Using Beta-Version mHealth Technology for Team-Based Care Management to Support Stroke Prevention: An Assessment of Utility and Challenges
Bibliographic record
Abstract
BACKGROUND: Beta versions of health information technology tools are needed in service delivery models with health care and community partnerships to confirm the key components and to assess the performance of the tools and their impact on users. We developed a care management technology (CMT) for use by community health workers (CHWs) and care managers (CMs) working collaboratively to improve risk factor control among recent stroke survivors. The CMT was expected to enhance the efficiency and effectiveness of the CHW-CM team. OBJECTIVE: The primary objective was to describe the Secondary Stroke Prevention by Uniting Community and Chronic Care Model Teams Early to End Disparities (SUCCEED) CMT and investigate CM and CHW perceptions of the CMT's usefulness and challenges for team-based care management. METHODS: We conducted qualitative interviews with all users of the beta-version SUCCEED CMT, namely two CMs and three CHWs. They were asked to demonstrate and describe their perceptions of the CMT's ease of use and usefulness for completing predefined key care management activities. They were also probed about their general perceptions of the CMT's information quality, ease of use, usefulness, and impact on CM and CHW roles. Interview transcripts were coded using a priori codes. Coded excerpts were grouped into broader themes and then related in a conceptual model of how the CMT facilitated care management. We also conducted a survey with 14 patients to obtain their perspective on CHW tablet use during CHW-patient interactions. RESULTS: Care managers and community health workers expressed that the CMT helped them keep track of patient interactions and plan their work. It guided CMs in developing and sharing care plans with CHWs. For CHWs, the CMT enabled electronic collection of clinical assessment data, provided decision support, and provided remote access to patients' risk factor values. Long loading times and downtimes due to outages were the most significant challenges encountered. Additional issues included extensive use of free-text responses and manual data transfer from the electronic medical record. Despite these challenges, patients overall did not perceive the tablet as interfering with CHW-patient interactions. CONCLUSIONS: Our findings suggest useful functionalities of CMTs supporting health care and community partners in collaborative chronic care management. However, usability issues need to be addressed during the development process. The SUCCEED CMT is an initial step toward the development of effective health information technology tools to support collaborative, team-based models of care and will need to be modified as the evidence base grows. Future research should assess the CMT's effects on team performance.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".