Interprofessional survey on medication reconciliation activities in the US Department of Veterans’ Affairs: development and validation of an Implementation Readiness Questionnaire
Bibliographic record
Abstract
BACKGROUND: Medication reconciliation (MR) can detect medication history discrepancies at interfaces-in-care and help avoid downstream adverse drug events. However, organisations have struggled to implement high-quality MR programmes. The literature has identified systems barriers, including technology capabilities and data interoperability. However, organisational culture as a root cause has been underexplored. OBJECTIVES: Our objectives were to develop an implementation readiness questionnaire and measure staff attitudes towards MR across a healthcare enterprise. METHODS: We developed and distributed a questionnaire to 170 Veterans' Health Affairs (VHA) sites using Research Electronic Data Capture (REDCap) software. The questionnaire contained 21 Likert-scale items that measured three constructs, such as: (1) the extent that clinicians valued MR; (2) perceptions of workflow compatibility and (3) perceptions concerning organisational climate of implementation. RESULTS: 8704 clinicians and staff responded to our questionnaire (142 of 170 VHA facilities). Most staff believed reconciling medications can improve medication safety (approximately 90% agreed it was 'important'). However, most (approximately 90%) also expressed concerns about changes to their workflow. One-third of respondents prioritised other duties over MR and reported barriers associated with implementation climate. Only 47% of respondents agreed they had enough resources to address discrepancies when identified. INTERPRETATION: Our findings indicate that an MR readiness assessment can forecast challenges and inform development of a context-sensitive implementation bundle. Clinicians surveyed struggled with resources, technology challenges and implementation climate. A strong campaign should include clear leadership messaging, credible champions and resources to overcome technical challenges. CONCLUSIONS: This manuscript provides a method to conduct a readiness assessment and highlights the importance of organisational culture in an MR campaign. The data can help assess site or network readiness for an MR change management programme.
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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 | 0.030 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".