Valuing Citizen Access to Digital Health Services: Applied Value-Based Outcomes in the Canadian Context and Tools for Modernizing Health Systems
Bibliographic record
Abstract
BACKGROUND: In publicly funded health systems, digital health technologies are strategies that aim to improve the quality and safety of health care service delivery and enhance patient experiences and outcomes. In Canada, governments and health organizations have invested in digital health technologies such as personal health records (PHRs) and other electronic service functionalities and innovation across provincial and territorial health systems. OBJECTIVE: Patients' access to their own information via secure, Web-based PHRs and integrated virtual care services are promising mechanisms for supporting patient engagement in health care. We draw on current evidence to develop an economic model that estimates the demonstrated and potential value of these digital health initiatives. METHODS: We first synthesized results from a variety of Canadian and international studies on the outcomes for patients and service providers associated with PHRs across a continuum of services, ranging from viewing information (eg, laboratory results) on the Web to electronic prescription renewal to email or video conferencing with care teams and providers. We then developed a quantitative model of estimated value, grounded in these demonstrated benefits and citizen use (2016-2017). In addition to estimating the costs saved from patient and system perspectives, we used a novel application of a compensating differential approach to assess the value (independent of costs) to society of improved health and well-being resulting from PHR use. RESULTS: Patients' access to a range of digital PHR functions generated value for Canadians and health systems by increasing health system productivity, and improving access to and quality of health care provided. As opportunities increased to interact and engage with health care providers via PHR functions, the marginal value generated by utilization of PHR functionalities also increased. Web-based prescription renewal generated the largest share of the total current value from the patient perspective. From the health systems perspective, Canadians' ability to view their information on the Web was the largest value share. If PHRs were to be implemented with more integrated virtual care services, the value generated from populations with chronic illnesses such as severe and persistent mental illness and diabetes could amount to between Can $800 million and Can $1 billion per year across Canadian health systems. CONCLUSIONS: PHRs with higher interactivity could yield substantial potential value from wider implementation in Canada and increased adoption rates in certain target groups-namely, high-frequency health system users and their caregivers. Further research is needed to tie PHR use to health outcomes across PHR functions, care settings, and patient populations.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".