No abstract

In recent years, Indonesia has introduced reforms to its bureaucracy in response to critiques of the quality of government policy design and delivery. The Grand Design of Bureaucratic Reform strategy seeks to reduce the number of civil servants employed in administrative or managerial positions (structural appointments) in favour of skills-based recruitment into ‘functional’ positions. Specifically, the introduction of the ‘policy analyst’ position as a functional position in the civil service has sought to improve evidence-based policy making and the quality of policy outcomes, by incorporating merit-based recruitment, appointment and promotion. The role of functional policy analysts (Jabatan Fungsional Analis Kebijakan or JFAKs) is to assist policy makers in identifying policy issues, analyse evidence available on these issues, and ultimately make policy recommendations. This report overviews the recent experiences of different policy analyst cohorts since the role’s creation in 2015. It investigates these experiences to better understand the extent to which policy analysts are playing the role intended for them, and the factors enabling or inhibiting this.

Purpose: Increasing digitalisation in the medical domain gives rise to large amounts of healthcare data which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to non-standardised data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the healthcare system. Despite the existence of standardised data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remains limited. Methods: We developed a data harmonisation pipeline (DHP) for clinical data sets relying on the common FHIR data standard. We validated the performance and usability of our FHIR-DHP with data from the MIMIC IV database including > 40,000 patients admitted to an intensive care unit. Results: We present the FHIR-DHP workflow in respect of transformation of “raw” hospital records into a harmonised, AI-friendly data representation. The pipeline consists of five key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonised data into the patient-model database and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. Conclusion: Our approach enables scalable and needs-driven data modelling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step towards increasing cooperation, interoperability and quality of patient care in the clinical routine and for medical research.

No abstract

No abstract