Belgian Integrated Health Record (BIHR): from 2025, all well-being and health data will be available for all members of the care team, including the patient himself. 
Bibliographic record
Abstract
•Why are you conducting a workshop? Please explain the problem, the context, or the case study, It would be an absolute added value for the BIHR-project to also hear the voice and opinion from other countries and experts: what went very smoothly? But also, what obstacles were they confronted with and how did they tackle these challenges. •Who is it for? Please explain who you would like to engage with. BIHR is a ‘Moonshot’: the expert group defined four major groups that are essential to bring BIHR to a successful project: the citizens, the healthcare providers, the various governments, and partners from the industry. In the panel debate we would like to have representatives from those different stakeholders around the table. It would also be an absolute added value to hear success stories from other countries and see if things could be realized in Belgium in the short and/or medium term. •Ilke Montag, MD, MSc: lead of the Belgian expert panel about BIHR •Jan De Maeseneer, MD, PhD: Head of WHO Collaborating Centre on Family Medicine and Primary Health Care - Ghent University Belgium •Bart Van den Bosch, MD, PHD: CIO at University Hospital Leuven Belgium •A representative of the European Patients Forum (European Patients' Forum - Home (eu-patient.eu)) •A representative of the International Hospital Federation (Home - IHF - International Hospital Federation (ihf-fih.org)) •Someone who can explain the concept and the functioning of EHRs in the Canadian Primary Health Care Services •Someone who can explain the concept and the functioning of Slovenian Community health centers with multidisciplinary teams •What are you going to do? Please explain the structure of the session. Please remember that workshops’ objective is to be interactive and engage with the audience - At least ½ of the time should be based on the interaction with the audience. •How are you going to engage with the audience? •How are you going to summarize the take home messages? We will start with a short 15 min. presentation/introduction of the concept of BIHR. Afterwards, the moderator asks for a short reflection on this concept by the various panel members. The panel members are then asked to name both a success story and the lessons learnt from less successful projects. To involve the public in the debate, statements for debate will be used, each time from a unique perspective (citizens, care providers, governments, industry). The panel members will be confronted with the voting results on the statements. The take home message will be a compilation of the success keys cited by each of the panel. There will also be a warm appeal to work together not only across the various levels in health care, but also across national borders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.059 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".