scholarly journals Improving Appropriate Diagnosis of Clostridioides difficile Infection Through an Enteric Pathogen Order Set With Computerized Clinical Decision Support: An Interrupted Time Series Analysis

2020 ◽  
Vol 7 (10) ◽  
Author(s):  
Catherine Liu ◽  
Kristine Lan ◽  
Elizabeth M Krantz ◽  
H Nina Kim ◽  
Jacqlynn Zier ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Outcomes ◽  
Clinical Decision Support ◽  
Medical Center ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Clostridioides Difficile ◽  
Pcr Test ◽  
Order Set

Abstract Background Inappropriate testing for Clostridioides difficile leads to overdiagnosis of C difficile infection (CDI). We determined the effect of a computerized clinical decision support (CCDS) order set on C difficile polymerase chain reaction (PCR) test utilization and clinical outcomes. Methods This study is an interrupted time series analysis comparing C difficile PCR test utilization, hospital-onset CDI (HO-CDI) rates, and clinical outcomes before and after implementation of a CCDS order set at 2 academic medical centers: University of Washington Medical Center (UWMC) and Harborview Medical Center (HMC). Results Compared with the 20-month preintervention period, during the 12-month postimplementation of the CCDS order set, there was an immediate and sustained reduction in C difficile PCR test utilization rates at both hospitals (HMC, −28.2% [95% confidence interval {CI}, −43.0% to −9.4%], P = .005; UWMC, −27.4%, [95% CI, −37.5% to −15.6%], P < .001). There was a significant reduction in rates of C difficile tests ordered in the setting of laxatives (HMC, −60.8% [95% CI, −74.3% to −40.1%], P < .001; UWMC, −37.3%, [95% CI, −58.2% to −5.9%], P = .02). The intervention was associated with an increase in the C difficile test positivity rate at HMC (P = .01). There were no significant differences in HO-CDI rates or in the proportion of patients with HO-CDI who developed severe CDI or CDI-associated complications including intensive care unit transfer, extended length of stay, 30-day mortality, and toxic megacolon. Conclusions Computerized clinical decision support tools can improve C difficile diagnostic test stewardship without causing harm. Additional studies are needed to identify key elements of CCDS tools to further optimize C difficile testing and assess their effect on adverse clinical outcomes.

scholarly journals Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study (Preprint)

2019 ◽  
Author(s):  
Liyuan Tao ◽  
Chen Zhang ◽  
Lin Zeng ◽  
Shengrong Zhu ◽  
Nan Li ◽  
...  
Keyword(s):  
Time Series ◽  
Logistic Regression ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision Support Systems ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Aided Diagnosis

BACKGROUND Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. OBJECTIVE The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice–aided diagnosis in real-world research. METHODS This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. RESULTS The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all <italic>P</italic>&lt;.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, <italic>P</italic>=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, <italic>P</italic>=.01). Similar results were obtained in the subgroup analysis. CONCLUSIONS The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians’ diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.

Chasing the rate: An interrupted time series analysis of interventions targeting reported hospital onset Clostridioides difficile, 2013–2018

2020 ◽  
Vol 41 (10) ◽  
pp. 1142-1147
Author(s):  
Michelle E. Doll ◽  
Jinlei Zhao ◽  
Le Kang ◽  
Barry Rittmann ◽  
Michael Alvarez ◽  
...  
Keyword(s):  
Time Series ◽  
Decision Support ◽  
Infection Control ◽  
Medical Center ◽  
Academic Medical Center ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Test Volume ◽  
Clostridioides Difficile ◽  
The Impact

AbstractObjective:To assess the impact of major interventions targeting infection control and diagnostic stewardship in efforts to decrease Clostridioides difficile hospital onset rates over a 6-year period.Design:Interrupted time series.Setting:The study was conducted in an 865-bed academic medical center.Methods:Monthly hospital-onset C. difficile infection (HO-CDI) rates from January 2013 through January 2019 were analyzed around 5 major interventions: (1) a 2-step cleaning process in which an initial quaternary ammonium product was followed with 10% bleach for daily and terminal cleaning of rooms of patients who have tested positive for C. difficile (February 2014), (2) UV-C device for all terminal cleaning of rooms of C. difficile patients (August 2015), (3) “contact plus” isolation precautions (June 2016), (4) sporicidal peroxyacetic acid and hydrogen peroxide cleaning in all patient areas (June 2017), (5) electronic medical record (EMR) decision support tool to facilitate appropriate C. difficile test ordering (March 2018).Results:Environmental cleaning interventions and enhanced “contact plus” isolation did not impact HO-CDI rates. Diagnostic stewardship via EMR decision support decreased the HO-CDI rate by 6.7 per 10,000 patient days (P = .0079). When adjusting rates for test volume, the EMR decision support significance was reduced to a difference of 5.1 case reductions per 10,000 patient days (P = .0470).Conclusion:Multiple aggressively implemented infection control interventions targeting CDI demonstrated a disappointing impact on endemic CDI rates over 6 years. This study adds to existing data that outside of an outbreak situation, traditional infection control guidance for CDI prevention has little impact on endemic rates.

scholarly journals Patient Outcomes With Prevented vs Negative Clostridioides difficile Tests Using a Computerized Clinical Decision Support Tool

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Gregory R Madden ◽  
Kyle B Enfield ◽  
Costi D Sifri
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Center ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Nucleic Acid Amplification ◽  
Retrospective Case ◽  
Support Tool ◽  
Clostridioides Difficile ◽  
Clinical Decision Support Tool

Abstract Background Overtesting and overdiagnosis of Clostridioides difficile infection are suspected to be common. Reducing inappropriate testing through interventions designed to promote evidence-based diagnostic testing (ie, diagnostic stewardship) may improve C. difficile test utilization. However, the safety of these interventions is not well understood despite the potential risk for missed or delayed diagnoses. Methods This retrospective case–control study examined the outcomes of patients admitted to the University of Virginia Medical Center following introduction of a computerized clinical decision support tool without hard-stops designed to reduce inappropriate tests. Outcomes were compared between patients with a prevented C. difficile nucleic acid amplification test and those with a negative result. Chart reviews were performed for patients with a subsequent positive within 7 days, as well as those patients who received C. difficile–active antibiotics after implementation of the computerized clinical decision support tool. Results Multivariate analysis of 637 cases (490 negative, 147 prevented) showed that a prevented test was not significantly associated with the primary composite outcome (inpatient mortality or intensive care unit transfer) compared with a negative test (adjusted odds ratio, 0.912; P = .747). Fifty-four of 147 (37%) prevented tests were followed by a completed test within 7 days; 11 of these results were positive, resulting in a potential delay in diagnosis. Individual case reviews found that either clinical changes warranted the delay in testing or no adverse events occurred attributable to C. difficile infection. C. difficile treatment without a positive test was not identified. Conclusions Diagnostic stewardship of C. difficile testing using computerized clinical decision support may be both safe and effective for reducing inappropriate inpatient testing.

scholarly journals Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study

10.2196/16912 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e16912 ◽  
Author(s):  
Liyuan Tao ◽  
Chen Zhang ◽  
Lin Zeng ◽  
Shengrong Zhu ◽  
Nan Li ◽  
...  
Keyword(s):  
Time Series ◽  
Logistic Regression ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision Support Systems ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Aided Diagnosis

Background Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. Objective The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice–aided diagnosis in real-world research. Methods This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. Results The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all P<.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, P=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, P=.01). Similar results were obtained in the subgroup analysis. Conclusions The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians’ diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.

scholarly journals Implementation Strategies of a Quality Improvement Initiative for Hospital-Acquired Clostridioides difficile Infection Prevention

2020 ◽  
Vol 41 (S1) ◽  
pp. s279-s280
Author(s):  
Nicole Lamont ◽  
Lauren Bresee ◽  
Kathryn Bush ◽  
Blanda Chow ◽  
Bruce Dalton ◽  
...  
Keyword(s):  
Decision Support ◽  
Focus Groups ◽  
Clinical Decision Support ◽  
Implementation Strategy ◽  
Clinical Decision ◽  
Medication Administration ◽  
Quality Improvement Initiative ◽  
Order Entry ◽  
Clostridioides Difficile ◽  
Number Of Patients

Background:Clostridioides difficile infection (CDI) is the most common cause of infectious diarrhea in hospitalized patients. Probiotics have been studied as a measure to prevent CDI. Timely probiotic administration to at-risk patients receiving systemic antimicrobials presents significant challenges. We sought to determine optimal implementation methods to administer probiotics to all adult inpatients aged 55 years receiving a course of systemic antimicrobials across an entire health region. Methods: Using a randomized stepped-wedge design across 4 acute-care hospitals (n = 2,490 beds), the probiotic Bio-K+ was prescribed daily to patients receiving systemic antimicrobials and was continued for 5 days after antimicrobial discontinuation. Focus groups and interviews were conducted to identify barriers, and the implementation strategy was adapted to address the key identified barriers. The implementation strategy included clinical decision support involving a linked flag on antibiotic ordering and a 1-click order entry within the electronic medical record (EMR), provider and patient education (written/videos/in-person), and local site champions. Protocol adherence was measured by tracking the number of patients on therapeutic antimicrobials that received BioK+ based on the bedside nursing EMR medication administration records. Adherence rates were sorted by hospital and unit in 48- and 72-hour intervals with recording of percentile distribution of time (days) to receipt of the first antimicrobial. Results: In total, 340 education sessions with >1,800 key stakeholders occurred before and during implementation across the 4 involved hospitals. The overall adherence of probiotic ordering for wards with antimicrobial orders was 78% and 80% at 48 and 72 hours, respectively over 72 patient months. Individual hospital adherence rates varied between 77% and 80% at 48 hours and between 79% and 83% at 72 hours. Of 246,144 scheduled probiotic orders, 94% were administered at the bedside within a median of 0.61 days (75th percentile, 0.88), 0.47 days (75th percentile, 0.86), 0.71 days (75th percentile, 0.92) and 0.67 days (75th percentile, 0.93), respectively, at the 4 sites after receipt of first antimicrobial. The key themes from the focus groups emphasized the usefulness of the linked flag alert for probiotics on antibiotic ordering, the ease of the EMR 1-click order entry, and the importance of the education sessions. Conclusions: Electronic clinical decision support, education, and local champion support achieved a high implementation rate consistent across all sites. Use of a 1-click order entry in the EMR was considered a key component of the success of the implementation and should be considered for any implementation strategy for a stewardship initiative. Achieving high prescribing adherence allows more precision in evaluating the effectiveness of the probiotic strategy.Funding: Partnerships for Research and Innovation in the Health System, Alberta Innovates/Health Solutions Funding: AwardDisclosures: None

scholarly journals 1074. Evaluation of a Pharmacist-led Antimicrobial and Anticoagulant Monitoring Initiative

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S380-S381
Author(s):  
Wei Hsiang Lin ◽  
Amanda Binkley ◽  
Christo L Cimino ◽  
Naasha J Talati ◽  
Jimish M Mehta ◽  
...  
Keyword(s):  
Renal Function ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Dose Adjustment ◽  
Adverse Drug Events ◽  
Medical Center ◽  
Clinical Decision

Abstract Background Adverse drug events are associated with an increase in hospital stay and cost. Risks from these events are minimized by adjusting a medication’s dose or frequency, and changes in renal function may necessitate adjustments. Currently, there is no formal procedure for a prospective audit of renal function over the weekend at our institution. This pharmacist-driven initiative will evaluate if a prospective review identified by real-time clinical decision support alerts over the weekend will reduce the time from change in renal function to dose adjustment of select antimicrobials and/or anticoagulants. Methods This monitoring initiative is comprised of a pre- and post-cohort population. The pre-cohort population included patients admitted to Penn Presbyterian Medical Center (PPMC) from January to March of 2018 on select antimicrobials and/or anticoagulants, who were identified to have a change in renal function (serum creatinine change of 0.3 mg/dL or greater) over the weekend. The post-cohort population was identified with a clinical decision support system (ILÚM Health Solutions, Kenilworth, NJ) and included patients admitted to PPMC from January to March of 2019. A pharmacy resident reviewed alerts in the clinical decision support system over the weekend and contacted providers with dose adjustment recommendations. The Mann–Whitney U test was used to analyze the primary endpoint while descriptive statistics were used for the secondary endpoints Results Eighteen interventions were completed within the 3-month post-cohort intervention period, with a time to dose adjustment between the pre/post-cohort being reduced by 50 hours (P = 0.0001) resulting in a median time to change of 11 hours in the post-cohort. All pharmacy recommendations were accepted by the provider, and 94% of medication adjustments were antimicrobials. Conclusion The application of this prospective weekend initiative utilizing a clinical decision support system demonstrated a clinically and statistically significant reduction in the time to dose adjustments for antimicrobials and/or anticoagulants. Implementation of this initiative will further establish a role for pharmacist-led evaluations and could potentially be expanded to other clinical areas. Disclosures All authors: No reported disclosures.

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