Adoption Of Clinical Decision Support Research Articles

Clinical Decision Support to Increase Emergency Department Naloxone Coprescribing: Implementation Report.

Coprescribing naloxone with opioid analgesics is a Centers for Disease Control and Prevention (CDC) best practice to mitigate the risk of fatal opioid overdose, yet coprescription by emergency medicine clinicians is rare, occurring less than 5% of the time it is indicated. Clinical decision support (CDS) has been associated with increased naloxone prescribing; however, key CDS design characteristics and pragmatic outcome measures necessary to understand replicability and effectiveness have not been reported. This study aimed to rigorously evaluate and quantify the impact of CDS designed to improve emergency department (ED) naloxone coprescribing. We hypothesized CDS would increase naloxone coprescribing and the number of naloxone prescriptions filled by patients discharged from EDs in a large health care system. Following user-centered design principles, we designed and implemented a fully automated, interruptive, electronic health record-based CDS to nudge clinicians to coprescribe naloxone with high-risk opioid prescriptions. "High-risk" opioid prescriptions were defined as any opioid analgesic prescription ≥90 total morphine milligram equivalents per day or for patients with a prior diagnosis of opioid use disorder or opioid overdose. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to evaluate pragmatic CDS outcomes of reach, effectiveness, adoption, implementation, and maintenance. Effectiveness was the primary outcome of interest and was assessed by (1) constructing a Bayesian structural time-series model of the number of ED visits with naloxone coprescriptions before and after CDS implementation and (2) calculating the percentage of naloxone prescriptions associated with CDS that were filled at an outpatient pharmacy. Mann-Kendall tests were used to evaluate longitudinal trends in CDS adoption. All outcomes were analyzed in R (version 4.2.2; R Core Team). Between November 2019 and July 2023, there were 1,994,994 ED visits. CDS reached clinicians in 0.83% (16,566/1,994,994) of all visits and 15.99% (16,566/103,606) of ED visits where an opioid was prescribed at discharge. Clinicians adopted CDS, coprescribing naloxone in 34.36% (6613/19,246) of alerts. CDS was effective, increasing naloxone coprescribing from baseline by 18.1 (95% CI 17.9-18.3) coprescriptions per week or 2,327% (95% CI 3390-3490). Patients filled 43.80% (1989/4541) of naloxone coprescriptions. The CDS was implemented simultaneously at every ED and no adaptations were made to CDS postimplementation. CDS was maintained beyond the study period and maintained its effect, with adoption increasing over time (τ=0.454; P<.001). Our findings advance the evidence that electronic health record-based CDS increases the number of naloxone coprescriptions and improves the distribution of naloxone. Our time series analysis controls for secular trends and strongly suggests that minimally interruptive CDS significantly improves process outcomes.

What characteristics of clinical decision support system implementations lead to adoption for regular use? A scoping review

IntroductionDigital healthcare innovation has yielded many prototype clinical decision support (CDS) systems, however, few are fully adopted into practice, despite successful research outcomes. We aimed to explore the characteristics of...

A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury

BackgroundVenous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes.This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation.MethodsThe SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a “Living Guideline” process model for the VTE prevention CDS system.DiscussionThe stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics.Trial registrationClinicaltrials.gov– NCT05628207. Prospectively registered 11/28/2022, https://classic.clinicaltrials.gov/ct2/show/NCT05628207.

Universal clinical decision support tool for thromboprophylaxis in hospitalized COVID-19 patients: post hoc analysis of the IMPROVE-DD cluster randomized trial

BackgroundInpatient and extended postdischarge thromboprophylaxis of COVID-19 patients remains suboptimal despite antithrombotic guidelines. ObjectivesTo determine whether a novel electronic health record–agnostic clinical decision support (CDS) tool incorporating the International Medical Prevention Registry on Venous Thromboembolism plus D-dimer (IMPROVE-DD) venous thromboembolism (VTE) scores increases appropriate inpatient and extended postdischarge thromboprophylaxis and improves outcomes in COVID-19 inpatients. MethodsThis post hoc analysis of the IMPROVE-DD cluster randomized trial evaluated thromboprophylaxis CDS among COVID-19 inpatients at 4 New York hospitals between December 21, 2020, and January 21, 2022. Hospitals were randomized 1:1 to CDS (intervention, n = 2) vs no CDS (usual care, n = 2). The primary outcome was rate of appropriate thromboprophylaxis. Secondary outcomes included rates of major thromboembolism, all-cause and VTE-related readmissions and death, major bleeding (MB), and all-cause mortality 30 days after discharge. ResultsTwo thousand four hundred fifty-two COVID-19 inpatients were analyzed (CDS, 1355; no CDS, 1097). Mean age was 73.7 ± 9.37 years; 50.1% of participants were male. CDS adoption was 96.8% (intervention group). CDS was associated with increased appropriate at-discharge extended thromboprophylaxis (42.6% vs 28.8%; odds ratio [OR], 1.83; 95% CI, 1.39-2.41; P < .001). CDS was associated with reduced VTE (OR, 0.54; 95% CI, 0.39-0.75; P < .001), arterial thromboembolism (OR, 0.10; 95% CI, 0.01-0.81; P = .01), total thromboembolism (OR, 0.50; 95% CI, 0.36-0.69; P < .001), and 30-day all-cause readmission/death (OR, 0.78; 95% CI, 0.62-0.99; P = .04). There were no differences in MB, VTE-related readmissions/death, or all-cause mortality. ConclusionElectronic health record–agnostic CDS incorporating IMPROVE-DD VTE scores had high adoption, was associated with increased appropriate at-discharge extended thromboprophylaxis, and reduced thromboembolism and all-cause readmission/death without increasing MB in COVID-19 inpatients.

Sustained Effect of Clinical Decision Support for Heart Failure: A Natural Experiment Using Implementation Science.

In a randomized controlled trial, we found that applying implementation science (IS) methods and best practices in clinical decision support (CDS) design to create a locally customized, "enhanced" CDS significantly improved evidence-based prescribing of β blockers (BB) for heart failure compared with an unmodified commercially available CDS. At trial conclusion, the enhanced CDS was expanded to all sites. The purpose of this study was to evaluate the real-world sustained effect of the enhanced CDS compared with the commercial CDS. In this natural experiment of 28 primary care clinics, we compared clinics exposed to the commercial CDS (preperiod) to clinics exposed to the enhanced CDS (both periods). The primary effectiveness outcome was the proportion of alerts resulting in a BB prescription. Secondary outcomes included patient reach and clinician adoption (dismissals). There were 367 alerts for 183 unique patients and 171 unique clinicians (pre: March 2019-August 2019; post: October 2019-March 2020). The enhanced CDS increased prescribing by 26.1% compared with the commercial (95% confidence interval [CI]: 17.0-35.1%), which is consistent with the 24% increase in the previous study. The odds of adopting the enhanced CDS was 81% compared with 29% with the commercial (odds ratio: 4.17, 95% CI: 1.96-8.85). The enhanced CDS adoption and effectiveness rates were 62 and 14% in the preperiod and 92 and 10% in the postperiod. Applying IS methods with CDS best practices was associated with improved and sustained clinician adoption and effectiveness compared with a commercially available CDS tool.

Examining Care Planning Efficiency and Clinical Decision Support Adoption in a System Tailoring to Nurses' Graph Literacy: National, Web-Based Randomized Controlled Trial.

The proliferation of health care data in electronic health records (EHRs) is fueling the need for clinical decision support (CDS) that ensures accuracy and reduces cognitive processing and documentation burden. The CDS format can play a key role in achieving the desired outcomes. Building on our laboratory-based pilot study with 60 registered nurses (RNs) from 1 Midwest US metropolitan area indicating the importance of graph literacy (GL), we conducted a fully powered, innovative, national, and web-based randomized controlled trial with 203 RNs. This study aimed to compare care planning time (CPT) and the adoption of evidence-based CDS recommendations by RNs randomly assigned to 1 of 4 CDS format groups: text only (TO), text+table (TT), text+graph (TG), and tailored (based on the RN's GL score). We hypothesized that the tailored CDS group will have faster CPT (primary) and higher adoption rates (secondary) than the 3 nontailored CDS groups. Eligible RNs employed in an adult hospital unit within the past 2 years were recruited randomly from 10 State Board of Nursing lists representing the 5 regions of the United States (Northeast, Southeast, Midwest, Southwest, and West) to participate in a randomized controlled trial. RNs were randomly assigned to 1 of 4 CDS format groups-TO, TT, TG, and tailored (based on the RN's GL score)-and interacted with the intervention on their PCs. Regression analysis was performed to estimate the effect of tailoring and the association between CPT and RN characteristics. The differences between the tailored (n=46) and nontailored (TO, n=55; TT, n=54; and TG, n=48) CDS groups were not significant for either the CPT or the CDS adoption rate. RNs with low GL had longer CPT interacting with the TG CDS format than the TO CDS format (P=.01). The CPT in the TG CDS format was associated with age (P=.02), GL (P=.02), and comfort with EHRs (P=.047). Comfort with EHRs was also associated with CPT in the TT CDS format (P<.001). Although tailoring based on GL did not improve CPT or adoption, the study reinforced previous pilot findings that low GL is associated with longer CPT when graphs were included in care planning CDS. Higher GL, younger age, and comfort with EHRs were associated with shorter CPT. These findings are robust based on our new innovative testing strategy in which a diverse national sample of RN participants (randomly derived from 10 State Board of Nursing lists) interacted on the web with the intervention on their PCs. Future studies applying our innovative methodology are recommended to cost-effectively enhance the understanding of how the RN's GL, combined with additional factors, can inform the development of efficient CDS for care planning and other EHR components before use in practice.

Targeted messaging to improve the adoption of clinical decision support for prescription drug monitoring program use.

Clinical decision support (CDS) can prevent medical errors and improve patient outcomes. Electronic health record (EHR)-based CDS, designed to facilitate prescription drug monitoring program (PDMP) review, has reduced inappropriate opioid prescribing. However, the pooled effectiveness of CDS has exhibited substantial heterogeneity and current literature does not adequately detail why certain CDS are more successful than others. Clinicians regularly override CDS, limiting its impact. No studies recommend how to help nonadopters recognize and recover from CDS misuse. We hypothesized that a targeted educational intervention would improve CDS adoption and effectiveness for nonadopters. Over 10 months, we identified 478 providers consistently overriding CDS (nonadopters) and sent each up to 3 educational message(s) via email or EHR-based chat. One hundred sixty-one (34%) nonadopters stopped consistently overriding CDS and started reviewing the PDMP after contact. We concluded that targeted messaging is a low-resource way to disseminate CDS education and improve CDS adoption and best practice delivery.

Implementation strategies to address the determinants of adoption, implementation, and maintenance of a clinical decision support tool for emergency department buprenorphine initiation: a qualitative study

BackgroundUntreated opioid use disorder (OUD) is a significant public health problem. Buprenorphine is an evidence-based treatment for OUD that can be initiated in and prescribed from emergency departments (EDs) and office settings. Adoption of buprenorphine initiation among ED clinicians is low. The EMBED pragmatic clinical trial investigated the effectiveness of a clinical decision support (CDS) tool to promote ED clinicians’ behavior related to buprenorphine initiation in the ED. While the CDS intervention was not associated with increased rates of buprenorphine treatment for patients with OUD at intervention ED sites, attending physicians at intervention EDs were more likely to initiate buprenorphine at least once over the duration of the study compared to those in the usual care arms (44.4% vs 34.0%, P = 0.01). This suggests the CDS intervention may be associated with increased adoption of buprenorphine initiation. As a secondary aim, we sought to identify the determinants of CDS adoption, implementation, and maintenance in a variety of ED settings and geographic locations.MethodsWe purposively sampled and conducted semi-structured, in-depth interviews with clinicians across EMBED trial sites randomized to the intervention arm from five healthcare systems. Interviews elicited clinician experiences regarding buprenorphine initiation and CDS use. Interviews were analyzed using directed content analysis informed by the Practical, Robust Implementation and Sustainability Model (PRISM). We used a hybrid approach (a priori codes informed by PRISM and emergent codes) for codebook development. ATLAS.ti (version 9.0) was used for data management. Coded data were analyzed within individual interview transcripts and across all interviews to identify major themes. This process involved (1) combining, comparing, and making connections between codes; (2) writing analytic memos about observed patterns; and (3) frequent team meetings to discuss emerging patterns.ResultsTwenty-eight interviews were conducted. Major themes that influenced the successful adoption, implementation, and maintenance of the EMBED intervention and ED-initiated BUP were organizational culture and commitment, clinician training and support, the ability to connect patients to ongoing treatment, and the ability to tailor implementation to each ED. These findings informed the identification of implementation strategies (framed using PRISM domains) to enhance the ED initiation of buprenorphine.ConclusionThe findings from this qualitative analysis can provide guidance to build better systems to promote the adoption of ED-initiated buprenorphine.

Nudging Health Care Providers’ Adoption of Clinical Decision Support: Protocol for the User-Centered Development of a Behavioral Economics–Inspired Electronic Health Record Tool

The improvements in care resulting from clinical decision support (CDS) have been significantly limited by consistently low health care provider adoption. Health care provider attitudes toward CDS, specifically psychological and behavioral barriers, are not typically addressed during any stage of CDS development, although they represent an important barrier to adoption. Emerging evidence has shown the surprising power of using insights from the field of behavioral economics to address psychological and behavioral barriers. Nudges are formal applications of behavioral economics, defined as positive reinforcement and indirect suggestions that have a nonforced effect on decision-making. Our goal is to employ a user-centered design process to develop a CDS tool-the pulmonary embolism (PE) risk calculator-for PE risk stratification in the emergency department that incorporates a behavior theory-informed nudge to address identified behavioral barriers to use. All study activities took place at a large academic health system in the New York City metropolitan area. Our study used a user-centered and behavior theory-based approach to achieve the following two aims: (1) use mixed methods to identify health care provider barriers to the use of an active CDS tool for PE risk stratification and (2) develop a new CDS tool-the PE risk calculator-that addresses behavioral barriers to health care providers' adoption of CDS by incorporating nudges into the user interface. These aims were guided by the revised Observational Research Behavioral Information Technology model. A total of 50 clinicians who used the original version of the tool were surveyed with a quantitative instrument that we developed based on a behavior theory framework-the Capability-Opportunity-Motivation-Behavior framework. A semistructured interview guide was developed based on the survey responses. Inductive methods were used to analyze interview session notes and audio recordings from 12 interviews. Revised versions of the tool were developed that incorporated nudges. Functional prototypes were developed by using Axure PRO (Axure Software Solutions) software and usability tested with end users in an iterative agile process (n=10). The tool was redesigned to address 4 identified major barriers to tool use; we included 2 nudges and a default. The 6-month pilot trial for the tool was launched on October 1, 2021. Clinicians highlighted several important psychological and behavioral barriers to CDS use. Addressing these barriers, along with conducting traditional usability testing, facilitated the development of a tool with greater potential to transform clinical care. The tool will be tested in a prospective pilot trial. DERR1-10.2196/42653.

Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers.

Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use. Analyses included 80195 encounters with 17931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data. Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider. CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications. Research is needed on improving SDM and CDS adoption in CHCs. ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care

BackgroundThe US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals. Recently, with attention to opioid-related risks, prescribing has declined. However, clinical experts have countered with concerns that some patients for whom opioid-related benefits outweigh risks may be inappropriately discontinued from opioids. Unfortunately, primary care clinicians lack usable tools to help them partner with their patients in choosing pain treatment options that best balance risks and benefits in the context of patient history, resources, symptoms, and goals. Thus, primary care clinicians and patients would benefit from patient-centered clinical decision support (CDS) for this shared decision-making process.MethodsThe objective of this 3-year project is to study the adaptation and implementation of an existing interoperable CDS tool for pain treatment shared decision making, with tailored implementation support, in new clinical settings in the OneFlorida Clinical Research Consortium. Our central hypothesis is that tailored implementation support will increase CDS adoption and shared decision making. We further hypothesize that increases in shared decision making will lead to improved patient outcomes, specifically pain and physical function. The CDS implementation will be guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. The evaluation will be organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. We will adapt and tailor PainManager, an open source interoperable CDS tool, for implementation in primary care clinics affiliated with the OneFlorida Clinical Research Consortium. We will evaluate the effect of tailored implementation support on PainManager’s adoption for pain treatment shared decision making. This evaluation will establish the feasibility and obtain preliminary data in preparation for a multi-site pragmatic trial targeting the effectiveness of PainManager and tailored implementation support on shared decision making and patient-reported pain and physical function.DiscussionThis research will generate evidence on strategies for implementing interoperable CDS in new clinical settings across different types of electronic health records (EHRs). The study will also inform tailored implementation strategies to be further tested in a subsequent hybrid effectiveness-implementation trial. Together, these efforts will lead to important new technology and evidence that patients, clinicians, and health systems can use to improve care for millions of Americans who suffer from pain and other chronic conditions.Trial registrationClinicalTrials.gov, NCT05256394, Registered 25 February 2022.

Pharmacogenomics decision support in the U-PGx project: Results and advice from clinical implementation across seven European countries

The clinical implementation of pharmacogenomics (PGx) could be one of the first milestones towards realizing personalized medicine in routine care. However, its widespread adoption requires the availability of suitable clinical decision support (CDS) systems, which is often impeded by the fragmentation or absence of adequate health IT infrastructures. We report results of CDS implementation in the large-scale European research project Ubiquitous Pharmacogenomics (U-PGx), in which PGx CDS was rolled out and evaluated across more than 15 clinical sites in the Netherlands, Spain, Slovenia, Italy, Greece, United Kingdom and Austria, covering a wide variety of healthcare settings. We evaluated the CDS implementation process through qualitative and quantitative process indicators. Quantitative indicators included statistics on generated PGx reports, median time from sampled upload until report delivery and statistics on report retrievals via the mobile-based CDS tool. Adoption of different CDS tools, uptake and usability were further investigated through a user survey among healthcare providers. Results of a risk assessment conducted prior to the implementation process were retrospectively analyzed and compared to actual encountered difficulties and their impact. As of March 2021, personalized PGx reports were produced from 6884 genotyped samples with a median delivery time of twenty minutes. Out of 131 invited healthcare providers, 65 completed the questionnaire (response rate: 49.6%). Overall satisfaction rates with the different CDS tools varied between 63.6% and 85.2% per tool. Delays in implementation were caused by challenges including institutional factors and complexities in the development of required tools and reference data resources, such as genotype-phenotype mappings. We demonstrated the feasibility of implementing a standardized PGx decision support solution in a multinational, multi-language and multi-center setting. Remaining challenges for future wide-scale roll-out include the harmonization of existing PGx information in guidelines and drug labels, the need for strategies to lower the barrier of PGx CDS adoption for healthcare institutions and providers, and easier compliance with regulatory and legal frameworks.

Applying Clinical Decision Support Design Best Practices With the Practical Robust Implementation and Sustainability Model Versus Reliance on Commercially Available Clinical Decision Support Tools: Randomized Controlled Trial.

BackgroundLimited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations.ObjectiveThis study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool.MethodsWe conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomized trial across 28 primary care clinics. Both alerts aimed to improve beta-blocker prescribing for heart failure. The enhanced alert was informed by CDS best practices and the Practical, Robust, Implementation, and Sustainability Model (PRISM) IS framework, whereas the commercial alert followed vendor-supplied specifications. Following PRISM, the enhanced alert was informed by iterative, multilevel stakeholder input and the dynamic interactions of the internal and external environment. Outcomes aligned with PRISM’s evaluation measures, including patient reach, clinician adoption, and changes in prescribing behavior. Clinicians exposed to each alert were interviewed to identify design features that might influence adoption. The interviews were analyzed using a thematic approach.ResultsBetween March 15 and August 23, 2019, the enhanced alert fired for 61 patients (106 alerts, 87 clinicians) and the commercial alert fired for 26 patients (59 alerts, 31 clinicians). The adoption and effectiveness of the enhanced alert were significantly higher than those of the commercial alert (62% vs 29% alerts adopted, P<.001; 14% vs 0% changed prescribing, P=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert.ConclusionsThe results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool.Trial RegistrationClinicalTrials.gov NCT04028557; http://clinicaltrials.gov/ct2/show/NCT04028557

A systematic review of theoretical constructs in CDS literature

BackgroundStudies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature.ObjectiveOur overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions.MethodsWe used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters.ResultsTwenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87).ConclusionWe found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.

Why provision of clinical decision support (CDS) is not enough? Factors influencing the CDS adoption

Abstract Background Clinical decision support (CDS) can improve the quality and efficiency of care by increasing access to evidence-based data at the point-of-care. However, CDS adoption is limited in clinical settings. The focus on the technical design have overlooked end-users’ perceptions, social and organisational factors that may influence uptake. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, we aimed to identify different challenges and opportunities for CDSS adoption in clinical settings. Methods A cross-sectional online survey was conducted from June-October 2019 with general practitioners, physicians and surgeons in Australia. To measure the impact of UTAUT moderators (age, gender and experience) and care settings (primary care vs. hospitals) on perceived barriers and facilitators to CDS implementation, multivariate logistic regression was carried out. Qualitative data was thematically analysed for emerging patterns and themes. Results Many respondents indicated that CDS can promote evidence-based care (79%) and improve quality and safety (52%). Overall CDS adoption is dependent on factors such as effort expectancy (ease of use), performance expectancy and a facilitating environment for system use. Clinician perceptions of barriers and facilitators to uptake are associated with type of care setting. There was higher likelihood of perceiving ease of use (OR 1.39, 95% CI 1.15-2.07), time limitations (OR 1.95, 95%CI 1.10-3.77), patient' preferences (OR 2.17, 95% CI 1.14-3.37) and threat to professional autonomy (OR 2.15, 95%CI 1.39-3.41) as factors that influence adoption in primary care. Providing relevant information in right format at right time is critical for successful CDS use. Conclusions CDS implementation requires a holistic approach addressing users' perceptions and preferences determined through stakeholder consultation. The system design must be easy to use but also facilitated through the provision of on-site training and support. Key messages CDS adoption is determined by systems’ ease of use and provision of enabling environment. Users and setting characteristics are important moderators and must be considered in CDS implementation.

Improving the adoption of an electronic clinical decision support tool and evaluating its effect on venous thromboembolism prophylaxis prescribing at a Sydney tertiary teaching hospital

AbstractBackgroundAn electronic venous thromboembolism (VTE) prophylaxis clinical decision support (CDS) tool was implemented in an electronic medication management system at an Australian tertiary teaching hospital. The VTE CDS had not been clinically validated nor widely adopted.AimThis study evaluated the effectiveness of the VTE CDS for improving risk‐appropriate prescribing of VTE prophylaxis, a peer‐to‐peer strategy for improving adoption of the VTE CDS and the effects of the strategy on VTE risk assessment documentation, risk‐appropriate prophylaxis prescribing rate and user acceptability of the VTE CDS.MethodsNominated junior medical officers were trained to educate and promote the use of the VTE CDS to target colleagues over 6 weeks. Pre‐ and postintervention audits of VTE risk assessment and prophylaxis prescribing were conducted. A user acceptance survey was distributed.ResultsAnalysis of pre‐ and postintervention audit data (n = 198 for each) revealed no significant differences in rates of VTE CDS adoption or risk‐appropriate VTE prophylaxis prescribed. More patients had risk‐appropriate prophylaxis prescribed when the VTE CDS was used (90%; 63/70) than when it was not used (71.5% (233/326); p = 0.001). Documented evidence of VTE risk assessment increased significantly from 51.5% (102/198) to 68.2% (135/198) following the intervention (p &lt; 0.001). Most survey responses were favourable towards VTE CDS usability despite limitations.ConclusionPeer‐to‐peer promotion was unsuccessful in improving VTE CDS adoption in this study. Findings suggest VTE CDS use is associated with more appropriate VTE prophylaxis prescribing decisions and may improve risk assessment documentation. Future studies should examine strategies to sustainably improve adoption of VTE CDS and patient outcomes.

Clinical decision support: the role of ACR Appropriateness Criteria.

Clinical decision support is a way to decrease inappropriate imaging exams and promote judicious use of imaging resources. The adoption of clinical decision support will be incentivized by requiring the use of approved mechanisms to qualify for Medicare reimbursement starting in January 2020. Insurance providers base their reimbursement policies on Medicare, so clinical decision support could soon become relevant to pediatric imaging. We present the process behind the American College of Radiology (ACR) Appropriateness Criteria (a set of appropriate use criteria developed by the ACR) that will form the basis for software that can be used to fulfill the criteria for clinical decision support. For most organizations, this software is expected to be the easiest way to implement clinical decision support. Clinical decision support will affect how providers order imaging exams. This article should help readers understand how clinical decision support is expected to change the practice of the ordering providers, how the ACR Appropriateness Criteria are related to clinical decision support and how the ACR Appropriateness Criteria are developed. This will help the interpreting radiologist better communicate with the referring clinician, including informing the latter about how the clinical decision support software is making decisions.

A qualitative framework-based evaluation of radiology clinical decision support initiatives: eliciting key factors to physician adoption in implementation.

ObjectivesTo illustrate key contextual factors that may have effects on clinical decision support (CDS) adoption and, ultimately, success.Materials and MethodsWe conducted a qualitative evaluation of 2 similar radiology CDS innovations for near-term endpoints affecting adoption and present the findings using an evaluation framework. We identified key contextual factors between these 2 innovations and determined important adoption differences between them.ResultsDegree of electronic health record integration, approach to education and training, key drivers of adoption, and tailoring of the CDS to the clinical context were handled differently between the 2 innovations, contributing to variation in their relative degrees of adoption and use. Attention to these factors had impacts on both near and later-term measures of success (eg, patient outcomes).DiscussionCDS adoption is a well-studied early-term measure of CDS success that directly impacts outcomes. Adoption requires attention throughout the design phases of an intervention especially to key factors directly affecting it, including how implementation across multiple sites and systems complicates adoption, which prior experience with CDS matters, and that practice guidelines invariably require tailoring to the clinical context.ConclusionWith better planning for the capture of early-term measures of successful CDS implementation, especially adoption, critical adjustments may be made to ensure that the CDS is effectively implemented to be successful.

Leveraging Health Information Technology in the Quest to Improve Health Care Value.

In the United States, patients receive only half of recommended medical care and nearly a third of the care received may be unnecessary. This disparity between usual and evidence-based clinical practice is associated with a third of hospital deaths and loss of an estimated $380 billion each year.1 The health care industry has invested billions of dollars in electronic medical record systems, believing that it would revolutionize the process of translating evidenced-based guidelines into clinical practice—leading to higher quality care at lower costs, and thereby, improving health care value. Despite these expectations, variability in clinical practice continues for common pediatric diagnoses. Such variability often leads to unsafe, less efficient, and costly health care. However, astute clinicians, policy makers, and payers recognize that evidence-based clinical practice guidelines can make care more reliable and efficient and narrow the evidence practice gap between what clinicians do and what scientific evidence supports. Current strategies to optimize health information technology as a tool to translate evidence into clinical practice include clinical decision support (CDS) tools, such as condition-specific order sets, documentation templates, and best practice advisories.2 Despite these strategies, inconsistences remain in medical care, widening gaps in population-level health experiences and outcomes for common pediatric diagnoses, including asthma, community-acquired pneumonia, and bronchiolitis.3,4 Poor adoption by providers is an important barrier to optimizing health information technology, specifically CDS, as a tool to translate evidence-based recommendations into clinical practice.4 Provider factors, such as their attitudes toward CDS, are typically not addressed during any stage of a CDS development, even though they represent an important factor for adoption.5 It is important to develop strategies that foster partnerships between end users, most often clinicians, and medical informatics personnel to learn what provider factors will enhance CDS adoption. Even a user-centered design CDS tool will not likely completely address the variability in current clinical practice. Thus, we must also focus on robust implementation strategies, which will foster consistent adoption of these tools and sustainability of improvement efforts. We have previously described one conceptual framework associated with sustained improvements in a variety of accountability measures at The Johns Hopkins Hospital.6 The model has also been implemented in 4 other hospitals in the Johns Hopkins Health System to translate evidence into practice and achieved similar improvements in performance measures.7 This model may provide a promising approach to promote CDS adoption and reduce variability in a sustainable manner.6 The model entails (1) clarifying and communicating the goals and measures of interest across all levels of the organization; (2) building capacity and using Lean Sigma or a similar change model for improvement; (3) transparent reporting and ensuring accountability for performance; and (4) developing a sustainability process. Here is how the model could support implementation, using pediatric asthma as an example. First, a multidisciplinary team involving a pediatric pulmonologist and hospitalist, respiratory therapist, nursing, and a pharmacist would agree on the overall goals of the CDS form. They would select or, if needed, identify complementary performance measures inclusive of process and outcome metrics. The aim would be to use national or standardized performance measures when these exist. For example, The Joint Commission has identified 3 performance measures for Children's Asthma Care that pediatric hospitalists should aim to achieve for patients hospitalized to treat asthma. Once the multidisciplinary team identifies appropriate measures, they would partner with health care informatics to integrate these measures into the CDS tool and to build a mechanism to easily track and report performance. The next step would be to implement and test the CDS tool. Implementation would be an iterative process and could use the Lean Sigma “define-measure-analyze-improve-control (DMAIC)”8 or a similar change framework to systematically identify where failures occur and determine ways to improve. It is important to have transparent reporting of performance during this process to ensure accountability and to determine when goals have been consistently achieved over time (step 3). Once goals have been consistently achieved, the last step would be to develop a sustainability plan to ensure that improvements persist outside of the targeted implementation phase. The potential of CDS tools in improving the translation of evidence-based recommendations into clinical practice has not been fully realized. A primary factor has been poor provider adoption of these tools and lackluster implementation strategies. Development of CDS tools should incorporate end user feedback to facilitate improvements in adoption. A comprehensive implementation model that involves end user clinicians in the initiative could promote adoption of such a tool and sustainable improvement of performance measures. The authors plan to study this model as a mechanism to standardize pediatric care.

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017.

Summary Introduction: Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry. Objective: Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017. Methods: Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally. Results: CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related “Big Data” sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of “app” ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions. Conclusion: Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.