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.

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.

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.

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.

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.

Examining the association between educational attainment and life expectancy in MENA region: A panel data analysis.

BACKGROUND: The purpose of this study is to investigate the association between educational attainment and life expectancy in 18 countries in MENA region. METHODS: We used World Bank database for a panel of 18 MENA countries during the years 1995 to 2009. We used Life Expectancy at Birth, as the key health care output measure. Additionally, we used six health care input independent variables. All variables were transformed into natural logarithms. We estimated the production function using Cobb-Douglas function. RESULTS: Results indicate that 1% increase in educational attainment of males 25 to 34 years old, males 25 years and older, females 25 to 34 years old, females 25 years and older, and females aged 15 to 44 years old will increase life expectancy by 0.14%, 0.07%, 0.04%, 0.03%, and 0.04%, respectively, while everything else remains constant. CONCLUSION: Our results suggest that for MENA region countries investing in education to broaden access would improve health outcomes and life expectancy. Boosting educational attainment for both male and female population may close the life expectancy gaps between the MENA region and other developed countries, and males and females within the same country. Education attainment has the potential to be a social remedy for better health outcomes in MENA countries.

Patient Perceptions of Servicescape in Healthcare: A Systematic Review of the Literature.

EXECUTIVE SUMMARY: Many hospitals are competing for survival in their service areas. Because of intense competition within markets, hospitals are developing strategies to differentiate themselves. One way to do so is to create a physical infrastructure and service environment that generate a positive impact on patient perceptions. The purpose of this study is to review the literature on servicescape (i.e., a total impression of a service encounter developed through the use of human senses) and its effects on service quality and patient outcomes in healthcare settings. Servicescape studies have taken place in various healthcare settings (i.e., teaching hospitals, dental clinics, outpatient clinics) in 10 countries. Although servicescape in healthcare settings is a rarely researched topic at both the national and international levels, research indicates a significant positive association between servicescape and patient perceptions, patient satisfaction, and patient emotions. In light of the increasing emphasis in quality and value-based purchasing initiatives on patient experience and outcomes, more servicescape research in healthcare settings is needed. This systematic review underscores this need and enhances the knowledge base in this area.

Longitudinal analysis of high-technology medical services and hospital financial performance.

BACKGROUND: U.S. hospitals have been investing in high-technology medical services as a strategy to improve financial performance. Despite the interest in high-tech medical services, there is not much information available about the impact of high-tech services on financial performance. PURPOSE: The aim of this study was to examine the impact of high-tech medical services on financial performance of U.S. hospitals by using the resource-based view of the firm as a conceptual framework. METHODOLOGY/APPROACH: Fixed-effects regressions with 2 years lagged independent variables using a longitudinal panel sample of 3,268 hospitals (2005-2010). It was hypothesized that hospitals with rare or large numbers (breadth) of high-tech medical services will experience better financial performance. FINDINGS: Fixed effects regression results supported the link between a larger breadth of high-tech services and total margin, but only among not-for-profit hospitals. Both breadth and rareness of high-tech services were associated with high total margin among not-for-profit hospitals. Neither breadth nor rareness of high-tech services was associated with operating margin. Although breadth and rareness of high-tech services resulted in lower expenses per inpatient day among not-for-profit hospitals, these lower costs were offset by lower revenues per inpatient day. PRACTICE IMPLICATIONS: Enhancing the breadth of high-tech services may be a legitimate organizational strategy to improve financial performance, especially among not-for-profit hospitals. Hospitals may experience increased productivity and efficiency, and therefore lower inpatient operating costs, as a result of newer technologies. However, the negative impact on operating revenue should caution hospital administrators about revenue reducing features of these technologies, which may be related to the payer mix that these technologies may attract. Therefore, managers should consider both the cost and revenue implications of these technologies.

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.

Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP.

Skin cancer is one of the most frequently occurring cancers worldwide, and early detection is crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, and strict time limits, which can negatively affect diagnostic outcomes. Deep learning-based diagnostic systems offer quick, accurate testing and enhanced research capabilities, providing significant support to dermatologists. In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA) in place of the conventional shifted window-based multi-head self-attention (SW-MSA). This adjustment enables the model to more efficiently process areas of skin cancer overlap, capture finer details, and manage long-range dependencies, while maintaining memory usage and computational efficiency during training. Additionally, the study replaces the standard multi-layer perceptron (MLP) in the Swin Transformer with a SwiGLU-based MLP, an upgraded version of the gated linear unit (GLU) module, to achieve higher accuracy, faster training speeds, and better parameter efficiency. The modified Swin model-base was evaluated using the publicly accessible ISIC 2019 skin dataset with eight classes and was compared against popular convolutional neural networks (CNNs) and cutting-edge vision transformer (ViT) models. In an exhaustive assessment on the unseen test dataset, the proposed Swin-Base model demonstrated exceptional performance, achieving an accuracy of 89.36%, a recall of 85.13%, a precision of 88.22%, and an F1-score of 86.65%, surpassing all previously reported research and deep learning models documented in the literature.

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.

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.

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.

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.

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.

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.

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.