Utility, Value, and Benefits of Contemporary Personal Health Records: Integrative Review and Conceptual Synthesis
Bibliographic record
Abstract
BACKGROUND: Contemporary personal health record (PHR) technologies offer a useful platform for individuals to maintain a lifelong record of personally reported and clinically sourced data from various points of medical care. OBJECTIVE: This paper presents an integrative review and synthesis of the extant literature on PHRs. This review draws upon multiple lenses of analysis and deliberates value perspectives of PHRs at the product, consumer, and industry levels. METHODS: Academic databases were searched using multiple keywords related to PHRs for the years 2001-2020. Three research questions were formulated and used as selection criteria in our review of the extant literature relevant to our study. RESULTS: We offer a high-level functional utility model of PHR features and functions. We also conceptualize a consumer value framework of PHRs, highlighting the applications of these technologies across various health care delivery activities. Finally, we provide a summary of the benefits of PHRs for various health care constituents, including consumers, providers, payors, and public health agencies. CONCLUSIONS: PHR products offer a myriad of content-, connectivity-, and collaboration-based features and functions for their users. Although consumers benefit from the tools provided by PHR technologies, their overall value extends across the constituents of the health care delivery chain. Despite advances in technology, our literature review identifies a shortfall in the research addressing consumer value enabled by PHR tools. In addition to scholars and researchers, our literature review and proposed framework may be especially helpful for value analysis committees in the health care sector that are commissioned for the appraisal of innovative health information technologies such as PHRs.
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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 | 0.041 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".