The Problem: Surgical patients face uncertainty in recovering from their operation. On one end of the spectrum, this uncertainty may lead to unnecessary emergency room or urgent care visits. At the other end of the spectrum, patient uncertainty may result in potentially life-threatening delays in care. Along the entire spectrum, there is avoidable angst and opportunities to meaningfully improve quality of care and patient experience.
Our Approach: We have built a platform that includes a mobile application for patients, a web-based dashboard for providers to monitor patients, and a database to learn from patterns of collected data.
During hospital discharge following surgery, we provide patients with a smartphone loaded with our app. The app is linked to a wearable device that tracks patients’ biometrics—step activity, heart rate, and sleep. Additionally, the app prompts patients with customizable symptom questionnaires and patient-recorded outcome measures. Biometric readings and questionnaire responses out of range, or non-responses, prompt alerts on the provider dashboard. Alerts can also be directly routed to responsible providers via text message, email, or app push notification. Patients deemed at-risk are contacted via the smartphone to assess their clinical status and need for further evaluation.
Our Aims: By discovering and potentially intervening earlier on deteriorating patients, we hope to stem the severity and duration of post-discharge surgical complications. Similarly, by granting patients more ready access to a provider with knowledge of their care, we aim to decrease unwarranted post-operative ER, urgent care, and unscheduled clinic visits.
Archived data linked to respective clinical data will be subjected to machine learning techniques. Refined pattern recognition will yield more accurate and rapid risk identification. The database will also serve as a repository that can be used for both clinical research and evaluation of patient experience.
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