Service Quality Assessment of Digital Health Solutions in Outpatient Care: Qualitative Item Repository Development Study
Bibliographic record
Abstract
BACKGROUND: The integration of digital health solutions (DHSs) into health care systems has the potential to significantly enhance service delivery and health outcomes. Despite their benefits, the adoption remains slow, especially in outpatient care, and is hindered by various barriers, such as unclear effectiveness and high costs. OBJECTIVE: This study aimed to address the uncertainties regarding the cost-benefit ratio of DHSs by developing a comprehensive instrument to evaluate their impact on health care service quality across diverse settings (eg, across different diseases or types of DHSs). METHODS: We conducted a multistaged rapid review and semistructured, qualitative interviews to identify and adapt existing instruments evaluating the effects of DHSs. The first rapid review screened 4957 records and included 40 relevant papers to identify instruments currently used for DHS assessment after their deployment, yielding a total of 126 reported outcomes. Subsequently, we conducted interviews with 19 health care practitioners across 4 countries to validate and refine the 7 health care service quality dimensions derived from merging the Outpatient Experience Questionnaire (OPEQ), selected after the first rapid review, and Health Care Service Quality (HEALTHQUAL), an established instrument for measuring health care service quality derived from gray literature. On the basis of the results of the interviews, a second rapid review with 35 papers out of 493 screened records was conducted to identify instruments used to measure patient satisfaction, yielding a total of 29 patient satisfaction instruments. RESULTS: From the first rapid review, OPEQ was selected out of 18 relevant instruments identified among the 126 reported outcomes and combined with HEALTHQUAL. The interviews with health care professionals confirmed the relevance of all 7 health care service quality dimensions derived from OPEQ and HEALTHQUAL. In addition, 4 interviewees mentioned patient satisfaction as a further dimension missing in the framework presented during the interviews. From the subsequent rapid review, the Patient Satisfaction Questionnaire-Short Form was selected out of 6 relevant instruments identified among the 29 identified patient satisfaction instruments. By combining HEALTHQUAL, OPEQ, and Patient Satisfaction Questionnaire-Short Form, we derived the Digital Healthcare Service Quality (DigiHEALTHQUAL) questionnaire, which consists of 51 items across 8 dimensions, including accessibility, efficiency, empathy, general satisfaction, degree of improvements of care services, information, safety, and tangibles. CONCLUSIONS: The DigiHEALTHQUAL questionnaire aims to provide a standardized approach for assessing the impact of DHSs on health care service quality across various use cases, therapeutic areas, and perspectives, facilitating comparison between DHSs and supporting decision makers in resource allocation and implementation decisions. Future research will focus on validating the DigiHEALTHQUAL in real-life settings and further refining it to comprehensively encompass both patient and health care practitioner perspectives.
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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 | 0.058 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".