TY - JOUR
T1 - Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes
T2 - Retrospective Observational Cohort Study
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
N1 - ©Courtenay R Bruce, Patricia Harrison, Tariq Nisar, Charlie Giammattei, Neema M Tan, Caitlin Bliven, Jamie Shallcross, Aroub Khleif, Nhan Tran, Sayali Kelkar, Noreen Tobias, Ana E Chavez, Dana Rivera, Angela Leong, Angela Romano, S Nicholas Desai, Josh R Sol, Kayla Gutierrez, Christopher Rappel, Eric Haas, Feibi Zheng, Kwan J Park, Stephen Jones, Paul Barach, Roberta Schwartz. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 26.06.2020.
PY - 2020/6/26
Y1 - 2020/6/26
N2 - 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.
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.
KW - communication programs
KW - hospital stay
KW - length of stay
KW - mHealth
KW - patient activation
KW - patient empowerment
KW - patient engagement
KW - patient involvement
KW - patient satisfaction
KW - patient-centered care
KW - Humans
KW - Male
KW - Biomedical Technology
KW - Telemedicine
KW - Technology
KW - Female
KW - Aged
KW - Retrospective Studies
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U2 - 10.2196/19333
DO - 10.2196/19333
M3 - Article
C2 - 32589161
AN - SCOPUS:85085310649
SN - 2291-5222
VL - 8
SP - e19333
JO - JMIR mHealth and uHealth
JF - JMIR mHealth and uHealth
IS - 6
M1 - e19333
ER -