12031 Background: An augmented intelligence (AI) tool using a machine learning algorithm was developed and validated to generate insights into risk for short-term mortality among patients with cancer. The algorithm, which scores patients every week as being at low, medium or high risk for death within 30 days, allowing providers to potentially intervene and modify care of those at medium to high risk based on established practice pathways. Deployment of the algorithm increased palliative care referrals in a large community hematology/oncology practice in the United States (Gajra et al, JCO 2020). The objective of this retrospective analysis was to evaluate the differences in survival and healthcare utilization (HCU) outcomes of patients previously scored as medium or high risk by the AI tool. Methods: Between 6/2018 – 10/2019, the AI tool scored patients on a weekly basis at the hematology/oncology practice. In 9/2020, a chart review was conducted for the 886 patients who had been identified by the algorithm as being at medium or high risk for 30-day mortality during the index period, to determine outcomes (including death, emergency department [ED] visits, and hospital admissions). Data are presented using descriptive statistics. Results: Of the 886 at-risk patients, 450 (50.8%) were deceased at the time of follow-up. Of these, 244 (54.2%) died within the first 180 days of scoring as at-risk, with median time to death 68 days (IQR 99). Among the 255 patients scored as high risk, 171 (67.1%) had died, vs. 279 (44.2%) of the 631 patients who were scored as medium risk (p < 0.001). Of the 601 patients who were scored more than once during the index period as medium or high risk, 342 (56.9%) had died, vs. 108 (37.9%) of the 285 who were scored as at risk only once (p < 0.001). A total of 363 patients (43.1%) had at least 1 ED visit, and 346 patients (41.1%) had at least 1 hospital admission. There was no difference in the proportion of patients scored as high risk compared with those scored as medium risk in ED visits (104 of 237 [43.9%] vs. 259 of 605 [42.8%], p = 0.778) or hospital admissions (100 of 237 [42.2%] vs. 246 of 605 [40.7%], p = 0.684, respectively). Compared with patients scored as medium or high risk only once during the index period, patients who were scored as at-risk more than once had more ED visits (282 of 593 [47.6%] vs. 81 of 249 [32.5%], p < 0.001) and hospital admissions (269 of 593 [45.4%] vs. 77 of 249 [30.9%], p < 0.001). Conclusions: This follow-up study found that half of the patients identified as at-risk for short-term mortality during the index period were deceased, with greater likelihood associated with high risk score and being scored more than once. Over 40% had visited an ED or were admitted to hospital. These findings have important implications for the use of the algorithm to guide treatment discussions, prevent acute HCU and to plan ahead for end of life care in patients with cancer.
External Validation of the Simple Clinical Score and the HOTEL Score, Two Scores for Predicting Short-Term Mortality after Admission to an Acute Medical Unit