Collaboration in healthcare: Building effective transdisciplinary collaborations in open innovation initiatives
Bibliographic record
Abstract
Emerging diseases like the current pandemic, COVID-19, and the increasing number of chronic diseases around the world are putting considerable pressure on the healthcare system, demanding for more services, with higher quality and more efficient. Hence the healthcare system needs to transition towards sustainable healthcare. An approach to achieve a transition towards sustainable healthcare is the Quadruple Aim. It is a practical framework that focuses on improving the health of the population, improving the work-life of care providers, enhancing patients experience and reducing health cost. To implement these four aims, it is necessary the collaboration between multiple disciplines and the implementation of an innovative approach. Open innovation provides a space for transdisciplinary collaboration and innovation to occurs. Hence, this graduation project explores transdisciplinary collaborations in the healthcare sector, focusing on open innovation initiatives. The research question for this study is how to build effective transdisciplinary collaborations in healthcare? This graduation project took place in two countries, the Netherlands and Mexico. In the Netherlands, I realised interview research in eight open innovation initiatives. Later, in Mexico, I did a case study at the Research and Technological Development Unit inside the General Hospital of Mexico ‘Eduardo Liceaga’. Afterwards, I realised a comparative analysis between both studies, followed by a design phase to build effective transdisciplinary collaborations in healthcare. The results of the two studies present a future vision and a future-oriented strategy for the Research and Technological Development Unit to improve collaboration between actors. The future vision contributes to the alignment of the actors involved in the unit so that they can work in the same direction. The strategy is formed by three strategic lines: organisational and project development support, building a knowledge community, and promote and consolidate the unit. These strategic lines aim to guide the actors towards the future vision by suggesting a series of steps. The success of this initiative could contribute to boost innovation in healthcare. A suggestion for future research is on how design can facilitate the implementation of the strategy by considering the collaboration of all the actors present in the unit.
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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 | 0.053 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".