Introduction: Health in all Policies (HiAP) is a valuable method for effective Healthcare policy development. Big data analysis can be useful to both individuals and clinicians so that the full potential of big data is employed.
Aim: The present paper deals with Health in All Policies, and how the use of Big Data can lead and support the development of new policies.
Methods: To this end, in the context of the CrowdHEALTH project, data from heterogeneous sources will be exploited and the Policy Development Toolkit (PDT) model will be used. In order to facilitate new insights to healthcare by exploiting all available data sources.
Results: In the case study that is being proposed, the NOHS Story Board (inpatient and outpatient health care) utilizing data from reimbursement of disease-related groups (DRGs), as well as medical costs for outpatient data, will be analyzed by the PDT.
Conclusion: PDT seems promising as an efficient decision support system for policymakers to align with HiAP as it offers Causal Analysis by calculating the total cost (expenses) per ICD-10, Forecasting Information by measuring the clinical effectiveness of reimbursement cost per medical condition, per gender and per age for outpatient healthcare, and Risk Stratification by investigating Screening Parameters, Indexes (Indicators) and other factors related to healthcare management. Thus, PDT could also support HiAP by helping policymakers to tailor various policies according to their needs, such as reduction of healthcare cost, improvement of clinical effectiveness and restriction of fraud.
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http://dx.doi.org/10.5455/aim.2020.28.65-70 | DOI Listing |
Biometrics
January 2025
Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States.
In the era of big data, increasing availability of data makes combining different data sources to obtain more accurate estimations a popular topic. However, the development of data integration is often hindered by the heterogeneity in data forms across studies. In this paper, we focus on a case in survival analysis where we have primary study data with a continuous time-to-event outcome and complete covariate measurements, while the data from an external study contain an outcome observed at regular intervals, and only a subset of covariates is measured.
View Article and Find Full Text PDFFront Public Health
January 2025
Division of Medical Statistics and Bioinformatics, Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City, Taiwan.
Background: Taiwan implemented global hospital budgeting with a floating-point value, which created a prisoner's dilemma. As a result, hospitals increased service volume, which caused the floating-point value to drop to less than one New Taiwan Dollar (NTD). The recent increase in the number of hospital beds and the call to enhance the floating-point value to one NTD raise concerns about the potential for increased financial burden without adding value to patient care if hospitals expand their bed capacity for volume-based competition.
View Article and Find Full Text PDFFront Immunol
January 2025
Microbiome-X, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
[This corrects the article DOI: 10.3389/fimmu.2024.
View Article and Find Full Text PDFFront Genet
January 2025
College of Animal Science and Technology, China Agricultural University, Beijing, China.
Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research.
View Article and Find Full Text PDFHeliyon
January 2025
Information Management Office, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
Background: This investigation quantifies the mean and median hearing thresholds and assesses the prevalence of age-related hearing loss within the senior population of Taipei.
Methods: In a substantive geriatric assessment supported by government initiative, 1696 individuals from a community hospital partook in this cross-sectional study (2016-2018). Detailed audiometric evaluations logged pure-tone thresholds across critical frequencies (0.
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