| Autor: |
Hernandez, Nuvia, Karam, Kate, Baugh, Nicole, Musale, Shilpa, Moses, Aditya Patrick, Raicu, Daniela, Furst, Jacob, McCabe, Kelly, Tchoua, Roselyne |
| Předmět: |
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| Zdroj: |
International Journal of Semantic Computing; Sep2024, Vol. 18 Issue 3, p383-403, 21p |
| Abstrakt: |
As the capabilities of machine learning have developed, more researchers and health care providers are beginning to consider applications for health informatics to improve health care outcomes and address issues of health equity. The Centers for Medicare and Medicaid Services considers 30-day readmission rates to the Emergency Department (ED) to be an "outcome of care" measure. Such measures show how well a hospital is doing in preventing complications, educating patients about their care needs, and helping patients make a smooth transition from the hospital to home or other care facilities. While certain readmissions are medically necessary, hospitals usually aim to decrease the rate of 30-day ED readmissions by decreasing the number of avoidable unplanned revisits. This work is an evidential study that demonstrates the positive impact of integrating Community Health Workers (CHWs) and Social Determinants of Health (SDoH) in decreasing the 30-day unplanned hospital ED readmissions at Sinai Chicago. Using data from the Sinai Urban Health Institute, we compare predicting the readmissions of patients with and without data pertaining to CHWs and SDoH, characterize the improvement in predictions, and discuss lessons learned in the process. We show that when CHWs engage with patients, the predictive accuracy of the classifier is higher by 13.0%–15.2%. Importantly, we show that the features (data characteristics) related to the CHWs are important to the classification, pointing to the importance of the program. We optimize the classifier for engaged patients and demonstrate the improvement in predictive capabilities of the classier using multiple metrics and sets of features. We use our results to make recommendations for improving patient care, discuss limitations and future work. Notably, our work points directly to the human connection between patients and CHWs as an important feature predictive of readmission rate. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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