Preventing clinical deterioration in cancer outpatients: Human centered design of a predictive model and response system.

e13567 Background: Patients (pts) with cancer in the outpatient setting are at a high-risk for adverse events, such as unplanned hospitalizations and ER visits. A recent study found that up to 30% of hospital admissions were preventable. Identifying pts at risk of avoidable clinical deterioration remains a challenge, as clinicians may not be aware of pts’ experiences at home. The growing use of health IT presents an opportunity to identify and respond to clinical deterioration in patients before an adverse event occurs. In this study, we describe a human-centered design (HCD) process used to develop a clinical deterioration risk prediction system to improve the detection of and response to deterioration in cancer outpatients. Methods: Predictive model: We enrolled eligible cancer pts and collected data from each one including: FitBit, geolocation, EHR, and weekly patient-reported outcome measures (PROMs). Pts and their family caregivers could also report non-routine events (NREs), defined as any deviation from expected optimal care. We also captured unplanned treatment events (UTEs), a clinically meaningful change in the pt’s treatment course or care pathway. We developed a predictive model that generates a pt’s 7-day risk of clinical deterioration. Response system: We are developing a risk communication system (RCS) to communicate predicted risk scores to clinical teams. Using a HCD process, we first conducted 36 observations across 100 patient encounters to understand the environment of use. Next, we conducted 18 clinician interviews to define user needs. We have conducted 7 multi-disciplinary design sessions to iteratively develop prototypes of the RCS. We are currently conducting formative usability testing to assess the prototype and gather clinician feedback. Results: Predictive model: We have enrolled 36 cancer outpatients (24 head & neck, 9 gastrointestinal, and 3 lung). Pts completed a total of 219 weekly PROM surveys, reported 107 NREs and experienced 18 UTEs (e.g., infection). So far, models using EHR and PROM data are the most sensitive and precise (AUC: 0.983; 0.999). More patient data are required to develop higher quality stable models. Response system: We identified key design elements to include in the RCS, such as the caregiver’s phone number and the pt’s weight over time. Preliminary findings demonstrate high usability of the prototype RCS. Oncologists identified opportunities for the system to better support team communication and coordination, and to improve the identification and response to clinical deterioration in cancer outpatients. Conclusions: We have developed and tested a clinical deterioration risk prediction system for cancer outpatients. Future studies will implement the response system and evaluate its impact on clinical care.