Identifying best approaches for engaging patients and family members in health informatics initiatives: a case study of the Group Priority Sort technique
Bibliographic record
Abstract
BACKGROUND: Patient engagement strategies in health service delivery have become more common in recent years. However, many healthcare organizations are challenged in identifying the best methods to engage patients in health information technology (IT) initiatives. Engaging with important stakeholders to identify effective opportunities can inform the development of a resource that addresses this issue and supports organizations in their endeavors. The purpose of this paper is to share our experience and lessons learned from applying a novel consensus-building technique in order to identify key elements for effective patient engagement in health IT initiatives. This will be done through a case study approach. METHODS: Patients, family members of patients, health professionals, researchers, students, vendor representatives and individuals who work in health IT roles in health organizations were engaged through a one-day symposium in Toronto, Canada in September, 2018. During the symposium, the Group Priority Sort technique was used to obtain structured feedback from symposium attendees in the context of small group discussions. Descriptive statistics and a content analysis were undertaken to analyze the data collected through the Group Priority Sort as well as participant feedback following the symposium. RESULTS: A total of 37 participants attended the symposium from a variety of settings and organizations. Using the Group Priority Sort technique, 30 topics were classified by priority to be included in a future resource. Participant feedback pertaining to the symposium and research methods was largely positive. Several areas of improvement, such as clarity of items, were identified from this case study. CONCLUSIONS: The Group Priority Sort technique was an efficient method for obtaining valuable suggestions from a diverse group of stakeholders, including patients and family members. The specific priorities and feedback obtained from the symposium will be incorporated into a resource for healthcare organizations to aid them in engaging patients in health IT initiatives. Additionally, five important considerations were identified when conducting future work with the Group Priority Sort technique and are outlined in this paper.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".