Decreasing the cost of care while improving the quality of care through digitally enabled technology is a majoraim of the digital health movement. Within the area of orthopedics, improving patient outcomes by focusing onupstream patient optimization has been shown to decrease complication rates. Further, monitoring qualitythrough the collection of Patient Reported Outcomes (PROs) has become a cornerstone of the move towardsvalue purchasing by payers. However, collecting PROs either at the POC or at home has been quite challenging asfew patients have the desire to answer numerous questions.In this study we aim to address both opportunities by collecting data both passively and actively through a sensorworn by patients before and after surgery and by using apps to support patient pre-operative rehabilitation andpostoperative recovery. To this data set we couple our existing PRO data collection database collected by theUCSF department of orthopedics. These three data sets (1) PRO data collected actively, 2) our passively collectedsensor data and 3) data collected through iPhone apps) will provide insight in two particular areas of interest: A)what impact on post-operative outcome does a course of pre-habilitation have on patient’s activity level aftersurgery, and B) how accurately does passively collected sensor data correlate with PRO scores? Our hypothesis isthat patients undergoing pre-habilitation would register a lower Length of Stay and higher overall satisfactionwith their hospital stay than non-pre-habilitated patients. In the context of bundled payments, such a correlationcould be easily turned into a risk mitigation tool for hospitals and providers keen on improving quality andlowering costs. Further, if it can be shown that a combination of data points gleaned passively through sensors orwearable devices would correlate with and be predictive of PROs, there would be a large market opportunity forimplant manufacturers, providers and insurers alike. For example, sensors build into implants could passivelycollect outcomes data that currently patients are unlikely to provide, and wearable devices could be engineeredto specifically focus on data collection and feedback loops to help patients recover quicker or identify patientswho need greater attention. There are many more potential applications.
Principal Investigator: Stefano Bini, MD
Trainees: Jeffrey Mulvihill, MD, Eli Kamara, MD, Ilya Bendich, MD,MBA, Kaitlin Vitek, Justin Krogue, MD, Kurt Yusi, MD, Austin Pitcher, MD, Kevin Choo, MD, Paul Yi,MD, Kevin Hwang, MD, Krishn Khanna, Joseph Patterson, MD