Connecting Medical Records: An Evaluation of Benefits and Challenges for Primary Care Practices
Bibliographic record
Abstract
BACKGROUND: Implementation of systems to support health information sharing has lagged other areas of healthcare IT, yet offers a strong possibility for benefit. Clinical acceptance is a key limiting factor in health IT adoption. OBJECTIVES: To assess the benefits and challenges experienced by clinicians using a custom-developed health information exchange system, and to show how perceptions of benefits and challenges influence perceptions of productivity and care-related outcomes. METHODS: We used a mixed methods design with two phases. First, we conducted interviews with stakeholders who were familiar with the health information exchange system to inform the development of a measure of benefits and challenges of the use of this system. Second, using this measure we conducted a survey of current and former users of the health information exchange system using a modified Dillman method. RESULTS: 105 current and former users completed the survey. The results showed information quality, ease of completing tasks and clinical process improvement as key benefits that reduced workload and improved patient care. Challenges related to system reliability, quality of reports and service quality increased workload and decreased impact on care, though the effect of the challenges was smaller than that of the benefits. CONCLUSIONS: Even very limited health information exchange capabilities can improve outcomes for primary care users. Improving perceptions of benefits may be even more important the removing challenges to use, though it is likely that a threshold of quality must be achieved for this to be true.
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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 | 0.044 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".