Algorithmic prediction of failure modes in healthcare.

BACKGROUND: Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. OBJECTIVES: To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. METHODS: The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants' working hours invested in each process and the adverse events, categorized as 'patient identification', before and after the recommendations resulted from the above processes were implemented. RESULTS: APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented. CONCLUSION: In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.

Developing a Pre-Testing Diagnostic Tool for COVID-19 Using Big Data Predictive Analytics (preprint)

Background: Standard Polymerase Chain reaction (PCR) tests for SARS-COV-2 are in short supply to meet demand in many countries presenting a need to improve testing efficiency. Pre-testing tools can be used to ensure continued public safety as systems move through the pandemic. In this study we set out to create an instrument based on big data predictive tools to assess pre-test probability for COVID-19. Methods: We analyzed data reported by the Israeli Ministry of Health (IMOH) for standard PCR tests done for SARS-COV-2 from March to April, 2020, overall 108,852 cases. Demographics and symptoms of the patients were collected at time of testing. Four supervised machine learning algorithms were used to analyze 20,537 test results of cases who presented with symptoms. Model results were used to develop efficient pre-test diagnostic tool. Findings: Of symptomatic patients tested, 6,427 (31.3%) tested positive for SARS-COV-2, and 14,110 (68.7%) tested negative. In all models used headache, shortness of breath, sore throat, fever, and having contact with an infected person came up as most predictive of a positive test. The area under the curve of the receiver operating characteristic curve for the test sample was found to be 0.88 and the misclassification rate was between 4.7% and 6.5% for all predictive models, demonstrating effective classification ability. Using our pre-test probability screening tool with conventional PCR testing can potentially increase efficiency by 141%. Interpretation: We suggest a simple diagnostic pre-test tool for assessing the probability of infection can increase efficiency of testing and effectiveness of public health COVID-19 programs. Funding: NoneDeclaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The authors noted that the analysis presented was approved by the IMOH Data Sharing Institutional Review Board.