Role of Individual Clinician Authority in the Implementation of Informatics Tools for Population-Based Medication Management: Qualitative Semistructured Interview Study
Bibliographic record
Abstract
BACKGROUND: Direct oral anticoagulant (DOAC) medications are frequently associated with inappropriate prescribing and adverse events. To improve the safe use of DOACs, health systems are implementing population health tools within their electronic health record (EHR). While EHR informatics tools can help increase awareness of inappropriate prescribing of medications, a lack of empowerment (or insufficient empowerment) of nonphysicians to implement change is a key barrier. OBJECTIVE: This study examined how the individual authority of clinical pharmacists and anticoagulation nurses is impacted by and changes the implementation success of an EHR DOAC Dashboard for safe DOAC medication prescribing. METHODS: We conducted semistructured interviews with pharmacists and nurses following the implementation of the EHR DOAC Dashboard at 3 clinical sites. Interview transcripts were coded according to the key determinants of implementation success. The intersections between individual clinician authority and other determinants were examined to identify themes. RESULTS: A high level of individual clinician authority was associated with high levels of key facilitators for effective use of the DOAC Dashboard (communication, staffing and work schedule, job satisfaction, and EHR integration). Conversely, a lack of individual authority was often associated with key barriers to effective DOAC Dashboard use. Positive individual authority was sometimes present with a negative example of another determinant, but no evidence was found of individual authority co-occurring with a positive instance of another determinant. CONCLUSIONS: Increased individual clinician authority is a necessary antecedent to the effective implementation of an EHR DOAC Population Management Dashboard and positively affects other aspects of implementation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13012-020-01044-5.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".