Barriers and Enablers to Integrating Patient-Generated Health Data in Shared Decision-Making from Health Care Professional and Patient Perspectives: Scoping Review (Preprint)
Bibliographic record
Abstract
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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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 | 0.186 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".