Signal from the Noise: A Mixed Graphical and Quantitative Process Mining Approach to Evaluate Care Pathways Applied to Emergency Stroke Care

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

BACKGROUND

Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.

OBJECTIVE

The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.

METHODS

We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.

RESULTS

We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.

CONCLUSIONS

The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15 767 634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.

A scoping review of medical practice variation research within the informatics literature

RATIONALE

We performed a scoping review of informatics core literature about medical practice variation (MPV) as an agile summary of the subject in our field.

MATERIALS AND METHODS

The Ovid integrated database was searched between 1946 and 2022 to identify MPV studies published in major informatics journals and conference proceedings. Two reviewers performed relevance screening, with assistance from another independent reviewer for adjudication. The included articles were then thematically analyzed and summarized through discussion among all three reviewers.

RESULTS

A total of 43 articles were included and went through the thematic analysis. About half (n = 21) of the included articles were published in conference proceedings. Five articles reported the effect of MPV on patient outcomes. The variation of interest was most frequently in treatment decisions. In terms of the role informatics played (multiple roles allowed), 39 (90.7%) articles pertained to detection of MPV, 5 were about prevention of MPV and 4 about learning from MPV.

DISCUSSION

MPV remains a critical issue in health care, yet most informatics research has been focused on simple tasks such as automating the detection of MPV and assessing compliance to decision-support systems, and less focused on addressing the causes of variation or supporting learning from variation.

CONCLUSION

Our scoping review found that informatics studies have focused on detecting of MPV, especially variability in treatments and deviation from practice guidelines. Technological advances should promote more informatics research focused on explaining and learning from MPV.

Process mining based on patient waiting time: an application in health processes

Purpose

Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method.

Design/methodology/approach

In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor.

Findings

The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors.

Research limitations/implications

The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution.

Originality/value

This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms.