Name
Abstract | ||
|---|---|---|
We present Sensi-steps, an application using patient-generated data (PGD) to prevent falls for geriatric and especially poststroke patients. The Sensi-steps tool incorporates a wearable wrist device, pedometer, pressure and proximity sensors, and tablet. PGD collection occurs through Timed Up and Go (TUG) tests and collection of physiological data, which is integrated into the EHR. Fall risk factor active tracking encourages new ways of shared decision-making between patients, caregivers, and practitioners. PGD will be managed at the primary care nurse or Care Manager level (see 3-tier PGD service proposal), presenting a novel way to incorporate PGD into clinical decision-support systems. We expect our solution to be easier to use routinely by the patient at home than other fall risk tracking solutions. Sensi-steps has the potential to improve patient care, help patients make informed decisions, and help clinicians understand patient-generated, environmental, and lifestyle information to deliver personalized, preventative healthcare. |
Year | Venue | Field |
|---|---|---|
2017 | AMIA | Stroke,Patient Generated Data,Physical medicine and rehabilitation,Medicine |
DocType | Volume | Citations |
Conference | 2017 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angela Smith | 1 | 8 | 2.54 |
| Ada Ng | 2 | 0 | 1.35 |
| Eleanor R. Burgess | 3 | 0 | 3.04 |
| Jennifer A Pacheco | 4 | 181 | 23.88 |
| Noah D. Weingarten | 5 | 0 | 0.34 |