Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining

[EN] In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations.This project was partially funded by Fondecyt Grants 1150365 and 11130577 from the Chilean National Commission on Scientific and Technological Research (CONICYT), the Ph.D. Scholarship Program of CONICYT Chile (CONICYT-Doctorado Nacional/2014-63140180), the Ph.D. Scholarship Program of CONICIT Costa Rica and by Universidad de Costa Rica Professor Fellowships.Rojas, E.; Sepúlveda, M.; Munoz-Gama, J.; Capurro, D.; Traver Salcedo, V.; Fernández Llatas, C. (2017). Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining. Applied Sciences. 7(3):1-29. https://doi.org/10.3390/app7030302S12973Welch, S. J., Asplin, B. R., Stone-Griffith, S., Davidson, S. 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Systems approach to assessing and improving local human research Institutional Review Board performance.

ObjectiveTo quantifying the interdependency within the regulatory environment governing human subject research, including Institutional Review Boards (IRBs), federally mandated Medicare coverage analysis and contract negotiations.MethodsOver 8000 IRB, coverage analysis and contract applications initiated between 2013 and 2016 were analyzed using traditional and machine learning analytics for a quality improvement effort to improve the time required to authorize the start of human research studies.ResultsStaffing ratios, study characteristics such as the number of arms, source of funding and number and type of ancillary reviews significantly influenced the timelines. Using key variables, a predictive algorithm identified outliers for a workflow distinct from the standard process. Improved communication between regulatory units, integration of common functions, and education outreach improved the regulatory approval process.ConclusionsUnderstanding and improving the interdependencies between IRB, coverage analysis and contract negotiation offices requires a systems approach and might benefit from predictive analytics

A multisite study of performance drivers among institutional review boards.

Introduction:The time required to obtain Institutional Review Board (IRB) approval is a frequent subject of efforts to reduce unnecessary delays in initiating clinical trials. This study was conducted by and for IRB directors to better understand factors affecting approval times as a first step in developing a quality improvement framework. Methods:807 IRB-approved clinical trials from 5 University of California campuses were analyzed to identify operational and clinical trial characteristics influencing IRB approval times. Results:High workloads, low staff ratios, limited training, and the number and types of ancillary reviews resulted in longer approval times. Biosafety reviews and the need for billing coverage analysis were ancillary reviews that contributed to the longest delays. Federally funded and multisite clinical trials had shorter approval times. Variability in between individual committees at each institution reviewing phase 3 multisite clinical trials also contributed to delays for some protocols. Accreditation was not associated with shorter approval times. Conclusions:Reducing unnecessary delays in obtaining IRB approval will require a quality improvement framework that considers operational and study characteristics as well as the larger institutional regulatory environment

Can the US Minimum Data Set Be Used for Predicting Admissions to Acute Care Facilities?

This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value

Automated Measurement of Adherence to Traumatic Brain Injury (TBI) Guidelines using Neurological ICU Data

Using a combination of physiological and treatment information from neurological ICU data-sets, adherence to traumatic brain injury (TBI) guidelines on hypotension, intracranial pressure (ICP) and cerebral perfusion pressure (CPP) is calculated automatically. The ICU output is evaluated to capture pressure events and actions taken by clinical staff for patient management, and are then re-expressed as simplified process models. The official TBI guidelines from the Brain Trauma Foundation are similarly evaluated, so the two structures can be compared and a quantifiable distance between the two calculated (the measure of adherence). The methods used include: the compilation of physiological and treatment information into event logs and subsequently process models; the expression of the BTF guidelines in process models within the real-time context of the ICU; a calculation of distance between the two processes using two algorithms (“Direct” and “Weighted”) building on work conducted in th e business process domain. Results are presented across two categories each with clinical utility (minute-by-minute and single patient stays) using a real ICU data-set. Results of two sample patients using a weighted algorithm show a non-adherence level of 6.25% for 42 mins and 56.25% for 708 mins and non-adherence of 18.75% for 17 minutes and 56.25% for 483 minutes. Expressed as two combinatorial metrics (duration/non-adherence (A) and duration * non-adherence (B)), which together indicate the clinical importance of the non-adherence, one has a mean of A=4.63 and B=10014.16 and the other a mean of A=0.43 and B=500.0