Erin Sarzynski1,2, Brian Decker3, Aaron Thul3, David Weismantel1,4, Ronald Melaragni5, Elizabeth Cholakis6, Megha Tewari1,6, Kristy Beckholt1,6, Michael Zaroukian6,7, Angie C Kennedy8, Charles Given1,2. 1. 1 Department of Family Medicine, College of Human Medicine, Michigan State University , East Lansing, Michigan. 2. 2 Institute for Health Policy, College of Human Medicine, Michigan State University , East Lansing, Michigan. 3. 3 Electronic Medical Office Logistics , LLC, Royal Oak, Michigan. 4. 4 Office of the University Physician, Michigan State University , East Lansing, Michigan. 5. 5 Sparrow Pharmacy Plus , Sparrow Health System, Lansing, Michigan. 6. 6 Sparrow Health System , Lansing, Michigan. 7. 7 Department of Medicine, College of Human Medicine, Michigan State University , East Lansing, Michigan. 8. 8 School of Social Work, Michigan State University , East Lansing, Michigan.
Abstract
BACKGROUND: We developed and beta-tested a patient-centered medication management application, PresRx optical character recognition (OCR), a mobile health (m-health) tool that auto-populates drug name and dosing instructions directly from patients' medication labels by OCR. MATERIALS AND METHODS: We employed a single-subject design study to evaluate PresRx OCR for three outcomes: (1) accuracy of auto-populated medication dosing instructions, (2) acceptability of the user interface, and (3) patients' adherence to chronic medications. RESULTS: Eight patients beta-tested PresRx OCR. Five patients used the software for ≥6 months, and four completed exit interviews (n = 4 completers). At baseline, patients used 3.4 chronic prescription medications and exhibited moderate-to-high adherence rates. Accuracy of auto-populated information by OCR was 95% for drug name, 98% for dose, and 96% for frequency. Study completers rated PresRx OCR 74 on the System Usability Scale, where scores ≥70 indicate an acceptable user interface (scale 0-100). Adherence rates measured by PresRx OCR were high during the first month of app use (93%), but waned midway through the 6-month testing period (78%). Compared with pharmacy fill rates, PresRx OCR underestimated adherence among completers by 3%, while it overestimated adherence among noncompleters by 8%. DISCUSSION: Results suggest smartphone applications supporting medication management are feasible and accurately assess adherence compared with objective measures. Future efforts to improve medication-taking behavior using m-health tools should target specific patient populations and leverage common application programming interfaces to promote generalizability. CONCLUSIONS: Our medication management application PresRx OCR is innovative, acceptable for patient use, and accurately tracks medication adherence.
BACKGROUND: We developed and beta-tested a patient-centered medication management application, PresRx optical character recognition (OCR), a mobile health (m-health) tool that auto-populates drug name and dosing instructions directly from patients' medication labels by OCR. MATERIALS AND METHODS: We employed a single-subject design study to evaluate PresRx OCR for three outcomes: (1) accuracy of auto-populated medication dosing instructions, (2) acceptability of the user interface, and (3) patients' adherence to chronic medications. RESULTS: Eight patients beta-tested PresRx OCR. Five patients used the software for ≥6 months, and four completed exit interviews (n = 4 completers). At baseline, patients used 3.4 chronic prescription medications and exhibited moderate-to-high adherence rates. Accuracy of auto-populated information by OCR was 95% for drug name, 98% for dose, and 96% for frequency. Study completers rated PresRx OCR 74 on the System Usability Scale, where scores ≥70 indicate an acceptable user interface (scale 0-100). Adherence rates measured by PresRx OCR were high during the first month of app use (93%), but waned midway through the 6-month testing period (78%). Compared with pharmacy fill rates, PresRx OCR underestimated adherence among completers by 3%, while it overestimated adherence among noncompleters by 8%. DISCUSSION: Results suggest smartphone applications supporting medication management are feasible and accurately assess adherence compared with objective measures. Future efforts to improve medication-taking behavior using m-health tools should target specific patient populations and leverage common application programming interfaces to promote generalizability. CONCLUSIONS: Our medication management application PresRx OCR is innovative, acceptable for patient use, and accurately tracks medication adherence.
Entities:
Keywords:
adherence; health information technology; m-health; medication management; smartphone
Authors: Joel Nathan Fishbein; Lauren Ellen Nisotel; James John MacDonald; Nicole Amoyal Pensak; Jamie Michele Jacobs; Clare Flanagan; Kamal Jethwani; Joseph Andrew Greer Journal: JMIR Res Protoc Date: 2017-04-20
Authors: Eulalia Roig; Sonia Mirabet; Mar Gomis-Pastor; Jan T De Pourcq; Irene Conejo; Anna Feliu; Vicens Brossa; Laura Lopez; Andreu Ferrero-Gregori; Anna Barata; M Antonia Mangues Journal: JMIR Mhealth Uhealth Date: 2020-02-04 Impact factor: 4.773