Development and Implementation of Electronic Health Record–Integrated Model‐Informed Clinical Decision Support Tools for the Precision Dosing of Drugs

2019 ◽  
Vol 107 (1) ◽  
pp. 129-135 ◽  
Author(s):  
Alexander A. Vinks ◽  
Richard W. Peck ◽  
Michael Neely ◽  
Diane R. Mould
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Integrated Model ◽  
Health Record ◽  
Decision Support Tools ◽  
Support Tools ◽  
Electronic Health ◽  
Clinical Decision Support Tools

scholarly journals Health Reform in Minnesota: An Analysis of Complementary Initiatives Implementing Electronic Health Record Technology and Care Coordination

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Karen Soderberg ◽  
Sripriya Rajamani ◽  
Douglas Wholey ◽  
Martin LaVenture
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Care Coordination ◽  
Health Reform ◽  
Clinical Decision ◽  
Health Record ◽  
Decision Support Tools ◽  
Primary Care Clinics ◽  
Support Tools ◽  
Clinical Decision Support Tools

Background: Minnesota enacted legislation in 2007 that requires all health care providers in the state to implement an interoperable electronic health record (EHR) system by 2015. 100% of hospitals and 98% of clinics had adopted EHR systems by end of 2015. Minnesota’s 2008 health reform included a health care home (HCH) program, Minnesota’s patient centered medical home. By end of 2014, 43% of HCH eligible clinics were certified with 335 certified HCHs and 430 eligible but not certified clinics.Objectives: To study the association between adoption and use of EHRs in primary care clinics and HCH certification, including use of clinical decision support tools, patient registries, electronic exchange of patient information, and availability of patient portals.Methods: Study utilized data from the 2015 Minnesota Health Information Technology Clinic Survey conducted annually by the Minnesota Department of Health. The response rate was 80% with 1,181 of 1,473 Minnesota clinics, including 662 HCH eligible primary care clinics. The comparative analysis focused on certified HCHs (311) and eligible but not certified clinics (351).Results: HCH clinics utilized the various tools of EHR technology at a higher rate than non-HCH clinics. This better utilization was noted across a range of functionalities: clinical decision support, patient disease registries, EHR to support quality improvement, electronic exchange of summary care records and availability of patient portals. HCH certification was significant for clinical decision support tools, registries and quality improvement.Conclusions: The rapid uptake in EHR technology, combined with health reform efforts focusing on accountability and care coordination, pose opportunities and challenges for providers. Opportunities are availability of tools that support decision making, quality improvement and reporting. Challenges remain for clinics to optimize health information exchange. The need to meet various care coordination requirements were likely drivers for better EHR utilization by HCH clinics. This research presents the synergy between complementary initiatives supporting EHR adoption and HCH certification.

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 72. Improving Antibiotic Prescribing in Interventional Radiology Using Clinical Decision Support Tools to Assess Penicillin Allergies

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S54-S54
Author(s):  
Vidya Atluri ◽  
Paula Marsland ◽  
Luke M Johnson ◽  
Rupali Jain ◽  
Paul Pottinger ◽  
...  
Keyword(s):  
Interventional Radiology ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Prophylactic Antibiotics ◽  
Decision Support Tools ◽  
First Line ◽  
Support Tools ◽  
History Of ◽  
Clinical Decision Support Tools

Abstract Background Patients labeled with penicillin allergies often receive alternative antibiotics, leading to increased cost, higher risk of adverse events, and decreased efficacy of procedural prophylaxis. However, most of those patients can tolerate a cephalosporin. University of Washington Medical Center – Montlake (UWMC-ML) Interventional Radiology (IR) frequently administer a pre-procedure prophylactic cephalosporin. We worked with the clinicians in IR to develop tools to allow them to better assess penicillin allergies, make the most appropriate antibiotic choice, and update the patient’s allergy documentation. Methods We identified all patients who underwent procedures in IR between 2017–2019. Chart review was done to determine the procedures performed, patient demographic information, allergies, allergy documentation, and prophylactic antibiotics received. In May 2020 we implemented new Clinical Decision Support tools, including an online assessment app (https://tinyurl.com/IRPCNAllAssess) and handouts to guide antibiotic decision making to clinicians in IR. Results From 2017 to 2019, 381 patients underwent 958 procedures in IR. Of those, 379 patients underwent 496 procedures for which the recommended first line choice for antibiotic prophylaxis is a cephalosporin. Of patients who received pre-procedure prophylactic antibiotics for those procedures, 15.9% [n=11] of patients with penicillin allergies received the first line antibiotic, compared to 89.9% [n=319] of patients without a reported penicillin allergy. Since implementation, the online app has been used to evaluate 9 patients, of whom 8 had penicillin allergies. All 8 patients safely received the first line antibiotic (3 were delabeled, 4 reported a history of mild reactions, and 1 reported a history of an immediate IgE mediated response to penicillin but safely received cefazolin). Conclusion IR evaluates hundreds of patients who may receive prophylactic antibiotics each year. By providing tools to assess penicillin allergies, we were able to improve both their prescribing and de-label patients which will provide a much broader impact on their care than on just their current procedure. Our free tool can be accessed at the website above, and we will demonstrate in person. Disclosures All Authors: No reported disclosures

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.

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