A Conceptual Model to Share Resources and Align Goals: Building Blockchain Application to Support Care Continuity Outside a Hospital
Bibliographic record
Abstract
The increased use of advanced technologies by consumers and hospitals is moving care closer to patients, and the challenge is one of how patient data can be shared with external care providers and patients. To support care continuity, patient data include both clinical data used by external care providers and non-clinical data used by social care providers. Care coordination of a patient outside a hospital requires peer-to-peer connectivity among a number of these clinical and social care providers, using a digital platform that aligns their goals and assigns their resource sharing responsibilities. With no single entity supporting such care coordination, most hospitals currently distribute this responsibility to several of its provider partners and patients. Such a division of responsibility with no real time feedback leads to discontinuous resource sharing, localized data analysis, and challenges in tailoring care to improve health outcomes. The goal of this paper is to propose a blockchain architecture model that uses a number of constructs for creating and assigning ownership to patient data so it can support peer-to-peer resource sharing and uses smart contracts to support goal alignment. Using two blockchain applications implemented in Hyperledger and illustrating their potential representation using the constructs in multi-chain, we develop a conceptual model for developing blockchain applications in general to support continuity of care. The generalizability of this model is illustrated by applying these constructs to four additional healthcare applications. Finally, we conclude the paper with a discussion of the limitations and directions for future research.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".