Impact Of Clinical Decision Support Research Articles

Impact of Clinical Decision Support with Mandatory versus Voluntary Venous Thromboembolism Risk Assessment in Hospitalized Patients.

Background Venous thromboembolism (VTE) causes significant preventable morbidity and mortality in hospitalized patients. Assessing VTE risk is essential to initiating appropriate prophylaxis and reducing VTE outcomes. Studies show that computerized clinical decision support (CDS) can improve VTE risk assessment (RA), prophylaxis, and outcomes but few examined the effectiveness of specific design features. From 2008 to 2016, University of Michigan Health implemented CDS for VTE prevention in four stages, which alternated between voluntary and mandatory RA using the 2005 Caprini model and generated inpatient orders for risk-appropriate prophylaxis based on CHEST guidelines. This cross-sectional study evaluated the impact of mandatory versus voluntary RA on VTE prophylaxis and outcomes for adult medical and surgical patients admitted to the health system. Methods Interrupted time series analysis was conducted to evaluate the trend in smart order set-recommended VTE prophylaxis by CDS stage. Logistic regression with CDS stage as the primary independent variable was used in pairwise comparisons of VTE during hospitalization and within 90 days post-discharge for mandatory versus voluntary RA. Adjusted odd ratios (ORs) were calculated for total, in-hospital, and post-discharge VTE. Results In this study of 223,405 inpatients over 8 years, smart order set-recommended prophylaxis increased from 65 to 79%; it increased significantly when voluntary RA in Stage 1 became mandatory in Stage 2 (10.59%, p < 0.001) and decreased significantly when it returned to voluntary in Stage 3 (-11.24%, p < 0.001). The rate increased slightly when mandatory RA was reestablished in Stage 4 (0.23%, p = 0.935). Adjusted ORs for VTE were lower for mandatory RA versus adjacent stages with voluntary RA. The adjusted OR for Stage 2 versus Stage 1 was 14% lower ( p < 0.05) and versus Stage 3 was 11% lower ( p < 0.05). The adjusted OR for Stage 4 versus Stage 3 was 4% lower ( p = 0.60). These results were driven by changes in in-hospital VTE. By contrast, the incidence of post-discharge VTE increased in each successive stage. Conclusion Mandatory RA was more effective in improving smart order set-recommended prophylaxis and VTE outcomes, particularly in-hospital VTE. Post-discharge VTE increased despite high adherence to risk-appropriate prophylaxis, indicating that guidelines for extended, post-discharge prophylaxis are needed to further reduce VTE for hospitalized patients.

Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists

ObjectivesTo assess urologist attitudes toward clinical decision support embedded into the electronic health record and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence, enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited. MethodsA sequential explanatory mixed methods study from 2019-2020 was performed. First, survey responses from the 2019 American Urological Association annual census evaluated attitudes toward an automatic clinical decision support tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on clinical decision support impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated. ResultsAmong a weighted sample of 12,366 practicing urologists, the majority agreed clinical decision support would help decision-making (70.9%, 95% CI 68.7–73.2%), aid patient counseling (78.5%, 95% CI 76.5–80.5%), save time (58.1%, 95% CI 55.7–60.5%), and improve patient outcomes (42.9%, 95% CI 40.5–45.4%). More years in practice was negatively associated with agreement (p<0.001). Urologists described how clinical decision support could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use. ConclusionsUrologists have favorable attitudes toward the potential for clinical decision support in the electronic health record. Smart design will be critical to ensure effective implementation and impact.

Clinical decision support for gastrointestinal panel testing.

This study aimed to assess the impact of clinical decision support (CDS) to improve ordering of multiplex gastrointestinal polymerase chain reaction (PCR) testing panel ("GI panel"). Single-center, retrospective, before-after study. Tertiary care Veteran's Affairs (VA) Medical Center provides inpatient, outpatient, and residential care. All patients tested with a GI panel between June 22, 2022 and April 20, 2023. We designed a CDS questionnaire in the electronic medical record (EMR) to guide appropriate ordering of the GI panel. A "soft stop" reminder at the point of ordering prompted providers to confirm five appropriateness criteria: 1) documented diarrhea, 2) no recent receipt of laxatives, 3) C. difficile is not the leading suspected cause of diarrhea, 4) time period since a prior test is >14 days or prior positive test is >4 weeks and 5) duration of hospitalization <72 hours. The CDS was implemented in November 2022. Compared to the pre-implementation period (n = 136), fewer tests were performed post-implementation (n = 92) with an IRR of 0.61 (p = 0.003). Inappropriate ordering based on laxative use or undocumented diarrhea decreased (IRR 0.37, p = 0.012 and IRR 0.25, p = 0.08, respectively). However, overall inappropriate ordering and outcome measures did not significantly differ before and after the intervention. Implementation of CDS in the EMR decreased testing and inappropriate ordering based on use of laxatives or undocumented diarrhea. However, inappropriate ordering of tests overall remained high post-intervention, signaling the need for continued diagnostic stewardship efforts.

Impact of Clinical Decision Support Within the Electronic Medical Record on Opioid Prescribing and Dispensing.

Clinical decision support tools support prescribers and pharmacists as they select and verify appropriate opioid regimens in efforts to combat the high variability in opioid prescribing. This study seeks to examine the impact of alerts within the electronic medical record and pharmacy system on day supply of initial opioid prescribing and dispensing. This retrospective study compared a 6-month pre- and postimplementation of clinical decision support tool alerts at an integrated health care system. Data were analyzed to assess changes in the day supply of an opioid at the point of initial prescribing and dispensing based on alerts. The best practice alert in the electronic medical record was associated with a 27% change (p = 0.007) in prescribing by the physician, which resulted in a reduction of average day supply from 12.09 to 6.58 days. The alert in the pharmacy system was associated with a 41.3% change (p < 0.001) in dispensing, which resulted in a reduction of average day supply from 13.46 to 6.96 days. To promote judicious opioid prescribing, the best practice alert in the electronic medical record led to a statistically significant change in prescribing. To support appropriate dispensing, the alert in the pharmacy system led to a statistically significant change in dispensing. Implementation of two clinical decision support tools that mirrored Centers for Disease Control and Prevention recommendations of prescribing less than a 7-day supply when initiating opioids resulted in a decrease in day supply of the opioid prescription for patients identified as opioid-naïve at the point of prescribing and dispensing.

Clinical Decision Support Implementation in Health Information Systems Across Radiology Departments

Introduction: Understanding the economic landscape is pivotal for gauging the feasibility and sustainability of Clinical Decision Support (CDS) integration across radiology, nursing, and laboratory departments. This review will delve into the economic considerations associated with CDS implementation, shedding light on clinical impact and cost-effectiveness. Methods: The systematic review employed a robust methodology, combining controlled vocabulary and free-text keywords in a comprehensive search across multiple databases. The inclusion criteria encompassed original research articles, systematic reviews, and meta-analyses in English, focusing on Clinical Decision Support (CDS) implementation in radiology, nursing, and laboratory departments within Health Information Systems. The two-step screening process, detailed data extraction, and methodological quality assessment were conducted with rigor by two reviewers, resolving discrepancies through discussion or consultation with a third reviewer. Results: The systematic review incorporated seven intervention studies spanning radiology, nursing, and laboratory departments within Health Information Systems (HIS). Findings revealed a broad range of sample sizes, from 152 to 805 participants, showcasing the diversity of healthcare professionals involved. Across these studies, CDS interventions demonstrated substantial positive impacts, particularly in radiology with a risk ratio of 1.75 (95% CI: 1.42-2.10) for improved diagnostic accuracy, in nursing with a 58% risk reduction in medication errors (95% CI: 0.30-0.58), and in laboratory services with a 65% lower risk of unnecessary tests (95% CI: 0.24-0.51). These consistent themes highlight the effectiveness of CDS interventions but underscore the need for ongoing customization to meet department-specific needs. Conclusions: The systematic review underscores the significant positive impact of Clinical Decision Support (CDS) implementation across radiology, nursing, and laboratory departments within Health Information Systems, as evidenced by improved diagnostic precision, medication management, and laboratory efficiency, while emphasizing the importance of continuous customization to address department-specific nuances.

Impact of clinical decision support on controlled substance prescribing

BackgroundPrescription drug overdose and misuse has reached alarming numbers. A persistent problem in clinical care is lack of easy, immediate access to all relevant information at the actionable time. Prescribers must digest an overwhelming amount of information from each patient’s record as well as remain up-to-date with current evidence to provide optimal care. This study aimed to describe prescriber response to a prospective clinical decision support intervention designed to identify patients at risk of adverse events associated with misuse of prescription opioids/benzodiazepines and promote adherence to clinical practice guidelines.MethodsThis study was conducted at a large multi-center healthcare system, using data from the electronic health record. A prospective observational study was performed as clinical decision support (CDS) interventions were sequentially launched (January 2016–July 2019). All data were captured from the medical record prospectively via the CDS tools implemented. A consecutive series of all patient encounters including an opioid/benzodiazepine prescription were included in this study (n = 61,124,172 encounters; n = 674,785 patients). Physician response to the CDS interventions was the primary outcome, and it was assessed over time using control charts.ResultsAn alert was triggered in 23.5% of encounters with a prescription (n = 555,626). The prescriber decision was influenced in 18.1% of these encounters (n = 100,301). As the number of risk factors increased, the rate of decision being influenced also increased (p = 0.0001). The effect of the alert differed by drug, risk factor, specialty, and facility.ConclusionThe delivery of evidence-based, patient-specific information had an influence on the final prescription in nearly 1 in 5 encounters. Our intervention was sustained with minimal prescriber fatigue over many years in a large and diverse health system.

The potential impact of clinical decision support on nonwaivered primary care clinicians' prescribing of buprenorphine.

Elimination of the X-waiver increased potential buprenorphine prescribers 13-fold, but growth in prescribing will likely be much lower. We explored self-assessments of nonwaivered primary care clinicians (PCCs) for factors affecting their likelihood to prescribe buprenorphine were the X-waiver eliminated (since realized January 2023) and the potential impacts of a clinical decision-support (CDS) tool for opioid use disorder (OUD). Cross-sectional survey data were obtained between January 2021 and March 2022 from 305 nonwaivered PCCs at 3 health systems. Factors explored were patient requests for buprenorphine, PCC access to an OUD-CDS, and PCC confidence and abilities for 5 OUD-care activities. Relationships were described using descriptive statistics and odds ratios. Only 26% of PCCs were more likely to prescribe buprenorphine upon patient request, whereas 63% were more likely to prescribe with the OUD-CDS. PCC confidence and abilities for some OUD-care activities were associated with increased prescribing likelihood from patient requests, but none were associated with the OUD-CDS. The OUD-CDS may increase buprenorphine prescribing for PCCs less likely to prescribe upon patient request. Future research is needed to develop interventions that increase PCC buprenorphine prescribing. Clinical trial registration: ClinicalTrials.gov. Identifier: NCT04198428. Clinical trial name: Clinical Decision Support for Opioid Use Disorders in Medical Settings (Compute 2.0).

Clinical decision support to reduce unnecessary diagnostic testing for heparin-induced thrombocytopaenia.

Appropriate evaluation of heparin-induced thrombocytopaenia (HIT) is imperative because of the potentially life-threatening complications. However, overtesting and overdiagnosis of HIT are common. Our goal was to evaluate the impact of clinical decision support (CDS) based on the HIT computerized-risk (HIT-CR) score, designed to reduce unnecessary diagnostic testing. This retrospective observational study evaluated CDS that presented a platelet count versus time graph and 4Ts score calculator to clinicians who initiated a HIT immunoassay order in patients with predicted low risk (HIT-CR score 0-2). The primary outcome was the proportion of immunoassay orders initiated but cancelled after firing of the CDS advisory. Chart reviews were conducted to assess anticoagulation usage, 4Ts scores and the proportion of patients who had HIT. In a 20-week period, 319 CDS advisories were presented to users who initiated potentially unnecessary HIT diagnostic testing. The diagnostic test order was discontinued in 80 (25%) patients. Heparin products were continued in 139 (44%) patients, and alternative anticoagulation was not given to 264 (83%). The negative predictive value of the advisory was 98.8% (95% CI: 97.2-99.5). HIT-CR score-based CDS can reduce unnecessary diagnostic testing for HIT in patients with a low pretest probability of HIT.

CO2 Evaluation of Clinical Decision Support to Improve Care for Patients with Atrial Fibrillation at Increased Stroke Risk: 2-Year Results

Effective stroke prevention with oral anticoagulation is the cornerstone of management in patients with atrial fibrillation (AF). However, previous literature has shown that guideline-concordant anticoagulation in patients with high stroke risk is often below 70% in the U.S. The purpose of this study is to evaluate the impact of clinical decision support (CDS) tools on anticoagulation rates.

2240. Implementation of a Clinical Decision Support Panel for Urine Culture Ordering

Abstract Background Standard urine culture collection techniques have a high risk of false positive results in catheterized patients. IDSA guidelines recommend replacing long-term catheters before specimen collection for cultures. Our goal was to evaluate the impact of clinical decision support (CDS) during the urine culture computerized provider order entry (CPOE) on test use and patient-level outcomes. Methods In March 2021, CDS was added to the urine culture CPOE in the electronic health record (EHR). The order panel includes education regarding appropriate clinical indications for urine culturing, and protocolizes urine collection to determine (A) if a urinalysis was ordered, (B) if special patient population (i.e., PICU), (C) the presence and duration of an indwelling urinary catheter, and (D) whether the catheter had difficulty with placement. Depending on the criteria identified in A, B, C, and D, the panel recommends catheter removal prior to urine culture (Figure 1). We retrospectively evaluated the impact of panel implementation in our pre- (7/2020- 3/2021) and post-intervention periods (4/2021- 3/2022) using an interrupted time series analysis with Poisson regression. Outcomes included urine cultures/1000 patient days (pd), number of catheter removal orders, catheter-associated urinary tract infection (CAUTI)/1000 catheter days, and days of antibiotic therapy with urinary tract infection (UTI) indication/1000pd Order Panel Framework Results Clinicians selected catheter removal in 183 of 2133 (8.5%) instances of ordering a urine culture when a catheter was detected by the panel (Figure 2). Analysis revealed a significant decrease in urine culture orders (1.1% decrease/month, p&amp;lt; 0.05), a decreasing trend of antibiotic use with UTI indications (Figure 3, 2.8% decrease/month, p&amp;lt; 0.05), but no significant change in CAUTI rates (Figure 4). Evaluation of the safety reporting system revealed no identifiable increase in safety events related to catheter removal or reinsertion. Catheter Associated Urinary Tract Infections Black boxes indicate Poisson regression model estimates Black boxes indicate Poisson regression model estimates Conclusion CDS can aid in improving urine culture practice habits, including removal of long-standing catheters before urine culture. The decreasing trend in total urine culture orders (Figure 5) may reflect a ”culture change” over time to avoid urine cultures in inappropriate instances. Total Urine Cultures Ordered Black boxes indicate Poisson regression model estimates. Red circles indicate predicted outcome without ‘intervention’. Disclosures Sonali D. Advani, MBBS, MPH, FIDSA, Locus Biosciences: Advisor/Consultant|Locus Biosciences: Honoraria|Sysmex America: Advisor/Consultant.

Patient experience of post-extraction pain management

IntroductionFew studies have assessed the impact of clinical decision support, with or without patient education, on patient-reported outcomes in dentistry. This cross-sectional survey was conducted to understand patients' experience with shared decision-making and pain management associated with a tooth extraction.MethodsWe administered a phone survey to 981 adult patients within a week of having a tooth extraction. The tooth extraction was performed at a large regional dental practice by a dentist participating in a randomized controlled trial of a clinical decision support system to reduce reliance on opioids for tooth extraction-related pain management. The survey compared respondents' answers by study arm: standard practice (SP) or clinical decision support with (CDS-E) or without patient education (CDS).ResultsOf respondents (n = 492, response rate: 52%), 91% reported that their dental provider talked about what to expect for pain in the days following the extraction, 92% stated that their dental provider talked about how to treat post-extraction pain, and 74% expressed they were confident in their ability to manage post-extraction pain at the end of the extraction visit. We found no statistically significant differences in patient responses across study arms for average pain in the 3 days following the extraction (p = 0.65, CDS vs. SP; p = 0.41, CDS-E vs. SP) or in shared decision-making (p = 0.38, CDS vs. SP; p = 0.70, CDS-E vs. SP).ConclusionMost patients reported adequate pain management and shared decision-making about pain management related to their tooth extraction. Further research will assess differences in analgesic recommendations across study arms.

Clinical Decision Support with or without Shared Decision Making to Improve Preventive Cancer Care: A Cluster-Randomized Trial.

Innovative interventions are needed to address gaps in preventive cancer care, especially in rural areas. This study evaluated the impact of clinical decision support (CDS) with and without shared decision making (SDM) on cancer-screening completion. In this 3-arm, parallel-group, cluster-randomized trial conducted at a predominantly rural medical group, 34 primary care clinics were randomized to clinical decision support (CDS), CDS plus shared decision making (CDS+SDM), or usual care (UC). The CDS applied web-based clinical algorithms identifying patients overdue for United States Preventive Services Task Force-recommended preventive cancer care and presented evidence-based recommendations to patients and providers on printouts and on the electronic health record interface. Patients in the CDS+SDM clinic also received shared decision-making tools (SDMTs). The primary outcome was a composite indicator of the proportion of patients overdue for breast, cervical, or colorectal cancer screening at index who were up to date on these 1 y later. From August 1, 2018, to March 15, 2019, 69,405 patients aged 21 to 74 y had visits at study clinics and 25,198 were overdue for 1 or more cancer screening tests at an index visit. At 12-mo follow-up, 9,543 of these (37.9%) were up to date on the composite endpoint. The adjusted, model-derived percentage of patients up to date was 36.5% (95% confidence interval [CI]: 34.0-39.1) in the UC group, 38.1% (95% CI: 35.5-40.9) in the CDS group, and 34.4% (95% CI: 31.8-37.2) in the CDS+SDM group. For all comparisons, the screening rates were higher than UC in the CDS group and lower than UC in the CDS+SDM group, although these differences did not reach statistical significance. The CDS did not significantly increase cancer-screening rates. Exploratory analyses suggest a deeper understanding of how SDM and CDS interact to affect cancer prevention decisions is needed. Trial registration: ClinicalTrials.gov ID: NCT02986230, December 6, 2016.

Outcomes of clinical decision support for outpatient management of Clostridioides difficile infection.

To determine the impact of clinical decision support on guideline-concordant Clostridioides difficile infection (CDI) treatment. Quasi-experimental study in >50 ambulatory clinics. Primary, specialty, and urgent-care clinics. Adult patients were eligible for inclusion if they were diagnosed with and treated for a first episode of symptomatic CDI at an ambulatory clinic between November 1, 2019, and November 30, 2020. An outpatient best practice advisory (BPA) was implemented to notify prescribers that "vancomycin or fidaxomicin are preferred over metronidazole for C.difficile infection" when metronidazole was prescribed to a patient with CDI. In total, 189 patients were included in the study: 92 before the BPA and 97 after the BPA. Their median age was 59 years; 31% were male; 75% were white; 30% had CDI-related comorbidities; 35% had healthcare exposure; 65% had antibiotic exposure; 44% had gastric acid suppression therapy within 90 days of CDI diagnosis. The BPA was accepted 23 of 26 times and was used to optimize the therapy of 16 patients in 6 months. Guideline-concordant therapy increased after implementation of the BPA (72% vs 91%; P = .001). Vancomycin prescribing increased and metronidazole prescribing decreased after the BPA. There was no difference in clinical response or unplanned encounter within 14 days after treatment initiation. Fewer patients after the BPA had CDI recurrence within 14-56 days of the initial episode (27% vs 7%; P < .001). Clinical decision support increased prescribing of guideline-concordant CDI therapy in the outpatient setting. A targeted BPA is an effective stewardship intervention and may be especially useful in settings with limited antimicrobial stewardship resources.

Effectiveness of Clinical Decision Support for Hypercoagulable Evaluations in One Hospital

Introduction: Venous Thromboembolism (VTE) is a potentially life-threatening disorder that may be provoked by venous stasis, endothelial injury or hypercoagulable states. Workups for hypercoagulable states are frequently undertaken, but tests performed are often either not indicated (if performed in older patients, provoked VTE) or uninterpretable (if functional tests are ordered while patient anticoagulated or with active thrombus). Clinical decision support at point of order entry has the potential to prevent this unnecessary testing. As part of a larger QI project to improve care of VTE patients, we studied the impact of clinical decision support embedded in order sets, measuring the rates of appropriate hypercoagulable evaluations as a primary outcome.Intervention: This study was undertaken from February 2013 - May 2015, and the effects of intervention were subsequently measured until May 2017. Hypercoagulable workups were defined as appropriate when ordered in patients who are < 45 years of age with unprovoked thrombus for all tests (gene and functional tests); and patients without active thrombi or anticoagulants for functional tests. A new order set was developed on the hospital health information system for VTE admissions. The order set provided evidenced-based guidance for ordering work-up. The order set was rolled out in April 2015, after extensive education of health care providers.Outcome: Over the 2 years after the addition of the clinical decision support intervention, there was a reduction of the total number of hypercoagulable state work-ups from 21% pre-intervention to 15.7% post intervention, the average number of inappropriate tests reduced from 33 to 14 per month - a 57% reduction. Cost savings realized per month from the intervention was approximately $4,500 (pre-intervention: $9,050.73, post-intervention $4,569.98; P - 0.0004) and totaled about $117,000 over 26 months post intervention.Conclusion: Clinical decision support interventions may result in a sustained decrease in unnecessary hypercoagulable workups. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

The impact of personalized clinical decision support on primary care patients\u2019 views of cancer prevention and screening: a cross-sectional survey

BackgroundFew studies have assessed the impact of clinical decision support (CDS), with or without shared decision-making tools (SDMTs), on patients’ perceptions of cancer screening or prevention in primary care settings. This cross-sectional survey was conducted to understand primary care patient’s perceptions on cancer screening or prevention.MethodsWe mailed surveys (10/2018–1/2019) to 749 patients aged 18 to 75 years within 15 days after an index clinical encounter at 36 primary care clinics participating in a clinic-randomized control trial of a CDS system for cancer prevention. All patients were overdue for cancer screening or human papillomavirus vaccination. The survey compared respondents’ answers by study arm: usual care; CDS; or CDS + SDMT.ResultsOf 387 respondents (52% response rate), 73% reported having enough time to discuss cancer prevention options with their primary care provider (PCP), 64% reported their PCP explained the benefits of the cancer screening choice very well, and 32% of obese patients reported discussing weight management, with two-thirds reporting selecting a weight management intervention. Usual care respondents were significantly more likely to decide on colorectal cancer screening than CDS respondents (p < 0.01), and on tobacco cessation than CDS + SDMT respondents (p = 0.02) and both CDS and CDS + SDMT respondents (p < 0.001).ConclusionsMost patients reported discussing cancer prevention needs with PCPs, with few significant differences between the three study arms in patient-reported cancer prevention care. Upcoming research will assess differences in screening and vaccination rates between study arms during the post-intervention follow-up period.Trial registrationclinicaltrials.gov, NCT02986230, December 6, 2016.

The role of experience and clinical decision support in clinical trial accrual within oncology.

1527 Background: Patient accrual for cancer clinical trials is suboptimal. The complexity of applying eligibility criteria and enrolling patients may deter oncologists from recommending patients for a trial. As such, there is a need to understand how experience, training, and clinical decision support impact physician practices and intentions related to trial accrual. Methods: From May to September 2017, we conducted a survey on clinical trial accrual in a national sample of medical, surgical, and radiation oncologists. The 20-minute survey assessed barriers and facilitators to clinical trial accrual, including experience (e.g., “In the past 5 years, have you been a study or site PI of a trial?”), training (e.g., “Did you receive training about trial design and recruitment as part of medical school, residency, or fellowship? After fellowship?”), and clinical decision support (e.g., “What kind of clinical decision support has your practice implemented?). We used logistic regression to identify factors associated with frequency of discussing trials (with ≥25% of patients) and likelihood of recommending a trial to a patient (likely or very likely) in the future. Results: Survey respondents (n = 1,030) were mostly medical oncologists (59%), age 35-54 years (67%), male (74%), and not in academic practice (58%). About 18% of respondents (n = 183) reported discussing trials with ≥25% of their patients, and 80% reported being likely or very likely to recommend a trial to a patient in the future. Prior experience as principal investigator of a trial was associated with both frequency of discussing trials (OR 3.27, 95% CI 2.25, 4.75) and likelihood of recommending a trial in the future (OR 5.22, 95% CI 3.71, 7.34), as was receiving additional training in clinical trials after fellowship (discussion with patients: OR 2.48, 95% CI 1.80, 3.42; recommend in future: OR 1.92, 95% CI 1.37, 2.69). Implementing clinical decision support was not associated with discussing trials with ≥25% of patients (OR 1.12, 95% CI 0.76, 1.67), but was associated with being likely to recommend a trial in the future (OR 1.73, 95% CI 1.11, 2.71). Conclusions: In a national survey of oncologists, we observed differences in physician practices and intention related to clinical trial accrual. Whereas the vast majority (80%) reported being likely or very likely to recommend trials in the future, far fewer (20%) reported discussing trials with their patients within the past 5 years. Implementation of clinical decision support – electronic tools intended to optimize patient care and identification of patient eligibility – was not associated with frequency of past discussion of clinical trials but was associated with recommending a trial in the future. Given the stronger association between experience as a site Principal Investigator and recommending a trial, future research should explore how improving opportunities to lead a clinical trial impact trial accrual.

Impact of Clinical Decision Support on Azithromycin Prescribing in Primary Care Clinics.

Inappropriate use of antibiotics in the outpatient setting is a common problem, yet literature evaluating best practices for stewardship interventions in this setting is sparse. To evaluate the impact of clinical decision support (CDS) order panels for azithromycin prescribing on the percentage of inappropriate azithromycin prescriptions in primary care clinics. Single-center, retrospective analysis of azithromycin prescribing within nine primary care clinics. Pre-intervention and post-intervention data included azithromycin prescriptions from November 2016 to April 2017 and February 2019 to July 2019, respectively. Key exclusion criteria included prescriptions for the treatment of a sexually transmitted infection or for prophylaxis against Mycobacterium avium complex. The azithromycin CDS panel was created to provide point-of-care information on appropriate use of azithromycin along with recommended alternatives based on indications. CDS panels were implemented on January 10, 2019. The primary composite outcome was the change in the percentage of inappropriate azithromycin prescribing before and after implementation of CDS panels. The composite outcome included prescriptions with inappropriate indications for azithromycin, unnecessary prescriptions, inappropriate treatment durations, and/or inappropriate dose. There were 306 and 263 prescriptions for azithromycin prescriptions included for analysis in the pre- and post-intervention periods, respectively. Inappropriate prescriptions decreased by 12.6% from the pre- to post-intervention period (81.4% vs. 68.8%; P < 0.001). In both the pre- and post-intervention period, bronchitis and unspecified upper respiratory tract infections (URI) were the two most common indications where azithromycin was prescribed inappropriately. Implementation of CDS order panels resulted in a reduction in inappropriate azithromycin prescribing. However, additional improvement in azithromycin prescribing is needed especially for the indications of bronchitis and unspecified URI.

Impact of clinical decision support on oritavancin prescribing in the emergency department

BackgroundEmergency department (ED) visits and hospitalizations from skin and soft tissue infections (SSTIs) have been on the rise and have led to an increased clinical and economic burden. Owing to their single-dose regimen, recently approved lipoglycopeptides such as oritavancin have the potential to shift the management of SSTIs from an inpatient to outpatient setting. Limited data exist regarding the use of these drugs in the ED setting. ObjectivesThe purpose of this study was to describe the impact that clinical decision support (CDS) incorporation into the computerized physician order entry (CPOE) system had on oritavancin use and to assess compliance with appropriate use guidelines in the ED. MethodsThis was a retrospective cohort study evaluating patients who received oritavancin from September 2016 to May 2018. The patients were assigned to the pre-CDS–implementation group if oritavancin was used between September 13, 2016, and June 28, 2017 and to the post-CDS–implementation group if oritavancin was used between August 28, 2017, and May 25, 2018. There was a 2-month transition period between the 2 study time periods. Patients were excluded if the administration occurred outside of the ED or during the transition period. The primary endpoints were oritavancin use and compliance with the appropriate use guidelines after the implementation. ResultsThere were 169 oritavancin orders in total, of which 119 met the inclusion criteria. There was a marked decrease in use post-CDS implementation (9.2 orders/mo vs. 3 orders/mo). Among those who were prescribed oritavancin, compliance with the appropriate use guidelines increased; however, this did not reach statistical significance. ConclusionThe implementation of the appropriate use guidelines with CDS integration into the CPOE system decreased overall oritavancin use but did not have an impact on compliance with the appropriate use guidelines.

Economic impact of clinical decision support interventions based on electronic health records

BackgroundUnnecessary healthcare utilization, non-adherence to current clinical guidelines, or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to improve quality of care and thereby yield substantial effects on reducing healthcare expenditure. In this article, we evaluate the economic impact of clinical decision support (CDS) interventions based on electronic health records (EHR).MethodsWe searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registry databases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practice application areas and categorized the investigated interventions according to an existing taxonomy of front-end CDS tools.Results and discussionTwenty-seven studies are investigated in this review. Of those, twenty-two studies indicate a reduction of healthcare expenditure after implementing an EHR based CDS system, especially towards prevalent application areas, such as unnecessary laboratory testing, duplicate order entry, efficient transfusion practice, or reduction of antibiotic prescriptions. On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance costs of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance of further high-quality economic evaluations for these CDS systems.ConclusionCurrent research studies lack consideration of comparative cost-outcome metrics as well as detailed cost components in their analyses. Nonetheless, the positive economic impact of EHR based CDS interventions is highly promising, especially with regard to reducing waste in healthcare.

Impact of Clinical Decision Support on Antibiotic Prescribing for Acute Respiratory Infections: a Cluster Randomized Implementation Trial.

Clinical decision support (CDS) is a promising tool for reducing antibiotic prescribing for acute respiratory infections (ARIs). To assess the impact of previously effective CDS on antibiotic-prescribing rates for ARIs when adapted and implemented in diverse primary care settings. Cluster randomized clinical trial (RCT) implementing a CDS tool designed to guide evidence-based evaluation and treatment of streptococcal pharyngitis and pneumonia. Two large academic health system primary care networks with a mix of providers. All primary care practices within each health system were invited. All providers within participating clinic were considered a participant. Practices were randomized selection to a control or intervention group. Intervention practice providers had access to an integrated clinical prediction rule (iCPR) system designed to determine the risk of bacterial infection from reason for visit of sore throat, cough, or upper respiratory infection and guide evidence-based evaluation and treatment. Change in overall antibiotic prescription rates. Frequency, rates, and type of antibiotics prescribed in intervention and controls groups. 33 primary care practices participated with 541 providers and 100,573 patient visits. Intervention providers completed the tool in 6.9% of eligible visits. Antibiotics were prescribed in 35% and 36% of intervention and control visits, respectively, showing no statistically significant difference. There were also no differences in rates of orders for rapid streptococcal tests (RR, 0.94; P= 0.11) or chest X-rays (RR, 1.01; P= 0.999) between groups. The iCPR tool was not effective in reducing antibiotic prescription rates for upper respiratory infections in diverse primary care settings. This has implications for the generalizability of CDS tools as they are adapted to heterogeneous clinical contexts. Clinicaltrials.gov (NCT02534987). Registered August 26, 2015 at https://clinicaltrials.gov.