Disruptive Innovation: Implementation of Electronic Consultations in a Veterans Affairs Health Care System
Bibliographic record
Abstract
BACKGROUND: Electronic consultations (e-consults) offer rapid access to specialist input without the need for a patient visit. E-consult implementation began in 2011 at VA Boston Healthcare System (VABHS). By early 2013, e-consults were available for all clinical services. In this implementation, the requesting clinician selects the desired consultation within the electronic health record (EHR) ordering menu, which creates an electronic form that is pre-populated with patient demographic information and allows free-text entry of the reason for consult. This triggers a message to the requesting clinician and requested specialty, thereby enabling bidirectional clinician-clinician communication. OBJECTIVE: The aim of this study is to examine the utilization of e-consults in a large Veterans Affairs (VA) health care system. METHODS: Data from the electronic health record was used to measure frequency of e-consult use by provider type (physician or nurse practitioner (NP) and/or physician assistant), and by the requesting and responding specialty from January 2012 to December 2013. We conducted chart reviews for a purposive sample of e-consults and semi-structured interviews with a purposive sample of clinicians and hospital leaders to better characterize the process, challenges, and usability of e-consults. RESULTS: A total of 7097 e-consults were identified, 1998 from 2012 and 5099 from 2013. More than one quarter (27.56%, 1956/7097) of the e-consult requests originated from VA facilities in New England other than VABHS and were excluded from subsequent analysis. Within the VABHS e-consults (72.44%, 5141/7097), variability in frequency and use of e-consults across provider types and specialties was found. A total of 64 NPs requested 2407 e-consults (median 12.5, range 1-415). In contrast, 448 physicians (including residents and fellows) requested 2349 e-consults (median 2, range 1-116). More than one third (37.35%, 1920/5141) of e-consults were sent from primary care to specialists. While most e-consults reflected a request for specialist input to a generalist's question in diagnosis or management in the ambulatory setting, we identified creative uses of e-consults, including requests for face-to-face appointments and documentation of pre-operative chart reviews; moreover, 7.00% (360/5141) of the e-consults originated from our sub-acute and chronic care inpatient units. In interviews, requesting providers reported high utility and usability. Specialists recognized the value of e-consults but expressed concerns about additional workload. CONCLUSIONS: The e-consult mechanism is frequently utilized for its initial intended purpose. It has also been adopted for unexpected clinical and administrative uses, developing into a "disruptive innovation" and highlighting existing gaps in mechanisms for provider communication. Further investigation is needed to characterize optimal utilization of e-consults within specialty and the medical center, and what features of the e-consult program, other than volume, represent valid measures of access and quality care.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".