Systematic Review and Meta-Analysis of the Financial Impact of 30-Day Readmissions for Selected Medical Conditions: A Focus on Hospital Quality Performance.

BACKGROUND: The Patient Protection and Affordable Care Act (ACA) established the Hospital Quality Initiative in 2010 to enhance patient safety, reduce hospital readmissions, improve quality, and minimize healthcare costs. In response, this study aims to systematically review the literature and conduct a meta-analysis to estimate the average cost of procedure-specific 30-day risk-standardized unplanned readmissions for Acute Myocardial Infarction (AMI), Heart Failure (HF), Pneumonia, Coronary Artery Bypass Graft (CABG), and Total Hip Arthroplasty and/or Total Knee Arthroplasty (THA/TKA). METHODS: Eligibility Criteria: This study included English language original research papers from the USA, encompassing various study designs. Exclusion criteria comprise studies lacking empirical evidence on hospital financial performance. INFORMATION SOURCES: A comprehensive search using relevant keywords was conducted across databases from January 1990 to December 2019 (updated in March 2021), covering peer-reviewed articles and gray literature. Risk of Bias: Bias in the included studies was assessed considering study design, adjustment for confounding factors, and potential effect modifiers. SYNTHESIS OF RESULTS: The review adhered to PRISMA guidelines. Employing Monte Carlo simulations, a meta-analysis was conducted with 100,000 simulated samples. Results indicated mean 30-day readmission costs: USD 16,037.08 (95% CI, USD 15,196.01-16,870.06) overall, USD 6852.97 (95% CI, USD 6684.44-7021.08) for AMI, USD 9817.42 (95% CI, USD 9575.82-10,060.43) for HF, and USD 21,346.50 (95% CI, USD 20,818.14-21,871.85) for THA/TKA. DISCUSSION: Despite the financial challenges that hospitals face due to the ACA and the Hospital Readmissions Reduction Program, this meta-analysis contributes valuable insights into the consistent cost trends associated with 30-day readmissions. CONCLUSIONS: This systematic review and meta-analysis provide comprehensive insights into the financial implications of 30-day readmissions for specific medical conditions, enhancing our understanding of the nexus between healthcare quality and financial performance.

A text mining and network analysis of topics and trends in major nursing research journals.

AIM: This study is set to determine the main topics of the nursing field and to show the changing perspectives over time by analysing the abstracts of several major nursing research journals using text mining methodology. DESIGN: Text mining and network analysis. METHODS: Text analysis combines automatic and manual operations to identify patterns in unstructured data. Detailed searches covering 1998-2021 were conducted in PubMed archives to collect articles from six nursing journals: Journal of Advanced Nursing, International Journal of Nursing Studies, Western Journal of Nursing Research, Nursing Research, Journal of Nursing Scholarship and Research in Nursing and Health. This study uses a four-phase text mining and network approach, gathering text data and cleaning, preprocessing, text analysis and advanced analyses. Analyses and data visualization were performed using Endnote, JMP, Microsoft Excel, Tableau and VOSviewer versions. From six journals, 17,581 references in PubMed were combined into one EndNote file. Due to missing abstract information, 2496 references were excluded from the study. The remaining references (n = 15,085) were used for the text mining analyses. RESULTS: Eighteen subjects were determined into two main groups; research method topics and nursing research topics. The most striking topics are qualitative research, concept analysis, advanced practice in the downtrend, and literature search, statistical analysis, randomized control trials, quantitative research, nurse practice environment, risk assessment and nursing science. According to the network analysis results, nursing satisfaction and burnout and nursing practice environment are highly correlated and represent 10% of the total corpus. This study contributes in various ways to the field of nursing research enhanced by text mining. The study findings shed light on researchers becoming more aware of the latest research status, sub-fields and trends over the years, identifying gaps and planning future research agendas. No patient or public contribution.

Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).

OBJECTIVE: Apply natural language processing (NLP) to Amazon consumer reviews to identify adverse events (AEs) associated with unapproved over the counter (OTC) homeopathic drugs and compare findings with reports to the US Food and Drug Administration Adverse Event Reporting System (FAERS). MATERIALS AND METHODS: Data were extracted from publicly available Amazon reviews and analyzed using JMP 16 Pro Text Explorer. Topic modeling identified themes. Sentiment analysis (SA) explored consumer perceptions. A machine learning model optimized prediction of AEs in reviews. Reports for the same time interval and product class were obtained from the FAERS public dashboard and analyzed. RESULTS: Homeopathic cough/cold products were the largest category common to both data sources (Amazon = 616, FAERS = 445) and were analyzed further. Oral symptoms and unpleasant taste were described in both datasets. Amazon reviews describing an AE had lower Amazon ratings (X2 = 224.28, P < .0001). The optimal model for predicting AEs was Neural Boosted 5-fold combining topic modeling and Amazon ratings as predictors (mean AUC = 0.927). DISCUSSION: Topic modeling and SA of Amazon reviews provided information about consumers' perceptions and opinions of homeopathic OTC cough and cold products. Amazon ratings appear to be a good indicator of the presence or absence of AEs, and identified events were similar to FAERS. CONCLUSION: Amazon reviews may complement traditional data sources to identify AEs associated with unapproved OTC homeopathic products. This study is the first to use NLP in this context and lays the groundwork for future larger scale efforts.

Resources availability and COVID-19 mortality among US counties.

The COVID-19 was declared a pandemic by WHO on 03/2020 has claimed millions of lives worldwide. The US leads all countries in COVID-19-related deaths. Individual level (preexisting conditions and demographics) and county-level (availability of resources) factors have been attributed to increased risk of COVID-19-related deaths. This study builds on previous studies to assess the relationship between county-level resources and COVID-19 mortality among 2,438 US counties. We merged 2019 data from AHA, AHRF, and USA FACTS. The dependent variable was the total number of COVID-19-related deaths. Independent variables included county-level resources: (1) hospital staffing levels (FTE RNs, hospitalists, and intensivists) per 10,000 population; (2) hospital capacity (occupancy rate, proportion of teaching hospitals, and number of airborne infection control rooms per 10,000 population); and (3) macroeconomic resources [per capita income and location (urban/rural)]. We controlled for population 65+, racial/ethnic minority, and COVID-19 deaths per 1,000 population. A negative binomial regression was used. Hospital staffing per 10,000 population {FTE RN [IRR = 0.997; CI (0.995-0.999)], FTE hospitalists [IRR = 0.936; CI (0.897-0.978)], and FTE intensivists [IRR = 0.606; CI (0.516-0.712)]} was associated with lower COVID-19-related deaths. Hospital occupancy rate, proportion of teaching hospitals, and total number of airborne infection control rooms per 10,000 population were positively associated with COVID-19-related deaths. Per capita income and being in an urban county were positively associated with COVID-19-related deaths. Finally, the proportion of 65+, racial/ethnic minorities, and the number of cases were positively associated with COVID-19-related deaths. Our findings suggest that focusing on maintaining adequate hospital staffing could improve COVID-19 mortality.

Contextual factors and business strategy choice: The case of us hospitals.

US hospitals are struggling with how to compete and remain viable in an increasingly turbulent and competitive environment. Using Porter's generic strategies and resource dependence theory, this study examined the relationship between environmental factors and business strategy choice among U.S. hospitals. The study used longitudinal data from 2006 to 2016 of US urban, general acute care hospitals from the American Hospital Association Annual Survey, Medicare cost reports, and Area Health Resource File. Multinomial regression was used to analyze the data. and Discussion: Our findings showed four types of hospital strategy: cost-leadership, differentiation, hybrid, and stuck-in-the-middle. A greater number of physicians (county-level) increases the likelihood of pursuing differentiation and hybrid strategy. On the other hand, a higher older adult population (65 years+) increases the likelihood of pursuing a cost-leadership strategy. Similarly, lower competition and higher Medicare Advantage penetration increases the likelihood of pursuing cost-leadership over hybrid strategy. An increase in the unemployment rate decreases the likelihood of pursuing differentiation and cost-leadership strategies versus the hybrid strategy. Finally, hospitals pursuing a differentiation strategy tended to be larger, teaching, and not-for-profit. The results showed the importance of environmental and organizational factors in predicting the strategy choice of hospitals.

Mapping 2 Decades of Research in Health Services Research, Health Policy, and Health Economics Journals.

OBJECTIVE: To identify major research topics and exhibit trends in these topics in 15 health services research, health policy, and health economics journals over 2 decades. DATA SOURCES: The study sample of 35,159 abstracts (1999-2020) were collected from PubMed for 15 journals. STUDY DESIGN: The study used a 3-phase approach for text analyses: (1) developing the corpus of 40,618 references from PubMed (excluding 5459 of those without abstract or author information); (2) preprocessing and generating the term list using natural language processing to eliminate irrelevant textual data and identify important terms and phrases; (3) analyzing the preprocessed text data using latent semantic analysis, topic analyses, and multiple correspondence analysis. PRINCIPAL FINDINGS: Application of analyses generated 16 major research topics: (1) implementation/intervention science; (2) HIV and women's health; (3) outcomes research and quality; (4) veterans/military studies; (5) provider/primary-care interventions; (6) geriatrics and formal/informal care; (7) policies and health outcomes; (8) medication treatment/therapy; (9) patient interventions; (10) health insurance legislation and policies; (11) public health policies; (12) literature reviews; (13) cost-effectiveness and economic evaluation; (14) cancer care; (15) workforce issues; and (16) socioeconomic status and disparities. The 2-dimensional map revealed that some journals have stronger associations with specific topics. Findings were not consistent with previous studies based on user perceptions. CONCLUSION: Findings of this study can be used by the stakeholders of health services research, policy, and economics to develop future research agendas, target journal submissions, and generate interdisciplinary solutions by examining overlapping journals for particular topics.

A text mining study of topics and trends in health care management journals: 1998-2018.

BACKGROUND: Advances in natural language processing and text mining provide a powerful approach to understanding trending themes in the health care management literature. PURPOSE: The aim of this study was to introduce machine learning, particularly text mining and natural language processing, as a viable approach to summarizing a subset of health care management research. The secondary aim of the study was to display the major foci of health care management research and to summarize the literature's evolution trends over a 20-year period. METHODOLOGY/APPROACH: Article abstracts (N = 2,813), from six health care management journals published from 1998 through 2018 were evaluated through latent semantic analysis, topic analysis, and multiple correspondence analysis. RESULTS: Using latent semantic analysis and topic analysis on 2,813 abstracts revealed eight distinct topics. Of the eight, three leadership and transformation, workforce well-being, and delivery of care issues were up-trending, whereas organizational performance, patient-centeredness, technology and innovation, and managerial issues and gender concerns exhibited downward trending. Finance exhibited peaks and troughs throughout the study period. Four journals, Frontiers of Health Services Management, Journal of Healthcare Management, Health Care Management Review, and Advances in Health Care Management, exhibited strong associations with finance, organizational performance, technology and innovation, managerial issues and gender concerns, and workforce well-being. The Journal of Health Management and the Journal of Health Organization and Management were more distant from the other journals and topics, except for delivery of care, and leadership and transformation. CONCLUSION: There was a close association of journals and research topics, and research topics evolved with changes in the health care environment. PRACTICE IMPLICATIONS: As scholars develop research agendas, focus should be on topics important to health care management practitioners for better informed decision-making.

Examining the relationship between self-reported lifetime cancer diagnosis and nativity: findings from the National Health and Nutrition Examination Survey (NHANES) 2011-2018.

PURPOSE: Cancer incidence in the USA remains higher among certain groups, regions, and communities, and there are variations based on nativity. Research has primarily focused on specific groups and types of cancer. This study expands on previous studies to explore the relationship between country of birth (nativity) and all cancer site incidences among USA and foreign-born residents using a nationally representative sample. METHODS: This is a cross-sectional study of (unweighted n = 22,554; weighted n = 231,175,933) participants between the ages of 20 and 80 from the National Health and Nutrition Examination Survey (NHANES) 2011-2018. Using weighted logistic regressions, we analyzed the impact of nativity on self-reported cancer diagnosis controlling for routine care, smoking status, overweight, race/ethnicity, age, and gender. We ran a partial model, adjusting only for age as a covariate, a full model with all other covariates, and stratified by race/ethnicity. RESULTS: In the partial and full models, our findings indicate that US-born individuals were more likely to report a cancer diagnosis compared to their foreign-born counterparts (OR 2.34, 95% CI [1.93; 2.84], p < 0.01) and (OR 1. 39, 95% CI [1.05; 1.84], p < 0.05), respectively. This significance persisted only among non-Hispanic Blacks when stratified by race. Non-Hispanic Blacks who were US-born were more likely to report a cancer diagnosis compared to their foreign-born counterparts (OR 2.30, 95% [CI 1.31; 4.02], p < 0.05). CONCLUSION: A variety of factors may reflect lower self-reported cancer diagnosis in foreign-born individuals in the USA other than a healthy immigrant advantage. Future studies should consider the factors behind the differences in cancer diagnoses based on nativity status, particularly among non-Hispanic Blacks.

Nursing-sensitive indicators for nursing care: A systematic review (1997-2017).

AIM: To provide a systematic review of the literature from 1997 to 2017 on nursing-sensitive indicators. DESIGN: A qualitative design with a deductive approach was used. DATA SOURCES: Original and Grey Literature references from Cochrane Library, Medline/PubMed, Embase, and CINAHL, Google Scholar Original and Grey Literature. REVIEW METHODS: Quality assessment was performed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: A total of 3,633 articles were identified, and thirty-nine studies met the inclusion criteria. The quantitative assessment of investigated relationships in these studies suggests that nursing staffing, mortality, and nosocomial infections were the most frequently reported nursing-sensitive indicators. CONCLUSION: This review provides a comprehensive list of nursing-sensitive indicators, their frequency of use, and the associations between these indicators and various outcome variables. Stakeholders of nursing research may use the findings to streamline the indicator development efforts and standardization of nursing-sensitive indicators. IMPACT: This review provides evidence-based results that health organizations can benefit from nursing care quality.

A critical analysis of COVID-19 research literature: Text mining approach.

OBJECTIVE: Among the stakeholders of COVID-19 research, clinicians particularly experience difficulty keeping up with the deluge of SARS-CoV-2 literature while performing their much needed clinical duties. By revealing major topics, this study proposes a text-mining approach as an alternative to navigating large volumes of COVID-19 literature. MATERIALS AND METHODS: We obtained 85,268 references from the NIH COVID-19 Portfolio as of November 21. After the exclusion based on inadequate abstracts, 65,262 articles remained in the final corpus. We utilized natural language processing to curate and generate the term list. We applied topic modeling analyses and multiple correspondence analyses to reveal the major topics and the associations among topics, journal countries, and publication sources. RESULTS: In our text mining analyses of NIH's COVID-19 Portfolio, we discovered two sets of eleven major research topics by analyzing abstracts and titles of the articles separately. The eleven major areas of COVID-19 research based on abstracts included the following topics: 1) Public Health, 2) Patient Care & Outcomes, 3) Epidemiologic Modeling, 4) Diagnosis and Complications, 5) Mechanism of Disease, 6) Health System Response, 7) Pandemic Control, 8) Protection/Prevention, 9) Mental/Behavioral Health, 10) Detection/Testing, 11) Treatment Options. Further analyses revealed that five (2,3,4,5, and 9) of the eleven abstract-based topics showed a significant correlation (ranked from moderate to weak) with title-based topics. CONCLUSION: By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.

Identifying research themes and trends in the top 20 cancer journals through textual analysis.

BACKGROUND: The rapid growth in cancer research continues expanding the literature. Text mining approaches help make sense of large bodies of scientific literature and integrate the mounting data into the health care delivery system. Our objective is to generate a comprehensive understanding of the themes and trends in cancer research. METHODS: We employed a three-step text mining process of corpus generation and term-list creation and analysis, including latent semantic analysis for dimension reduction and factor analysis for topic identification to analyze 93,423 abstracts from the top 20 cancer/oncology journals for the period between 1999 and 2020. RESULTS: We identified 14 distinct topics in cancer literature. The results revealed the uptrend topics - including cell signaling (T-2), immunotherapy (T-3), clinical trials (T-5), disparities and epidemiology (T-7), cancer practice and policy (T-8), outcome research (T-9), and molecular therapeutics (T-10). - and downtrend topics such as cell death (T-1), early phase clinical trials (T-4), angiogenesis (T-6), cancer screening (T-12), and transplant (T-13). The topics of biomarkers(T-11) and cancer genetics(T-16) remained relatively stable. While the topics of angiogenesis (n = 10,490) and cell death (n = 10,258) included the highest number of abstracts, biomarkers (n = 3203), and cancer genetic (n = 4322) themes included the least number of articles. These findings suggest that despite having the lowest numbers of publications, certain topics such as cancer genetic (T-16) and biomarkers (T-11) have been exhibiting a stable trend and drawing a steady amount of attention from cancer researchers over the past 20 years. CONCLUSION: Findings of this study contribute explanatory insight about themes and trends in cancer research, which can help researchers and stakeholders to identify areas for future studies. POLICY SUMMARY STATEMENT: The findings indicate the increasing efforts to improve cancer practice and cancer care through policy efforts. Therefore, policymakers and other stakeholders may use the findings in prioritization and funding of specific topics.

Do Medicare and Medicaid Payer-Mix Change After the Privatization of Public Hospitals?

This study assessed the impact of public hospitals' privatization on payer-mix. We used a national sample of nonfederal, acute care, public hospitals in 1997 and followed them through 2013, resulting in a cohort of 492 hospitals (8,335 hospital-year observations). Privatization to for-profit (FP) status was associated with a greater increase in Medicare payer-mix (ß = 0.13; p ≤ .001), compared with a smaller increase for privatization to not-for-profit (NFP) status (ß = 0.02; p ≤ .05). FP privatization was associated with a greater decrease in Medicaid payer-mix (ß = -0.09; p ≤ .001), compared with NFP privatization (nonsignificant). There is a larger change in payer-mix after FP privatization than after NFP privatization.

Healthcare Research and Analytics Data Infrastructure Solution: A Data Warehouse for Health Services Research.

BACKGROUND: Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. OBJECTIVE: This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. METHODS: The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. RESULTS: The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. CONCLUSIONS: To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.

Predictors of Hospital-Acquired Clostridioides difficile Infection: A Systematic Review.

BACKGROUND: Clostridioides difficile infections (CDIs) have been identified as a major health concern due to the high morbidity, mortality, and cost of treatment. The aim of this study was to review the extant literature and identify the various patient-related, medication-related, and organizational risk factors associated with developing hospital-acquired CDIs in adult patients in the United States. METHODS: A systematic review of four (4) online databases, including Scopus, PubMed, CINAHL, and Cochrane Library, was conducted to identify empirical studies published from 2007 to 2017 pertaining to risk factors of developing hospital-acquired CDIs. FINDINGS: Thirty-eight studies (38) were included in the review. Various patient-level and medication-related risk factors were identified including advanced patient age, comorbidities, length of hospital stay, previous hospitalizations, use of probiotic medications and proton pump inhibitors. The review also identified organizational factors such as room size, academic affiliation, and geographic location to be significantly associated with hospital-acquired CDIs. CONCLUSION: Validation of the factors associated with high risk of developing hospital-acquired CDIs identified in this review can aid in the development of risk prediction models to identify patients who are at a higher risk of developing CDIs and developing quality improvement interventions that might improve patient outcomes by minimizing risk of infection.

Research Themes and Trends in Ten Top-Ranked Nephrology Journals: A Text Mining Analysis.

BACKGROUND: Nephrology research is expanding, and harnessing the much-needed information and data for the practice of evidence-based medicine is becoming more challenging. In this study, we used the natural language processing and text mining approach to mitigate some of these challenges. METHODS: We analyzed 17,412 abstracts from the top-10 nephrology journals over 10 years (2007-2017) by using latent semantic analysis and topic analysis. RESULTS: The analyses revealed 10 distinct topics (T) for nephrology research ranging from basic science studies, using animal modeling (T-1), to dialysis vascular access-related issues -(T-10). The trend analyses indicated that while the majority of topics stayed relatively stable, some of the research topics experienced increasing popularity over time such as studies focusing on mortality and survival (T-4) and Patient-related Outcomes and Perspectives of Clinicians (T-5). However, some research topics such as studies focusing on animal modeling (T-1), predictors of acute kidney injury, and dialysis access (T-10) exhibited a downward trend. CONCLUSION: Stakeholders of nephrology research may use these trends further to develop priorities and enrich the research agenda for the future.

Introduction to "health informatics, healthcare quality and safety, and healthcare simulation: the new triad to advance healthcare operations".

This special themed international issue explores the multiple facets of health informatics, healthcare quality and safety, and healthcare simulation from different parts of the world. The papers in this issue fall into two broad themes. The first theme uses the intersection to address better management of care including physical design layout. The second theme examines innovative uses of the triad to prevent critical and non-critical safety events. The collection of papers culminates with a position paper reporting on the interdependence that is emerging as an important triad for research and practice within medical education, system development and testing, and teamwork and communication and concludes with reducing imprecision and factual errors in handoffs. Findings from the special collection of papers can inform managers and leaders on advancing operations in healthcare settings.

The emerging literature for the triad of health informatics, healthcare quality and safety, and healthcare simulation.

The areas of health informatics, healthcare quality and safety, and healthcare simulation are often thought of as separate domains. The purpose of this position paper is to report on the interdependence that is emerging as an important triad across the healthcare/health system continuum. A qualitative review of 24 studies suggests the interdependence of health informatics, healthcare quality and safety, and healthcare simulation reaches much broader than traditional utilisation of simulation. We suggest ways that organisations can take advantage of the interdependence of this triad across a broader variety of healthcare environments, including teamwork, communication, and complex system relationships. In conclusion, the reviewed 24 studies suggest that the research in the triad focuses on simulation education and computerised simulation, and when coupled with health informatics, bears greater strength on quality improvement or patient safety.

Patient Engagement Functionalities in U.S. Hospitals: Is Early Adoption Associated With Financial Performance?

EXECUTIVE SUMMARY: U.S. hospitals are in various stages in their adoption of health information technology (HIT) with patient engagement functionalities. The Health Information Technology for Economic and Clinical Health Act of 2009 allocated $30 billion to incentivize the adoption and use of HIT. This study aims to identify hospital characteristics of early patient engagement functionality adoption and compare the financial performance of groups of hospitals that offer these functionalities according to Rogers' adopter categories. The combined data from the American Hospital Association Annual Survey and Information Technology Supplement, Centers for Medicare & Medicaid cost reports, and Health Resources & Services Administration Area Health Resource Files from 2008 to 2013 yielded a sample of 696 unique acute care hospitals. Three adopter categories-early adopters, early majority, and late majority-were created. Generalized estimating equations were used to examine the financial performance (operating margin, return on assets, total margin, operating expenses, revenue per inpatient day) across the adopter types. Compared to early adopter hospitals, operating margins were lower for early majority hospitals (ß = -.407, p < .05) and late majority hospitals (ß = -.608, p < .05). Moreover, compared to early adopter hospitals, late majority hospitals exhibited significantly lower operating revenue (ß = -.087, p < .01) and operating expenses (ß = -.064, p < .01) per inpatient day. No significant relationships were observed when comparing these groups based on total margin and return on assets. Hospital administrators should consider the positive financial outcomes associated with early adoption of patient engagement functionalities in the decision-making process.

Reduction in Hospitals' Readmission Rates: Role of Hospital-Based Skilled Nursing Facilities.

Hospital readmission within 30 days of discharge is an important quality measure given that it represents a potentially preventable adverse outcome. Approximately, 20% of Medicare beneficiaries are readmitted within 30 days of discharge. Many strategies such as the hospital readmission reduction program have been proposed and implemented to reduce readmission rates. Prior research has shown that coordination of care could play a significant role in lowering readmissions. Although having a hospital-based skilled nursing facility (HBSNF) in a hospital could help in improving care for patients needing short-term skilled nursing or rehabilitation services, little is known about HBSNFs' association with hospitals' readmission rates. This study seeks to examine the association between HBSNFs and hospitals' readmission rates. Data sources included 2007-2012 American Hospital Association Annual Survey, Area Health Resources Files, the Centers for Medicare and Medicaid Services (CMS) Medicare cost reports, and CMS Hospital Compare. The dependent variables were 30-day risk-adjusted readmission rates for acute myocardial infarction (AMI), congestive heart failure, and pneumonia. The independent variable was the presence of HBSNF in a hospital (1 = yes, 0 = no). Control variables included organizational and market factors that could affect hospitals' readmission rates. Data were analyzed using generalized estimating equation (GEE) models with state and year fixed effects and standard errors corrected for clustering of hospitals over time. Propensity score weights were used to control for potential selection bias of hospitals having a skilled nursing facility (SNF). GEE models showed that the presence of HBSNFs was associated with lower readmission rates for AMI and pneumonia. Moreover, higher SNFs to hospitals ratio in the county were associated with lower readmission rates. These findings can inform policy makers and hospital administrators in evaluating HBSNFs as a potential strategy to lower hospitals' readmission rates.