Creating a foundation for implementing an electronic health records (EHR)-integrated Social Knowledge Networking (SKN) system on medication reconciliation
Bibliographic record
Abstract
Background: In fall 2016, Augusta University received a two-year grant from AHRQ, to implement a Social Knowledge Networking (SKN) system for enabling its health system, AU-Health, to progress from “limited use” of electronic health records (EHR) Medication Reconciliation (MedRec) Technology, to “meaningful use”. Phase 1 sought to identify a comprehensive set of issues related to EHR MedRec encountered by practitioners at AU-Health. These efforts helped develop a Reporting Tool, which, along with a Discussion Tool, was incorporated into the AU-Health EHR, at the end of Phase 1. Phase 2 (currently underway), comprises a 52-week pilot of the EHR-integrated SKN system in outpatient and inpatient medicine units. The purpose of this paper is to describe the methods and results of Phase 1.Methods: Phase 1 utilized an exploratory mixed-method approach, involving two rounds of data collection. This included 15 individual interviews followed by a survey of 200 practitioners, i.e., physicians, nurses, and pharmacists, based in the outpatient and inpatient medicine service at AU Health.Results: Thematic analysis of interviews identified 55 issue-items related to EHR MedRec under 9 issue-categories. The survey sought practitioners’ importance-rating of all issue-items identified from interviews. A total of 127 (63%) survey responses were received. Factor analysis served to validate the following 6 of the 9 issue-categories, all of which, were rated “Important” or higher (on average), by over 70% of all respondents: 1) Care-Coordination (CCI); 2) Patient-Education (PEI); 3) Ownership-and-Accountability (OAI); 4) Processes-of-Care (PCI); 5) IT-Related (ITRI); and 6)Workforce-Training (WTI). Significance-testing of importance-rating by professional affiliation revealed no statistically significant differences for CCI and PEI; and some statistically significant differences for OAI, PCI, ITRI, and WTI.Conclusions: There were two key gleanings from the issues related to EHR MedRec unearthed by this study: 1) there was an absence of shared understanding among practitioners, of the value of EHR MedRec in promoting patient safety, which contributed to workarounds, and suboptimal use of the EHR MedRec system; and 2) there was a socio-technical dimension to many of the issues, creating an added layer of complexity. These gleanings in turn, provide insights into best practices for managing both clinical transitions-of-care in the EHR MedRec process; and socio-technical challenges encountered in EHR MedRec implementation.
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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 | 0.039 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".