Some thoughts on health and healthcare

Abstract Introducing Artificial Intelligence (AI), Large Language Models (LLMs), and Robotic Process Automation (RPA) in a Belgian hospital should not be treated primarily as an IT procurement exercise. It is a clinical, legal, organizational, and ethical transformation. AI can support diagnosis, triage, logistics, population health, and administrative optimization; LLMs can assist with documentation, translation, summarization, coding, and knowledge retrieval; and RPA can automate repetitive digital workflows such as data entry, report routing, claims preparation, and system monitoring. In Belgium, however, implementation must be aligned with the General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act, the European Health Data Space (EHDS) Regulation, Belgian health-data governance structures, and the hospital’s duty to preserve clinical safety, security and patient trust. The safest introduction model is therefore phased: establish governance, select low-risk hi...

Introduction Healthcare reform is not merely an exercise in repairing defects in an inherited administrative order. It is, more fundamentally, an exercise in institutional design: deciding what kind of health system a society wants, what outcomes it values, and how governance, financing, service delivery, workforce policy, and information systems should be aligned to achieve those outcomes. Contemporary reform agendas across advanced health systems increasingly converge around stronger primary care, better care integration, digital transformation, affordability, and more people-centred care. That convergence already suggests that reform is guided by a desired future state, not by passive extrapolation from legacy arrangements. For that reason, healthcare reform should begin conceptually from the “ to be ” state. A target-state approach clarifies the normative goals of reform and helps orient institutions toward outcomes that matter: better patient experience, better population health, ...

Inleiding  Met de toenemende druk op de gezondheidszorg om beter om te springen met de beschikbare middelen, wordt het belangrijker om zorgprocessen efficiënt en effectief te organiseren. Dat geldt zowel voor de normale werking als in tijden van gezondheidscrisissen. Nog steeds worden theoretische modellen gemaakt van zorgpaden die niet overeenstemmen met hoe het er echt aan toe gaat op de werkvloer. Process Mining kan helpen om in kaart te brengen hoe een zorgproces in de praktijk verloopt, zodat theorie en praktijk beter op mekaar kunnen worden afgestemd. Proces Mining Process mining is een analysetechniek die IT-logbestanden (event logs) gebruikt om werkelijke bedrijfsprocessen te visualiseren, analyseren en verbeteren. Het maakt inefficiënties, knelpunten en afwijkingen zichtbaar, wat kan leiden tot efficiëntere processen, lagere operationele kosten en hogere patiënt- en medewerkerstevredenheid (change management).  Process mining in de gezondheidszorg analyseert d...

Abstract Co-creation (and related “co-production” and “co-design”) is increasingly used to shape healthcare policy and to accelerate innovation adoption by involving patients, citizens, clinicians, payers, regulators, and industry in joint problem framing, design, and evaluation.  Conceptually, co-creation can increase the relevance, legitimacy, implementability, and equity of innovation-supporting policies, thereby improving efficiency, effectiveness, quality, and outcomes. Empirically, however, systematic reviews repeatedly conclude that outcome evidence is inconsistent, under-measured, and often limited to process or experiential benefits rather than system-level value. This essay (i) synthesizes documented strengths and weaknesses of co-creation as a policy approach, (ii) maps these to mechanisms and real-world examples across three policy levels (national/regional; payer reimbursement; hospital/provider) and four innovation types (digital health; payment models; workforce rede...