Maxim Topaz1, Kyungmi Woo, Miriam Ryvicker, Maryam Zolnoori, Kenrick Cato. 1. Maxim Topaz, PhD, RN, is Associate Professor, Columbia University School of Nursing, New York City, New York. Kyungmi Woo, PhD, RN, CCM, is Postdoctoral Scientist, Columbia University School of Nursing, New York City, New York. Miriam Ryvicker, PhD, is Senior Researcher, Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York City. Maryam Zolnoori, PhD, is Postdoctoral Scientist, Columbia University School of Nursing, New York City, New York. Kenrick Cato, PhD, RN, FAAN, is Assistant Professor, Columbia University School of Nursing, New York City, New York.
Abstract
BACKGROUND: About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. OBJECTIVES: The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes). METHODS: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). RESULTS: Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories. DISCUSSION: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources.
BACKGROUND: About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. OBJECTIVES: The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes). METHODS: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). RESULTS: Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories. DISCUSSION: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources.
Authors: Zfania Tom Korach; Kenrick D Cato; Sarah A Collins; Min Jeoung Kang; Christopher Knaplund; Patricia C Dykes; Liqin Wang; Kumiko O Schnock; Jose P Garcia; Haomiao Jia; Frank Chang; Jessica M Schwartz; Li Zhou Journal: Appl Clin Inform Date: 2019-12-18 Impact factor: 2.342
Authors: Daniel T Linnen; Gabriel J Escobar; Xiao Hu; Elizabeth Scruth; Vincent Liu; Caroline Stephens Journal: J Hosp Med Date: 2019-03 Impact factor: 2.960
Authors: Amol S Navathe; Feiran Zhong; Victor J Lei; Frank Y Chang; Margarita Sordo; Maxim Topaz; Shamkant B Navathe; Roberto A Rocha; Li Zhou Journal: Health Serv Res Date: 2017-03-13 Impact factor: 3.402
Authors: Patricia C Dykes; Ronen Rozenblum; Anuj Dalal; Anthony Massaro; Frank Chang; Marsha Clements; Sarah Collins; Jacques Donze; Maureen Fagan; Priscilla Gazarian; John Hanna; Lisa Lehmann; Kathleen Leone; Stuart Lipsitz; Kelly McNally; Conny Morrison; Lipika Samal; Eli Mlaver; Kumiko Schnock; Diana Stade; Deborah Williams; Catherine Yoon; David W Bates Journal: Crit Care Med Date: 2017-08 Impact factor: 9.296
Authors: Brian J Douthit; Rachel L Walden; Kenrick Cato; Cynthia P Coviak; Christopher Cruz; Fabio D'Agostino; Thompson Forbes; Grace Gao; Theresa A Kapetanovic; Mikyoung A Lee; Lisiane Pruinelli; Mary A Schultz; Ann Wieben; Alvin D Jeffery Journal: Appl Clin Inform Date: 2022-02-09 Impact factor: 2.342