Challenges to Personal Information Sharing in Interorganizational Settings: Learning from the Quebec Health Smart Card Project
Bibliographic record
Abstract
DEPLOYMENTS of national health information infrastructures (NHII) are slowly spreading throughout the world. Because they span organizational boundaries and involve many parties with different strategie interests, they are considered interorganizational systems (IOS). The body of literature on IOS poses problems for the health care sector since the latter usually encompasses more types of stakeholders than the usual users and suppliers, its information systems contain sensitive information and it is not driven by profit. Moreover, the conventional MIS models have not been adapted quite yet to account for this more complex reality. Studies about NHII infrastructures have offered fragmented results so far. In this research, I address this gap in the literature by examining what happened during the pre-implementation phase of the Quebec Health Smart Card project, a provincial health infrastructure project which lasted close to 15 years. In order to achieve this, I use Pierre Bourdieu’s Theory of Practice and Bruno Latour’s Actor-Network-Theory. These sociological methods allow me to investigate the process that unfolded over the years with a critical eye so that in addition to answering usual questions such as who participated, what their concerns were and how the debate took place, I am addressing questions such as why it happened that way. The different perspective offered by these alternative theories provided good hindsight to refine resistance to change and user participation (MIS) models so that they reflect more accurately situations in interorganizational settings. Also, it fostered interesting thoughts about the similarities between private and public sector’s information systems. Finally, it engendered three statements that can be applied to other situations and/or other sectors that share certain similarities with the case studied. In addition to these benefits to MIS researchers, the present result can also help practitioners in managing the still ongoing provincial health infrastructure project, now renamed Quebec’s Health Record. This is important since recent events proved that most issues and protagonists taking part in the controversy are the same as before, that the same history seems to be repeating.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.024 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".