A Computerized Pharmacy Decision Support System (PDSS) for Headache Management: Observational Pilot Study
Bibliographic record
Abstract
BACKGROUND: Headaches are common and often lead patients to seek advice from a pharmacist and consequently self-medicate for relief. Computerized pharmacy decision support systems (PDSSs) may be a valuable resource for health care professionals, particularly for community pharmacists when counseling patients with headache, to guide treatment with over-the-counter medications and recognize patients who require urgent or specialist care. OBJECTIVE: This observational pilot study aimed to evaluate a newly developed PDSS web app for the management of patients seeking advice from a pharmacy for headache. This study examined the use of the PDSS web app and if it had an impact on patient or pharmacy personnel counseling, pharmacy personnel perception, and patient perception. METHODS: The PDSS web app was developed according to Francophone des Sciences Pharmaceutiques Officinales (SFSPO) recommendations for headache management, and was made available to pharmacies in 2 regions of France: Hauts de France and New Aquitaine. Pharmacy personnel received 2 hours of training before using the PDSS web app. All people who visited the pharmacies for headache between June 29, 2020, and December 31, 2020, were offered an interview based on the PDSS web app and given information about the next steps in the management of headaches and advice on the proper use of their medication. Patients and pharmacy personnel reported satisfaction with the PDSS web app following consultations or during a follow-up period (January 18 to 25, 2021). RESULTS: Of the 44 pharmacies that received the PDSS web app, 38 pharmacies representing 179 pharmacy personnel used the PDSS web app, and 435 people visited these pharmacies for headache during the study period. Of these, 70.0% (305/435) asked for immediate over-the-counter analgesics for themselves and consulted with pharmacy personnel with the use of the PDSS web app. The majority of these patients were given advice and analgesics for self-medication (346/435, 79.5%); however, 17.0% (74/435) were given analgesics and referred to urgent medical services, and 3.5% (15/435) were given analgesics and referred to their general practitioner. All pharmacy personnel (n=45) were satisfied or very satisfied with the use of the PDSS web app, and a majority thought it improved the quality of their care (41/44, 93.2%). Most pharmacy personnel felt that the PDSS web app modified their approach to management of headache (29/45, 64.4%). Most patients were very satisfied with the PDSS web app during their consultation (96/119, 80.7%), and all felt mostly or completely reassured. CONCLUSIONS: Use of the PDSS web app for the management of patients with headache improved the perceived quality of care for pharmacy personnel and patients. The PDSS web app was well accepted and effectively identified patients who required specialist medical management. Further studies should identify additional "red flags" for more effective screening and management of patients via the PDSS web app. Larger studies can measure the impact of the PDSS web app on the lives of patients and how safe or appropriate pharmacy personnel recommendations are.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".