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

A theoretical framework for linking hospitals longitudinally: demonstrated using German Hospital Quality Reports 2016-2020

BACKGROUND

In longitudinal health services research, hospital identification using an ID code, often supplemented with several additional variables, lacks clarity regarding representativeness and variable influence. This study presents an operational method for hospital identity delimitation and a novel longitudinal identification approach, demonstrated using a case study.

METHODS

The conceptualisation considers hospitals as evolving entities, identifying "similar enough" pairs across two time points using an automated similarity matrix. This method comprises key variable selection, similarity scoring, and tolerance threshold definition, tailored to data source characteristics and clinical relevance. This linking method is tested by applying the identification of minimum caseload requirements-related German hospitals, utilizing German Hospital Quality Reports (GHQR) 2016-2020.

RESULTS

The method achieved a success rate (min: 97.9% - max: 100%, mean: 99.9%) surpassing traditional hospital ID-code linkage (min: 91.5% - max: 98.8%, mean: 96.6%), with a remarkable 99% reduction in manual work through automation.

CONCLUSIONS

This method, rooted in a comprehensive understanding of hospital identities, offers an operational, automated, and customisable process serving diverse clinical topics. This approach has the advantage of simultaneously considering multiple variables and systematically observing temporal changes in hospitals. It also enhances the precision and efficiency of longitudinal hospital identification in health services research.

Hospital process performance and the adoption of medical devices: An organization-based view

Over the past two decades, there has been a growing scholarly interest in the adoption of technology in healthcare. While numerous studies have delved into the effects of specific technologies on the performance of different organizational units and medical specialties, the findings have often been divergent. Unlike the established literature, our approach focuses on the organization's perspective to analyze how technology impacts process performance in hospital settings. More precisely, we compiled a tailored dataset from 56 healthcare organizations in Italy and conducted a comprehensive analysis of panel data from 2016 to 2019, utilizing Ordinary Least Squares (OLS) regression as our main analytical tool. The data shows a clear relationship between an organization's use of medical devices and its overall process performance. Our research highlights the importance of achieving substantial improvements in process performance by strategically integrating new technologies and devices. Policymakers are encouraged to consider introducing incentives to drive hospitals to invest in innovative technologies. Furthermore, monitoring expenditures on new devices could serve as a valuable metric for assessing the extent of technology adoption within clinical practices.

An Exploratory Analysis of the Association between Hospital Quality Measures and Financial Performance

Hospitals are perpetually challenged by concurrently improving the quality of healthcare and maintaining financial solvency. Both issues are among the top concerns for hospital executives across the United States, yet some have questioned if the efforts to enhance quality are financially sustainable. Thus, the aim of this study is to examine if efforts to improve quality in the hospital setting have a corresponding association with hospital profitability. Recent and directly relevant research on this topic is very limited, leaving practitioners uncertain about the wisdom of their investments in interventions which enhance quality and patient safety. We assessed if eight different quality measures were associated with our targeted measure of hospital profitability: the net patient revenue per adjusted discharge. Using multivariate regression, we found that improving quality was significantly associated with our targeted measure of hospital profitability: the net patient revenue per adjusted discharge. Significant findings were reported for seven of eight quality measures tested, including the HCAHPS Summary Star Rating (p < 0.001), Hospital Compare Overall Rating (p < 0.001), All-Cause Hospital-Wide Readmission Rate (p < 0.01), Total Performance Score (p < 0.001), Safety Domain Score (p < 0.01), Person and Community Engagement Domain Score (p < 0.001), and the Efficiency and Cost Reduction Score (p < 0.001). Failing to address quality and patient safety issues is costly for US hospitals. We believe our findings support the premise that increased attention to the quality of care delivered as well as patients' perceptions of care may allow hospitals to accentuate profitability and advance a hospital's financial position.

Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm

With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial services and management platforms cannot effectively alleviate the current financial crisis. In the face of increasingly complex financial risks, traditional financial service and management platforms cannot achieve effective information sharing, which leads to continued low service and management efficiency and frequent financial risk problems. Support vector machine is a data classification algorithm based on supervision, which can realize data sharing and improve the efficiency of data processing. The article firstly readjusted the underlying architecture of the financial service and management platform to break through the barriers of data interaction. Then on this basis, the article further combines the support vector machine algorithm and extends it from binary data classification to multivariate classification. Finally, the paper redesigns the financial service and management platform considering support vector machines. After a series of experiments, it can be found that the financial service and management platform based on the support vector machine algorithm can reduce the financial risk by 17.2%, improve the financial service level by 30.2%, and improve the financial comprehensive service level by 45.2%. At the same time, thanks to information sharing and interaction, the financial service and management platform can effectively predict financial risks, and the accuracy of the prediction basically reaches 78.9%. This shows that a financial service and management platform that takes into account the support vector machine algorithm can effectively prevent financial risks and improve the efficiency of financial services and management.

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.

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.

Postoperative ward monitoring - Why and what now?

The postoperative ward is considered an ideal nursing environment for stable patients transitioning out of the hospital. However, approximately half of all in-hospital cardiorespiratory arrests occur here and are associated with poor outcomes. Current monitoring practices on the hospital ward mandate intermittent vital sign checks. Subtle changes in vital signs often occur at least 8-12 h before an acute event, and continuous monitoring of vital signs would allow for effective therapeutic interventions and potentially avoid an imminent cardiorespiratory arrest event. It seems tempting to apply continuous monitoring to every patient on the ward, but inherent challenges such as artifacts and alarm fatigue need to be considered. This review looks to the future where a continuous, smarter, and portable platform for monitoring of vital signs on the hospital ward will be accompanied with a central monitoring platform and machine learning-based pattern detection solutions to improve safety for hospitalized patients.

Strategies of High-Performing Teaching Hospitals

Teaching hospitals are large and complex, and under constant financial pressure. In this study, we examine the financial performance of 80 large teaching hospitals in the 20 largest cities in the U.S. over the last five years, to identify which strategic and operational management factors separate high-performing hospitals from lower-performing ones. Results suggest that growth strategies should continue to be sought for improving long-term financial condition. Operational efficiency was less important than market share, economic status of surrounding community, hospital size, and teaching intensity. This study's findings should help guide strategic planning for teaching hospitals.