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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.The study sample of 35,159 abstracts (1999-2020) were collected from PubMed for 15 journals.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.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.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.

BACKGROUND: With the increasing development and use of mobile technologies, an increasing amount of research on mobile health is being conducted. The purpose of the study was to capture the trends in mHealth research by mining terms related to medical conditions, interventions, study populations, and the relationships between these terms. METHODS: This study analyzed 5,600 journal articles published in Web of Science from 2008 to 2018. Using text mining techniques, a total of 39,292 terms extracted from the titles and abstracts of the journal articles were independently reviewed to identify meaningful terms related to medical conditions, interventions, and study populations. RESULTS: A total of 48 different types of medical conditions were identified in the dataset. Mood disorders appeared to be the most frequently identified medical condition in mHealth research. Thirty interventions were identified. Cell phone-, SMS-, and Internet-based interventions appeared to be the most prominent types, and “female” appeared to be the most frequently identified term related to the studied population. Females appeared to have been studied in the widest range of medical conditions, including pregnancy issues, overnutrition, neoplasms, and AIDS. Older adults were the least studied population in mHealth. CONCLUSIONS: Knowledge gaps that have not been explored in previous studies in mHealth research were identified, which should be addressed by researchers.

Background Although general trends in cancer outcomes are improving, racial/ethnic disparities in patient outcomes continue to widen, suggesting disparity-related shortcomings in cancer research designs. Methods Using convenience sampling, a total of 24 data sources, representing several research designs and 5 high-burden tumor types, were included for analyses. The percentages of races/ethnicities across each design/tumor type were compared with those of the 2017 US Census data. The authors used a framework based on the Belmont principles to evaluate the ethical strengths and/or weaknesses of each design. Results In all designs, white individuals were found to be overrepresented. African American and Asian individuals were underrepresented, and Native Americans had consistently poor or no representation. In general, ethical concerns varied according to the study design. Clinical trials were high with regard to respect for persons and beneficence but low for equitable subject selection related to the inclusion of race/ethnicity. Observational study designs were more inclusive for race/ethnicity compared with clinical and translational studies, but their clinical usefulness was less. Conclusions The authors proposed that ethical concerns should vary according to the study design. Because observational designs have strengths in inclusiveness for race/ethnicity, their clinical usefulness can be improved by extending the Learning Health System in hospital catchment populations, the use of health records linked to biospecimens, and minority oversampling. Likewise, minority enrollment into clinical trials can be improved through Learning Health System linking and by using National Cancer Institute-mandated Community Outreach and Engagement Cores. This will allow precision medicine for otherwise overlooked minority subgroups.

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., Graphical abstract Image 1

Originality/value – This is the latest and most comprehensive systematic review summarizing the effects of transparency of quality metrics on hospital care outcomes.

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.

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. 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. 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. 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. 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.

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 = 3,203), and cancer genetic (n = 4,322) 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.

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. CLINICALTRIAL

Machine learning methods, a subdomain of artificial intelligence, in healthcare have been experiencing rapid growth and development but these methods have also been criticized. This article explores some of the more frequent criticisms leveled at machine learning approaches and offers suggestions on how to address problems of bias, “black box” systems, appropriate training for users, and monitoring model performance. Some of these problems can potentially be addressed using machine learning methods. We advocate for more involvement of clinicians in the development and evaluation of machine learning systems in healthcare and more regular monitoring of performance to take advantage of the strengths and minimize the weaknesses of these systems.

Background: All states in the USA have established Workers&rsquo, Compensation (WC) insurance systems/programs. WC systems address key occupational safety and health concerns. This effort uses data from a large insurance provider for the years 2011&ndash, 2018 to provide estimates for WC payments, stratified by the claim severity, i.e., medical only, and indemnity. Methods: Besides providing descriptive statistics, we used generalized estimating equations to analyze the association between the key injury characteristics (nature, source, and body part injured) and total WC payments made. We also provide the overall cost burden for the former. Results: Out of the total 151,959 closed claims, 83% were medical only. The mean overall WC payment per claim for the claims that resulted in a payment was $1477 (SD: $7221). Adjusted models showed that mean payments vary by claim severity. For example, among medical only claims, the mean payment was the highest for amputations ($3849, CI: $1396, $10,608), and among disability and death related claims, ruptures cost the most ($14,285, $7772, $26,255). With frequencies taken into account, the overall cost burden was however the highest for strains. Conclusions: Workplace interventions should prioritize both the costs of claims on average and the frequency.

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.

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.

In recent years, because of the advancements in communication and networking technologies, mobile technologies have been developing at an unprecedented rate. mHealth, the use of mobile technologies in medicine, and the related research has also surged parallel to these technological advancements. Although there have been several attempts to review mHealth research through manual processes such as systematic reviews, the sheer magnitude of the number of studies published in recent years makes this task very challenging. The most recent developments in machine learning and text mining offer some potential solutions to address this challenge by allowing analyses of large volumes of texts through semi-automated processes. The objective of this study is to analyze the evolution of mHealth research by utilizing text-mining and natural language processing (NLP) analyses. The study sample included abstracts of 5,644 mHealth research articles, which were gathered from five academic search engines by using search terms such as mobile health, and mHealth. The analysis used the Text Explorer module of JMP Pro 13 and an iterative semi-automated process involving tokenizing, phrasing, and terming. After developing the document term matrix (DTM) analyses such as single value decomposition (SVD), topic, and hierarchical document clustering were performed, along with the topic-informed document clustering approach. The results were presented in the form of word-clouds and trend analyses. There were several major findings regarding research clusters and trends. First, our results confirmed time-dependent nature of terminology use in mHealth research. For example, in earlier versus recent years the use of terminology changed from "mobile phone" to "smartphone" and from "applications" to "apps". Second, ten clusters for mHealth research were identified including (I) Clinical Research on Lifestyle Management, (II) Community Health, (III) Literature Review, (IV) Medical Interventions, (V) Research Design, (VI) Infrastructure, (VII) Applications, (VIII) Research and Innovation in Health Technologies, (IX) Sensor-based Devices and Measurement Algorithms, (X) Survey-based Research. Third, the trend analyses indicated the infrastructure cluster as the highest percentage researched area until 2014. The Research and Innovation in Health Technologies cluster experienced the largest increase in numbers of publications in recent years, especially after 2014. This study is unique because it is the only known study utilizing text-mining analyses to reveal the streams and trends for mHealth research. The fast growth in mobile technologies is expected to lead to higher numbers of studies focusing on mHealth and its implications for various healthcare outcomes. Findings of this study can be utilized by researchers in identifying areas for future studies.

Background: As a tumor suppressor and a factor in glucose metabolism, p53 is an alluring point of focus in the study of obesity and cancer. Population-based studies show that single nucleotide polymorphisms (SNPs) at codon 72 of p53 are individually linked with development of breast cancer, high BMI, and type II diabetes. In this study cohort of African American (AA) and Caucasian (CA) women with breast cancer, we assessed the relationships between arginine (Arg) and proline (Pro) phenotypic variants and obesity and type II diabetes, accounting for confounding molecular and demographic influences. We also evaluated the prognostic relevance of these factors. Methods: DNA samples isolated from breast tumor tissues collected from 190 (AA=90 and CA= 100) women were genotyped to detect SNPs at codon 72 of the p53 gene and were sequenced by using exon-specific primers (4 through 9). These exons were selected because more than 90% of mutations are located in these exons of p53. For all women, information on BMI and type II diabetes status was collected from electronic health records. Obese was defined as BMI ≥ 30. The closest measure of BMI within 5 years of the date of the diagnostic sample was used, and the time difference between BMI and sample date was included as a confounding variable in adjusted analyses. Only pre-tumor sample diagnoses of type II diabetes were considered. Clinical and demographic variables were evaluated for their association to p53 SNPs, using χ2 tests for categorical analysis and F-tests for continuous variables. Linear regression models were used to examine interactions with BMI. Cox proportional hazard regression models were used for all time-to-event analyses. Separate models were built for each BMI-defined category of weight (underweight, normal, overweight, or obese). Results: Both SNP72 and BMI were strongly associated with race. For AAs, 68% were obese, and 49% were Pro/Pro phenotype. CAs were less obese (24%), and most were Arg/Arg (60%). Most cases of type II diabetes (78%) were AAs, but 23 total patients with diabetes was insufficient for BMI-stratified analysis. As determined in a linear regression model, there was an interaction between race, SNP72 and BMI. AAs with Arg/Arg had significantly higher BMI values than other SNP/race groups (P=0.003). In a Cox model of obese-only patients, adjusted for race, p53 somatic mutation, time of BMI measure, tumor stage, and molecular subtype, Arg/Arg women had 3.81 times higher hazard of death from cancer than Pro/Pro women (95% CI: 1.08,13.48). Type II diabetes apparently did not influence survival, but this observation may be due to insufficient sample size. Conclusions: The association between SNP 72 and BMI varied according to race. African American women who exhibited Arg/Arg had an average BMI of 35. For obese patients, Arg/Arg had a negative influence upon survival, which was independent of established risk factors, including race. Citation Format: Michael Behring, Bunyamin Ozaydin, Upender Manne. Codon 72 polymorphisms of p53 are associated with obesity, diabetes, and race in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4209.

Aim To examine associations between retinal thickness and rod-mediated dark adaptation in older adults with non-exudative age-related maculopathy (ARM) or normal macular health. Methods A cross-sectional study was conducted with 74 adults ≥50 years old from the comprehensive ophthalmology and retina services of an academic eye centre. ARM presence and disease severity in the enrolment eye was defined by the masked grading of stereofundus photos using the Clinical Age-Related Maculopathy grading system. High-definition, spectral-domain optical coherence tomography was used to estimate retinal thickness in a grid of regions in the macula. Rod-mediated dark adaptation, recovery of light sensitivity after a photo-bleach, was measured over a 20-min period for a 500 nm target presented at 5° on the inferior vertical meridian. Main outcomes of interest were retinal thickness in the macula (μm) and parameters of rod-mediated dark adaptation (second slope, third slope, average sensitivity, final sensitivity). Results In non-exudative disease retinal thickness was decreased in greater disease severity; thinner retina was associated with reductions in average and final rod-mediated sensitivity even after adjustment for age and visual acuity. Conclusions Impairment in rod-mediated dark adaptation in non-exudative ARM is associated with macular thinning.

The relationship between competition and hospital financial performance has been investigated by many researchers for the last 20 years. Although considerable research has been devoted to understanding the relationship between competition and hospital financial performance, less attention has been paid to summarizing the mixed and sometimes conflicting findings of these studies and defining an overall effect of competition on hospital performance. This study is designed to evaluate the magnitude of the competition effect on hospital financial performance by using meta-analytic methods. As a measure of competition, we focused on the Herfindahl-Hirschman Index (HHI), the most frequently used measure of competition in the empirical literature. As financial performance indicators, we examined cost, profitability, and revenue ratios. In our final meta-analysis, we utilized 60 comparisons in 41 studies using random effect models. The results indicated overall effect size at positive 1% for profitability, at -1% for cost, and -3% for revenue, with only revenue being significant due to considerable heterogeneity across studies. The study findings suggest that hospitals located in less competitive markets (high HHI) experience higher profitability, lower cost, and lower revenue. Further advanced meta-analyses were also performed to understand underlying factors that led to high heterogeneity across studies.

Data mining is a process of pattern and relationship discovery within large sets of data. Because of the large volume of data generated in healthcare settings, it is not surprising that healthcare organizations have been interested in data mining to enhance physician practices, disease management, and resource utilization. This chapter discusses a variety of data mining techniques that have been used to develop clinical decision support systems, including decision trees, neural networks, logistic regression, nearest neighbor classifiers. In addition, genetic algorithms, biologic and quantum computing, and big data analytics as well as methods of evaluating and comparing the different approaches are also discussed.

To evaluate the effects of oxidant/antioxidant mechanisms and levels of trace elements on trauma-stimulated moderate pulmonary contusions after vitamin E administration. Materials and methods: Sixty-three male Sprague Dawley rats were used. Animals were studied in 4 groups. Vitamin E (150 mg/kg) was injected intraperitoneally 30 min after trauma and on the first and second days. Blood samples were obtained for nitric oxide (NO) levels and superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities. Zinc (Zn^{+2}), copper (Cu^{+2}), and iron (Fe^{+3}) were measured in serum. Results: Lung contusion increased serum and tissue NO levels and SOD activities and decreased GSH-Px activities (P < 0.05). Vitamin E significantly (P < 0.05) decreased NO levels and SOD activities and increased GSH-Px. Serum Zn^{+2}, Cu^{+2}, and Fe^{+3}) levels were statistically significantly influenced by the administration of vitamin E (P < 0.05). Group 4 had lower scores compared to Group 3 (P < 0.05) and no difference compared to Group 1 (P > 0.05). Conclusion: These results suggest that treatment with vitamin E reduces lung oxidative stress and related mechanisms in isolated lung contusion as demonstrated by an experimental rat model.