Personalized Digital Care Pathways Enable Enhanced Patient Management as Perceived by Health Care Professionals: Mixed-Methods Study
Bibliographic record
Abstract
Background: Clinical decision support systems are known to improve adherence to clinical practice guidelines and patient outcomes by providing clinicians with timely, accurate, and appropriate knowledge. Objective: This study investigates the perceived usefulness and practical implementation of UpHill Route v3, a personalized digital care pathway (PDCP) system, in enhancing clinical decision-making and patient management across various clinical settings. Methods: A mixed-methods retrospective study was conducted among medical doctors and nurses from four National Health System-Local Health Units in Portugal. Data were collected from May 2023 to April 2024. The primary data source was an anonymous questionnaire assessing health care professionals' perceptions of UpHill Route v3's usefulness using the Likert scale ranging from 0 (do not agree) to 10 (totally agree). Secondary analysis involved quantifying decisions across heart failure, multimorbidity, diabetes, and colorectal and breast cancer clinical pathways. These data were collected from user interactions with UpHill Route v3 as well as from its internal database. Descriptive and bivariate statistics were used to analyze the data. Results: A total of 22 health care professionals with mean age 44.7 (SD 10.6) years, including 15 (68%) female participants and 9 (41%) physicians were included in the study. High ratings for adherence to clinical protocols, mean score 8.06 (SD 1.73); clinical decision support, mean score 8.05 (SD 1.73); patient care improvement, mean score 7.63 (SD 2.22); and confidence in patient management, mean score 8.26 (SD 1.56) were reported. Secondary analysis showed that across 3574 patients, 25,741 clinical decisions were informed, and 9254 actions were performed with the assistance of the PDCP tool. Conclusions: The UpHill Route v3 PDCP tool is highly valued by health care professionals for its ability to support clinical decision-making and improve operational efficiency across various clinical settings. Our findings suggest that this tool can effectively bridge the gap between clinical guidelines and real-world practice.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".