Assessing electronic health record systems in emergency departments: Using a decision analytic Bayesian model

The role of information systems in emergency department decision-making-a literature review

OBJECTIVES

Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED.

MATERIALS AND METHODS

We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes.

RESULTS

Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes.

DISCUSSION

Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation.

CONCLUSION

We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Nursing minimum data sets: Findings from an umbrella review

OBJECTIVES

This study explores the evidence available on Nursing Minimum Data Sets (NMDSs) by summarising: (a) the main methodological and reporting features of the reviews published in this field to date; (b) the recommendations developed and published in such reviews regarding the NMDSs, and (c) the categories and items that should be included in the NMDSs according to the available reviews.

METHODS

An Umbrella Review was performed. A search of secondary studies published up to November 2021 that were focused on NMDSs for adult hospitalised patients was conducted using MEDLINE (via PubMed), CINAHL and Scopus databases. The included studies were critically evaluated by using the Checklist for Systematic Review and Research Syntheses. The full review process was performed according to the Preferred Reporting Items for Systematic reviews and the Meta-Analyses statement.

RESULTS

From the initial 1311 studies that were retrieved, a total of eight reviews published from 1995 to 2018 were included. Their methodological quality was variable; these reviews offered four types of recommendations, namely at the overall, clinical, research and management levels. Additionally, seven NMDSs emerged with different purposes, elements, target populations and taxonomies. A list of categories and items that should be included in NMDSs have been summarised.

CONCLUSIONS

Nurses are daily involved in the nursing care documentation; however, which elements are recorded is mainly defined at the local levels and relies on paper and pencil. NMDS might provide a point of reference, specifically in the time of health digitalisation. Alongside other priorities as underlined in available recommendations, and the need to improve the quality of the reviews in this field, there is a need to develop a common NMDS by establishing its core elements, deciding on a standardised language and identifying linkages with other datasets.

Emergency Department Visits and Subsequent Hospital Admission Trends for Patients with Chest Pain and a History of Coronary Artery Disease

INTRODUCTION

Hospitalization is the largest component of health care spending in the United States. Most hospitalized patients first visit the emergency department (ED), where hospitalization decisions are made. Optimal utilization of hospital resources is critical for all stakeholders.

METHODS

We performed a population-based, cross-sectional study evaluating ED visits and subsequent inpatient admissions for patients with coronary artery disease (CAD) and chest pain (CP) suggestive of CAD from 2006 to 2013 using the Nationwide Emergency Department Sample database weighted for national estimates. We analyzed trends using a generalized linear regression model with a Poisson distribution and Wald test.

RESULTS

From 2006 to 2013, there was a 15% decrease in ED visits for CAD (p < 0.01), while ED visit rates for CP increased 31% (p < 0.01). Subsequent inpatient admission rates decreased 18% for CAD (p < 0.01) and 33% for CP (p < 0.01). Trends were not modified by patient and hospital strata.

CONCLUSION

ED visits and subsequent inpatient admissions resulting from CAD decreased from 2006 to 2013. Patients with CP had a substantially higher number of ED visits, with a significant decline in inpatient admissions.

The Evolution of Publication Hotspots in Electronic Health Records from 1957 to 2016 and Differences Among Six Countries

This study aims to reveal the evolution of publication hotspots in the field of electronic health records (EHRs) and differences among countries. We applied keyword frequency analysis, keyword co-occurrence analysis, principal component analysis, multidimensional scaling analysis, and visualization technology to compare the high-frequency Medical Subject Heading (MeSH) terms in six countries during the periods 1957-2008 and 2009-2016. After 2009, the number of MeSH terms reflecting information exchange and information mining increased, and various types of evaluations based on EHRs and cohort studies significantly increased. The top 20 MeSH terms between 2009 and 2016 constitute five relatively larger knowledge groups. Thus, we conclude that publication hotspots in EHR field have shifted from issues related to the adoption of EHRs to the utilization of EHRs, and the knowledge structure has become systematic. The publication's focus was different in the six countries, which may relate to their national characteristics.

Exploring trajectories of emergency department visits using a laboratory-based indicator of serious illness

Repeated emergency department visits have become a serious challenge worldwide. Despite prior research indicating that laboratory results may provide early alerts about such patients on their upcoming adverse events, few studies have examined their role as a critical indicator of the stability of a patient's medical condition over time. We model and analyze the developmental trajectories of patients' creatinine levels, a key laboratory marker of serious illness, as a potential risk stratification mechanism across many emergency department visits. We apply group-based statistical methodology to electronic health record data of 70,385 patients, with 3-15 emergency department visits, to identify and profile these trajectories for the entire population, for males and for females. Results reveal three distinct creatinine-based trajectory groups over time with significantly differing characteristics that may enable targeted interventions for each group. Future research will incorporate additional disease markers to identify longitudinal factors leading to repeated emergency department visits.

The effect of a test ordering software intervention on the prescription of unnecessary laboratory tests - a randomized controlled trial

Background

The way software for electronic health records and laboratory tests ordering systems are designed may influence physicians’ prescription. A randomised controlled trial was performed to measure the impact of a diagnostic and laboratory tests ordering system software modification.

Methods

Participants were family physicians working and prescribing diagnostic and laboratory tests.

The intervention group had a modified software with a basic shortcut menu changes, where some tests were withdrawn or added, and with the implementation of an evidence-based decision support based on United States Preventive Services Task Force (USPSTF) recommendations. This intervention group was compared with usual software (control group).

The outcomes were the number of tests prescribed from those: withdrawn from the basic menu; added to the basic menu; marked with green dots (USPSTF’s grade A and B); and marked with red dots (USPSTF’s grade D).

Results

Comparing the monthly average number of tests prescribed before and after the software modification, from those tests that were withdrawn from the basic menu, the control group prescribed 33.8 tests per 100 consultations before and 30.8 after (p = 0075); the intervention group prescribed 31.3 before and 13.9 after (p < 0001). Comparing the tests prescribed between both groups during the intervention, from those tests that were withdrawn from the basic menu, the intervention group prescribed a monthly average of 14.0 vs. 29.3 tests per 100 consultations in the control group (p < 0.001). From those tests that are USPSTF’s grade A and B, intervention group prescribed 66.8 vs. 74.1 tests per 100 consultations in the control group (p = 0.070). From those tests categorised as USPSTF grade D, the intervention group prescribed an average of 9.8 vs. 11.8 tests per 100 consultations in the control group (p = 0.003).

Conclusions

Removing unnecessary tests from a quick shortcut menu of the diagnosis and laboratory tests ordering system had a significant impact and reduced unnecessary prescription of tests.

The fact that it was not possible to perform the randomization at the family physicians’ level, but only of the computer servers is a limitation of our study. Future research should assess the impact of different tests ordering systems during longer periods.

Trial registration

ISRCTN45427977, May 1^st 2014 (retrospectively registered).

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

INTRODUCTION

Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction.

OBJECTIVE

This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms.

METHODS

PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study.

RESULT

In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms.

DISCUSSION

This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy.

CONCLUSION

The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.