Readmission Risk Assessment Technologies and the Anchoring and Adjustment Heuristic Approximately 23% of patients discharged from primary healthcare facilities are readmitted within 30 days at an annual cost of roughly 42 billion. To remedy this problem, healthcare providers are attempting to deploy readmission risk estimation tools, but how they might be used in the traditional, humanexpertcentered decision process is not well understood. One such tool estimates readmission risk based on 50 patientspecific factors. This paper reports on a study performed in collaboration with Order of St. Francis Healthcare to determine how healthcare workersxe2x80x99 own risk estimates are influenced by the tool, specifically testing the hypothesis that they will first anchor towards tool results while making adjustments based on their expertise, and then make further adjustments when additional human expert opinions are presented. Task analysis was performed, fictional patient scenarios were developed, and a survey of 56 subjects in two stratified groups of case managers was conducted. Data from the control and experiment groups were analyzed using ANOVAGLM and ttests. Results indicate that the healthcare workersxe2x80x99 risk estimates were influenced by the anchor provided by the tool, then adjusted based on their expertise. The workers further adjusted their estimates in response to new expert human inputs. Thus, a reliance on both the predictive model and human expertise was observed.

Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning Heart failure hospitalization is a severe burden on healthcare. How to predict and therefore prevent readmission has been a significant challenge in outcomes research. To address this, we propose a deep learning approach to predict readmission from clinical notes. Unlike conventional methods that use structured data for prediction, we leverage the unstructured clinical notes to train deep learning models based on convolutional neural networks (CNN). We then use the trained models to classify and predict potentially highrisk admissionspatients. For evaluation, we trained CNNs using the discharge summary notes in the MIMIC III database. We also trained regular machine learning models based on random forest using the same datasets. The result shows that deep learning models outperform the regular models in prediction tasks. CNN method achieves a F1 score of 0.756 in general readmission prediction and 0.733 in 30day readmission prediction, while random forest only achieves a F1 score of 0.674 and 0.656 respectively. We also propose a chisquare test based method to interpret key features associated with deep learning predicted readmissions. It reveals clinical insights about readmission embedded in the clinical notes. Collectively, our method can make the human evaluation process more efficient and potentially facilitate the reduction of readmission rates.