Reduction in Hospitals\u27 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

Does the Provision of High-Technology Health Services Change After the Privatization of Public Hospitals?

Background: Public hospitals hold a key role in providing health care services especially to individuals without health insurance, those who are partially covered by health insurance, and low income population. However, some of these hospitals have converted to private status. The objective of this study was to assess the effect of the ownership conversion of public hospitals into private status on the provision of high-technology health services. Methods: This study used a non-experimental longitudinal design based on merged secondary data from the American Hospital Association annual survey, the Area Health Resources File, and the Local Area Unemployment Statistics [1997–2013]. The dependent variable “high-technology health services” was measured using Saidin index. There were 492 non-federal acute care public hospitals (n=8,335 hospital-year observations) in our sample, of which 104 (21%) converted to private status (75 converted to private not-for-profit and 29 converted to for-profit hospitals). The independent variable “privatization” referred to ownership conversion from public to either private not-for-profit or private for-profit status. We ran two fixed-effects linear regressions to measure the impact of privatization on high-technology services offering. Results: Our key findings suggested that privatization was associated with a decrease in Saidin index (β=−0.74; P=0.016; 95% CI: −1.34 to −1.38). For-profit privatization was associated with a greater decrease in Saidin index (β=−1.29; P=0.024; 95% CI: −2.41 to −0.17), compared with an insignificant decrease for not-for-profit privatization (β=−0.56; P=0.106; 95% CI: −1.25 to 0.12). Conclusions: Given the excessive cost of high-technology health services and the change in the hospitals’ mission after privatization, privatized hospitals tend to reduce the number of high-technology health services they provide

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.3417985

A Practical and Empirical Comparison of Three Topic Modeling Methods Using a COVID-19 Corpus: LSA, LDA, and Top2Vec

This study was prepared as a practical guide for researchers interested in using topic modeling methodologies. This study is specially designed for those with difficulty determining which methodology to use. Many topic modeling methods have been developed since the 1980s namely, latent semantic indexing or analysis (LSI/LSA), probabilistic LSI/LSA (pLSI/pLSA), naïve Bayes, the Author-Recipient-Topic (ART), Latent Dirichlet Allocation (LDA), Topic Over Time (TOT), Dynamic Topic Models (DTM), Word2Vec, Top2Vec, and \variation and combination of these techniques. Researchers from disciplines other than computer science may find it challenging to select a topic modeling methodology. We compared a recently developed topic modeling algorithm Top2Vec with two of the most conventional and frequently-used methodologiesLSA and LDA. As a study sample, we used a corpus of 65,292 COVID-19-focused abstracts. Among the 11 topics we identified in each methodology, we found high levels of correlation between LDA and Top2Vec results, followed by LSA and LDA and Top2Vec and LSA. We also provided information on computational resources we used to perform the analyses and provided practical guidelines and recommendations for researchers