OBJECTIVES: Palliative care has been demonstrated to have positive effects for patients, families, health care providers, and health systems. Early identification of patients who are likely to benefit from palliative care would increase opportunities to provide these services to those most in need. This study predicted all-cause mortality of patients as a surrogate for patients who could benefit from palliative care. STUDY DESIGN: Claims and electronic health record (EHR) data for 59,639 patients from a large integrated health care system were utilized. METHODS: A deep learning algorithm-a long short-term memory (LSTM) model-was compared with other machine learning models: Deep neural networks, random forest, and logistic regression. We conducted prediction analyses using combined claims data and EHR data, only claims data, and only EHR data, respectively. In each case, the data were randomly split into training (80%), validation (10%), and testing (10%) data sets. The models with different hyperparameters were trained using the training data, and the model with the best performance on the validation data was selected as the final model. The testing data were used to provide an unbiased performance evaluation of the final model. RESULTS: In all modeling scenarios, LSTM models outperformed the other 3 models, and using combined claims and EHR data yielded the best performance. CONCLUSIONS: LSTM models can effectively predict mortality by using a combination of EHR data and administrative claims data. The model could be used as a promising clinical tool to aid clinicians in early identification of appropriate patients for palliative care consultations.

Original languageEnglish
Pages (from-to)E7-E15
JournalAmerican Journal of Managed Care
Issue number1
StatePublished - Jan 2021


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