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.

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.

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.

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.

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.

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.

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.

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.

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.