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Data Driven Insights in Healthcare
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Data Driven Insights in Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 6664

Special Issue Editors

Prof. Dr. Victor R. Prybutok

E-Mail Website
Guest Editor
Department of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, Denton, TX 76201, USA
Interests: quantitative methods; data modeling; quality control; service quality
Special Issues, Collections and Topics in MDPI journals
Dr. Gayle Linda Prybutok

E-Mail Website
Guest Editor
Department of Rehabilitation and Health Services, College of Health and Public Service, University of North Texas, Denton, TX 76201, USA
Interests: health communication; health education; healthcare quality improvement

Special Issue Information

Dear Colleagues,

Current research is typically composed of theory-driven studies. However, it is also critical that we learn from observational experiences; big data provide an opportunity to mine data sets for information in a novel manner. The insights gained from data exploration will provide insights that are worthy of future studies because the discoveries found in these data will improve healthcare practice and delivery. This Special Issue will showcase how data-driven discoveries provide insights and value for future research.

We are pleased to invite you to submit your data-driven investigations, including, but not limited to, the qualitative analysis of comments, the data mining of large data sets, exploratory data techniques including the use of AI, and any other appropriate method of gaining insights from large data sets. This Special Issue is also open to review articles that focus on what can be learned from healthcare data sets.

This Special Issue aims to show how data exploration can result in new possibilities and improve our understanding of current theories and practice Studies on the use of AI and machine learning are encouraged, but this Special Issue is not limited to such methods.

The themes and article types include using data analytic methods on data sets to gain insights about the relationships among variables and the insights the data provide to healthcare researchers and practitioners. In addition, review articles that address how such data-driven approaches can be used in the future are welcome.

We look forward to receiving your contributions.

Prof. Dr. Victor R. Prybutok
Dr. Gayle Linda Prybutok
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven health insights
  • health data mining
  • health care analytics
  • health informatics
  • data-driven patient insights
  • health care modeling

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

22 pages, 3610 KiB  
Article
A Comparative Study of Hospitalization Mortality Rates between General and Emergency Hospitalized Patients Using Survival Analysis
by Haegak Chang, Seiyoung Ryu, Ilyoung Choi, Angela Eunyoung Kwon and Jaekyeong Kim
Healthcare 2024, 12(19), 1982; https://doi.org/10.3390/healthcare12191982 - 4 Oct 2024
Viewed by 377
Abstract
Background/Objectives: In Korea’s emergency medical system, when an emergency patient arises, patients receive on-site treatment and care during transport at the pre-hospital stage, followed by inpatient treatment upon hospitalization. From the perspective of emergency patient management, it is critical to identify the high [...] Read more.
Background/Objectives: In Korea’s emergency medical system, when an emergency patient arises, patients receive on-site treatment and care during transport at the pre-hospital stage, followed by inpatient treatment upon hospitalization. From the perspective of emergency patient management, it is critical to identify the high death rate of patients with certain conditions in the emergency room. Therefore, it is necessary to compare and analyze the determinants of the death rate of patients admitted via the emergency room and generally hospitalized patients. In fact, previous studies investigating determinants of survival periods or length of stay (LOS) primarily used multiple or logistic regression analyses as their main research methodology. Although medical data often exhibit censored characteristics, which are crucial for analyzing survival periods, the aforementioned methods of analysis fail to accommodate these characteristics, presenting a significant limitation. Methods:Therefore, in this study, survival analyses were performed to investigate factors affecting the dying risk of general inpatients as well as patients admitted through the emergency room. For this purpose, this study collected and analyzed the sample cohort DB for a total of four years from 2016 to 2019 provided by the Korean National Health Insurance Services (NHIS). After data preprocessing, the survival probability was estimated according to sociodemographic, patient, health checkup records, and institutional features through the Kaplan–Meier survival estimation. Then, the Cox proportional hazards models were additionally utilized for further econometric validation. Results: As a result of the analysis, in terms of the ‘city’ feature among the sociodemographic characteristics, the small and medium-sized cities exert the most influence on the death rate of general inpatients, whereas the metropolitan cities exert the most influence on the death rate of inpatients admitted through the emergency room. In terms of institution characteristics, it was found that there is a difference in determinants affecting the death rate of the two groups of study, such as the number of doctors per 100 hospital beds, the number of nurses per 100 hospital beds, the number of hospital beds, the number of surgical beds, and the number of emergency beds. Conclusions: Based on the study results, it is expected that an efficient plan for distributing limited medical resources can be established based on inpatients’ LOS. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
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14 pages, 867 KiB  
Article
Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence
by Marwah Ahmed Halwani and Manal Ahmed Halwani
Healthcare 2024, 12(17), 1694; https://doi.org/10.3390/healthcare12171694 - 26 Aug 2024
Viewed by 747
Abstract
Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, [...] Read more.
Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methods: A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. Results: The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. Conclusions: Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
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24 pages, 1882 KiB  
Article
A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort
by Kaylla Richardson, Sankari Penumaka, Jaleesa Smoot, Mansi Reddy Panaganti, Indu Radha Chinta, Devi Priya Guduri, Sucharitha Reddy Tiyyagura, John Martin, Michael Korvink and Laura H. Gunn
Healthcare 2024, 12(10), 983; https://doi.org/10.3390/healthcare12100983 - 10 May 2024
Viewed by 1438
Abstract
Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are [...] Read more.
Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are not always available, complete, or homogeneous, and clinically driven metrics to assess medical practices are not logistically feasible at the population level, particularly in non-centralized healthcare delivery systems and/or for those who only have access to claims data. Data-driven approaches that incorporate all available information are needed to explore coding specificity practices. Using N = 487,775 hospitalization records of individuals diagnosed with dementia and discharged in 2022 from a large all-payor administrative claims dataset, we fitted logistic regression models using patient and facility characteristics to explain the coding specificity of principal and secondary diagnoses of dementia. A two-step approach was produced to allow for the flexible clustering of patient-level outcomes. Model outcomes were then used within a Poisson binomial model to identify facilities that over- or under-specify dementia diagnoses against healthcare industry standards across hospitalizations. The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727). The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map. This study’s findings provide healthcare facilities with a benchmark for assessing coding specificity practices and developing quality enhancements to align with healthcare industry standards, ultimately contributing to better patient care and healthcare system reliability. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
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16 pages, 1041 KiB  
Article
The Role of Technology in Online Health Communities: A Study of Information-Seeking Behavior
by LeAnn Boyce, Ahasan Harun, Gayle Prybutok and Victor R. Prybutok
Healthcare 2024, 12(3), 336; https://doi.org/10.3390/healthcare12030336 - 29 Jan 2024
Cited by 1 | Viewed by 2174
Abstract
This study significantly contributes to both theory and practice by providing valuable insights into the role and value of healthcare in the context of online health communities. This study highlights the increasing dependence of patients and their families on online sources for health [...] Read more.
This study significantly contributes to both theory and practice by providing valuable insights into the role and value of healthcare in the context of online health communities. This study highlights the increasing dependence of patients and their families on online sources for health information and the potential of technology to support individuals with health information needs. This study develops a theoretical framework by analyzing data from a cross-sectional survey using partial least squares structural equation modeling and multi-group and importance–performance map analysis. The findings of this study identify the most beneficial technology-related issues, like ease of site navigation and interaction with other online members, which have important implications for the development and management of online health communities. Healthcare professionals can also use this information to disseminate relevant information to those with chronic illnesses effectively. This study recommends proactive engagement between forum admins and participants to improve technology use and interaction, highlighting the benefits of guidelines for effective technology use to enhance users’ information-seeking processes. Overall, this study’s significant contribution lies in its identification of factors that aid online health community participants in the information-seeking process, providing valuable information to professionals on using technology to disseminate information relevant to chronic illnesses like COPD. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
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30 pages, 4044 KiB  
Article
Team Size and Composition in Home Healthcare: Quantitative Insights and Six Model-Based Principles
by Yoram Clapper, Witek ten Hove, René Bekker and Dennis Moeke
Healthcare 2023, 11(22), 2935; https://doi.org/10.3390/healthcare11222935 - 9 Nov 2023
Cited by 1 | Viewed by 1211
Abstract
The aim of this constructive study was to develop model-based principles to provide guidance to managers and policy makers when making decisions about team size and composition in the context of home healthcare. Six model-based principles were developed based on extensive data analysis [...] Read more.
The aim of this constructive study was to develop model-based principles to provide guidance to managers and policy makers when making decisions about team size and composition in the context of home healthcare. Six model-based principles were developed based on extensive data analysis and in close interaction with practice. In particular, the principles involve insights in capacity planning, travel time, available effective capacity, contract types, and team manageability. The principles are formalized in terms of elementary mathematical models that capture the essence of decision-making. Numerical results based on real-life scenarios reveal that efficiency improves with team size, albeit more prominently for smaller teams due to diminishing returns. Moreover, it is demonstrated that the complexity of managing and coordinating a team becomes increasingly more difficult as team size grows. An estimate for travel time is provided given the size and territory of a team, as well as an upper bound for the fraction of full-time contracts, if split shifts are to be avoided. Overall, it can be concluded that an ideally sized team should serve (at least) around a few hundreds care hours per week. Full article
(This article belongs to the Special Issue Data Driven Insights in Healthcare)
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