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Background: Rising health care costs and consequent increases inMedicare reimbursements have led tomany payment reforms over the years. Implementation of the prospective payment system (PPS) for hospitals in 1983 incentivized hospitals to either purchase skilled nursing facilities (SNFs) or utilize their excess capacity to establish one within the hospital. With PPS reimbursement being applied to SNFs in 1998, prior monetary incentives for hospitals to own an SNF disappeared. However, despite the reduction in numbers, many hospitals continued to operate their hospitalbased skilled nursing facilities (HBSNFs). Purpose: This study examines the organizational andmarket-level factors associated with the survival of HBSNFs using the population ecology of organizations framework. Methodology: Using American Hospital Association survey data, event histories of all U.S. acute care hospitals with an open HBSNF in 1998 were plotted to examine if a hospital closed its HBSNF during a 22-year period (1998-2020). The primary independent variables included hospital size, ownership, totalmargin, market competition, andMedicare Advantage penetration. The independent and control variableswere lagged by 1 year. Cox regressionswere conducted to estimate the hazard ratios capturing the risk of HBSNF closure. Results: The results showed that HBSNFs located in large, not-for-profit hospitals and those operating in less competitive markets had greater odds of surviving. Practice Implications: The HBSNF administrators of small, for-profit hospitals and those operating in highly competitive markets could utilize the findings of this study to judiciously allocate slack resources to theirHBSNFs to keep those open given the current emphasis on continuity of care by regulatory bodies. [ABSTRACT FROM AUTHOR]

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 (X 2 = 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (β = 0.13; p ≤ .001), compared with a smaller increase for privatization to not-for-profit (NFP) status (β = 0.02; p ≤ .05). FP privatization was associated with a greater decrease in Medicaid payer-mix (β = -0.09; p ≤ .001), compared with NFP privatization (nonsignificant). There is a larger change in payer-mix after FP privatization than after NFP privatization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]