Exploring the Impact of the Digital Health Drug Repository in Ontario
Bibliographic record
Abstract
Central repositories of drug‐related information have the potential to reduce adverse events and inappropriate prescribing by enabling clinicians to access relevant details at the point of care. In 2016, the Ontario Ministry of Health developed the Digital Health Drug Repository (DHDR) to support clinicians in developing a best possible medication history (BPMH). We conducted a formative evaluation of the DHDR to understand (1) the perceived clinical value DHDR; and (2) the barriers and enablers to adoption and meaningful use. A multimethod approach including semistructured interviews and an online clinician survey. Interview data were thematically analyzed, and survey data were analyzed using descriptive statistics. Clinicians included physicians, nurses, pharmacists, and allied health providers who were eligible to use the DHDR (irrespective of use). Thirty‐three interviews were conducted. Most participants were female (60%, n = 20), worked in acute care settings (46%, n = 15), and self‐reported using the DHDR > 4 times (78%, n = 26). Participants were satisfied with the DHDR as source of secondary information, but the absence of specific data such as medication instructions and prescribed medications that were not dispensed limited its utility. Poor integration with point‐of‐care systems further limited potential, with no perceived impact on the development of a BPMH. Of the 167 survey participants, the majority were female (82%, n = 137) and worked in acute care settings (58%, n = 90). Only 24% (n = 40) were actively using DHDR. DHDR users were neutral in their perceptions of the utility of DHDR (mean scores ranged 4.11‐4.76 on a 7‐point adjectival scale). Of the 76% (n = 127) who were not using the DHDR, many found access to medication information very important (mean scores ranged 6.22‐5.97). Reasons for not using DHDR included cumbersome process to gain access to DHDR and the perception that the repository was incomplete. Findings from this evaluation suggest that there is potential untapped value if a digital centralized medication repository is operationalized to align with clinician needs. Specifically, (1) integration with point‐of‐care systems; (2) comprehensive clinical data; and (3) quick and streamlined onboarding processes would facilitate meaningful use. Digital drug repositories can be a valuable tool for clinicians when determining a BPMH for a patient. Access to comprehensive medication information across the health care system can improve efficiency and reduce medical errors. These applied insights can inform the operationalization and implementation of system‐wide strategies to improve their uptake and impact. Ontario Ministry of Health Canada.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| 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".