Implementation Of Decision Support Research Articles

Development and implementation of a guideline-based decision support system for etiological diagnosis of Sustained Monomorphic Ventricular Tachycardia (SMVT)

Abstract Background Guideline (GL)-based decision support systems (DSSs) play a significant role in supporting physicians in their decision-making processes by providing structured clinical guidance. While these systems have shown significant success in enhancing practitioner performance and patient outcomes in general practice, their application in the context of ventricular arrhythmias remains limited. Moreover, the clinical etiological diagnosis of ventricular arrhythmias presents various challenges for healthcare professionals, such as adherence to updated diagnostic criteria and understanding of complex arrhythmia mechanisms. Purpose In this study, we aim to design, implement, and evaluate a guideline-based decision support systems for etiological diagnosis of sustained monomorphic ventricular tachycardia (SMVT), which could potentially address critical challenges associated with the disease diagnosis and management. Methods To comprehensively capture the semantics embedded in clinical guidelines, we first developed a dedicated knowledge graph tailored to the clinical decision-making. This involved extracting entities and relationships pertinent to key decision procedures from ESC guidelines for ventricular arrhythmias, clinical textbooks or literatures. Subsequently, we translated instances within the knowledge graph into archetypes within openEHR, amplifying the interoperability of clinical information. To enhance the interpretability of the DSS, we established executable diagnostic and treatment processes to offer visual representation of clinical steps in diagnosis and treatment. Ultimately, we constructed a decision support engine to implement the GL-DSS, and its effectiveness was evaluated with anonymous real-world data. Results The proposed GL-DSS was found to be quite feasible and could provide etiological diagnosis for SMVT. The knowledge base for the constructed GL-DDS included more than 2000 entities and relations, which played a vital role in representing disease pathogenesis. The proposed system can map the clinical entities to a set of openEHR archetypes, thus mitigate the interoperability issues of the hospital information systems and CDSS data integration. Also, this module improved system usability and ensured that guidelines could be easily translated and updated into decision support tools. Real-word medical records from 20 patient were collected for model evaluation, the results showed that the GL-DSS covered most of the entities and relations between diseases and diagnostic evidence recorded in clinical texts and achieved a diagnosis accuracy of 80%. Conclusions The results demonstrated that our proposed GL-DSS could completely and accurately capture the diagnostic information under the concrete EHR data. Thus, the implement decision support system could have the potential to improve clinical decision-making, patient care, and outcomes in this specialized medical domain.

Use of web-based decision support to improve informed choice for chemoprevention: a qualitative analysis of pre-implementation interviews (SWOG S1904)

BackgroundWomen with high-risk breast lesions, such as atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS), have a 4- to tenfold increased risk of breast cancer compared to women with non-proliferative breast disease. Despite high-quality data supporting chemoprevention, uptake remains low. Interventions are needed to break down barriers.MethodsThe parent trial, MiCHOICE, is a cluster randomized controlled trial evaluating the effectiveness and implementation of patient and provider decision support tools to improve informed choice about chemoprevention among women with AH or LCIS. For this pre-implementation analysis, 25 providers participated in semi-structured interviews prior to accessing decision support tools. Interviews sought to understand attitudes/beliefs and barriers/facilitators to chemoprevention.ResultsInterviews with 25 providers (18 physicians and 7 advanced practice providers) were included. Providers were predominantly female (84%), white (72%), and non-Hispanic (88%). Nearly all providers (96%) had prescribed chemoprevention for eligible patients. Three themes emerged in qualitative analysis. The first theme describes providers’ confidence in chemoprevention and the utility of decision support tools. The second theme elucidates barriers to chemoprevention, including time constraints, risk communication and perceptions of patients’ fear of side effects and anxiety. The third theme is the need for early implementation of decision support tools.ConclusionsThis qualitative study suggests that providers were interested in the early inclusion of decision aids (DA) in their chemoprevention discussion workflow. The DAs may help overcome certain barriers which were elucidated in these interviews, including patient level concerns about side effects, clinic time constraints and difficulty communicating risk. A multi-faceted intervention with a DA as one active component may be needed.Trial registrationThis trial was registered with the NIH clinical trial registry, clinicaltrials.gov, NCT04496739.

Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system’s interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system’s performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren’t needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.

Implementation of Clinical Decision Support to Identify and Improve Treatment for Severe PIVIE.

Pediatric patients are at high risk of peripheral intravenous infiltration or extravasation (PIVIE) leading to injury and increased costs. Most of the work in addressing PIVIE has focused on the implementation of workflow bundles and evidenced based guidelines. This project showed that Clinical Decision Support can be used to help support identification and treatment of Severe PIVIE through use of an interruptive alert that increases placement of vascular access team consults.

Implementation and Evaluation of Clinical Decision Support for Apixaban Dosing in a Community Teaching Hospital.

This study aimed to describe and evaluate the transition from a specialty service-based prospective order approval system to a computerized clinical decision support (CCDS) tool for apixaban dosing at a community teaching hospital. The primary objective was to assess the impact of the transition on the appropriateness of apixaban prescribing. A CCDS tool for apixaban dosing was developed and implemented using interprofessional collaboration. A retrospective chart review was conducted for apixaban orders placed before (preimplementation) and after (postimplementation) the CCDS transition. The primary outcome was the percent change in inappropriate apixaban orders, with secondary outcomes exploring percent change of apixaban orders with inappropriate dosing in different patient groups and indications per package insert. Fifty orders were assessed in both arms, with 8% of orders preimplementation and 10% postimplementation deemed inappropriate. After accounting for questionable orders, overall appropriateness of prescribing was 88% preimplementation and 84% postimplementation ( P = 0.7). Challenges with implementation of CCDS included working with available information technology resources and facilitating acceptance of a new ordering process. The implementation of a CCDS tool for apixaban dosing at a community teaching hospital demonstrated comparable rates of appropriateness to the previous specialty service-based approval process. While the transition streamlined resources and improved efficiency, ongoing efforts are needed to address specific dosing challenges. Future research should explore the sustainability and generalizability of CCDS tools in diverse healthcare settings.

Implementasi Metode TOPSIS Untuk Menentukan Dosen Terbaik (Studi Kasus : STMIK Bina Nusantara Jaya Lubuklinggau)

STMIK Bina Nusantara Jaya Lubuklinggau is one of the universities in Lubuklinggau which was founded in 2007. Currently there are 20 permanent lecturers. At the higher education level, lecturers are professional staff who are appointed through statutory regulations. As teaching staff, lecturers have an obligation to provide knowledge to students in higher education. As an effort to support the teaching and learning process on campus, selection of the best lecturers is often held directly by students. However, the process of selecting the best lecturer still has shortcomings, namely that students only choose their favorite lecturer without looking at the teaching and learning process provided by the lecturer. Therefore, this research will implement decision support methods to help make more accurate decisions in determining the best lecturer. The method implemented in the decision support system for determining the best lecturers is the TOPSIS method. There are 6 criteria used to determine the best lecturers, namely length of service, achievements, semester learning plans, teaching materials, learning media, and lecturer presence. Based on the calculation results of the implementation of the TOPSIS method on 5 lecturers who have been certified, Robi Yanto is the best lecturer with the highest score, namely 0,86.

Nurses' perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study.

The purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption. Qualitative descriptive study. Nurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke. Four major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools. This study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy. This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support.

Making Informed Choices On Incorporating Chemoprevention into carE (MiCHOICE, SWOG 1904): Design and methods of a cluster randomized controlled trial

IntroductionWomen with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS) have a significantly increased risk of breast cancer, which can be substantially reduced with antiestrogen therapy for chemoprevention. However, antiestrogen therapy for breast cancer risk reduction remains underutilized. Improving knowledge about breast cancer risk and chemoprevention among high-risk patients and their healthcare providers may enhance informed decision-making about this critical breast cancer risk reduction strategy. Methods/designWe are conducting a cluster randomized controlled trial to evaluate the effectiveness and implementation of patient and provider decision support tools to improve informed choice about chemoprevention among women with AH or LCIS. We have cluster randomized 26 sites across the U.S. through the SWOG Cancer Research Network. A total of 415 patients and 200 healthcare providers are being recruited. They are assigned to standard educational materials alone or combined with the web-based decision support tools. Patient-reported and clinical outcomes are assessed at baseline, after a follow-up visit at 6 months, and yearly for 5 years. The primary outcome is chemoprevention informed choice after the follow-up visit. Secondary endpoints include other patient-reported outcomes, such as chemoprevention knowledge, decision conflict and regret, and self-reported chemoprevention usage. Barriers and facilitators to implementing decision support into clinic workflow are assessed through patient and provider interviews at baseline and mid-implementation. Results/discussionWith this hybrid effectiveness/implementation study, we seek to evaluate if a multi-level intervention effectively promotes informed decision-making about chemoprevention and provide valuable insights on how the intervention is implemented in U.S. clinical settings. Trial RegistrationNCT04496739

A Quality Initiative to Improve Appropriate Medication Dosing in Pediatric Patients with Obesity.

Emerging evidence supports the use of alternative dosing weights for medications in patients with obesity. Pediatric obesity presents a particular challenge because most medications are dosed based on patient weight. Additionally, building system-wide pediatric obesity safeguards is difficult due to pediatric obesity definitions of body mass index-percentile-for-age via the Center for Disease Control growth charts. We describe a quality initiative to increase appropriate medication dosing in inpatients with obesity. The specific aim was to increase appropriate dosing for 7 high-risk medications in inpatients with obesity ≥2 years old from 37% to >74% and to sustain for 1 year. The Institute for Healthcare Improvement model for improvement was used to plan interventions and track outcomes progress. Interventions included a literature review to establish internal dosing guidance, electronic health record (EHR) functionality to identify pediatric patients with obesity, a default selection for medication weight with an opt-out, and obtaining patient heights in the emergency department. Appropriate dosing weight use in medication ordered for patients with obesity increased from 37% to 83.4% and was sustained above the goal of 74% for 12 months. Implementation of EHR-based clinical decision support has increased appropriate evidence-based dosing of medications in pediatric and adult inpatients with obesity. Future studies should investigate the clinical and safety implications of using alternative dosing weights in pediatric patients.

Clinical Impact of an AI Decision Support System for Detection of Intracranial Hemorrhage in CT Scans.

This study aimed to evaluate the predictive value and clinical impact of a clinically implemented artificial neural network software model. The software detects intracranial hemorrhage (ICH) from head computed tomography (CT) scans and artificial intelligence (AI)-identified positive cases are then annotated in the work list for early radiologist evaluation. The index test was AI detection by the program Zebra Medical Vision-HealthICH+. Radiologist-confirmed ICH was the reference standard. The study compared whether time benefits from using the AI model led to faster escalation of patient care or surgery within the first 24 h. A total of 2,306 patients were evaluated by the software, and 288 AI-positive cases were included. The AI tool had a positive predictive value of 0.823. There was, however, no significant time reduction when comparing the patients who required escalation of care and those who did not. There was also no significant time reduction in those who required acute surgery compared with those who did not. Among the individual patients with reduced time delay, no cases with evident clinical benefit were identified. Although the clinically implemented AI-based decision support system showed adequate predictive value in identifying ICH, there was no significant clinical benefit for the patients in our setting. While AI-assisted detection of ICH shows great promise from a technical perspective, there remains a need to evaluate the clinical impact and perform external validation across different settings.

Game-theoretic analysis of competition between access service providers utilizing a Nash genetic algorithm

Fiber-to-the-home (FTTH) technology is a promising solution for providing advanced service delivery to end-users, but its implementation requires substantial capital expenditures. To minimize investment risks and aid decision-making for access service providers, we propose a game theoretic framework based on a modified Nash genetic algorithm. We illustrate how this framework can be applied to analyze the competition between access providers offering a flat-rate FTTH service on multiple geographical areas. Each provider determines its price for all areas simultaneously and decides whether to invest on a particular area depending on the anticipated revenues. Two distinct demand models are adopted to describe different types of consumer behavior. A solution engine, based on a modified mixed-variable Nash genetic algorithm is implemented under an open-source license. The significance and practical implications of the equilibrium points obtained for both single and multiple area games are discussed. The proposed framework and the solution engine developed, aid both providers and regulatory bodies to analyze competitive environments. They can also be used to implement decision support tools for similar problems as well. The paper concludes by pointing out further research directions in this context.

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Low-back pain (LBP) is a complex health problem requiring accurate diagnosis and effective treatment. However, the current decision support system (DSS) for LBP only considers the patient’s pain intensity and treatment suitability, which may not lead to optimal outcomes. This paper proposes a novel DSS that combines machine learning (ML) and expert input to classify LBP types and provide more reliable and personalized recommendations. We used an open-source dataset to train and test various ML models, including an ensemble model that combines multiple classifiers. We also performed data analysis and feature extraction to enhance the model’s predictive power. We developed a prototype tool to demonstrate the model’s performance and usability. Our results show that the ensemble model achieved the highest accuracy of 92.02%, followed by random forest (RF) (91.01%), multilayer perceptron (MP) (91.01%), and support vector machine (SVM) (87.88%). Our findings suggest that ML can help LBP specialists diagnose and treat LBP more effectively by learning from historical data and predicting LBP categories. Our DSS can potentially improve the quality of life for LBP patients and reduce the burden on the healthcare system.

Implementation of clopidogrel pharmacogenetic clinical decision support for a preemptive return of results program.

To describe our experiences implementing and iterating CYP2C19 genotype-guided clopidogrel pharmacogenetic clinical decision support (CDS) tools over time in the setting of a large health system-wide, preemptive pharmacogenomics program. Clopidogrel-treated patients who are genetically predicted cytochrome P450 isozyme 2C19 (CYP2C19) intermediate or poor metabolizers have an increased risk of atherothrombotic events, some of which can be life-threatening. The Clinical Pharmacogenetics Implementation Consortium provides guidance for the use of clopidogrel based on CYP2C19 genotype in patients with cardiovascular and cerebrovascular diseases. Our multidisciplinary team implemented an automated, interruptive alert that fires when clopidogrel is ordered or refilled for biobank participants with structured CYP2C19 intermediate or poor metabolizer genomic indicators in the electronic health record. The implementation began with a narrow cardiovascular indication and setting and was then scaled in 4 primary dimensions: (1) clinical indication; (2) availability across health-system locations; (3) care venue (e.g., inpatient vs outpatient); and (4) provider groups (eg, cardiology and neurology). We iterated our approach over time based on evolving clinical evidence and proactive strategies to optimize CDS maintenance and sustainability. A key facilitator of expansion was socialization of the broader pharmacogenomics initiative among our academic medical center community, accompanied by clinician acceptance of pharmacogenetic alerts in practice. A multidisciplinary collaboration is recommended to facilitate the use of CYP2C19 genotype-guided antiplatelet therapy in patients with cardiovascular and cerebrovascular diseases. Evolving clopidogrel pharmacogenetic evidence necessitates thoughtful iteration of implementation efforts and strategies to optimize long-term maintenance and sustainability.

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires. Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors. Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models. The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.

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.

APPLICATIONS OF KNOWLEDGE DISCOVERY AND DATABASE (KDD) IN CLINICAL DECISION SUPPORT SYSTEM

Applications of Knowledge Discovery and Database (KDD) in Clinical Decision Support Systems) is a research work that aims to develop a medical system that has the ability to detect and suggest cure for an ailment with minimal effort. Healthcare system has been a major source of worry across the globe in recent time due to emergence of different types of diseases and epidemics. The number of health care personnel in various hospitals falls short of the number required most especially in terms of specialists. The medical system in Nigeria today suffers from lack of fast, accurate, reliable and intelligent software solutions that can help healthcare practitioners make decisions that would solve urgent, and in some cases, complex medical problems in real-time. Also, the cost of processing and analyzing large volumes of data in a medical environment is high most especially in terms of time consumption. So in this research design and implement clinical decision support system using knowledge discovery in database (KDD) was the major focus. The design required a computerized database for storing medical records as well as software for improved intelligence-based medical procedures that were going to be simpler to use, versatile, adaptable, savvy, and effortless at combining and evaluating medical data, allowing medical practitioners at all levels to make realistic, intelligent, and real-time decisions on critical health issues. The system was designed using the Object-Oriented Design and Analysis Methodology (OOADM).

Implementation of Electronic Health Record Integration and Clinical Decision Support to Improve Emergency Department Prescription Drug Monitoring Program Use

To evaluate the implementation of 3 electronic health record (EHR)-based interventions to increase prescription drug monitoring program (PDMP) use in the emergency department (ED): EHR-PDMP integration, addition of a PDMP risk score, and addition of EHR-based clinical decision support alert to review the PDMP when prescribing an opioid. Three intervention stages were implemented using a prospective stepped-wedge design at 5 university-affiliated EDs split into 3 practice groups. The PDMP use and prescribing rates during the 3 stages were compared with baseline before EHR integration and a sustainability stage where the clinical decision support alert was removed, but EHR integration and risk score remained. Generalized linear mixed model with logit link function and a random intercept for clinicians was analyzed. The ED provider PDMP review before opioid prescribing was low in all stages. The highest review rate occurred during interruptive clinical decision support alerts, 23.8% (interquartile range 10.6 to 37.5). Overall, opioid prescribing declined, and PDMP review was not associated with a decrease in opioid prescribing. PDMP review was associated with a reduction in the probability of prescribing an opioid as the number of prior opioid prescriptions increased (odds ratio: 0.92 [95% confidence interval: 0.91 to 0.94] for every additional prescription). The EHR-PDMP integration did not increase PDMP use in the ED, but a PDMP risk score and a clinical decision support alert were associated with modest increases in the probability of PDMP review. When the PDMP is reviewed, ED clinicians are less likely to prescribe opioids to patients with a high number of prior opioid prescriptions.

Differences in Use of Clinical Decision Support Tools and Implementation of Aspirin, Blood Pressure Control, Cholesterol Management, and Smoking Cessation Quality Metrics in Small Practices by Race and Sex

Practice-level evidence is needed to clarify the value of population-based clinical decision support (CDS) tools in reducing racial and sex disparities in cardiovascular care. To evaluate the association between CDS tools and racial and sex disparities in the aspirin use, blood pressure control, cholesterol management, and smoking cessation (ABCS) care quality metrics among smaller primary care practices. This cross-sectional study used practice-level data from the Agency for Healthcare Research and Quality-funded EvidenceNOW initiative. The national initiative from May 1, 2015, to April 30, 2021, spanned 12 US states and focused on improving cardiovascular preventive care by providing quality improvement support to smaller primary care practices. A total of 576 primary care practices in EvidenceNOW submitted both survey data and electronic health record (EHR)-derived ABCS data stratified by race and sex. Practice-level estimates of disparities between Black and White patients and between male and female patients were calculated as the difference in proportions of eligible patients within each practice meeting ABCS care quality metrics. The association between CDS tools (EHR prompts, standing orders, and clinical registries) and disparities was evaluated by multiply imputed multivariable models for each CDS tool, adjusted for practice rurality, ownership, and size. Across the 576 practices included in the analysis, 219 (38.0%) had patient panels that were more than half White and 327 (56.8%) had panels that were more than half women. The proportion of White compared with Black patients meeting metrics for blood pressure (difference, 5.16% [95% CI, 4.29%-6.02%]; P < .001) and cholesterol management (difference, 1.49% [95% CI, 0.04%-2.93%] P = .04) was higher; the proportion of men meeting metrics for aspirin use (difference, 4.36% [95% CI, 3.34%-5.38%]; P < .001) and cholesterol management (difference, 3.88% [95% CI, 3.14%-4.63%]; P < .001) was higher compared with women. Conversely, the proportion of women meeting practice blood pressure control (difference, -1.80% [95% CI, -2.32% to -1.28%]; P < .001) and smoking cessation counseling (difference, -1.67% [95% CI, -2.38% to -0.95%]; P < .001) metrics was higher compared with men. Use of CDS tools was not associated with differences in race or sex disparities except for the smoking metric. Practices using CDS tools showed a higher proportion of men meeting the smoking counseling metric than women (coefficient, 3.82 [95% CI, 0.95-6.68]; P = .009). The findings of this cross-sectional study suggest that practices using CDS tools had small disparities that were not statistically significant, but CDS tools were not associated with reductions in disparities. More research is needed on effective practice-level interventions to mitigate disparities.

Adaptation and Formative Evaluation of Online Decision Support to Implement Evidence-Based Strategies to Increase HPV Vaccination Rates in Pediatric Clinics.

Human papilloma virus (HPV) vaccination rates remain below national goals in the United States despite the availability of evidence-based strategies to increase rates. The Adolescent Vaccination Program (AVP) is a multi-component intervention demonstrated to increase HPV vaccination rates in pediatric clinics through the implementation of six evidence-based strategies. The purpose of this study, conducted in Houston, Texas, from 2019-2021, was to adapt the AVP into an online decision support implementation tool for standalone use and to evaluate its feasibility for use in community clinics. Phase 1 (Adaptation) comprised clinic interviews (n = 23), literature review, Adolescent Vaccination Program Implementation Tool (AVP-IT) design documentation, and AVP-IT development. Phase 2 (Evaluation) comprised usability testing with healthcare providers (HCPs) (n = 5) and feasibility testing in community-based clinics (n = 2). AVP-IT decision support provides an Action Plan with tailored guidance on implementing six evidence-based strategies (immunization champions, assessment and feedback, continuing education, provider prompts, parent reminders, and parent education). HCPs rated the AVP-IT as acceptable, credible, easy, helpful, impactful, and appealing (≥80% agreement). They rated AVP-IT supported implementation as easier and more effective compared to usual practice (p ≤ 0.05). The clinic-based AVP-IT uses facilitated strategy implementation by 3-month follow-up. The AVP-IT promises accessible, utilitarian, and scalable decision support on strategies to increase HPV vaccination rates in pediatric clinic settings. Further feasibility and efficacy testing is indicated.