No abstract

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.

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.

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.

Purpose The purpose of this paper is to determine the relationships between the health tourists’ perceptions on decisive factors (i.e. experience, technological infrastructure, flight distance, legal and moral restrictions, touristic attractions, religious similarity, waiting time and price of health tourism) and Turkey as their choice of healthcare tourism destination. Design/methodology/approach The data for this empirical study were collected from 288 patients in Turkey, all of whom being health tourists from various countries. Descriptive statistics and Kruskal–Wallis difference tests were utilized for analyses. Findings Statistically significant differences were found among health tourists in regards to the geographical regions of their residence. These finding suggest that differences among health tourists in regards to the geographical regions of their residence contributed to the healthcare tourists’ behaviors and health tourism market segmentations in Turkey. Research limitations/implications Among the constraints of the study are the time and funding limitations coupled with the limitations on the scale development attempts in the health tourism literature and limitation and biases related to primary data collection. Despite all these limitations, by being the first study exploring the health tourism market segmentations in Turkey, this study contributes to the literature about the perceptions of health tourists and their reasons in choosing Turkey as a health tourism destination. Practical implications Determining the Turkey’s health tourism market segmentations will generate the positive effect on the target market which is currently heterogeneous for health tourism operators and intermediary institutions. Moreover, this knowledge would allow the target market to be divided into homogeneous groups, with different marketing mixes for each group. Homogenized groups exhibit unified purchasing behaviors for their needs. Therefore, it is very important for health tourism operators and intermediary institutions to know how the preferences of health tourists from different geographical regions vary. Originality/value The paper fulfills a need for advancing the knowledge on the decisive factors in determining Turkey as the health tourism destination by revealing perceptions of health tourists from various geographical regions. This information is very valuable for the Turkey’s healthcare tourism marketing managers who desire to implement the strategies to achieve competitive advantage in the global health tourism market.

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.

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.

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.