TY  - JOUR
AU  - Bruce, Courtenay R
AU  - Harrison, Patricia
AU  - Nisar, Tariq
AU  - Giammattei, Charlie
AU  - Tan, Neema M
AU  - Bliven, Caitlin
AU  - Shallcross, Jamie
AU  - Khleif, Aroub
AU  - Tran, Nhan
AU  - Kelkar, Sayali
AU  - Tobias, Noreen
AU  - Chavez, Ana E
AU  - Rivera, Dana
AU  - Leong, Angela
AU  - Romano, Angela
AU  - Desai, S Nicholas
AU  - Sol, Josh R
AU  - Gutierrez, Kayla
AU  - Rappel, Christopher
AU  - Haas, Eric
AU  - Zheng, Feibi
AU  - Park, Kwan J
AU  - Jones, Stephen
AU  - Barach, Paul
AU  - Schwartz, Roberta
PY  - 2020
DA  - 2020/6/26
TI  - Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes: Retrospective Observational Cohort Study
JO  - JMIR Mhealth Uhealth
SP  - e19333
VL  - 8
IS  - 6
KW  - mHealth
KW  - patient-centered care
KW  - patient satisfaction
KW  - length of stay
KW  - patient activation
KW  - patient empowerment
KW  - patient engagement
KW  - patient involvement
KW  - hospital stay
KW  - communication programs
AB  - Background: Despite the growth of and media hype about mobile health (mHealth), there is a paucity of literature supporting the effectiveness of widespread implementation of mHealth technologies. Objective: This study aimed to assess whether an innovative mHealth technology system with several overlapping purposes can impact (1) clinical outcomes (ie, readmission rates, revisit rates, and length of stay) and (2) patient-centered care outcomes (ie, patient engagement, patient experience, and patient satisfaction). Methods: We compared all patients (2059 patients) of participating orthopedic surgeons using mHealth technology with all patients of nonparticipating orthopedic surgeons (2554 patients). The analyses included Wilcoxon rank-sum tests, Kruskal-Wallis tests for continuous variables, and chi-square tests for categorical variables. Logistic regression models were performed on categorical outcomes and a gamma-distributed model for continuous variables. All models were adjusted for patient demographics and comorbidities. Results: The inpatient readmission rates for the nonparticipating group when compared with the participating group were higher and demonstrated higher odds ratios (ORs) for 30-day inpatient readmissions (nonparticipating group 106/2636, 4.02% and participating group 54/2048, 2.64%; OR 1.48, 95% CI 1.03 to 2.13; P=.04), 60-day inpatient readmissions (nonparticipating group 194/2636, 7.36% and participating group 85/2048, 4.15%; OR 1.79, 95% CI 1.32 to 2.39; P<.001), and 90-day inpatient readmissions (nonparticipating group 261/2636, 9.90% and participating group 115/2048, 5.62%; OR 1.81, 95% CI 1.40 to 2.34; P<.001). The length of stay for the nonparticipating cohort was longer at 1.90 days, whereas the length of stay for the participating cohort was 1.50 days (mean 1.87, SD 2 vs mean 1.50, SD 1.37; P<.001). Patients treated by participating surgeons received and read text messages using mHealth 83% of the time and read emails 84% of the time. Patients responded to 60% of the text messages and 53% of the email surveys. Patients were least responsive to digital monitoring questions when the hospital asked them to do something, and they were most engaged with emails that did not require action, including informational content. A total of 96% (558/580) of patients indicated high satisfaction with using mHealth technology to support their care. Only 0.40% (75/2059) patients opted-out of the mHealth technology program after enrollment. Conclusions: A novel, multicomponent, pathway-driven, patient-facing mHealth technology can positively impact patient outcomes and patient-reported experiences. These technologies can empower patients to play a more active and meaningful role in improving their outcomes. There is a deep need, however, for a better understanding of the interactions between patients, technology, and health care providers. Future research is needed to (1) help identify, address, and improve technology usability and effectiveness; (2) understand patient and provider attributes that support adoption, uptake, and sustainability; and (3) understand the factors that contribute to barriers of technology adoption and how best to overcome them. 
SN  - 2291-5222
UR  - http://mhealth.jmir.org/2020/6/e19333/
UR  - https://doi.org/10.2196/19333
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32589161
DO  - 10.2196/19333
ID  - info:doi/10.2196/19333
ER  -