Health Care Worker Perspectives Inform Optimization of Patient Panel-Support Tools: A Qualitative Study
Why this work is in the frame
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Bibliographic record
Abstract
Electronic decision-support systems appear to enhance care, but improving both tools and work practices may optimize outcomes. Using qualitative methods, the authors' aim was to evaluate perspectives about using the Patient Panel-Support Tool (PST) to better understand health care workers' attitudes toward, and adoption and use of, a decision-support tool. In-depth interviews were conducted to elicit participant perspectives about the PST-an electronic tool implemented in 2006 at Kaiser Permanente Northwest. The PST identifies "care gaps" and recommendations in screening, medication use, risk-factor control, and immunizations for primary care panel patients. Primary care physician (PCP) teams were already grouped (based on performance pre- and post-PST introduction) into lower, improving, and higher percent-of-care-needs met. Participants were PCPs (n=21), medical assistants (n=11), and quality and other health care managers (n=20); total n=52. Results revealed that the most commonly cited benefit of the PST was increased in-depth knowledge of patient panels, and empowerment of staff to do quality improvement. Barriers to PST use included insufficient time, competing demands, suboptimal staffing, tool navigation, documentation, and data issues. Facilitators were strong team staff roles, leadership/training for tool implementation, and dedicated time for tool use. Higher performing PCPs and their assistants more often described a detailed team approach to using the PST. In conclusion, PCP teams and managers provided important perspectives that could help optimize use of panel-support tools to improve future outcomes. Improvements are needed in tool function and navigation; training; staff accountability and role clarification; and panel management time.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it