Service Quality Assessment of Digital Health Solutions in Outpatient Care: Qualitative Item Repository Development Study
Notice bibliographique
Résumé
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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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 | 0,058 | 0,096 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».