The Impact of a Computerized Clinical Decision Support Tool on Inappropriate Clostridium difficile Testing

2017 ◽  
Vol 38 (10) ◽  
pp. 1204-1208 ◽  
Author(s):  
Duncan R. White ◽  
Keith W. Hamilton ◽  
David A. Pegues ◽  
Asaf Hanish ◽  
Craig A. Umscheid
Keyword(s):  
Decision Support ◽  
Clostridium Difficile ◽  
Clinical Decision Support ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Baseline Period ◽  
Support Tool ◽  
Number Of Patients ◽  
The Impact ◽  
Order Set

OBJECTIVETo evaluate the effectiveness of a computerized clinical decision support intervention aimed at reducing inappropriate Clostridium difficile testingDESIGNRetrospective cohort studySETTINGUniversity of Pennsylvania Health System, comprised of 3 large tertiary-care hospitalsPATIENTSAll adult patients admitted over a 2-year periodINTERVENTIONProviders were required to use an order set integrated into a commercial electronic health record to order C. difficile toxin testing. The order set identified patients who had received laxatives within the previous 36 hours and displayed a message asking providers to consider stopping laxatives and reassessing in 24 hours prior to ordering C. difficile testing. Providers had the option to continue or discontinue laxatives and to proceed with or forgo testing. The primary endpoint was the change in inappropriate C. difficile testing, as measured by the number of patients who had C. difficile testing ordered while receiving laxatives.RESULTSCompared to the 1-year baseline period, the intervention resulted in a decrease in the proportion of inappropriate C. difficile testing (29.6% vs 27.3%; P=.02). The intervention was associated with an increase in the number of patients who had laxatives discontinued and did not undergo C. difficile testing (5.8% vs 46.4%; P<.01) and who had their laxatives discontinued and underwent testing (5.4% vs 35.2%; P<.01). We observed a nonsignificant increase in the proportion of patients with C. difficile related complications (5.0% vs 8.9%; P=.11).CONCLUSIONSA C. difficile order set was successful in decreasing inappropriate C. difficile testing and improving the timely discontinuation of laxatives.Infect Control Hosp Epidemiol 2017;38:1204–1208

scholarly journals A Quality Improvement Initiative to Decrease Platelet Ordering Errors and a Proposed Model for Evaluating Clinical Decision Support Effectiveness

2019 ◽  
Vol 10 (03) ◽  
pp. 505-512
Author(s):  
Julia Whitlow Yarahuan ◽  
Amy Billet ◽  
Jonathan D. Hron
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Quality ◽  
Platelet Transfusion ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Care Quality ◽  
Inpatient Service ◽  
The Impact ◽  
Order Set

Background and Objectives Clinical decision support (CDS) and computerized provider order entry have been shown to improve health care quality and safety, but may also generate previously unanticipated errors. We identified multiple CDS tools for platelet transfusion orders. In this study, we sought to evaluate and improve the effectiveness of those CDS tools while creating and testing a framework for future evaluation of other CDS tools. Methods Using a query of an enterprise data warehouse at a tertiary care pediatric hospital, we conducted a retrospective analysis to assess baseline use and performance of existing CDS for platelet transfusion orders. Our outcome measure was the percentage of platelet undertransfusion ordering errors. Errors were defined as platelet transfusion volumes ordered which were less than the amount recommended by the order set used. We then redesigned our CDS and measured the impact of our intervention prospectively using statistical process control methodology. Results We identified that 62% of all platelet transfusion orders were placed with one of two order sets (Inpatient Service 1 and Inpatient Service 2). The Inpatient Service 1 order set had a significantly higher occurrence of ordering errors (3.10% compared with 1.20%). After our interventions, platelet transfusion order error occurrence on Inpatient Service 1 decreased from 3.10 to 0.33%. Conclusion We successfully reduced platelet transfusion ordering errors by redesigning our CDS tools. We suggest that the use of collections of clinical data may help identify patterns in erroneous ordering, which could otherwise go undetected. We have created a framework which can be used to evaluate the effectiveness of other similar CDS tools.

scholarly journals Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience

2020 ◽  
Vol 21 (6) ◽  
pp. 375-386 ◽  
Author(s):  
Christina L Aquilante ◽  
David P Kao ◽  
Katy E Trinkley ◽  
Chen-Tan Lin ◽  
Kristy R Crooks ◽  
...  
Keyword(s):  
Decision Support ◽  
Health System ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Support Tool ◽  
University Of Colorado ◽  
The University ◽  
The Impact

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

scholarly journals Estimating the impact of deploying an electronic clinical decision support tool as part of a national practice improvement project

2019 ◽  
Vol 26 (7) ◽  
pp. 630-636 ◽  
Author(s):  
Ellen K Kerns ◽  
Vincent S Staggs ◽  
Sarah D Fouquet ◽  
Russell J McCulloh
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Market Area ◽  
Improvement Project ◽  
Support Tool ◽  
Febrile Infant ◽  
The Impact ◽  
Electronic Clinical Decision Support

Abstract Objective Estimate the impact on clinical practice of using a mobile device–based electronic clinical decision support (mECDS) tool within a national standardization project. Materials and Methods An mECDS tool (app) was released as part of a change package to provide febrile infant management guidance to clinicians. App usage was analyzed using 2 measures: metric hits per case (metric-related screen view count divided by site-reported febrile infant cases in each designated market area [DMA] monthly) and cumulative prior metric hits per site (DMA metric hits summed from study month 1 until the month preceding the index, divided by sites in the DMA). For each metric, a mixed logistic regression model was fit to model site performance as a function of app usage. Results An increase of 200 cumulative prior metric hits per site was associated with increased odds of adherence to 3 metrics: appropriate admission (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.18), appropriate length of stay (OR, 1.20; 95% CI, 1.12-1.28), and inappropriate chest x-ray (OR, 0.82; 95% CI, 0.75-0.91). Ten additional metric hits per case were also associated: OR were 1.18 (95% CI, 1.02-1.36), 1.36 (95% CI, 1.14-1.62), and 0.74 (95% CI, 0.62-0.89). Discussion mECDS tools are increasingly being implemented, but their impact on clinical practice is poorly described. To our knowledge, although ecologic in nature, this report is the first to link clinical practice to mECDS use on a national scale and outside of an electronic health record. Conclusions mECDS use was associated with changes in adherence to targeted metrics. Future studies should seek to link mECDS usage more directly to clinical practice and assess other site-level factors.

scholarly journals Reduced Clostridium difficile Tests and Laboratory-Identified Events With a Computerized Clinical Decision Support Tool and Financial Incentive

2018 ◽  
Vol 39 (6) ◽  
pp. 737-740 ◽  
Author(s):  
Gregory R. Madden ◽  
Ian German Mesner ◽  
Heather L. Cox ◽  
Amy J. Mathers ◽  
Jason A. Lyman ◽  
...  
Keyword(s):  
Decision Support ◽  
Clostridium Difficile ◽  
Clinical Decision Support ◽  
Financial Incentive ◽  
Decision Support Tool ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Support Tool ◽  
Time Series Analyses ◽  
Clinical Decision Support Tool

We hypothesized that a computerized clinical decision support tool for Clostridium difficile testing would reduce unnecessary inpatient tests, resulting in fewer laboratory-identified events. Census-adjusted interrupted time-series analyses demonstrated significant reductions of 41% fewer tests and 31% fewer hospital-onset C. difficile infection laboratory-identified events following this intervention.Infect Control Hosp Epidemiol 2018;39:737–740

Impact of a clinical decision support tool targeting QT-prolonging medications

2020 ◽  
Vol 77 (Supplement_4) ◽  
pp. S111-S117
Author(s):  
Katie Chernoby ◽  
Michael F Lucey ◽  
Carrie L Hartner ◽  
Michelle Dehoorne ◽  
Stephanie B Edwin
Keyword(s):  
Risk Factors ◽  
Decision Support ◽  
Adverse Events ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Study Groups ◽  
Pharmacist Intervention ◽  
Support Tool ◽  
The Impact

Abstract Purpose To evaluate the impact of a newly implemented clinical decision support (CDS) tool targeting QT interval–prolonging medications on order verification and provider interventions. Methods A multicenter, retrospective quasi-experimental study was conducted to evaluate provider response to CDS alerts triggered during ordering of QT-prolonging medications for adult patients. The primary outcome was the proportion of orders triggering QTc alerts that were continued without intervention during a specified preimplementation phase (n = 49) and during a postimplementation phase (n = 100). Patient risk factors for QTc prolongation, provider alert response, and interventions to reduce the risk of QTc-associated adverse events were evaluated. Results The rate of order continuation without intervention was 82% in the preimplementation phase and 37% in the postimplementation phase, representing an 55% reduction in continued verified orders following implementation of the QT-focused CDS tool. Most alerts were initially responded to by the prescriber, with pharmacist intervention needed in only 33% of cases. There were no significant differences in patient QTc-related risk factors between the 2 study groups (P = 0.11); the postimplementation group had a higher proportion of patients using at least 2 QTc-prolonging medications (48%, compared to 26% in the preimplementation group; P = 0.02). Conclusion Implementation of the CDS tool was associated with a reduction in the proportion of orders continued without intervention in patients at high risk for QTc-related adverse events.

scholarly journals Impact of an electronic hard-stop clinical decision support tool to limit repeat Clostridioides difficile toxin enzyme immunoassay testing on test utilization

2019 ◽  
Vol 40 (12) ◽  
pp. 1423-1426 ◽  
Author(s):  
Jennie H. Kwon ◽  
Kimberly A. Reske ◽  
Tiffany Hink ◽  
Ronald Jackups ◽  
Carey-Ann D. Burnham ◽  
...  
Keyword(s):  
Decision Support ◽  
Enzyme Immunoassay ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Testing Rate ◽  
Clostridioides Difficile ◽  
Clinical Decision Support Tool ◽  
The Impact

AbstractWe performed an intervention evaluating the impact of an electronic hard-stop clinical decision support tool on repeat Clostridioides difficile (CD) toxin enzyme immunoassay (T-EIA) testing. The CD testing rate and number of admissions with repeat tests decreased significantly postintervention (P < .01 for both); the percentage of positive tests was unchanged (P = .27).

scholarly journals Impact of a Clinical Decision Support Intervention on Albumin Utilization and Appropriateness of Use in an Academic Healthcare System

2021 ◽  
Author(s):  
Prashant R. Mudireddy ◽  
Nikhil K. Mull ◽  
Kendal Williams ◽  
Jennifer M. Bushen ◽  
Nishaminy Kasbekar ◽  
...  
Keyword(s):  
Decision Support ◽  
Healthcare System ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
P Value ◽  
Albumin Concentration ◽  
Before And After ◽  
The Impact ◽  
Over Time ◽  
Order Set

Background: Albumin is expensive compared to crystalloid intravenous fluids and may be used for inappropriate indications, resulting in low value care. Aim/Purpose: To study the impact of a computerized clinical decision support (CDS) intervention on albumin utilization and appropriateness of use in an academic healthcare system. Methods: A systematic review examining appropriate indications for albumin use in the healthcare setting was used by an interprofessional group of stakeholders locally to develop a CDS intervention to improve the appropriateness of albumin utilization. The order set was implemented across our healthcare system on 4/12/2011, included a list of appropriate indications, and automatically provided albumin concentration, dose and frequency based on the indication selected and patient weight and creatinine. We measured units of albumin ordered across the healthcare system and individually at each of three hospitals in the healthcare system 12 months before and after intervention implementation. An interrupted time series analysis using monthly data examined changes in the level and slope of albumin use during pre- versus post-implementation periods. We also reviewed charts of all adult inpatients receiving albumin in the 3 months prior to and following implementation of the order set at two of the three hospitals within the healthcare system, to determine if appropriateness of use had changed, as defined by our consensus criteria. We selected the two hospitals with the most frequent use of albumin in the pre-period. We used chi square tests to compare changes in the proportion of appropriate instances and grams of albumin used. We considered a p-value <0.05 as statistically significant. Results: The number of patient encounters analyzed in the 12 months before and after the albumin CDS intervention was 79,108, and 78,240, respectively. There was a statistically significant decrease in mean units of albumin ordered immediately post-intervention across the healthcare system (-4.98 units per 1000 patient days, confidence interval -9.64 to -0.33, p=0.04). At Hospital 1, there were no statistically significant changes in albumin ordering over time. At Hospital 2, albumin ordering significantly increased up to the intervention, but decreased significantly immediately following the intervention and continued to decrease significantly over time following the intervention; the pre and post implementation slopes were significantly different. At Hospital 3, albumin ordering was statistically unchanged up to the intervention, decreased significantly immediately following the intervention, and significantly increased over time following the intervention, but the pre and post slopes were not statistically different. At Hospitals 1 and 3, there was a statistically significant improvement in appropriateness of albumin use in the three months following implementation. Conclusions: Implementation of a CDS intervention was associated with an increase in the amount of albumin administered appropriately at two hospitals within an academic healthcare system and an overall decrease in albumin utilization across the healthcare system.

scholarly journals Implementation of a Clinical Decision Support Tool for Stool Cultures and Parasitological Studies in Hospitalized Patients

2017 ◽  
Vol 55 (12) ◽  
pp. 3350-3354 ◽  
Author(s):  
D. Nikolic ◽  
S. S. Richter ◽  
K. Asamoto ◽  
R. Wyllie ◽  
R. Tuttle ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Cost Savings ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Content Type ◽  
Support Tool ◽  
Clinical Decision Support Tool ◽  
The Impact ◽  
Stool Cultures

ABSTRACTThere is substantial evidence that stool culture and parasitological examinations are of minimal to no value after 3 days of hospitalization. We implemented and studied the impact of a clinical decision support tool (CDST) to decrease the number of unnecessary stool cultures (STCUL), ova/parasite (O&P) examinations, andGiardia/Cryptosporidiumenzyme immunoassay screens (GC-EIA) performed for patients hospitalized >3 days. We studied the frequency of stool studies ordered before or on day 3 and after day 3 of hospitalization (i.e., categorical orders/total number of orders) before and after this intervention and denoted the numbers and types of microorganisms detected within those time frames. This intervention, which corresponded to a custom-programmed hard-stop alert tool in the Epic hospital information system, allowed providers to override the intervention by calling the laboratory, if testing was deemed medically necessary. Comparative statistics were employed to determine significance, and cost savings were estimated based on our internal costs. Before the intervention, 129/670 (19.25%) O&P examinations, 47/204 (23.04%) GC-EIA, and 249/1,229 (20.26%) STCUL were ordered after 3 days of hospitalization. After the intervention, 46/521 (8.83%) O&P examinations, 27/157 (17.20%) GC-EIA, and 106/1,028 (10.31%) STCUL were ordered after 3 days of hospitalization. The proportions of reductions in the number of tests performed after 3 days and the associatedPvalues were 54.1% for O&P examinations (P< 0.0001), 22.58% for GC-EIA (P= 0.2807), and 49.1% for STCUL (P< 0.0001). This was estimated to have resulted in $8,108.84 of cost savings. The electronic CDST resulted in a substantial reduction in the number of evaluations of stool cultures and the number of parasitological examinations for patients hospitalized for more than 3 days and in a cost savings while retaining the ability of the clinician to obtain these tests if clinically indicated.

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