scholarly journals Importance of clinical decision support system response time monitoring: a case report

2019 ◽  
Vol 26 (11) ◽  
pp. 1375-1378
Author(s):  
David Rubins ◽  
Adam Wright ◽  
Tarik Alkasab ◽  
M Stephen Ledbetter ◽  
Amy Miller ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Unintended Consequences ◽  
System Response ◽  
Record System ◽  
Web Based ◽  
Monitoring Programs ◽  
System Response Time ◽  
Electronic Health

Abstract Clinical decision support (CDS) systems are prevalent in electronic health records and drive many safety advantages. However, CDS systems can also cause unintended consequences. Monitoring programs focused on alert firing rates are important to detect anomalies and ensure systems are working as intended. Monitoring efforts do not generally include system load and time to generate decision support, which is becoming increasingly important as more CDS systems rely on external, web-based content and algorithms. We report a case in which a web-based service caused significant increase in the time to generate decision support, in turn leading to marked delays in electronic health record system responsiveness, which could have led to patient safety events. Given this, it is critical to consider adding decision support-time generation to ongoing CDS system monitoring programs.

scholarly journals Developing Clinical Decision Support within a Commercial Electronic Health Record System to Improve Antimicrobial Prescribing in the Neonatal ICU

2014 ◽  
Vol 05 (02) ◽  
pp. 368-387 ◽  
Author(s):  
K. Cato ◽  
B. Sheehan ◽  
S. Patel ◽  
J. Duchon ◽  
P. DeLaMora ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Electronic Health Record System ◽  
Health Record ◽  
Record System ◽  
Antimicrobial Prescribing ◽  
Neonatal Icu ◽  
Electronic Health

SummaryObjective: To develop and implement a clinical decision support (CDS) tool to improve antibiotic prescribing in neonatal intensive care units (NICUs) and to evaluate user acceptance of the CDS tool.Methods: Following sociotechnical analysis of NICU prescribing processes, a CDS tool for empiric and targeted antimicrobial therapy for healthcare-associated infections (HAIs) was developed and incorporated into a commercial electronic health record (EHR) in two NICUs. User logs were reviewed and NICU prescribers were surveyed for their perceptions of the CDS tool.Results: The CDS tool aggregated selected laboratory results, including culture results, to make treatment recommendations for common clinical scenarios. From July 2010 to May 2012, 1,303 CDS activations for 452 patients occurred representing 22% of patients prescribed antibiotics during this period. While NICU clinicians viewed two culture results per tool activation, prescribing recommendations were viewed during only 15% of activations. Most (63%) survey respondents were aware of the CDS tool, but fewer (37%) used it during their most recent NICU rotation. Respondents considered the most useful features to be summarized culture results (43%) and antibiotic recommendations (48%).Discussion: During the study period, the CDS tool functionality was hindered by EHR upgrades, implementation of a new laboratory information system, and changes to antimicrobial testing methodologies. Loss of functionality may have reduced viewing antibiotic recommendations. In contrast, viewing culture results was frequently performed, likely because this feature was perceived as useful and functionality was preserved.Conclusion: To improve CDS tool visibility and usefulness, we recommend early user and information technology team involvement which would facilitate use and mitigate implementation challenges.Citation: Hum RS, Cato K, Sheehan B, Patel S, Duchon J, DeLaMora P, Ferng YH, Graham P, Vawdrey DK, Perlman J, Larson E, Saiman L. Developing clinical decision support within a commercial electronic health record system to improve antimicrobial prescribing in the neonatal ICU. Appl Clin Inf 2014; 5: 368–387 http://dx.doi.org/10.4338/ACI-2013-09-RA-0069

scholarly journals The Use of Evidence-Based, Problem-Oriented Templates as a Clinical Decision Support in an Inpatient Electronic Health Record System

2016 ◽  
Vol 07 (03) ◽  
pp. 790-802 ◽  
Author(s):  
Nila Radhakrishnan ◽  
Carrie Warring ◽  
Ankur Jain ◽  
Jorge Fuentes ◽  
Angela Dolganiuc ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Outcome Measurement ◽  
Clinical Decision ◽  
Electronic Health Record System ◽  
Evidence Based ◽  
Health Record ◽  
Record System ◽  
Electronic Health

SummaryThe integration of clinical decision support (CDS) in documentation practices remains limited due to obstacles in provider workflows and design restrictions in electronic health records (EHRs). The use of electronic problem-oriented templates (POTs) as a CDS has been previously discussed but not widely studied.We evaluated the voluntary use of evidence-based POTs as a CDS on documentation practices.This was a randomized cohort (before and after) study of Hospitalist Attendings in an Academic Medical Center using EPIC EHRs. Primary Outcome measurement was note quality, assessed by the 9-item Physician Documentation Quality Instrument (PDQI-9). Secondary Outcome measurement was physician efficiency, assessed by the total charting time per note.Use of POTs increased the quality of note documentation [score 37.5 vs. 39.0, P = 0.0020]. The benefits of POTs scaled with use; the greatest improvement in note quality was found in notes using three or more POTs [score 40.2, P = 0.0262]. There was no significant difference in total charting time [30 minutes vs. 27 minutes, P = 0.42].Use of evidence-based and problem-oriented templates is associated with improved note quality without significant change in total charting time. It can be used as an effective CDS during note documentation. Citation: Mehta R, Radhakrishnan NS, Warring CD, Jain A, Fuentes J, Dolganiuc A, Lourdes LS, Busigin J, Leverence RR. The use of evidence-based, problemoriented templates as a clinical decision support in an inpatient electronic health record system.

scholarly journals An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support

2019 ◽  
Vol 6 (5) ◽  
pp. e9766
Author(s):  
Sofian Berrouiguet ◽  
Romain Billot ◽  
Mark Erik Larsen ◽  
Jorge Lopez-Castroman ◽  
Isabelle Jaussent ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Suicide Attempts ◽  
Regression Trees ◽  
Clinical Decision ◽  
Clustering Methods ◽  
Web Based ◽  
Suicide Attempters ◽  
Electronic Health

Background In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health–based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.

scholarly journals An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support (Preprint)

2018 ◽  
Author(s):  
Sofian Berrouiguet ◽  
Romain Billot ◽  
Mark Erik Larsen ◽  
Jorge Lopez-Castroman ◽  
Isabelle Jaussent ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Suicide Attempts ◽  
Regression Trees ◽  
Clinical Decision ◽  
Clustering Methods ◽  
Web Based ◽  
Suicide Attempters ◽  
Electronic Health

BACKGROUND In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health–based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. OBJECTIVE The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. METHODS We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. RESULTS We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. CONCLUSIONS Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.

scholarly journals Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record

2021 ◽  
Vol 147 ◽  
pp. 104349
Author(s):  
Thomas McGinn ◽  
David A. Feldstein ◽  
Isabel Barata ◽  
Emily Heineman ◽  
Joshua Ross ◽  
...  
Keyword(s):  
Child Abuse ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  
Keyword(s):  
Decision Support ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Centers ◽  
Web Based ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Association Patterns of Ontological Features Signify Electronic Health Records in Liver Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lawrence W. C. Chan ◽  
S. C. Cesar Wong ◽  
Choo Chiap Chiau ◽  
Tak-Ming Chan ◽  
Liang Tao ◽  
...  
Keyword(s):  
Decision Support ◽  
Liver Cancer ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Computed Tomography Imaging ◽  
Health Records ◽  
Association Pattern ◽  
Electronic Health ◽  
Systematized Nomenclature Of Medicine ◽  
Primary Predictor

Electronic Health Record (EHR) system enables clinical decision support. In this study, a set of 112 abdominal computed tomography imaging examination reports, consisting of 59 cases of hepatocellular carcinoma (HCC) or liver metastases (so-called HCC group for simplicity) and 53 cases with no abnormality detected (NAD group), were collected from four hospitals in Hong Kong. We extracted terms related to liver cancer from the reports and mapped them to ontological features using Systematized Nomenclature of Medicine (SNOMED) Clinical Terms (CT). The primary predictor panel was formed by these ontological features. Association levels between every two features in the HCC and NAD groups were quantified using Pearson’s correlation coefficient. The HCC group reveals a distinct association pattern that signifies liver cancer and provides clinical decision support for suspected cases, motivating the inclusion of new features to form the augmented predictor panel. Logistic regression analysis with stepwise forward procedure was applied to the primary and augmented predictor sets, respectively. The obtained model with the new features attained 84.7% sensitivity and 88.4% overall accuracy in distinguishing HCC from NAD cases, which were significantly improved when compared with that without the new features.

Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements

2021 ◽  
Vol 12 (01) ◽  
pp. 182-189
Author(s):  
Adam Wright ◽  
Skye Aaron ◽  
Allison B. McCoy ◽  
Robert El-Kareh ◽  
Daniel Fort ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Organizations ◽  
Software Tool ◽  
Clinical Decision ◽  
Unintended Consequences ◽  
Boolean Logic ◽  
Safety Issues ◽  
Automated Algorithm ◽  
Logic Minimization

Abstract Objective Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements. Methods Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors. Results Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback. Discussion An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations. Conclusion Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

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