The Effect of a Mobile Health App on Treatment Adherence and Revenue at Physical Health Clinics: Retrospective Record Review
Notice bibliographique
Résumé
BACKGROUND: A significant number of patients do not adhere to their prescribed course of physical therapy or discharge themselves from care. Adhering to prescribed physical therapy, including attending physical therapy clinic appointments, contributes to patients achieving the goals of therapy including reducing pain and increasing functionality. Web-based platforms have been demonstrated to be effective means for managing clinical patients with musculoskeletal pain, similar to managing them in person. Behavior change techniques introduced through digital or web-based platforms can reduce nonadherence with prescribed physical therapy and improve patient outcomes. Literature also indicates that a phone-based app provided to patients, which includes a reward-incentive gamification to complement their care, contributed to a greater number of kept appointments in a physical therapy clinic. OBJECTIVE: This study aims to compare the rate of provider discharge with self-discharge and the number of clinic visits among patients attending a physical health clinic who did and did not choose to adopt a phone-based app to complement their care. A secondary purpose was to compare the revenue generated by patients attending a physical health clinic who did and did not choose to adopt a phone-based app to complement their care. METHODS: A retrospective analysis of all new outpatient medical records (N=5328) from a multisite physical health practice was conducted between January 2018 and December 2019. Patients in the sample self-selected the 2018 Usual Care, the 2019 Usual Care, or the 2019 Kanvas App groups. Kanvas is a customized private practice app, designed for patient engagement with their specific health care provider. This app included a gamification system that provided rewards to the patient for attending their scheduled clinic appointments. According to their medical record, each patient was classified as completing their prescribed therapy (provider discharged) or not completing their prescribed therapy (self-discharged). Additionally, the total number of clinic visits each patient attended, the total charges for services, and the total payments received by the clinic per patient were extracted from each patient's medical record. RESULTS: Patients in the 2019 Kanvas App Group exhibited a higher rate of provider discharge compared to patients who did not adopt the app. This greater rate of provider discharges among the patients who adopted the Kanvas app likely contributed to this group attending more clinic visits (13.21, SD 12.09) than the other study groups who did not download the app (10.72, SD 9.80 to 11.35, SD 11.10). This greater number of clinic visits in turn contributed to the patients who adopted the app generating more clinic charges and payments. CONCLUSIONS: Future investigators need to employ more rigorous methods to confirm these findings, and clinicians need to weigh the anticipated benefits against the cost and staff involvement in managing the Kanvas app.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,010 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,009 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».