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Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".

Deadline for manuscript submissions: closed (22 March 2023) | Viewed by 10004

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Special Issue Editors

Prof. Dr. Teresa Magalhães

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Guest Editor

1. Department of Public Health and Forensic Sciences, and Medical Education, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
2. TOXRUN – Toxicology Research Unit, University Institute of Health Sciences, Advanced Polytechnic and University Cooperative (CESPU), CRL, 4585-116 Gandra, Portugal
3. MTG Research and Development Lab, 4200-604 Porto, Portugal
Interests: legal medicine; forensic sciences; abuse and neglect; data science; medical education; pedagogical innovation; personal injury assessment; insurance medicine

Prof. Dr. Tiago Taveira-Gomes

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Co-Guest Editor

1.Department of Community Medicine, Information and Decision in Health, Faculty of Medicine, University of Porto, 4050-313 Porto, Portugal
2.MTG Research and Development Lab, 4200-604 Porto, Portugal
3.Center for Health Technology and Services Research, 4200-450 Porto, Portugal
4.Faculty of Health Sciences, University Fernando Pessoa, 4249-004 Porto, Portugal
55TOXRUN – Toxicology Research Unit, University Institute of Health Sciences, Advanced Polytechnic and University Cooperative (CESPU), CRL, 4585-116 Gandra, Portugal
Interests: real-world evidence; implementation science; preventive medicine; healthcare data science; artificial intelligence; medical education; software development

Prof. Dr. Ricardo Jorge Dinis-Oliveira

E-Mail Website
Co-Guest Editor

1. TOXRUN – Toxicology Research Unit, University Institute of Health Sciences, Advanced Polytechnic and University Cooperative (CESPU), CRL, 4585-116 Gandra, Portugal
2. Department of Public Health and Forensic Sciences, and Medical Education, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
3. UCIBIO-REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
4. MTG Research and Development Lab, 4200-604 Porto, Portugal
Interests: real-world evidence; implementation science; toxicology; forensic sciences; psychoactive substances; drugs; biomedical research; scientometrics; scientific medical writing; pedagogical Innovation
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Dear Colleagues,

The potential benefits of leveraging real-world data (RWD), stored in electronic health records (EHR) and other sources, have become even more evident with the COVID-19 pandemic. Generating real-world evidence (RWE) from these sources is currently a great avenue of scientific opportunity for improving the understanding of diseases, the effectiveness of clinical guidelines and patient management strategies, as well as the socioeconomic impact of healthcare policy from the local up to the global level. RWE, which can virtually be generated from every healthcare institution around the globe, holds promise to transform the means through which healthcare is provided, by enabling healthcare professionals and decision makers to derive meaningful patient profiling, inform missed opportunities for diagnosis and treatment, strengthen preventive and predictive medicine approaches, etc., ultimately increasing life expectancy, reducing disease burden, reducing healthcare expenditure and overall contributing to the sustainability of healthcare systems.

Indeed, the successful implementation of EHRs in institutions across the globe has opened new challenges for the scientific research community in the healthcare informatics and data science fields, namely, through the adaptation of methods of exploratory data analysis, hypothesis testing and statistical modelling, to the analysis of RWD at scale, which must take into consideration local clinical practice specificities and their evolution across time. Thus, in this novel research era, the proper handling of RWD has brought all sorts of novel challenges to derive clinically meaningful RWE.

In this Special Issue, we are interested in receiving original articles, reviews, technical notes, protocols, guidelines, etc., with no restriction on the length of the papers, exploring the usefulness of RWD and RWE offering insight on diseases, pathophysiology, the assessment of missed opportunities for diagnosis and treatment of diseases, novel predictive models of disease identification, the assessment of treatment effectiveness and safety, morbidity, mortality and cost estimation, among any other topics generating RWE to advance scientific and medical knowledge.

Prof. Dr. Teresa Magalhães
Prof. Dr. Tiago Taveira-Gomes
Prof. Dr. Ricardo Jorge Dinis-Oliveira
Guest Editors

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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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

real-world evidence
real-world data
big data
digital health
healthcare
preventive medicine
implementation science
research 2.0
epidemiology
public health
medical informatics

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Editorial

Jump to: Research

3 pages, 285 KiB

Open AccessEditorial

Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers

by Teresa Magalhães, Ricardo Jorge Dinis-Oliveira and Tiago Taveira-Gomes

Int. J. Environ. Res. Public Health 2022, 19(14), 8364; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19148364 - 8 Jul 2022

Cited by 8 | Viewed by 2355

Abstract

Real world data (RWD) and real-world evidence (RWE) plays an increasingly important role in clinical research since scientific knowledge is obtained during routine clinical large-scale practice and not experimentally as occurs in the highly controlled traditional clinical trials. Particularly, the electronic health records (EHRs) are a relevant source of data. Nevertheless, there are also significant challenges in the correct use and interpretation of EHRs data, such as bias, heterogeneity of the population, and missing or non-standardized data formats. Despite the RWD and RWE recognized difficulties, these are easily outweighed by the benefits of ensuring the efficacy, safety, and cost-effectiveness in complement to the gold standards of the randomized controlled trial (RCT), namely by providing a complete picture regarding factors and variables that can guide robust clinical decisions. Their relevance can be even further evident as healthcare units develop more accurate EHRs always in the respect for the privacy of patient data. This editorial is an overview of the RWD and RWE major aspects of the state of the art and supports the Special Issue on “Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers” aimed to explore all the potential and the utility of RWD and RWE in offering insights on diseases in a broad spectrum. Full article

(This article belongs to the Special Issue Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers)

Research

Jump to: Editorial

16 pages, 369 KiB

Open AccessArticle

Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions

by Timo Schulte, Tillmann Wurz, Oliver Groene and Sabine Bohnet-Joschko

Int. J. Environ. Res. Public Health 2023, 20(6), 4693; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20064693 - 7 Mar 2023

Cited by 1 | Viewed by 2043

Abstract

The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8% of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model. One result was that both models achieve a generally comparable performance with c-values above 0.75, whereas the Random Forest model reached slightly higher c-values. The prediction models developed in this study reached c-values comparable to existing study results of prediction models for (avoidable) hospitalization from the literature. The prediction models were designed in such a way that they can support integrated care or public and population health interventions with little effort with an additional risk assessment tool in the case of availability of claims data. For the regions analyzed, the logistic regression revealed that switching to a higher age class or to a higher level of long-term care and unit from prior hospitalizations (all-cause and due to an ambulatory care-sensitive condition) increases the odds of having an ambulatory care-sensitive hospitalization in the upcoming year. This is also true for patients with prior diagnoses from the diagnosis groups of maternal disorders related to pregnancy, mental disorders due to alcohol/opioids, alcoholic liver disease and certain diseases of the circulatory system. Further model refinement activities and the integration of additional data, such as behavioral, social or environmental data would improve both model performance and the individual risk scores. The implementation of risk scores identifying populations potentially benefitting from public health and population health activities would be the next step to enable an evaluation of whether ambulatory care-sensitive hospitalizations can be prevented. Full article

(This article belongs to the Special Issue Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers)

12 pages, 376 KiB

Open AccessArticle

Health Outcomes in Women Victims of Intimate Partner Violence: A 20-Year Real-World Study

by Maria Clemente-Teixeira, Teresa Magalhães, Joana Barrocas, Ricardo Jorge Dinis-Oliveira and Tiago Taveira-Gomes

Int. J. Environ. Res. Public Health 2022, 19(24), 17035; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192417035 - 19 Dec 2022

Cited by 4 | Viewed by 2941

Abstract

Intimate partner violence is characterized by violent actions against a person perpetrated by his or her former or current partner, regardless of cohabitation. It most frequently affects women, and one of its most relevant outcomes is the health problems associated with the experience of repeated violence. Thus, the main objective of this study is to analyse the prevalence of health problems among women for whom there was a medical suspicion of being victims of intimate partner violence. The specific objectives are to analyse the prevalence of (a) health risk behaviours; (b) traumatic injuries and intoxications; (c) mental health conditions; and (d) somatic diseases. We conducted a real-world, retrospective, observational, cross-sectional and multicentric study based on secondary data analyses of electronic health records and health care register data in patients of the Local Healthcare Unit of Matosinhos (between 2001 and 2021). The identified data were extracted from electronic health records corresponding to the Health Insurance Portability and Accountability Act Safe Harbor Standard. Information was obtained considering the International Classification of Diseases, the International Classification of Primary Care, and the Anatomical Therapeutic Chemical Classification System, as well as clinical notes (according to previously defined keywords). Considering all information sources, 1676 cases were obtained. This number means that just 2% of the women observed at this health care unit were suspected of being victims of intimate partner violence, which is far from the known statistics. However, we found much higher rates of all health risk behaviours, trauma and intoxication cases, mental health conditions, and somatic disorders we looked for, when compared to the general population. Early detection of these cases is mandatory to prevent or minimize their related health outcomes. Full article

(This article belongs to the Special Issue Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers)

16 pages, 3035 KiB

Open AccessArticle

Development and Evaluation of Machine Learning-Based High-Cost Prediction Model Using Health Check-Up Data by the National Health Insurance Service of Korea

by Yeongah Choi, Jiho An, Seiyoung Ryu and Jaekyeong Kim

Int. J. Environ. Res. Public Health 2022, 19(20), 13672; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192013672 - 21 Oct 2022

Cited by 5 | Viewed by 1858

Abstract

In this study, socioeconomic, medical treatment, and health check-up data from 2010 to 2017 of the National Health Insurance Service (NHIS) of Korea were analyzed. This year’s socioeconomic, treatment, and health check-up data are used to develop a predictive model for high medical expenses in the next year. The characteristic of this study is to derive important variables related to the high cost of domestic medical expenses users by using data on health check-up items conducted by the country. In this study, we tried to classify data and evaluate its performance using classification supervised learning algorithms for high-cost medical expense prediction. Supervised learning for predicting high-cost medical expenses was performed using the logistic regression model, random forest, and XGBoost, which have been known to result the best performance and explanatory power among the machine learning algorithms used in previous studies. Our experimental results show that the XGBoost model had the best performance with 77.1% accuracy. The contribution of this study is to identify the variables that affect the prediction of high-cost medical expenses by analyzing the medical bills using the health check-up variables and the Korea Classification Disease (KCD) large group as input variables. Through this study, it was confirmed that musculoskeletal disorders (M) and respiratory diseases (J), which are the most frequently treated diseases, as important KCD disease groups for high-cost prediction in Korea, affect the future high cost prediction. In addition, it was confirmed that malignant neoplasia diseases (C) with high medical cost per treatment are a group of diseases related to high future medical cost prediction. Unlike previous studies, it is the result of analyzing all disease data, so it is expected that the study will be more meaningful when compared with the results of other national health check-up data. Full article

(This article belongs to the Special Issue Digital Health and Big Data Analytics: Implications of Real-World Evidence for Clinicians and Policymakers)

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