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

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.

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

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2017.161 ◽

2017 ◽

Vol 38 (10) ◽

pp. 1204-1208 ◽

Cited By ~ 26

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

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

10.1101/2021.04.05.21254943 ◽

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.

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Behavioral Economics Interventions in Clinical Decision Support Systems

Yearbook of Medical Informatics ◽

10.1055/s-0038-1641221 ◽

2018 ◽

Vol 27 (01) ◽

pp. 114-121 ◽

Cited By ~ 5

Author(s):

Insook Cho ◽

David Bates

Keyword(s):

Decision Support ◽

Behavioral Economics ◽

Clinical Decision Support ◽

Health Care Quality ◽

Cognitive Biases ◽

Controlled Trial ◽

Clinical Decision ◽

The United States ◽

Care Quality ◽

Design Strategies

Background: Clinical decision support (CDS) systems can improve safety and facilitate evidence-based practice. However, clinical decisions are often affected by the cognitive biases and heuristics of clinicians, which is increasing the interest in behavioral and cognitive science approaches in the medical field. Objectives: This review aimed to identify decision biases that lead clinicians to exhibit irrational behaviors or responses, and to show how behavioral economics can be applied to interventions in order to promote and reveal the contributions of CDS to improving health care quality. Methods: We performed a systematic review of studies published in 2016 and 2017 and applied a snowball citationsearch method to identify topical publications related to studies forming part of the BEARI (Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections) multisite, cluster-randomized controlled trial performed in the United States. Results: We found that 10 behavioral economics concepts with nine cognitive biases were addressed and investigated for clinician decision-making, and that the following five concepts, which were actively explored, had an impact in CDS applications: social norms, framing effect, status-quo bias, heuristics, and overconfidence bias. Conclusions: Our review revealed that the use of behavioral economics techniques is increasing in areas such as antibiotics prescribing and preventive care, and that additional tests of the concepts and heuristics described would be useful in other areas of CDS. An improved understanding of the benefits and limitations of behavioral economics techniques is also still needed. Future studies should focus on successful design strategies and how to combine them with CDS functions for motivating clinicians.

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A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists

Applied Clinical Informatics ◽

10.1055/s-0041-1722916 ◽

2021 ◽

Vol 12 (02) ◽

pp. 199-207

Author(s):

Liang Yan ◽

Thomas Reese ◽

Scott D. Nelson

Keyword(s):

Patient Safety ◽

Quality Of Care ◽

Decision Support ◽

Clinical Decision Support ◽

Clinical Decision ◽

Controlled Trials ◽

Clinical Pharmacists ◽

Randomized Controlled ◽

The Impact

Abstract Objective Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. Methods We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle–Ottawa Scale. Results Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. Conclusion These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

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Utilization and effects of mobile electronic clinical decision support on pediatric asthma care quality in the emergency department and inpatient setting

JAMIA Open ◽

10.1093/jamiaopen/ooab019 ◽

2021 ◽

Vol 4 (2) ◽

Author(s):

Ellen Kerns ◽

Russell McCulloh ◽

Sarah Fouquet ◽

Corrie McDaniel ◽

Lynda Ken ◽

...

Keyword(s):

Emergency Department ◽

Decision Support ◽

Clinical Decision Support ◽

Pediatric Asthma ◽

Clinical Decision ◽

Care Quality ◽

Asthma Care ◽

Mixed Effect ◽

National Quality ◽

Electronic Clinical Decision Support

Abstract Objective To determine utilization and impacts of a mobile electronic clinical decision support (mECDS) on pediatric asthma care quality in emergency department and inpatient settings. Methods We conducted an observational study of a mECDS tool that was deployed as part of a multi-dimensional, national quality improvement (QI) project focused on pediatric asthma. We quantified mECDS utilization using cumulative screen views over the study period in the city in which each participating site was located. We determined associations between mECDS utilization and pediatric asthma quality metrics using mixed-effect logistic regression models (adjusted for time, site characteristics, site-level QI project engagement, and patient characteristics). Results The tool was offered to clinicians at 75 sites and used on 286 devices; cumulative screen views were 4191. Children’s hospitals and sites with greater QI project engagement had higher cumulative mECDS utilization. Cumulative mECDS utilization was associated with significantly reduced odds of hospital admission (OR: 0.95, 95% CI: 0.92–0.98) and higher odds of caregiver referral to smoking cessation resources (OR: 1.08, 95% CI: 1.01–1.16). Discussion We linked mECDS utilization to clinical outcomes using a national sample and controlling for important confounders (secular trends, patient case mix, and concomitant QI efforts). We found mECDS utilization was associated with improvements in multiple measures of pediatric asthma care quality. Conclusion mECDS has the potential to overcome barriers to dissemination and improve care on a broad scale. Important areas of future work include improving mECDS uptake/utilization, linking clinicians’ mECDS usage to clinical practice, and studying mECDS’s impacts on other common pediatric conditions.

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Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2019-0390-oa ◽

2019 ◽

Vol 144 (7) ◽

pp. 869-877 ◽

Cited By ~ 1

Author(s):

Marios A. Gavrielides ◽

Meghan Miller ◽

Ian S. Hagemann ◽

Heba Abdelal ◽

Zahra Alipour ◽

...

Keyword(s):

Decision Support ◽

Ovarian Carcinoma ◽

Clinical Decision Support ◽

Clinical Decision ◽

Subspecialty Training ◽

Reference Standard ◽

Observer Study ◽

Subtype Classification ◽

The Impact ◽

Whole Slide Images

Context.— Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. Objective.— To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. Design.— Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. Results.— Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. Conclusions.— Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks.

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The Changing Nature of Clinical Decision Support Systems: a Focus on Consumers, Genomics, Public Health and Decision Safety

Yearbook of Medical Informatics ◽

10.1055/s-0038-1638644 ◽

2009 ◽

Vol 18 (01) ◽

pp. 84-95 ◽

Cited By ~ 3

Author(s):

A. Y. S. Lau ◽

G. Tsafnat ◽

V. Sintchenko ◽

F. Magrabi ◽

E. Coiera

Keyword(s):

Public Health ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Genetic Data ◽

Clinical Decision ◽

Translational Bioinformatics ◽

The Impact

Summary Objectives To review the recent research literature in clinical decision support systems (CDSS). Methods A review of recent literature was undertaken, focussing on CDSS evaluation, consumers and public health, the impact of translational bioinformatics on CDSS design, and CDSS safety. Results In recent years, researchers have concentrated much less on the development of decision technologies, and have focussed more on the impact of CDSS in the clinical world. Recent work highlights that traditional process measures of CDSS effectiveness, such as document relevance are poor proxy measures for decision outcomes. Measuring the dynamics of decision making, for example via decision velocity, may produce a more accurate picture of effectiveness. Another trend is the broadening of user base for CDSS beyond front line clinicians. Consumers are now a major focus for biomedical informatics, as are public health officials, tasked with detecting and managing disease outbreaks at a health system, rather than individual patient level. Bioinformatics is also changing the nature of CDSS. Apart from personalisation of therapy recommendations, translational bioinformatics is creating new challenges in the interpretation of the meaning of genetic data. Finally, there is much recent interest in the safety and effectiveness of computerised physicianorderentry (CPOE) systems, given that prescribing and administration errors are a significant cause of morbidity and mortality. Of note, there is still much controversy surrounding the contention that poorly designed, implemented or used CDSS may actually lead to harm. Conclusions CDSS research remains an active and evolving area of research, as CDSS penetrate more widely beyond their traditional domain into consumer decision support, and as decisions become more complex, for example by involving sequence level genetic data.

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“Send & hold” clinical decision support rules improvement to reduce unnecessary testing of vitamins A, E, K, B1, B2, B3, B6 and C

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2017-1031 ◽

2018 ◽

Vol 56 (7) ◽

pp. 1063-1070 ◽

Cited By ~ 4

Author(s):

Enrique Rodriguez-Borja ◽

Africa Corchon-Peyrallo ◽

Esther Barba-Serrano ◽

Celia Villalba Martínez ◽

Arturo Carratala Calvo

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Primary Care Physicians ◽

Vitamin B1 ◽

Clinical Decision ◽

Health Department ◽

Leadership Role ◽

Control Group ◽

Vitamin B3 ◽

The Impact

Abstract Background: We assessed the impact of several “send & hold” clinical decision support rules (CDSRs) within the electronical request system for vitamins A, E, K, B1, B2, B3, B6 and C for all outpatients at a large health department. Methods: When ordered through electronical request, providers (except for all our primary care physicians who worked as a non-intervention control group) were always asked to answer several compulsory questions regarding main indication, symptomatology, suspected diagnosis, vitamin active treatments, etc., for each vitamin test using a drop-down list format. After samples arrival, tests were later put on hold internally by our laboratory information system (LIS) until review for their appropriateness was made by two staff pathologists according to the provided answers and LIS records (i.e. “send & hold”). The number of tests for each analyte was compared between the 10-month period before and after CDSRs implementation in both groups. Results: After implementation, vitamins test volumes decreased by 40% for vitamin A, 29% for vitamin E, 42% for vitamin K, 37% for vitamin B1, 85% for vitamin B2, 68% for vitamin B3, 65% for vitamin B6 and 59% for vitamin C (all p values 0.03 or lower except for vitamin B3), whereas in control group, the majority increased or remained stable. In patients with rejected vitamins, no new requests and/or adverse clinical outcome comments due to this fact were identified. Conclusions: “Send & hold” CDSRs are a promising informatics tool that can support in utilization management and enhance the pathologist’s leadership role as tests specialist.

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Examining Workflow in a Pediatric Emergency Department to Develop a Clinical Decision Support for an Antimicrobial Stewardship Program

Applied Clinical Informatics ◽

10.1055/s-0038-1641594 ◽

2018 ◽

Vol 09 (02) ◽

pp. 248-260 ◽

Cited By ~ 10

Author(s):

Mustafa Ozkaynak ◽

Danny Wu ◽

Katia Hannah ◽

Peter Dayan ◽

Rakesh Mistry

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Antimicrobial Stewardship ◽

Time Allocation ◽

Care Delivery ◽

Tertiary Care ◽

Clinical Decision ◽

Design And Implementation ◽

Decision Points ◽

Stewardship Program

Background Clinical decision support (CDS) embedded into the electronic health record (EHR), is a potentially powerful tool for institution of antimicrobial stewardship programs (ASPs) in emergency departments (EDs). However, design and implementation of CDS systems should be informed by the existing workflow to ensure its congruence with ED practice, which is characterized by erratic workflow, intermittent computer interactions, and variable timing of antibiotic prescription. Objective This article aims to characterize ED workflow for four provider types, to guide future design and implementation of an ED-based ASP using the EHR. Methods Workflow was systematically examined in a single, tertiary-care academic children's hospital ED. Clinicians with four roles (attending, nurse practitioner, physician assistant, resident) were observed over a 3-month period using a tablet computer-based data collection tool. Structural observations were recorded by investigators, and classified using a predetermined set of activities. Clinicians were queried regarding timing of diagnosis and disposition decision points. Results A total of 23 providers were observed for 90 hours. Sixty-four different activities were captured for a total of 6,060 times. Among these activities, nine were conducted at different frequency or time allocation across four roles. Moreover, we identified differences in sequential patterns across roles. Decision points, whereby clinicians then proceeded with treatment, were identified 127 times. The most common decision points identified were: (1) after/during examining or talking to patient or relative; (2) after talking to a specialist; and (3) after diagnostic test/image was resulted and discussed with patient/family. Conclusion The design and implementation of CDS for ASP should support clinicians in various provider roles, despite having different workflow patterns. The clinicians make their decisions about treatment at different points of overall care delivery practice; likewise, the CDS should also support decisions at different points of care.

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

BMJ Quality & Safety ◽

10.1136/bmjqs-2018-008765 ◽

2019 ◽

Vol 28 (9) ◽

pp. 762-768 ◽

Cited By ~ 12

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.

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