A Smartphone App With a Digital Care Pathway for Patients Undergoing Spine Surgery: Development and Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: There is a great unmet clinical need to provide patients undergoing spinal surgery and their caregivers with ongoing, high-quality care before and after surgery in an efficiency-focused health care environment. OBJECTIVE: The objective of this study is to design, develop, and evaluate the acceptability and feasibility of a novel planning-, outcomes-, and analytics-based smartphone app called ManageMySurgery (MMS) in patients undergoing elective spine surgery (MMS-Spine). METHODS: The development process of the MMS app was conducted over 2 sequential stages: (1) an evidence-based intervention design with refinement from surgeon and patient feedback and (2) feasibility testing in a clinical pilot study. We developed a novel, mobile-based, Health Insurance Portability and Accountability Act-compliant platform for interventional and surgical procedures. It is a patient-centric mobile health app that streamlines patients' interactions with their care team. MMS divides the patient journey into phases, making it feasible to provide customized care pathways that meet patients' unique needs. Patient-reported outcomes are easily collected and conform to the National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS) standard. RESULTS: We tested the feasibility of the MMS-Spine app with patients undergoing elective spine surgery at a large academic health system. A total of 47 patients undergoing elective spine surgery (26 cervical spine and 21 lumbar spine surgeries) downloaded and used MMS-Spine to navigate their surgical journey, quantify their baseline characteristics and postoperative outcomes, and provide feedback on the utility of the app in preparing for and recovering from their spinal surgery. The median age was 59.0 (range 33-77) years, 22 of the 47 patients (47%) were women, and 26 patients (55%) had commercial insurance. Of the 47 patients, a total of 33 (70%) logged in on an iOS device, 11 (23%) on an Android device, and 3 (6%) on a computer or tablet. A total of 17 of the 47 patients (36%) added a caregiver, of which 7 (41%) logged in. The median number of sign-ins was 2. A total of 38 of 47 patients (81%) completed their baseline preoperative PROMIS-29 outcomes, and 14 patients (30%) completed at least one PROMIS-29 survey during the postoperative period. Of the 24 patients who completed the MMS survey, 21 (88%) said it was helpful during preparation for their procedure, 16 (67%) said it was helpful during the postoperative period, and 23 (96%) said that they would recommend MMS to a friend or family member. CONCLUSIONS: We used a patient-centered approach based on proven behavior change techniques to develop a comprehensive smartphone app for patients undergoing elective spine surgery. The optimized version of the app is ready for formal testing in a larger randomized clinical study to establish its cost-effectiveness and effect on patients' self-management skills and long-term outcomes.
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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 | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».