The provider perspective: investigating the effect of the Electronic Patient-Reported Outcome (ePRO) mobile application and portal on primary care provider workflow
Bibliographic record
Abstract
Aim This qualitative study investigates how the Electronic Patient-Reported Outcome (ePRO) mobile application and portal system, designed to capture patient-reported measures to support self-management, affected primary care provider workflows. BACKGROUND: The Canadian health system is facing an ageing population that is living with chronic disease. Disruptive innovations like mobile health technologies can help to support health system transformation needed to better meet the multifaceted needs of the complex care patient. However, there are challenges with implementing these technologies in primary care settings, in particular the effect on primary care provider workflows. METHODS: Over a six-week period interdisciplinary primary care providers (n=6) and their complex care patients (n=12), used the ePRO mobile application and portal to collaboratively goal-set, manage care plans, and support self-management using patient-reported measures. Secondary thematic analysis of focus groups, training sessions, and issue tracker reports captured user experiences at a Toronto area Family Health Team from October 2014 to January 2015. Findings Key issues raised by providers included: liability concerns associated with remote monitoring, increased documentation activities due to a lack of interoperability between the app and the electronic patient record, increased provider anxiety with regard to the potential for the app to disrupt and infringe upon appointment time, and increased demands for patient engagement. Primary care providers reported the app helped to focus care plans and to begin a collaborative conversation on goal-setting. However, throughout our investigation we found a high level of provider resistance evidenced by consistent attempts to shift the app towards fitting with existing workflows rather than adapting much of their behaviour. As health systems seek innovative and disruptive models to better serve this complex patient population, provider change resistance will need to be addressed. New models and technologies cannot be disruptive in an environment that is resisting change.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".