Strengthening Digital Transformation and Innovation in the Health Care System: Protocol for the Design and Implementation of a Multidisciplinary National Health Innovation Research School
Bibliographic record
Abstract
BACKGROUND: Digital health technologies have the potential to transform health care services to be more cost-effective, coordinated, and accessible on equal terms for entire populations. In the future, people will be assisted by such technologies to monitor their health status, take preventive measures, and have more control of their health situation. An increase in digital supplementation or substitution of physical care visits can potentially add value to patients and care providers by increasing accessibility, safety, and quality of care. However, health care organizations struggle with the challenges of developing and implementing digital health technologies and services in practice. As a response to this, we have developed a national multidisciplinary research school to increase competence and capacity for research on the development, implementation, and dissemination of digital health technology solutions. The overall aim of the research school is to increase national competence and capacity for the development, implementation, and dissemination of digital health technology to increase the preparedness to support and facilitate the ongoing digital transformation in the health care system. OBJECTIVE: The purpose of this paper is to outline the protocol for the development and implementation of a national multidisciplinary doctoral education program of health innovation supporting digital transformation in the health care system. METHODS: A national multidisciplinary research school for health innovation was planned in collaboration between 7 Swedish universities and their partners from industry and the public sector. The research school will run over 6 years, of which 5 years are dedicated for the doctoral education program and 1 year for the project start-up and closing. In this paper, we outline the methodological approach of the research school; the combining of knowledge and expertise of the universities that are important to run the research school; the jointly formulated research-oriented and societally relevant research focus, goals, and objectives for the research school; the established and developed relationships with partners from industry and the public sector for joint research training projects; the forms of collaboration in the research school; and the format of the doctoral education process. RESULTS: The research school was funded in December 2021 and started in March 2022. The research school starts with an initiation period from March 2022 to December 2022 where the infrastructure and the action plans to run the school are set up. The PhD projects start in January 2023, and these projects will be completed in 5 years. Additional activities within the research program are doctoral courses, networking activities, and dissemination of results. CONCLUSIONS: The network of several partners from industry, public sector, and academia enables the research school to pose research questions that can contribute to solving relevant societal problems related to the development, evaluation, implementation, and dissemination of methods and processes assisted by digital technologies. Ultimately, this will promote innovation to improve health outcomes, quality of care, and prioritizations of resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46595.
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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 | 0.199 | 0.149 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.064 | 0.020 |
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".