BACKGROUND: Electronic health information platforms have the potential to support standardized care delivery models, engage patients, and deliver patient self-assessment tools. STUDY DESIGN: We tested whether an e-health platform could support the delivery and acquisition of patient-reported outcomes (PROs) during hospitalization after cardiac surgery. Secondarily, we tested if patient reported data were predictive of resource use (length of stay) or outcomes (discharge disposition). Subjects were 149 cardiac surgical patients, over age 50 years, undergoing routine surgery, with an expected length of stay of 5 to 7 days. While hospitalized, patients were provided with iPads (Apple), which delivered a personalized care plan. That plan included daily patient "To Do" lists with self-assessment modules that included recovery-related patient reported outcomes. Those included an early screen for discharge planning (ESDP) as well as daily pain and mobility self-assessments using the visual analog pain scale and the I-MOVE mobility scale. We measured completion rates for the self assessments, determined length of stay (short, intermediate, or long) and discharge disposition (home independently or other), and evaluated whether patient self-assessments were predictive of these outcomes. RESULTS: Patients completed 98% of the 1,418 self-assessments that were delivered. The ESDP and mobility self-assessments were predictive of length of hospital stay (p = 0.004 and p = 0.012, respectively) and of discharge disposition (p < 0.0001 and p < 0.007, respectively). Lower ESDP scores and higher I-MOVE scores were predictive of shorter lengths of stay and a higher likelihood of discharge to home independently. CONCLUSIONS: Our trial demonstrated that an e-health platform, combined with mobile computing, can effectively deliver customized care plans with which patients can interact. Furthermore, patient self-reported data are predictive of length of stay and discharge disposition.
BACKGROUND: Electronic health information platforms have the potential to support standardized care delivery models, engage patients, and deliver patient self-assessment tools. STUDY DESIGN: We tested whether an e-health platform could support the delivery and acquisition of patient-reported outcomes (PROs) during hospitalization after cardiac surgery. Secondarily, we tested if patient reported data were predictive of resource use (length of stay) or outcomes (discharge disposition). Subjects were 149 cardiac surgical patients, over age 50 years, undergoing routine surgery, with an expected length of stay of 5 to 7 days. While hospitalized, patients were provided with iPads (Apple), which delivered a personalized care plan. That plan included daily patient "To Do" lists with self-assessment modules that included recovery-related patient reported outcomes. Those included an early screen for discharge planning (ESDP) as well as daily pain and mobility self-assessments using the visual analog pain scale and the I-MOVE mobility scale. We measured completion rates for the self assessments, determined length of stay (short, intermediate, or long) and discharge disposition (home independently or other), and evaluated whether patient self-assessments were predictive of these outcomes. RESULTS:Patients completed 98% of the 1,418 self-assessments that were delivered. The ESDP and mobility self-assessments were predictive of length of hospital stay (p = 0.004 and p = 0.012, respectively) and of discharge disposition (p < 0.0001 and p < 0.007, respectively). Lower ESDP scores and higher I-MOVE scores were predictive of shorter lengths of stay and a higher likelihood of discharge to home independently. CONCLUSIONS: Our trial demonstrated that an e-health platform, combined with mobile computing, can effectively deliver customized care plans with which patients can interact. Furthermore, patient self-reported data are predictive of length of stay and discharge disposition.
Keywords:
ESDP; HHC; LOS; PRO; SNF; early screen for discharge planning; home health care; length of stay; patient-reported outcomes; skilled nursing facility
Authors: Renee A Cowan; Rudy S Suidan; Vaagn Andikyan; Youssef A Rezk; M Heather Einstein; Kaity Chang; Jeanne Carter; Oliver Zivanovic; Elizabeth J Jewell; Nadeem R Abu-Rustum; Ethan Basch; Dennis S Chi Journal: Gynecol Oncol Date: 2016-09-13 Impact factor: 5.482
Authors: David J Cook; Anilga Moradkhani; Kristin S Vickers Douglas; Sharon K Prinsen; Erin N Fischer; Darrell R Schroeder Journal: Telemed J E Health Date: 2014-01-20 Impact factor: 3.536
Authors: Juan Mata; Nicolò Pecorelli; Pepa Kaneva; Dan Moldoveanu; Alexandre Gosselin-Tardiff; Mohsen Alhashemi; Stephan Robitaille; Saba Balvardi; Lawrence Lee; Barry L Stein; Sender Liberman; Patrick Charlebois; Julio F Fiore; Liane S Feldman Journal: Surg Endosc Date: 2019-05-13 Impact factor: 4.584