An Intelligent Algorithm for Assessing Patient Safety Culture and Adverse Events Voluntary Reporting Using Pca and Anfis Publisher Pubmed



Omidi L¹ ; Akbari R² ; Hadavandi E³ ; Zareid E^{4, 5} Show Affiliations
Source: International Journal of Risk and Safety in Medicine Published:2019

Abstract

Patient safety culture (PSC) as a main component of the organizational culture plays a key role in providing safe, effective and economic cares and services in healthcare organizations. PSC provides a way to assist hospitals in order to improve patient safety and prevent medical errors. OBJECTIVE: The present study aimed to measure PSC and healthcare professionals' attitude towards voluntary reporting of adverse events in two hospitals in Iran and to develop a hybrid intelligent approach for modeling PSC grades. METHODS: The Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire and a two-part questionnaire were used for examining the PSC and healthcare professionals' attitude towards voluntary reporting of adverse events, respectively. Principal component analysis (PCA) was applied to extract of the main components in the HSOPSC questionnaire and to construct 12 dimensions of patient safety culture. The overall grade of patient safety culture was modeled using adaptive neuro-fuzzy inference systems (ANFIS) as a classification problem. RESULTS: Almost half of the participants have experienced a medical error and adverse events. The PSC grade was acceptable from the point of view of 55.5% and 50% of participants in hospital No.1 and hospital No.2, respectively. The overall accuracy of ANFIS in modeling overall grades of patient safety culture in both study hospitals was 0.84. Of those individuals gave an acceptable grade on patient safety culture in both study hospitals, more than 50% believed that all medical errors and near misses should be reported. CONCLUSIONS: The ANFIS algorithm was proposed for modeling and predicting of PSC for healthcare organizations. The results confirm the capability of the proposed model to predict patient safety grades in healthcare settings. © 2018/2019 - IOS Press and the authors. All rights reserved.