scholarly journals Multi-Institutional Implementation of Clinical Decision Support for APOL1, NAT2, and YEATS4 Genotyping in Antihypertensive Management

2021 ◽  
Vol 11 (6) ◽  
pp. 480
Author(s):  
Thomas M. Schneider ◽  
Michael T. Eadon ◽  
Rhonda M. Cooper-DeHoff ◽  
Kerri L. Cavanaugh ◽  
Khoa A. Nguyen ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Disease Gene ◽  
Antihypertensive Agents ◽  
Clinical Decision ◽  
Antihypertensive Medications ◽  
Context Sensitive ◽  
Laboratory Results ◽  
Workflow Integration ◽  
Selection Of

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus; 2. actionability; 3. context-sensitive triggers; 4. workflow integration; 5. feasibility; 6. interpretability; 7. portability; and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for APOL1, NAT2, and YEATS4 was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS.

scholarly journals Computerized Clinical Decision Support: Contributions from 2014

2015 ◽  
Vol 24 (01) ◽  
pp. 119-124 ◽  
Author(s):  
V. Koutkias ◽  
J. Bouaud ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Information Technologies ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Editorial Team ◽  
Bibliographic Databases ◽  
Health Information Technologies ◽  
Selection Of

Summary Objective: To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.Method: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Results: Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy. Conclusions: As health information technologies spread more and more meaningfully, CDSSs are improving to answer users’ needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term.

scholarly journals Decision Support: Time for Collaboration

2011 ◽  
Vol 20 (01) ◽  
pp. 102-104
Author(s):  
A. Guardia ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Articles ◽  
Health Record ◽  
Excellent Research ◽  
Support Time ◽  
Selection Of

SummaryTo summarize current outstanding research in the field of decision support.A selection of excellent research articles published in 2010 in the field of computerized clinical decision support systems.This selection of articles shows that deci- sion support systems (DSS) are getting better integrated into the electronic health record systems (EHR) and into the clinician’s workflow. As a result, there is a better collaboration between physicians and DSS, which improves the care of patients.

The Effect of a Clinical Decision Support for Pending Laboratory Results at Emergency Department Discharge

2019 ◽  
Vol 56 (1) ◽  
pp. 109-113
Author(s):  
Brian E. Driver ◽  
Sarah K. Scharber ◽  
Erik T. Fagerstrom ◽  
Lauren R. Klein ◽  
Jon B. Cole ◽  
...  
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Laboratory Results ◽  
Emergency Department Discharge

Workflow integration analysis of a human factors-based clinical decision support in the emergency department

2021 ◽  
Vol 97 ◽  
pp. 103498
Author(s):  
Megan E. Salwei ◽  
Pascale Carayon ◽  
Peter L.T. Hoonakker ◽  
Ann Schoofs Hundt ◽  
Douglas Wiegmann ◽  
...  
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Human Factors ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Workflow Integration ◽  
Integration Analysis

scholarly journals Assessment of Readiness for Clinical Decision Support to Aid Laboratory Monitoring of Immunosuppressive Care at U.S. Liver Transplant Centers

2014 ◽  
Vol 05 (04) ◽  
pp. 988-1004 ◽  
Author(s):  
C. Weir ◽  
R. S. Evans ◽  
C. Staes ◽  
J. Jacobs
Keyword(s):  
Decision Support ◽  
Liver Transplant ◽  
Clinical Decision Support ◽  
Clinical Information System ◽  
Laboratory Data ◽  
Clinical Information ◽  
Clinical Decision ◽  
Laboratory Monitoring ◽  
Post Transplant ◽  
Laboratory Results

SummaryBackground: Following liver transplantation, patients require lifelong immunosuppressive care and monitoring. Computerized clinical decision support (CDS) has been shown to improve post-transplant immunosuppressive care processes and outcomes. The readiness of transplant information systems to implement computerized CDS to support post-transplant care is unknown.Objectives: a) Describe the current clinical information system functionality and manual and automated processes for laboratory monitoring of immunosuppressive care, b) describe the use of guidelines that may be used to produce computable logic and the use of computerized alerts to support guideline adherence, and c) explore barriers to implementation of CDS in U.S. liver transplant centers.Methods: We developed a web-based survey using cognitive interviewing techniques. We surveyed 119 U.S. transplant programs that performed at least five liver transplantations per year during 2010–2012. Responses were summarized using descriptive analyses; barriers were identified using qualitative methods.Results: Respondents from 80 programs (67% response rate) completed the survey. While 98% of programs reported having an electronic health record (EHR), all programs used paper-based manual processes to receive or track immunosuppressive laboratory results. Most programs (85%) reported that 30% or more of their patients used external laboratories for routine testing. Few programs (19%) received most external laboratory results as discrete data via electronic interfaces while most (80%) manually entered laboratory results into the EHR; less than half (42%) could integrate internal and external laboratory results. Nearly all programs had guidelines regarding pre-specified target ranges (92%) or testing schedules (97%) for managing immunosuppressive care. Few programs used computerized alerting to notify transplant coordinators of out-of-range (27%) or overdue laboratory results (20%).Conclusions: Use of EHRs is common, yet all liver transplant programs were largely dependent on manual paper-based processes to monitor immunosuppression for post-liver transplant patients. Similar immunosuppression guidelines provide opportunities for sharing CDS once integrated laboratory data are available.Citation: Jacobs J, Weir C, Evans RS, Staes C. Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers. Appl Clin Inf 2014; 5: 988–1004http://dx.doi.org/10.4338/ACI-2014-08-RA-0060

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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