Lin Zhu1, Iris Reychav1, Roger McHaney2, Aric Broda3, Yossi Tal4, Orly Manor5. 1. Industrial Engineering & Management Department, Ariel University, Ariel, Israel. 2. Daniel D. Burke Chair for Exceptional Faculty, Professor and University Distinguished Teaching Scholar, Management Information Systems, Kansas State University, Manhattan, KS, USA. 3. Head of Risk Management Department, Maccabi Healthcare Services, Tel Aviv, Israel. 4. Risk Management and Patient Safety Advisor, Maccabi Healthcare Services, Tel Aviv, Israel. 5. Deputy of Risk Management Department, Maccabi Healthcare Services, Tel Aviv, Israel.
Abstract
OBJECTIVE: To compare primary medical adverse event keywords from reporters (e.g. physicians and nurses) and harm level perspectives to explore the underlying behaviors of medical adverse events using social network analysis (SNA) and latent Dirichlet allocation (LDA) leading to process improvements. DESIGN: Used SNA methods to explore primary keywords used to describe the medical adverse events reported by physicians and nurses. Used LDA methods to investigate topics used for various harm levels. Combined the SNA and LDA methods to discover common shared topic keywords to better understand underlying behaviors of physicians and nurses in different harm level medical adverse events. SETTING: Maccabi Healthcare Community is the second largest healthcare organization in Israel. DATA: 17,868 medical adverse event data records collected between 2000 and 2017. METHODS: Big data analysis techniques using social network analysis (SNA) and latent Dirichlet allocation (LDA). RESULTS: Shared topic keywords used by both physicians and nurses were determined. The study revealed that communication, information transfer, and inattentiveness were the most common problems reported in the medical adverse events data. CONCLUSIONS: Communication and inattentiveness were the most common problems reported in medical adverse events regardless of healthcare professional reporting or harm levels. Findings suggested that an information-sharing and feedback mechanism should be implemented to eliminate preventable medical adverse events. Healthcare institutions managers and government officials should take targeted actions to decrease these preventable medical adverse events through quality improvement efforts.
OBJECTIVE: To compare primary medical adverse event keywords from reporters (e.g. physicians and nurses) and harm level perspectives to explore the underlying behaviors of medical adverse events using social network analysis (SNA) and latent Dirichlet allocation (LDA) leading to process improvements. DESIGN: Used SNA methods to explore primary keywords used to describe the medical adverse events reported by physicians and nurses. Used LDA methods to investigate topics used for various harm levels. Combined the SNA and LDA methods to discover common shared topic keywords to better understand underlying behaviors of physicians and nurses in different harm level medical adverse events. SETTING: Maccabi Healthcare Community is the second largest healthcare organization in Israel. DATA: 17,868 medical adverse event data records collected between 2000 and 2017. METHODS: Big data analysis techniques using social network analysis (SNA) and latent Dirichlet allocation (LDA). RESULTS: Shared topic keywords used by both physicians and nurses were determined. The study revealed that communication, information transfer, and inattentiveness were the most common problems reported in the medical adverse events data. CONCLUSIONS: Communication and inattentiveness were the most common problems reported in medical adverse events regardless of healthcare professional reporting or harm levels. Findings suggested that an information-sharing and feedback mechanism should be implemented to eliminate preventable medical adverse events. Healthcare institutions managers and government officials should take targeted actions to decrease these preventable medical adverse events through quality improvement efforts.
Keywords:
Patient safety; Social Network Analysis (SNA); latent Dirichlet allocation zzm321990(LDA); medical adverse event; medical adverse event reporting system