Electronic Clinical Decision Support Research Articles

Can Electronic Clinical Decision Support Systems Improve the Diagnosis of Urinary Tract Infections? A Systematic Review and Meta-Analysis.

Urinary tract infection (UTI) is a commonly misdiagnosed infectious syndrome. Diagnostic stewardship interventions can reduce rates of asymptomatic bacteriuria treatment but are often labor intensive, and thus an automated means of reducing unnecessary urine testing is preferred. In this systematic review and meta-analysis, we sought to identify studies describing interventions utilizing clinical decision support (CDS) to optimize UTI diagnosis and to characterize the effectiveness of these interventions. We conducted a comprehensive electronic search and manual reference list review for peer-reviewed articles published before July 2, 2021. Publications describing an intervention intending to enhance UTI diagnosis via CDS were included. The primary outcome was urine culture test rate. The electronic search identified 5013 studies for screening. After screening and full-text review, 9 studies met criteria for inclusion, and a manual reference list review identified 5 additional studies, yielding a total of 14 studies included in the systematic review. The most common CDS intervention was urinalysis with reflex to urine culture based on prespecified urinalysis parameters. All 9 studies that provided statistical comparisons reported a decreased urine culture rate postintervention, 8 of which were statistically significant. A meta-analysis including 4 studies identified a pooled urine culture incidence rate ratio of 0.56 (95% confidence interval, .52-.60) favoring the postintervention versus preintervention group. In this systematic review and meta-analysis, CDS appeared to be effective in decreasing urine culture rates. Prospective trials are needed to confirm these findings and to evaluate their impact on antimicrobial prescribing, patient-relevant outcomes, and potential adverse effects.

1746. Electronic medical record-embedded clinical decision support decreases inappropriate duration of antibiotics for outpatient pediatric skin and soft tissue infections

Abstract Background Most antibiotics are prescribed outside of the hospital, and up to 50% are unnecessary or inappropriate. Studies of ambulatory stewardship interventions have primarily targeted respiratory infections. We investigated the impact of an electronic medical record (EMR)-embedded clinical decision support tool on antibiotic duration for outpatient pediatric skin and soft tissue infection (SSTI). Methods Institutional SSTI treatment guidelines for pediatric and adult patients were developed in 7/2019, followed in 5/2020 by dedicated education for primary care pediatricians. In 12/2020, a SmartSet (EMR-embedded tool with a templated progress note and antibiotic guidance) was created for pediatrics only. In this quasi-experimental pre-post study, we included adult and pediatric patients who were prescribed antibiotics for SSTI at a primary care or surgery clinic (in person, virtual, or phone) from 7/2019 to 12/2021. Inappropriate pediatric antibiotic duration ( >7 days) was compared pre- and post-intervention with interrupted time series (ITS) analysis of aggregate quarterly data. The trend in adult antibiotic duration was modeled with linear regression. Results We included 3,786 (948 pediatric and 2,838 adult) pre-intervention and 2,122 (469 pediatric and 1,653 adult) post-intervention visits. Pre-intervention, the percentage of inappropriate duration for pediatric patients was decreasing 1.6 percentage points per quarter (p < 0.01). After SmartSet implementation, there was an immediate decrease of 10.3 percentage points (p < 0.01), a relative decrease of 40% from the modeled percentage just prior to intervention (Figure 1). Inappropriate antibiotic duration for adults averaged 22.9% over the same period and did not change (p = 0.88). Figure 1Figure 1. Raw and modeled percentages of inappropriate antibiotic duration for skin and soft tissue infection before (quarters -6 to 0, encompassing Q3 2019 through Q4 2020) and after (quarters 0 to 4, encompassing Q1 2021 through Q4 2021) implementation of an EMR-embedded clinical decision support tool for pediatric patients. Time is represented in 3-month intervals, with quarter 0 being indicating the intervention at the end of December 2020. Conclusion Following guideline implementation and education, inappropriate antibiotic duration for ambulatory pediatric SSTI declined only modestly. An EMR-embedded clinical decision support tool was associated with an additional relative decrease of 40% in inappropriate antibiotic duration. In the same health system with comparable adult SSTI guidelines, the percentage of inappropriate antibiotic duration for adults was unchanged, suggesting timely EMR nudges can provide additional benefit over guidelines alone. Disclosures All Authors: No reported disclosures.

790. Can Electronic Clinical Decision Support Systems Improve the Diagnosis of Urinary Tract Infections?: A Systematic Review and Meta-Analysis

Abstract Background Urinary tract infection (UTI) is a commonly misdiagnosed infectious condition. Stewardship interventions which successfully reduce rates of asymptomatic bacteriuria treatment (ASB) are often labor intensive, and thus a systematic solution is desirable. In this study, our objective was to systematically review studies to evaluate the effect of implementing a clinical decision support system on the ordering of urine culture tests for the diagnosis of UTI across diverse clinical settings. Methods We conducted a comprehensive electronic search of 5 databases and manual reference list review for peer-reviewed articles published prior to July 2, 2021. Studies which described an intervention designed to reduce unnecessary or incorrect diagnosis of UTI through utilization of CDS were included. The primary outcome of interest was rate of urine culture tests ordered. Two investigators independently screened studies and extracted and compiled data. Results A total of 5,013 studies were screened, of which 14 met inclusion criteria for the systematic review (Table 1). The most common intervention was urinalysis with reflex to urine culture based on pre-specified urinalysis parameters, such as a threshold level of pyuria. All 9 studies with statistical comparisons reported a decreased urine culture rate post-intervention, 8 of which were statistically significant (Table 2). Overall, a 37% decrease in urine culture rate was detected after CDS implementation (Table 3). Several studies also reported improvements in antimicrobial-related measures such as days of therapy and guideline-concordant therapy. Catheter-associated urinary tract infection rate was decreased in 2 studies and unchanged in 1 study. A limited number of included studies reported no adverse outcomes including increased bloodstream infection and mortality rates. Conclusion Electronic clinical decision support systems appear to be effective in decreasing unnecessary urine culture tests and improving UTI diagnosis. Prospective studies are needed to evaluate the impact on antimicrobial prescribing, patient-relevant outcomes, and potential adverse effects. Disclosures Elie Saade, MD, MPH, Janssen: Advisor/Consultant Curtis Donskey, MD, Pfizer, Clorox, Ecolab, and Professional Disposables International: Grant/Research Support Abhishek Deshpande, MD, PhD, Clorox: Grant/Research Support|Merck & Co., Inc., Kenilworth, NJ, USA: Advisor/Consultant|Merck & Co., Inc., Kenilworth, NJ, USA: Stocks/Bonds|Seres Therapeutics: Grant/Research Support.

Improving guideline‐concordant thromboprophylaxis prescribing for children admitted to hospital with COVID‐19

The incidence of venous thrombo-embolism (VTE) in hospitalized children has increased by 130%-200% over the last two decades. Given this increase, many centers utilize electronic clinical decision support (CDS) to prognosticate VTE risk and recommend prophylaxis. SARS-CoV-2 infection (COVID-19) is a risk factor for VTE; however, CDS developed before the COVID-19 pandemic may not accurately prognosticate VTE risk in children with COVID-19. This study's objective was to identify areas to improve thromboprophylaxis recommendations for children with COVID-19. Inpatients with a positive COVID-19 test at admission were identified at a quaternary-care pediatric center between March 1, 2020 and January 20, 2022. The results of the institution's automated CDS thromboprophylaxis recommendations were compared to institutional COVID-19 thromboprophylaxis guidelines and to the actual thromboprophylaxis received. CDS optimization was performed to improve adherence to COVID-19 thromboprophylaxis recommendations. Of the 329 patients included in this study, 106 (28.2%) were prescribed pharmaco-prophylaxis, 167 (50.8%) were identified by the institutional COVID-19 guidelines as requiring pharmaco-prophylaxis, and 45 (13.2%) were identified by the CDS as needing pharmaco-prophylaxis. On univariate analysis, only age 12years or more was associated with recipient of appropriate prophylaxis (OR 1.78, 95% CI: 1.13-2.82, p = .013). Five patients developed VTEs; three had symptoms at presentation, two were identified as high risk for VTE by both the automated and best practice assessments but were not prescribed pharmaco-prophylaxis. Automated thromboprophylaxis recommendations developed prior to the COVID-19 pandemic may not identify all COVID-19 patients needing pharmaco-prophylaxis. Existing CDS tools need to be updated to reflect COVID-19-specific risk factors for VTEs.

Where Do Real-Time Prescription Benefit Tools Fit in the Landscape of High US Prescription Medication Costs? A Narrative Review.

The problem of unaffordable prescription medications in the United States is complex and can result in poor patient adherence to therapy, worse clinical outcomes, and high costs to the healthcare system. While providers are aware of the financial burden of healthcare for patients, there is a lack of actionable price transparency at the point of prescribing. Real-time prescription benefit (RTPB) tools are new electronic clinical decision support tools that retrieve patient- and medication-specific out-of-pocket cost information and display it to clinicians at the point of prescribing. The rise in US healthcare costs has been a major driver for efforts to increase medication price transparency, and mandates from the Centers for Medicare & Medicaid Services for Medicare Part D sponsors to adopt RTPB tools may spur integration of such tools into electronic health records. Although multiple factors affect the implementation of RTPB tools, there is limited evidence on outcomes. Further research will be needed to understand the impact of RTPB tools on end results such as prescribing behavior, out-of-pocket medication costs for patients, and adherence to pharmacologic treatment. We review the terminology and concepts essential in understanding the landscape of RTPB tools, implementation considerations, barriers to adoption, and directions for future research that will be important to patients, prescribers, health systems, and insurers.

Abstract 10401: Development and Pilot Implementation of Two Targeted Approaches to Ambulatory Clinical Decision Support for Cardiologists to Optimize Medical Therapy for Heart Failure

Introduction: Shortfalls in prescribing of proven therapies for patients with heart failure and reduced ejection fraction (HFrEF), particularly for mineralocorticoid receptor antagonists (MRA), account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing, but optimal timing and format of CDS tools are unknown. Objective: We developed two CDS interventions to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: 1) a best practice alert (BPA) that notifies cardiologists at the time of patient visit, and 2) an automated in-basket message that allows for review between visits. Methods: We designed CDS tools using an established CDS framework and findings from semi-structured interviews with cardiologists. We then pilot-tested both CDS tools at two cardiology practices for 12 weeks in a cross-over design, and further enhanced them based on additional semi-structured interviews. Results: Based on pilot testing (n=596 patients) and semi-structured interviews (n=11 cardiologists), in-basket messages were modified to reduce the number of patients listed, include future visits, and list date of next visit. The BPA was modified to improve noticeability, reduce extraneous information on guidelines, and include key information on contraindications. Final CDS interventions after incorporation of feedback are depicted in the Figure. Conclusions: Pilot testing and semi-structured interviews were instrumental in the development of CDS interventions and should be employed for other similar studies. We will compare the effectiveness of the BPA vs the in-basket message vs usual care on the primary outcome of MRA prescribing in The BETTER CARE-HF trial (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) (Clinicaltrials.gov NCT05275920)

Abstract 11295: Design and Rationale for the BETTER CARE-HF Trial: A Pragmatic Cluster-Randomized Controlled Trial Comparing Two Targeted Approaches to Ambulatory Clinical Decision Support for Cardiologists

Introduction: Heart failure with reduced ejection fraction (HFrEF) is a leading cause of morbidity and mortality. However, shortfalls in prescribing of proven therapies, particularly mineralocorticoid receptor antagonist (MRA) therapy, account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing of therapies. However, the optimal timing and format of CDS tools is unknown. We developed two CDS tools to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: 1) a best practice alert (BPA) that notifies cardiologists at the time of patient visit, and 2) an automated in-basket message that allows for review between visits (Figure). Objective: The BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) trial aims to compare the effectiveness of a BPA vs. in-basket message vs. usual care on the primary outcome of MRA prescribing. Methods: This is a pragmatic, cluster-randomized, three-arm intervention trial. Patients are randomized as a cluster at the level of their cardiologist in a 1:1:1 fashion (180 cardiologists included). The primary outcome is MRA prescription at the end of the study period, and secondary outcomes include prescribing of other GDMT, time to prescription, and provider-level changes in prescribing rates. We will require a total of 1,503 patients (501 per arm) to detect 10% improvement in prescribing with 80% power, and estimate a trial duration of 6 months. Implications: To our knowledge, no study has directly compared the efficacy of a BPA vs. in-basket message. If effective, our findings can be rapidly disseminated to improve morbidity and mortality for patients with HFrEF, and can also inform the development of future CDS interventions for other disease states. Trial registration: NCT05275920

Reducing Volatile Anesthetic Waste Using a Commercial Electronic Health Record Clinical Decision Support Tool to Lower Fresh Gas Flows.

Volatile anesthetic consumption can be reduced by minimizing excessive fresh gas flows (FGFs). Currently, it is unknown whether decision support tools embedded within commercial electronic health record systems can be successfully adopted to achieve long-term reductions in FGF rates. The authors describe the implementation of an electronic health record-based clinical decision support tool aimed at reducing FGF and evaluate the effectiveness of this intervention in achieving sustained reductions in FGF rates and volatile anesthetic consumption. On August 29, 2018, we implemented a decision support tool within the Epic Anesthesia Information Management System (AIMS) to alert providers of high FGF (>0.7 L/min for desflurane and >1 L/min for sevoflurane) during maintenance of anesthesia. July 22, 2015, to July 10, 2018, served as our baseline period before the intervention. The intervention period spanned from August 29, 2018, to December 31, 2019. Our primary outcomes were mean FGF (L/min) and volatile agent consumption (mL/MAC-h). Because a simple comparison of 2 time periods may result in false conclusions due to underlying trends independent of the intervention, we performed segmented regression of the interrupted time series to assess the change in level at the start of the intervention and the differences in slopes before and after the intervention. The analysis was also adjusted for potential confounding variables. Data included 44,899 cases using sevoflurane preintervention with 26,911 cases postintervention, and 17,472 cases using desflurane with 1185 cases postintervention. Segmented regression of the interrupted times series demonstrated a decrease in mean FGF by 0.6 L/min (95% CI, 0.6-0.6 L/min; P < .0001) for sevoflurane and 0.2 L/min (95% CI, 0.2-0.3 L/min; P < .0001) for desflurane immediately after implementation of the intervention. For sevoflurane, mL/MAC-h decreased by 3.8 mL/MAC-h (95% CI, 3.6-4.1 mL/MAC-h; P < .0001) after implementation of the intervention and decreased by 4.1 mL/MAC-h (95% CI, 2.6-5.6 mL/MAC-h; P < .0001) for desflurane. Slopes for both FGF and mL/MAC-h in the postintervention period were statistically less negative than the preintervention slopes (P < .0001 for sevoflurane and P < .01 for desflurane). A commercial AIMS-based decision support tool can be adopted to change provider FGF management patterns and reduce volatile anesthetic consumption in a sustainable fashion.

Overcoming barriers to implementing new guideline-directed therapies for chronic kidney disease.

For the first time in many years, guideline-directed drug therapies have emerged that offer substantial cardiorenal benefits, improved quality of life and longevity in patients with chronic kidney disease (CKD) and type 2 diabetes. These treatment options include sodium-glucose cotransporter-2 inhibitors, nonsteroidal mineralocorticoid receptor antagonists and glucagon-like peptide-1 receptor agonists. However, despite compelling evidence from multiple clinical trials, their uptake has been slow in routine clinical practice, reminiscent of the historical evolution of angiotensin-converting enzyme inhibitor and angiotensin II receptor blocker use. The delay in implementation of these evidence-based therapies highlights the many challenges to optimal CKD care, including: (i) clinical inertia; (ii) low CKD awareness; (iii) suboptimal kidney disease education among patients and providers; (iv) lack of patient and community engagement; (v) multimorbidity and polypharmacy; (vi) challenges in the primary care setting; (vii) fragmented CKD care; (viii) disparities in underserved populations; (ix) lack of public policy focused on health equity; and (x) high drug prices. These barriers to optimal cardiorenal outcomes can be ameliorated by a multifaceted approach, using the Chronic Care Model framework, to include patient and provider education, patient self-management programs, shared decision making, electronic clinical decision support tools, quality improvement initiatives, clear practice guidelines, multidisciplinary and collaborative care, provider accountability, and robust health information technology. It is incumbent on the global kidney community to take on a multidimensional perspective of CKD care by addressing patient-, community-, provider-, healthcare system- and policy-level barriers.

Using Electronic Clinical Decision Support to Examine Vision Rehabilitation Referrals and Practice Guidelines in Ophthalmology.

PurposeTo examine ophthalmologist use of an electronic health record (EHR)-based clinical decision support system (CDSS) to facilitate low vision rehabilitation (LVR) care referral.MethodsThe CDSS alert was designed to appear when best documented visual acuity was <20/40 or hemianopia or quadrantanopia diagnosis was identified during an ophthalmology encounter from November 6, 2017, to April 5, 2019. Fifteen ophthalmologists representing eight subspecialties from an academic medical center were required to respond to the referral recommendation (order, don't order). LVR referral rates and ophthalmologist user experience were assessed. Encounter characteristics associated with LVR referrals were explored using multilevel logistic regression analysis.ResultsThe alert appeared for 3625 (8.9%) of 40,931 eligible encounters. The referral rate was 14.8% (535/3625). Of the 3413 encounters that met the visual acuity criterion only, patients who were worse than 20/60 were more likely to be referred, and 32.4% of referred patients were between 20/40 and 20/60. Primary reasons for deferring referrals included active medical or surgical treatment, refractive-related issues, and previous connection to LVR services. Eleven of the 13 ophthalmologists agreed that the alert was useful in identifying candidates for LVR services.ConclusionsA CDSS for patient identification and referral offers an acceptable mechanism to apply practice guidelines and prompt ophthalmologists to facilitate LVR care. Further study is warranted to optimize ophthalmologist user experience while refining alert criteria beyond visual acuity.Translational RelevanceThe CDSS provides the framework for multi-center research to assess the development of pragmatic algorithms and standards for facilitating LVR care.

Auditing tools for antimicrobial prescribing in solid organ transplant recipients: The why, the how, and an assessment of current options.

Antimicrobial stewardship (AMS) aims to optimize antimicrobial use. Auditing and reporting of antimicrobial prescribing are essential. Auditing tools for solid organ transplant (SOT) patients should tailor to their needs. We reviewed published data describing auditing tools in the general and SOT population. We focused on three internationally or nationally available auditing tools. The National Antimicrobial Prescribing Survey (NAPS) is web-based tool to report antimicrobial consumption and assess appropriateness using standardized definitions based on consensus guidelines. In the absence of guidelines, adjudication is based on AMS principles. An automated dashboard, analyses by indication or antimicrobial, and benchmarking reports are available. The National Healthcare Safety Network Antimicrobial Use/Resistance module was developed by the Centers for Disease Control and Prevention for hospitals to upload monthly data, which are standardized for benchmarking. It does not assess appropriateness or address SOT wards. The Global-Point Prevalence Survey from bioMérieux collects data on antimicrobial regimen, indication and microbial resistance. Variables unique to SOT include comorbidities and devices. Assessment of appropriateness is limited to guideline adherence, and benchmarking may require prearrangement with bioMérieux. Benchmarking requires prearrangement. Advances in electronic health record systems and clinical decision support tools can improve the efficiency of the auditing process. Each AMS auditing tool has unique features for SOT patients. Capturing immunosuppression, source control, organ dysfunction, donor-derived infection, serology, and colonization status will enhance their applicability.

310 Emergency Department Oral Anticoagulation Prescribing Practices for Acute Atrial Fibrillation: Pre-Implementation of an Electronic Clinical Decision Support Tool

Study Objectives: Atrial fibrillation (AF) is the most common arrhythmia; and often, first diagnosed in the emergency department (ED). AF increases the risk of stroke, which can be reduced by 64% if prescribed anticoagulation (OAC) in appropriate at-risk patients. An ED evaluation is a teaching moment that can change the trajectory of care for patients, however, stroke prophylaxis has not been well-appreciated in the ED. The ED is a prime target for initiating stroke prophylaxis with the use of clinical decision support tools to guide shared-decision making.

Improving Antibiotic Stewardship for Diarrheal Disease With Probability-Based Electronic Clinical Decision Support

Inappropriate use of antibiotics for diarrheal illness can result in adverse effects and increase in antimicrobial resistance. To determine whether the diarrheal etiology prediction (DEP) algorithm, which uses patient-specific and location-specific features to estimate the probability that diarrhea etiology is exclusively viral, impacts antibiotic prescriptions in patients with acute diarrhea. A randomized crossover study was conducted to evaluate the DEP incorporated into a smartphone-based electronic clinical decision-support (eCDS) tool. The DEP calculated the probability of viral etiology of diarrhea, based on dynamic patient-specific and location-specific features. Physicians were randomized in the first 4-week study period to the intervention arm (eCDS with the DEP) or control arm (eCDS without the DEP), followed by a 1-week washout period before a subsequent 4-week crossover period. The study was conducted at 3 sites in Bangladesh from November 17, 2021, to January 21, 2021, and at 4 sites in Mali from January 6, 2021, to March 5, 2021. Eligible physicians were those who treated children with diarrhea. Eligible patients were children between ages 2 and 59 months with acute diarrhea and household access to a cell phone for follow-up. Use of the eCDS with the DEP (intervention arm) vs use of the eCDS without the DEP (control arm). The primary outcome was the proportion of children prescribed an antibiotic. A total of 30 physician participants and 941 patient participants (57.1% male; median [IQR] age, 12 [8-18] months) were enrolled. There was no evidence of a difference in the proportion of children prescribed antibiotics by physicians using the DEP (risk difference [RD], -4.2%; 95% CI, -10.7% to 1.0%). In a post hoc analysis that accounted for the predicted probability of a viral-only etiology, there was a statistically significant difference in risk of antibiotic prescription between the DEP and control arms (RD, -0.056; 95% CI, -0.128 to -0.01). No known adverse effects of the DEP were detected at 10-day postdischarge. Use of a tool that provides an estimate of etiological likelihood did not result in a significant change in overall antibiotic prescriptions. Post hoc analysis suggests that a higher predicted probability of viral etiology was linked to reductions in antibiotic use. Clinicaltrials.gov Identifier: NCT04602676.

Attitudes on pharmacogenomic results as secondary findings among medical geneticists.

As evidence mounts supporting the utility of pharmacogenomic-guided medication management, incorporating pharmacogenomic genes into secondary finding results from sequencing panels is increasingly under consideration. We studied medical geneticists' attitudes on receiving pharmacogenomic results as secondary finding. Four focus groups with 16 medical geneticists total were conducted followed by thematic analysis. All participants ordered genetic sequencing tests; however, the majority had rarely or never ordered pharmacogenomic tests (10/16) or prescribed medications with established response variability (11/16). In total 81.3% expressed low comfort interpreting pharmacogenomic results without appropriate clinical resources (13/16). The positives of receiving pharmacogenomic results as secondary finding included prevention of adverse drug reactions in adults, grateful information-seeking patients, the ability to rapidly prescribe more effective treatments and appreciation of the recent advances in both pharmacogenomic knowledge and available guidelines. Negatives included laboratory reporting issues, exclusivity of pharmacogenomic results to certain populations, lengthy reports concealing pharmacogenomic results in patient charts and laboratories marketing to individuals without prior pharmacogenomic knowledge or targeting inappropriate populations. The most desirable pharmacogenomic resources included a universal electronic health record clinical decision support tool to assist identifying and implementing pharmacogenomic results, a specialized pharmacist as part of the care team, additional pharmacogenomic training during medical/graduate school, and a succinct interpretation of pharmacogenomic results included on laboratory reports. The majority of participants agreed that adding certain actionable pharmacogenomic genes to the American College of Medical Genetics and Genomics SF list is reasonable; however, this was qualified with a need for additional resources to support implementation.

Improving Asthma Care Documentation with a Digital Tool-Experience in a Pediatric Institution.

Asthma affects approximately 10% of Australian children. Electronic medical record (EMR) systems and clinical decision support initiatives have been shown to improve the delivery of asthma care. Our institution implemented an EMR-based asthma "hub," which collates asthma-related information to a central location within a patient's record, provides a template to collect relevant clinical information, allows clinicians to evaluate a patient's history and presentation in a systematic manner and prompts relevant actions. The aim of the study is to measure year-on-year improvement in asthma-related documentation and provide a key gold-standard aspects of asthma management after the introduction of an EMR asthma "hub" tool in the outpatient setting. The asthma "hub" was introduced in November 2020. A chart review was conducted of all patients who attended the Complex Asthma Clinic between January-April 2020 and January-April 2021. The provision and presence of documentation of core aspects of asthma care were described in percentages and comparisons of pre- and post-introduction of the asthma "hub" were assessed. There was a significant increase in the documentation of asthma triggers, including smoking/smoker exposure, (47.5-92.6%, <i>p</i> &lt;0.001), current asthma action plans (70.4-86.3%, <i>p</i> = 0.02), and severity scores (46.3-81%, <i>p</i> &lt;0.001) post the introduction of the asthma "hub." There was no significant difference in documentation of reliever (as required) or regular preventer medications. An evidence-based EMR intervention improved the documentation and provision of aspects of asthma care in an outpatient clinic setting at a tertiary pediatric hospital, suggesting replication in the inpatient and emergency settings would be worthwhile. Further research is required to understand the tool's impact on clinical outcomes and on clinical efficiency and workflow.

Introduction of an Electronic Clinical Decision Support Tool to Inform Prescribing for Pediatric Diarrhea in Bangladesh and Mali: Do Provider Expectations Predict Experiences?

ABSTRACT.Nonindicated antibiotics for childhood diarrhea is a major contributor to global antimicrobial resistance. Electronic clinical decision support tools (eCDSTs) may reduce unnecessary antibiotics. This study examined how providers’ expectations of an eCDST to predict diarrhea etiology compared with their experiences using the tool. Providers were enrolled from public hospitals in Bangladesh (n = 15) and Mali (n = 15), and surveys were completed at baseline and after using the eCDST. Baseline surveys assessed expectations (utility, ease of use, and threat to autonomy), and post surveys assessed experiences in the same domains. Providers’ experiences with ease of use exceeded their baseline expectations, and providers reported less experienced threat to autonomy after use, compared with baseline expectations. Providers’ expectations of threat to autonomy significantly predicted their experienced threat to autonomy. Findings suggest that an eCDST to inform antimicrobial prescribing for diarrhea is feasible and acceptable, but training should promote local ownership for sustainability.

Effectiveness of an electronic clinical decision support system in improving the management of childhood illness in primary care in rural Nigeria: an observational study

ObjectivesTo evaluate the impact of ALgorithm for the MANAgement of CHildhood illness (‘ALMANACH’), a digital clinical decision support system (CDSS) based on the Integrated Management of Childhood Illness, on health...

How much do we need electronic clinical decision support systems to select patients to perform HIV tests in clinical settings?

Infectious Diseases Unit, San Camillo De Lellis Hospital, Rieti/National Institute for Infectious Diseases ‘Lazzaro Spallanzani’, Rome, Italy. Correspondence to Dr Mauro Zaccarelli, Infectious Diseases Unit, San Camillo de Lellis Hospital, Viale J.F. Kennedy, Rieti 02100, Italy. E-mail: [email protected] Received 17 March, 2022 Revised 8 April, 2022 Accepted 8 April, 2022

Assessing the Usability of a Clinical Decision Support System: Heuristic Evaluation.

BackgroundPoor usability is a primary cause of unintended consequences related to the use of electronic health record (EHR) systems, which negatively impacts patient safety. Due to the cost and time needed to carry out iterative evaluations, many EHR components, such as clinical decision support systems (CDSSs), have not undergone rigorous usability testing prior to their deployment in clinical practice. Usability testing in the predeployment phase is crucial to eliminating usability issues and preventing costly fixes that will be needed if these issues are found after the system’s implementation.ObjectiveThis study presents an example application of a systematic evaluation method that uses clinician experts with human-computer interaction (HCI) expertise to evaluate the usability of an electronic clinical decision support (CDS) intervention prior to its deployment in a randomized controlled trial.MethodsWe invited 6 HCI experts to participate in a heuristic evaluation of our CDS intervention. Each expert was asked to independently explore the intervention at least twice. After completing the assigned tasks using patient scenarios, each expert completed a heuristic evaluation checklist developed by Bright et al based on Nielsen’s 10 heuristics. The experts also rated the overall severity of each identified heuristic violation on a scale of 0 to 4, where 0 indicates no problems and 4 indicates a usability catastrophe. Data from the experts’ coded comments were synthesized, and the severity of each identified usability heuristic was analyzed.ResultsThe 6 HCI experts included professionals from the fields of nursing (n=4), pharmaceutical science (n=1), and systems engineering (n=1). The mean overall severity scores of the identified heuristic violations ranged from 0.66 (flexibility and efficiency of use) to 2.00 (user control and freedom and error prevention), in which scores closer to 0 indicate a more usable system. The heuristic principle user control and freedom was identified as the most in need of refinement and, particularly by nonnursing HCI experts, considered as having major usability problems. In response to the heuristic match between system and the real world, the experts pointed to the reversed direction of our system’s pain scale scores (1=severe pain) compared to those commonly used in clinical practice (typically 1=mild pain); although this was identified as a minor usability problem, its refinement was repeatedly emphasized by nursing HCI experts.ConclusionsOur heuristic evaluation process is simple and systematic and can be used at multiple stages of system development to reduce the time and cost needed to establish the usability of a system before its widespread implementation. Furthermore, heuristic evaluations can help organizations develop transparent reporting protocols for usability, as required by Title IV of the 21st Century Cures Act. Testing of EHRs and CDSSs by clinicians with HCI expertise in heuristic evaluation processes has the potential to reduce the frequency of testing while increasing its quality, which may reduce clinicians’ cognitive workload and errors and enhance the adoption of EHRs and CDSSs.

Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine

PurposeThe study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.Design/methodology/approachThis study is a narrative review of the literature.FindingsThe body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.Originality/valueWhile research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.