Barriers and Enablers to Integrating Patient-Generated Health Data in Shared Decision-Making from Health Care Professional and Patient Perspectives: Scoping Review (Preprint)
Notice bibliographique
Résumé
Background: Advances in sensor technologies and increased adoption of wearables and smartphones by individuals have led to an abundance of patient-generated health data (PGHD). This data, when used effectively, could help to further augment the process of shared decision-making (SDM) to enable patient-centered care. However, the possible integration and usage of PGHD introduces complexities and challenges, which warrant considering both health care professional (HCP) and patient perspectives. Objective: Summarize the relevant works from the past 10 years that reflect the perspectives of both HCPs and patients as key stakeholders on potential barriers and enablers to the integration of PGHD for SDM. We analyzed both perspectives to surface key challenges and opportunities with integrating PGHD throughout patient journeys, as well as clinical workflows. Methods: Electronic searches were done using 3 databases: PubMed, ACM Digital Library, and IEEE Xplore for papers published between March 2013 and March 2023. Enablers and barriers mentioned by the stakeholders in included papers were extracted and analyzed using thematic analysis. An existing six-stage workflow model for integrating PGHD was used as a reference for deductive coding. Subsequently, considering barriers and enablers faced by both the HCPs and patients uncovered various tensions and alignments of perspectives, which could be addressed in future work and can inform concepts, designs, and development in PGHD for SDM. Results: A total of 53 publications were included in the scoping review. Six main overarching themes for barriers and enablers were identified: (1) patient-provider relationship, (2) patient characteristics, (3) organizational factors, (4) medical ethics and law, (5) data-driven workflow, and (6) design and technology. The 6-stage workflow was further expanded based on the new findings to include 4 additional stages, which include contextual considerations outside of traditional clinical environments. In addition to partially corroborating previously established barriers in the 6-stage workflow model, several new barriers and enablers were identified throughout all stages. This model helps to further align the needs of HCPs and patients beyond the clinical setting and could benefit system designers who plan to integrate PGHD for SDM. Conclusions: This scoping review demonstrates that there are several factors to consider for effectively integrating PGHD into health-related SDM. Notably, such factors extend outside the boundaries of traditional clinical settings. Although there is agreement between HCPs and patients on certain factors, there are also tensions to be addressed. Our augmented 10-stage workflow model offers system designers an overview of the challenges and enablers to consider while designing for PGHD integration in clinical workflows and patient journeys to improve SDM.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,186 | 0,411 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,007 |
| Bibliométrie | 0,009 | 0,012 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,015 | 0,011 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».