Training the First Generation of Health Care Performance Intelligence Professionals in Europe and Canada
Bibliographic record
Abstract
To the Editor: Fiske and colleagues1 argue that a new type of professional called an information counselor needs to be trained to turn data into meaningful information for clinical practice, supporting providers and patients. Indeed, there is a lack of training in producing valid, reliable, and actionable “health care performance intelligence,” and stakeholders using the intelligence lack critical assessment skills, especially with respect to intelligence derived from big data. We argue that such training should go beyond clinical practice to serve all stakeholders in health care: decisionmakers to steer the health care system; funders to purchase high quality health care; health care managers to optimize quality, costs, and patient experiences; and citizens to make informed health care decisions. Furthermore, training should cover all layers of the “health care performance intelligence pyramid,” turning big data into reliable, valid indicators (layer 1), inferring useful information from indicators (layer 2) that is translated into knowledge (layer 3) upon which stakeholders can act (layer 4).2,3 This covers three major research areas: health care performance measurement, performance-based health care governance mechanisms, and the utilization of health care performance intelligence by different end users. To cover all layers of the pyramid and all three research areas, the European Commission launched the international training network on Healthcare Performance Intelligence Professionals (HealthPros) in September 2018.4 Coordinated by an international consortium, it provides an innovative, three-year program of collaborative, multidisciplinary, and entrepreneurial training to 13 doctoral students with varying backgrounds (e.g., health sciences, medical informatics, medicine, biological sciences, business administration, statistics, and economics), who will work on a cohesive set of individual research projects to obtain a PhD degree. Students will be trained to master a set of required competencies and multidisciplinary skills that are not well covered in existing research and training programs. Moreover, through secondments, data hackathons, and network events, the HealthPros will closely interact with an immersion community (e.g., academia, industry, pharma, governance, and funders) as part of the educational process. This will train the HealthPros in understanding different perspectives, politics, and change processes of stakeholders in health systems and will simultaneously function as a means of increasing the uptake of health performance research results. The program involves Canada, Denmark, Germany, Hungary, Italy, the Netherlands, and the United Kingdom. The program design is geared toward a direct flow between research outputs and innovation that is enhanced through education. Ultimately, HealthPros is about using knowledge that is available on health care system performance to achieve improved quality of care for all and more sustainable health care. Dionne S. Kringos, PhDAssistant professor, Department of Public Health, Amsterdam Public Health research institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; [email protected]; ORCID: https://orcid.org/0000-0003-2711-4713.Oliver Groene, PhDVice chairman of the board, OptiMedis AG, Hamburg, Germany, and honorary senior lecturer in health services research, London School of Hygiene and Tropical Medicine, London, United Kingdom; ORCID: https://orcid.org/0000-0002-1099-2950.Søren Paaske Johnsen, MD, PhDProfessor in clinical health services research, Department of Clinical Medicine, Danish Center for Clinical Health Services Research, Aalborg University, Aalborg, Denmark; ORCID: https://orcid.org/0000-0003-0053-5649.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".