management.

Background:Electronic health record (EHR)–based clinical decision support tools can improve the use of evidence-based clinical guidelines for preeclampsia management that can reduce maternal mortality and morbidity. No study has investigated the organizational capabilities that enable hospitals to use EHR-based decision support tools to manage preeclampsia.Objective:To examine the association of organizational capabilities and hospital adoption of EHR-based decision support tools for preeclampsia management.Methods:Cross-sectional analyses of hospitals providing obstetric care in 2017. In total, 739 hospitals responded to the 2017-2018 National Survey of Healthcare Organizations and Systems (NSHOS) and were linked to the 2017 American Hospital Association (AHA) Annual Survey Database and the Area Health Resources File (AHRF). A total of 425 hospitals providing obstetric care across 49 states were included in the analysis. The main outcome was whether a hospital adopted EHR-based clinical decision support tools for preeclampsia management. Hospital organizational capabilities assessed as predictors include EHR functions, adoption of evidence-based clinical treatments, use of quality improvement methods, and dissemination processes to share best patient care practices. Logistic regression estimated the association of hospital organizational capabilities and hospital adoption of EHR-based decision support tools to manage preeclampsia, controlling for hospital structural and patient sociodemographic characteristics.Results:Two-thirds of the hospitals (68%) adopted EHR-based decision support tools for preeclampsia, and slightly more than half (56%) of hospitals had a single EHR system. Multivariable regression results indicate that hospitals with a single EHR system were more likely to adopt EHR-based decision support tools for preeclampsia (17.4 percentage points; 95% CI, 1.9 to 33.0; P < .05) than hospitals with a mixture of EHR and paper-based systems. Compared with hospitals having multiple EHRs, on average, hospitals having a single EHR were also more likely to adopt the tools by 9.3 percentage points, but the difference was not statistically significant (95% CI, −1.3 to 19.9). Hospitals with more processes to aid dissemination of best patient care practices were also more likely to adopt EHR-based decision-support tools for preeclampsia (0.4 percentage points; 95% CI, 0.1 to 0.6, for every 1-unit increase in dissemination processes; P < .01).Conclusion:Standardized EHRs and policies to disseminate evidence are foundational hospital capabilities that can help advance the use of EHR-based decision support tools for preeclampsia management in the approximately one-third of US hospitals that still do not use them.

Polypharmacy in Older Adults Hospitalized for Heart Failure.

Despite potential harm that can result from polypharmacy, real-world data on polypharmacy in the setting of heart failure (HF) are limited. We sought to address this knowledge gap by studying older adults hospitalized for HF derived from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke). We examined 558 older adults aged ≥65 years with adjudicated HF hospitalizations from 380 hospitals across the United States. We collected and examined data from the REGARDS baseline assessment, medical charts from HF-adjudicated hospitalizations, the American Hospital Association annual survey database, and Medicare's Hospital Compare website. We counted the number of medications taken at hospital admission and discharge; and classified each medication as HF-related, non-HF cardiovascular-related, or noncardiovascular-related. The vast majority of participants (84% at admission and 95% at discharge) took ≥5 medications; and 42% at admission and 55% at discharge took ≥10 medications. The prevalence of taking ≥10 medications (polypharmacy) increased over the study period. As the number of total medications increased, the number of noncardiovascular medications increased more rapidly than the number of HF-related or non-HF cardiovascular medications. Defining polypharmacy as taking ≥10 medications might be more ideal in the HF population as most patients already take ≥5 medications. Polypharmacy is common both at admission and hospital discharge, and its prevalence is rising over time. The majority of medications taken by older adults with HF are noncardiovascular medications. There is a need to develop strategies that can mitigate the negative effects of polypharmacy among older adults with HF.

The Impact of Market Conditions on RN Staffing in Hospitals: Using Resource Dependence Theory and Information Uncertainty Perspective.

PurposeDue to a limited number of studies with generalizable findings on the relationships between market conditions and RN staffing levels in hospitals, this study examined such relationships employing a longitudinal design with a representative national sample.Materials and MethodsWe used longitudinal panel datasets from 2006 to 2010, drawn from various datasets including the American Hospital Association Annual Survey Database and the Area Health Resource File. A random-effects linear regression model was used to measure the influence of market conditions on RN staffing levels.ResultsThe results of this study showed that market conditions were significantly associated with RN staffing levels in hospitals. First, an increase in per capita income and being located in urban rather than rural areas were associated with a greater number of RNs per 1,000 inpatient days and a higher ratio of RNs to LPNs and nursing aides. In addition, an increase in the number of physician specialists was associated with an increase in the number of RNs per 1,000 inpatient days. Second, an increase in Medicare HMO penetration in the environment was related to an increase in the RNs to LPNs and nursing aides ratio. Lastly, an increase in market competition was associated with an increase in the number RNs per 1,000 inpatient days and the ratio of RNs to LPNs and nursing aides.ConclusionThe findings of this study suggest that staffing decision makers in hospitals should consider how to best align their RN staffing levels with their operating environment. In addition, health policy makers may improve the levels the RN supply in communities that needs more RNs by modulating external environmental forces (eg, specialist resources) that influence RN staffing levels in hospitals.

Pulmonary complications in trauma: Another bellwether for failure to rescue?

BackgroundPulmonary complications are the most common adverse event after injury and second greatest cause of failure to rescue (death after pulmonary complications). It is not known whether readily accessible trauma center data can be used to stratify center-level performance for various complications. Performance variation between trauma centers would allow sharing of best practices among otherwise similar hospitals. We hypothesized that high-, average-, and low-performing centers for pulmonary complication and failure to rescue could be identified and that hospital factors associated with success and failure could be discovered. MethodsPennsylvania state trauma registry data (2007–2015) were abstracted for pulmonary complications. Burns and age <17 were excluded. Multivariable logistic regression models were developed for pulmonary complication and failure to rescue, using demographics, comorbidities, and injuries/physiology. Expected event rates were compared with observed rates to identify outliers. Center-level variables associated with outcomes of interest were taken from the American Hospital Association Annual Survey Database and assessed for inclusion. ResultsIncluded in the study were 283,121 patients (male [60%] blunt trauma [92%]). Of these patients, 3% (8,381 of 283,121) developed pulmonary complications (center-level range 0.18%–5.8%). The percentage of failure-to-rescue patients was 13.4% (1,120/8,381, center-level range 0.0%-22.6%). For pulmonary complications, 13 out of 27 centers were high performers (95% CI for O:E ratio <1) and 7 out of 27 were low (95% CI for an O:E ratio >1). For failure-to-rescue patients, 2 out of 27 centers were low performers and the remainder average. There was little concordance between performance for pulmonary complications and failure to rescue. Research programs, large non-teaching hospitals, those with advanced practice providers, and those with health maintenance organizations had reduced failure-to-rescue patients. ConclusionFactors associated with complications were distinct from those affecting failure to rescue and center-level success in reducing complications often did not translate into success in preventing death once they occurred. Our data demonstrate that high- and low-performing centers and the factors driving success or failure are identifiable. This work serves as a guide for comparing practices and improving outcomes with readily available data.

The Relationship Between Nurse Staffing, Quality, And Financial Performance In Hospitals

Little evidence exists the relationship of nurse staffing and quality with financial performance in hospitals. This study aimed to measure the relationship between nurse staffing, quality of care, and profitability in hospitals. This study used longitudinal panel datasets from 2006 to 2010, drawn from various datasets including the American Hospital Association Annual Survey Database, Medicare Cost Report, and Hospital Compare Data. This study used the random-effects linear regression model to measure the relationship between nurse staffing, quality, and profitability. In addition, we tested a mediating effect of quality on the relationship between nurse staffing and profitability. This study found nurse staffing’s significant association with quality and profitability in hospitals. First, compared to hospitals in the lowest quintile of RNs per 1,000 inpatient days, hospitals in the higher quintiles had lower pneumonia readmission rates, and higher total profit margins, operating margins, and cash flow margins. In addition, hospitals with lower pneumonia readmission rates were found to have higher total profit margins and cash flow margins. Lastly, the current study found that the positive relationship between RNs per 1,000 inpatient days and total profit margin and cash flow margin was partially mediated by pneumonia readmission rates. In conclusion, our finding that nurse staffing is positively associated with both quality of care and profitability in hospitals suggests that the idea of hospitals responding to financial pressures by cutting RN resources with a goal of greater profitability should be called into question. The influence of lower RN staffing levels on higher profitability for hospitals is uncertain, while it is possible that RN staff reductions may compromise the quality of patient care.

Exploring association between certified EHRs adoption and patient experience in U.S. psychiatric hospitals.

ObjectiveCertified Electronic Health Records (EHR) have been shown to improve the health service quality in some health settings, but there is scant evidence related to its adoption in psychiatric hospitals. This paper aimed to examine the relationship between certified EHR adoption and patient experience across psychiatric hospitals in the United States.MethodsA cross-sectional study design compared the difference in patient experience measures between psychiatric hospitals with and without certified EHR. Data were drawn from the American Hospital Association (AHA) Annual Survey Database and Hospital Compare datasets. Eleven publicly reported measures for patient experience from the Consumer Assessment of Healthcare Providers and Systems Hospital Survey (HCAHPS) were applied for analysis. Independent relationship of certified EHR adoption and patient experience was explored with multiple linear regression models adjusted for hospital organizational characteristics.ResultsPositive associations were identified between certified EHR adoption and five patient perception measures—“recommend hospital” (β = 0.66, 95% CI = [0.16,1.16]; t = 2.68, p = 0.010), “overall hospital rating” (β = 0.39, 95% CI = [0.03,0.75]; t = 2.11, p = 0.035), “discharge information” (β = 0.45, 95% CI = [0.03,0.86]; t = 2.09, p = 0.037), “care transition” (β = 0.44, 95% CI = [0.14, 0.75]; t = 2.84, p = 0.005), and “responsiveness of hospital staff” (β = 0.47, 95% CI = [0.04, 0.90]; t = 2.13, p = 0.033).ConclusionOur results suggest the positive association between certified EHR adoption and patient experience. More studies are needed to explore impacts of certified EHR adoption and potential improvement in patient experience to quality of care.

Does the Halo Effect for Level 1 Trauma Centers Apply to High-Acuity Nonsurgical Admissions?

The halo effect describes the improved surgical outcomes at trauma centers for nontrauma conditions. To determine whether level 1 trauma centers have improved inpatient mortality for common but high-acuity nonsurgical diagnoses (eg, acute myocardial infarction [AMI], congestive heart failure [CHF], and pneumonia [PNA]) compared with non--level 1 trauma centers. The authors conducted a population-based, retrospective cohort study analyzing data from the Healthcare Cost and Utilization Project State Inpatient Database and the American Hospital Association Annual Survey Database. Patients who were admitted with AMI, CHF, and PNA between 2006-2011 in Florida and California were included. Level 1 trauma centers were matched to non-level 1 trauma centers using propensity scoring. The primary outcome was risk-adjusted inpatient mortality for each diagnosis (AMI, CHF, or PNA). Of the 190,474 patients who were hospitalized for AMI, CHF, or PNA, 94,037 patients (49%) underwent treatment at level 1 trauma centers. The inpatient mortality rates at level 1 trauma centers vs non-level 1 trauma centers for patients with AMI was 8.10% vs 8.40%, respectively (P=.73); for patients with CHF, 2.26% vs 2.71% (P=.90); and for patients with PNA, 2.30% vs 2.70% (P=.25). Level 1 trauma center designation was not associated with improved mortality for high-acuity, nonsurgical medical conditions in this study.

Hospital Characteristics Associated with Palliative Care Program Prevalence

Background: Over the past two decades, the number of hospitals with palliative care has increased significantly. Objective: This study analyzes the availability of palliative care in U.S. hospitals and examines the variation by hospital characteristics, community-level socioeconomic demographics, health care markets, and geographic characteristics. Methods: Data were obtained from the American Hospital Association Annual Survey Database for 2017 and supplemented with 2016 for nonresponders, the United States Census Bureau's 2017 American Community Survey, the Dartmouth Atlas of Health Care's 2016 Spending and 2011 Hospital and Physician Capacity datasets, the National Palliative Care Registry™, state-level directories on palliative care, and web-based searches. Multivariable logistic regression and average marginal effects were used to examine predictors of hospital palliative care programs. Results: Seventy-two percent of hospitals with 50 or more beds had palliative care programs. Hospital and geographic characteristics were significantly associated with the presence of palliative care. Most notably, nonprofit hospitals were 24.5 percentage points more likely than for-profit hospitals to have palliative care, and metropolitan areas were 15.4 percentage points more likely than rural areas, controlling for other variables. Conclusion: This study demonstrates that availability of palliative care in U.S. hospitals is determined by where patients live and the type of hospital to which they are admitted. Equitable and reliable availability to quality palliative care must improve across the nation.

Hospital Characteristics Associated with Certified EHR Adoption among US Psychiatric Hospitals.

ObjectiveThe aim of this study was to explore the relationship between hospital characteristics and certified electronic health record (EHR) adoption in psychiatric hospitals in the US.MethodsData were drawn from the American Hospital Association Annual Survey Database and the Centers for Medicare and Medicaid Services Hospital Compare data sets in 2016. Binary logistic regression analysis and χ2 tests were performed to examine the relationship between certified EHR adoption and hospital characteristics.ResultsOf 1,059 psychiatric hospitals in the US, 502 (47.4%) have adopted certified EHR technology. Large hospitals (OR 2.29, 95% CI 1.52–3.44; p<0.001), not-for-profit hospitals (OR 1.74, 95% CI 1.22–2.49; p=0.008), and hospitals participating in a network (OR 1.78, 95% CI 1.34–2.37; p<0.001) were more likely to adopt certified EHRs. Hospitals in the northeast were less likely to implement certified EHRs compared to other regions. However, there was no significant association found between EHR utilization and system affiliation, urban location, teaching status, or participation of health-maintenance organizations and preferred provider organizations.ConclusionThe study results suggested variations in EHR adoption according to hospital location, size, ownership, and network participation. This study fills a gap in previous work on certified EHR adoption that focused exclusively on general hospitals, but overlooked psychiatric hospitals. Future policies designed to influence the implementation of certified EHRs should take into consideration how hospital size, ownership, and network-affiliation status affect certified EHR adoption among psychiatric hospitals.

Sustainers: Hospitals with sustained superior performance.

The purpose of this article was to investigate the organizational and market-level variables associated with sustained superior hospital performance on Value-Based Purchasing total performance scores (TPS). TPS for 2014 through 2017 was obtained from the Centers for Medicare & Medicaid Services Hospital Compare website. Market-level data were from the 2017 Area Health Resource File, and hospital-level data were from the 2014 American Hospital Association Annual Survey database. We specified a logistic regression model to identify significant predictors of hospitals with sustained superior performance on TPS, that is, "sustainers." Only 8.4% of hospitals were classified as sustainers. Hospitals located in rural markets with a high Medicare Advantage penetration had a higher likelihood of being classified as sustainers. High RN staffing levels, lower Medicare share of inpatient days, not-for-profit ownership, and small size were all significant organizational predictors of sustained superior performance. Both modifiable characteristics, such as nurse staffing levels, and nonmodifiable characteristics, such as rural markets and small hospital size, are associated with the likelihood of hospitals sustaining superior performance over time. Managers need to carefully examine their staffing levels as they pursue interventions to sustain high TPS overtime. Moreover, factors such as Medicare share of inpatient days and size need to be considered when understanding barriers to sustained performance on Value-Based Purchasing domains.

Care Fragmentation And Failure To Rescue In Readmissions Following Hepatopancreatobiliary And Gastric Oncologic Surgery: Surgical Volume At The Reamdisson Hospital Is Associated With Mortality

Presenter: David Brauer MD, MPHS | Washington University, St. Louis Background: The surgical management of hepatopancreatobiliary (HPB) and gastric malignancies regularly occurs at major referral centers and is associated with a high rate of postoperative readmissions. Prior studies have shown that care fragmentation following surgery, including readmission to a hospital other than the index surgical hospital (an ‘outside hospital’ (OSH)), is associated with worse outcomes. No prior study has examined hospital-level variables associated with outcomes following readmission in this patient population. Methods: Patients undergoing HPB or gastric oncologic surgeries were identified from select State Inpatient Databases from the Healthcare Cost and Utilization Project from 2006-2014. Follow-up occurred up to the earliest of 90 days after discharge, receipt of chemotherapy, or discharge to hospice. The primary outcome was readmission mortality, with secondary outcomes of total readmission inpatient cost and total readmission inpatient length of stay. Annual hospital surgical volume was calculated using HCUP data for oncologic and non-oncologic HPB and gastric surgeries. Additional hospital-level variables were linked using the American Hospital Association Annual Survey Database. Due to data use agreements, hospitals with less than 10 visits during the inclusion period were excluded. Kruskal-Wallis test, logistic regression, and Youden’s index were used. Results: 31,526 patients were discharged following HPB or gastric oncologic surgery. 7,536 (24%) were readmitted within 90 days to a total of 636 hospitals. 28% of readmissions (n=2,124) were to OSH. For patients readmitted to OSH, 90-day postoperative mortality was 50% higher than mortality for patients readmitted to the index surgical hospital (8.0% vs 5.4%; OR 1.5, 95% CI 1.2 – 1.9). Looking solely at the hospital the patient was readmitted to, 188 hospitals had 10 or more readmissions during the inclusion period, accounting for 75% of all readmissions. Hospital size, measured by bed count, was not associated with readmission mortality, cost, or length of stay. However, a hospital's annual volume of HPB and gastric surgeries was associated with readmission mortality: patients were 37% more likely to die during their readmission if they were readmitted to a hospital with less than 100 combined annual HPB and gastric procedures (6.4% vs. 4.7%; OR 1.37, 95% CI 1.1 – 1.7). When comparing large volume (100 or more surgeries annually) and small volume hospitals, total 90-day readmission inpatient cost was 23% higher at large volume hospitals (median $15,000 vs $12,000; p<0.01). This could be a reflection of the costs of achieving rescue through surgical intervention, as significantly more surgeries were performed at larger hospitals (8.3% vs 5.3%, p<0.01). Total 90-day readmission length of stay was no different between the groups (median 6 days; p=0.82). Conclusion: Alongside sound clinical judgement, this data should be used to inform the decision-making process for where a patient should be readmitted following HPB and gastric surgery. To promote successful rescue after complications and readmission following surgery, efforts should be made to reduce care fragmentation and to direct readmissions to hospitals performing 100 or more annual HPB and gastric surgeries.

Hospitalists staffing levels and hospital performance.

To examine the association between hospitalists staffing levels and contract type with CMS Total Performance Score (TPS). Total performance scores were obtained from CMS, hospital-level data from the 2015 American Hospital Association Annual Survey Database, and unemployment rates from the Area Resource Health File. We used cluster analysis to classify hospitals based on the distribution of various hospitalist contracts, and we used regression analysis to examine the association between TPS and hospitalist staffing levels and contract distributions. Hospital-level predictors included hospitalists staffing levels, RN staffing levels, and Magnet status. Market-level variables were unemployment rates and competition. Higher staffing levels of employed hospitalists or hospitalists with a group contract are associated with higher TPS (with coefficient estimates of 0.85 and 0.83, respectively, and the same standard error of 0.22). Higher staffing levels of hospitalists under individual contract are negatively associated with TPS (with coefficient estimate of -0.43 and standard error of 0.21). Based on the regression analysis using hospital clusters as independent variables, hospitals with individual contracts or without hospitalists providing care had significantly worse TPS compared to hospitals that predominantly employ hospitalists (with coefficient estimate of -1.80 and standard error of 0.61). Magnet status, RN staffing levels, and small and medium size were positively associated with TPS. Medicare share of inpatient days, teaching status, AMCs, and for-profit and public nonfederal ownership were negatively associated with TPS. Adequate hospitalist staffing level is important for hospitals to achieve better performance. Hospitals need to consider the mix of arrangements or contracts that they have with hospitalists.