Achieving Meaningful Use of Electronic Health Records: Prospects for Blockchain in Ontario's Health Care System
Bibliographic record
Abstract
Over the past decade, the Government of Ontario has devoted significant resources to the digitization of patient health records with the goal of improving data storage, management, transfers and, ultimately, patient care. Adoption rates for digitized records, known as electronic health records (EHR), and accompanying systems, has been high among health care providers in Ontario. Yet, research has demonstrated that a number of barriers appear to inhibit the effective use of EHRs among clinicians. These barriers can impede or delay meaningful use of EHRs and accordingly, limit their ability improve information exchanges, service delivery and patient care.\nThis paper reviews the challenges of achieving meaningful use of EHRs in health care service delivery. It also examines whether an emerging technology for data management, blockchain, may overcome the most prominent barriers to meaningful use of EHRs. A strong focus of this research concerns the legal aspects of EHRs and the legal issues surrounding their use.\nThe difficulties in achieving meaningful use of EHRs can stem from the time and resources required for training and change management activities, the skill-level of users and the usability of the systems adopted.\nThis paper proposes recommendations including a greater emphasis by the government and industry groups on designated initiatives to support meaningful use, stronger compliance measures and incentives for health care providers, and investments in new and emerging health care positions. The legal community can assist by engaging in collaborative efforts that aid in increasing certainty about the laws concerning EHRs.\nThese findings may provide guidance to health care industry professionals and legal practitioners, to enhance preparation for technology changes in the area of information management, and encourage activities which support meaningful use.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".