Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm

Abstract

ImportanceGoal-concordant care is an ongoing challenge in hospital settings. Identification of high mortality risk within 30 days may call attention to the need to have serious illness conversations, including the documentation of patient goals of care.ObjectiveTo examine goals of care discussions (GOCDs) in a community hospital setting with patients identified as having a high risk of mortality by a machine learning mortality prediction algorithm.Design, Setting, and ParticipantsThis cohort study took place at community hospitals within 1 health care system. Participants included adult patients with a high risk of 30-day mortality who were admitted to 1 of 4 hospitals between January 2 and July 15, 2021. Patient encounters of inpatients in the intervention hospital where physicians were notified of the computed high risk mortality score were compared with patient encounters of inpatients in 3 community hospitals without the intervention (ie, matched control).InterventionPhysicians of patients with a high risk of mortality within 30 days received notification and were encouraged to arrange for GOCDs.Main Outcomes and MeasuresThe primary outcome was the percentage change of documented GOCDs prior to discharge. Propensity-score matching was completed on a preintervention and postintervention period using age, sex, race, COVID-19 status, and machine learning-predicted mortality risk scores. A difference-in-difference analysis validated the results.ResultsOverall, 537 patients were included in this study with 201 in the preintervention period (94 in the intervention group; 104 in the control group) and 336 patients in the postintervention period. The intervention and control groups included 168 patients per group and were well-balanced in age (mean [SD], 79.3 [9.60] vs 79.6 [9.21] years; standardized mean difference [SMD], 0.03), sex (female, 85 [51%] vs 85 [51%]; SMD, 0), race (White patients, 145 [86%] vs 144 [86%]; SMD 0.006), and Charlson comorbidities (median [range], 8.00 [2.00-15.0] vs 9.00 [2.00 to 19.0]; SMD, 0.34). Patients in the intervention group from preintervention to postintervention period were associated with being 5 times more likely to have documented GOCDs (OR, 5.11 [95% CI, 1.93 to 13.42]; P = .001) by discharge compared with matched controls, and GOCD occurred significantly earlier in the hospitalization in the intervention patients as compared with matched controls (median, 4 [95% CI, 3 to 6] days vs 16 [95% CI, 15 to not applicable] days; P < .001). Similar findings were observed for Black patient and White patient subgroups.Conclusions and RelevanceIn this cohort study, patients whose physicians had knowledge of high-risk predictions from machine learning mortality algorithms were associated with being 5 times more likely to have documented GOCDs than matched controls. Additional external validation is needed to determine if similar interventions would be helpful at other institutions.

Clinician- and Patient-Directed Communication Strategies for Patients With Cancer at High Mortality Risk

“…Secondary outcomes were outpatient palliative care referral (which may be prompted by earlier SICs 45 ) and, among decedents, aggressive end-of-life care (composite of any 1 of the following: chemotherapy within 14 days before death, hospitalization within 30 days before death, or admission to hospice within 3 days before death). 46 eTable 5 in Supplement 2 details all outcomes.…”

Looking Beyond Mortality Prediction: Primary Care Physician Views of Patients' Palliative Care Needs Predicted by a Machine Learning Tool

“…risk patients, [30][31][32][33][34] there is limited evidence about the extent to which mortality prediction can serve as a proxy predictor for unmet clinical palliative care needs. Therefore, in this study, we assessed the extent to which PCPs' perceptions of which patients may benefit from palliative care and serious illness communication in the primary care setting overlapped with assessments of mortality risk made by a machine learning-based mortality prediction tool.…”