Disenfranchised Patients: A Network Analysis of IS Integration in the Context of Patient-Centered Care
Bibliographic record
Abstract
Healthcare reform has emphasized coordinated and integrated care - patient-centered care - for a decade. To that end, policymakers have invested in integration of healthcare providers' information flows. Research to date has studied healthcare actors' information needs but overlooked communicative exchanges among all participants in coordinating treatment plan decisions. Consequently, while medical literature asserts that patients should depend on information from healthcare providers to enable patients' participation in treatment plan decisions, the assertion has not been tested. To ameliorate this oversight, we conducted an empirical study of a patient-centered healthcare environment. Our study draws on dependency network diagramming (DND) and social network analysis (SNA) to elucidate the nature and structure of actors' communications in support of their information dependencies. The findings illustrate that although patients are well supported by personal communications with healthcare providers, they are disenfranchised from the integrated information technology within and between healthcare providers and its potential to support patients' participation in coordinated "patient-centered care" decisions. Furthermore, knowledge asymmetry between patients and healthcare providers should be considered in the selection and tailoring of healthcare information systems (IS).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".