Healthcare service innovation based on information technology: The role of social values alignment
Bibliographic record
Abstract
The electronic personal health record (ePHR) is an information technology (IT) de- signed for patients’ empowerment in health self-management. Its actual implementation remains less than expected due to two main barriers that must be addressed by ePHRs’ providers: lack of trust in providers with regards to data privacy and lack of flexibility of the tool. In this study, we suggest that to potentially overcome these two challenges, ePHRs could be provided by health cooperatives (co-ops) in collaboration with open source devel- opment communities that share similar values. Based on the concept of social alignment that focuses on values, we explore the potential social bi-alignments between the values underlying the mission of health co-ops and the purpose of ePHRs, and between the founda- tional values of health co-ops and open source development communities. We also explore the effect of such potential social values alignments on health co-ops’ interest in innovat- ing with an ePHR-based service. To achieve our research objectives, 17 interviews were conducted in health co-ops in Quebec, a province of Canada where the network of health co-ops is particularly active. Our findings show that the concept of social values alignment is useful in the context of ePHR-based service innovation in health co-ops. However, our data analysis shows that social values alignment is not sufficient for healthcare service innovation to happen. Indeed, our findings lead us toward the concept of organizational readiness to better understand what is required to increase the likelihood of ePHR-based service innovation in health co-ops. This study culminates with the undertaking of theo- retical development where we propose a conceptual model of IT-based service innovation in healthcare organizations by expanding on our findings and on insights from the liter- ature.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".