scholarly journals 1049. Using Alternative Alerts in the Electronic Health Record to Guide Antimicrobial Selection Decisionmaking at the Point of Order Entry

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S370-S370
Author(s):  
Steven C Ebert
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Record ◽  
Original Order ◽  
Fifth Generation ◽  
Electronic Health

Abstract Background In a White Paper published in 2019, SHEA describes “The role of electronic health record and ‘add-on’ clinical decision support systems to enhance antimicrobial stewardship programs.” Modifications of the electronic health record and add-on clinical decision support systems are compared and contrasted. Some disadvantages of modification of the EHR include the need to include all end-user affiliates in modifications, lack of order set utilization, and heavy demands of IT staff to modify the EHR. We have utilized alternative alerts which may be customized to individual affiliates and are relatively easy to build which fire when specific medications are ordered (whether within order sets or not) and guide clinicians to more appropriate antibiotic choices. Methods From an antimicrobial stewardship perspective, alternative alerts are activated during the ordering of antibiotics for which routine use is discouraged (e.g., carbapenems, fluoroquinolones). When a provider enters an order, the alternative alert will pop up. The alert consists of two sections: an alert section describing the reason for the alert and a list of therapeutic options for the targeted drug; and links to orders for alternative antibiotics/combinations. The alerts may be configured to allow or not allow the orderer to continue with the original order. Different alternative alerts can be created and used at different facilities using the same EHR platform. We designed alternative alerts for fluoroquinolones, carbapenems, and fifth-generation cephalosporins that allowed providers to continue with the original order. We tested their impact on antimicrobial prescribing for 18 months after implementation, measured as quarterly days of therapy (DOT)/1000 pt-days. Results We noted marked reductions in quarterly DOT/1000 pt-days for fluoroquinolones (-70%) and fifth-generation cephalosporins (-90%). The impact on carbapenem prescribing was more variable. Conclusion Alternative alerts represent an easily created, customizable means to guide providers’ antimicrobial selections. We plan to incorporate more alternative alerts into our antimicrobial ordering process and strengthen the alert for carbapenems. Disclosures All authors: No reported disclosures.

Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data

2017 ◽  
Vol 56 (03) ◽  
pp. 238-247 ◽  
Author(s):  
Georgy Kopanitsa
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data ◽  
Health Record ◽  
Electronic Health

SummaryBackground: The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse.Objectives: In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration.Materials and Methods: Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS.Results: Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records’ normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users.Conclusions: The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid data set with known structure and constraints. The standard based nature of the archetype approach allows an easy integration of CDSSs with existing EHR systems.

scholarly journals Development of an antimicrobial stewardship module in an electronic health record: Options to enhance daily antimicrobial stewardship activities

2021 ◽  
Author(s):  
Kathryn Dzintars ◽  
Valeria M Fabre ◽  
Edina Avdic ◽  
Janessa Smith ◽  
Victoria Adams-Sommer ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Johns Hopkins Hospital ◽  
Health Record ◽  
Support Tool ◽  
Electronic Health

Abstract Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose The purpose of this manuscript is to describe our experience developing an antimicrobial stewardship (AS) module as a clinical decision support tool in the Epic electronic health record (EHR). Summary Clinical decision support systems within the EHR can be used to decrease use of broad-spectrum antibiotics, improve antibiotic selection and dosing, decrease adverse effects, reduce antibiotic costs, and reduce the development of antibiotic resistance. The Johns Hopkins Hospital constructed an AS module within Epic. Customized stewardship alerts and scoring systems were developed to triage patients requiring stewardship intervention. This required a multidisciplinary approach with a team comprising AS physicians and pharmacists and Epic information technology personnel, with assistance from clinical microbiology and infection control when necessary. In addition, an intervention database was enhanced with stewardship-specific interventions, and workbench reports were developed specific to AS needs. We herein review the process, advantages, and challenges associated with the development of the Epic AS module. Conclusion Customizing an AS module in an EHR requires significant time and expertise in antimicrobials; however, AS modules have the potential to improve the efficiency of AS personnel in performing daily stewardship activities and reporting through a single system.

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 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 Integrating pharmacogenetic information and clinical decision support into the electronic health record

2014 ◽  
Vol 21 (3) ◽  
pp. 522-528 ◽  
Author(s):  
Barry R Goldspiel ◽  
Willy A Flegel ◽  
Gary DiPatrizio ◽  
Tristan Sissung ◽  
Sharon D Adams ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

2017 ◽  
Vol 25 (5) ◽  
pp. 496-506 ◽  
Author(s):  
Adam Wright ◽  
Angela Ai ◽  
Joan Ash ◽  
Jane F Wiesen ◽  
Thu-Trang T Hickman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Quantitative Methods ◽  
Clinical Decision ◽  
The United States ◽  
Health Record ◽  
Qualitative And Quantitative Methods ◽  
Electronic Health

Abstract Objective To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems

2017 ◽  
pp. 184-201 ◽  
Author(s):  
Jitendra Jonnagaddala ◽  
Hong-Jie Dai ◽  
Pradeep Ray ◽  
Siaw-Teng Liaw
Keyword(s):  
Decision Support ◽  
Text Mining ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Association Studies ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Genome Wide ◽  
Electronic Health

Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.

scholarly journals Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation

2019 ◽  
Vol 28 (9) ◽  
pp. 762-768 ◽  
Author(s):  
Norman Lance Downing ◽  
Joshua Rolnick ◽  
Sarah F Poole ◽  
Evan Hall ◽  
Alexander J Wessels ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Health Record ◽  
Hospitalised Patients ◽  
Significant Difference ◽  
Electronic Health

BackgroundSepsis remains the top cause of morbidity and mortality of hospitalised patients despite concerted efforts. Clinical decision support for sepsis has shown mixed results reflecting heterogeneous populations, methodologies and interventions.ObjectivesTo determine whether the addition of a real-time electronic health record (EHR)-based clinical decision support alert improves adherence to treatment guidelines and clinical outcomes in hospitalised patients with suspected severe sepsis.DesignPatient-level randomisation, single blinded.SettingMedical and surgical inpatient units of an academic, tertiary care medical centre.Patients1123 adults over the age of 18 admitted to inpatient wards (intensive care units (ICU) excluded) at an academic teaching hospital between November 2014 and March 2015.InterventionsPatients were randomised to either usual care or the addition of an EHR-generated alert in response to a set of modified severe sepsis criteria that included vital signs, laboratory values and physician orders.Measurements and main resultsThere was no significant difference between the intervention and control groups in primary outcome of the percentage of patients with new antibiotic orders at 3 hours after the alert (35% vs 37%, p=0.53). There was no difference in secondary outcomes of in-hospital mortality at 30 days, length of stay greater than 72 hours, rate of transfer to ICU within 48 hours of alert, or proportion of patients receiving at least 30 mL/kg of intravenous fluids.ConclusionsAn EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.

scholarly journals Clinical Decision Support Systems Governance and the Pharmacy Department

2013 ◽  
Vol 48 (7) ◽  
pp. 607-608
Author(s):  
Brent I. Fox ◽  
Bill G. Felkey
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Meaningful Use ◽  
Clinical Decision ◽  
Health Record ◽  
Pharmacy Department ◽  
Core Requirements

The Meaningful Use criteria include a variety of core requirements that all electronic health record systems must demonstrate. An increasing level of functionality from clinical decision support systems is one of those requirements. In this article, we discuss general and specific aspects of clinical decision support systems, including potential roles for pharmacy in governance.

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