Health in All Policy Making Utilizing Big Data

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.