Implementation Of Clinical Decision Support System Research Articles

Improvements in appropriate placement of dental sealants after implementation of a clinical decision support system

BackgroundDental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS. MethodsA validated dental quality measure was adapted to assess a patient’s need for dental sealant placement. A CDSS was implemented to advise care team members whether a child was at elevated risk of developing caries and had sealant-eligible first or second molars. Data on dental sealant placement at examination visits during a 5-year period were analyzed, including 32 months before CDSS implementation and 28 months after CDSS implementation. ResultsFrom January 1, 2018, through December 31, 2022, the authors assessed 59,047 examination visits for children at elevated risk of developing caries and with sealant-eligible teeth. With the implementation of a CDSS and training to support the clinical care team members in September 2020, the appropriate placement of dental sealants at examination visits increased from 27% through 60% (P < .00001). ConclusionsIntegration of a CDSS into the EHR as part of a quality improvement program was effective in increasing the delivery of sealants in eligible first and second molars of children aged 5 through 15 years and considered at high risk of developing caries. Practical ImplicationsAn EHR–based CDSS can be implemented to improve standardization and provide timely and appropriate patient care in dental practices.

The Effect of an Electronic Medical Record-Based Clinical Decision Support System on Adherence to Clinical Protocols in Inflammatory Bowel Disease Care: Interrupted Time Series Study.

Clinical decision support systems (CDSSs) embedded in electronic medical records (EMRs), also called electronic health records, have the potential to improve the adoption of clinical guidelines. The University of Alberta Inflammatory Bowel Disease (IBD) Group developed a CDSS for patients with IBD who might be experiencing disease flare and deployed it within a clinical information system in 2 continuous time periods. This study aims to evaluate the impact of the IBD CDSS on the adherence of health care providers (ie, physicians and nurses) to institutionally agreed clinical management protocols. A 2-period interrupted time series (ITS) design, comparing adherence to a clinical flare management protocol during outpatient visits before and after the CDSS implementation, was used. Each interruption was initiated with user training and a memo with instructions for use. A group of 7 physicians, 1 nurse practitioner, and 4 nurses were invited to use the CDSS. In total, 31,726 flare encounters were extracted from the clinical information system database, and 9217 of them were manually screened for inclusion. Each data point in the ITS analysis corresponded to 1 month of individual patient encounters, with a total of 18 months of data (9 before and 9 after interruption) for each period. The study was designed in accordance with the Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI) guidelines for health informatics evaluations. Following manual screening, 623 flare encounters were confirmed and designated for ITS analysis. The CDSS was activated in 198 of 623 encounters, most commonly in cases where the primary visit reason was a suspected IBD flare. In Implementation Period 1, before-and-after analysis demonstrates an increase in documentation of clinical scores from 3.5% to 24.1% (P<.001), with a statistically significant level change in ITS analysis (P=.03). In Implementation Period 2, the before-and-after analysis showed further increases in the ordering of acute disease flare lab tests (47.6% to 65.8%; P<.001), including the biomarker fecal calprotectin (27.9% to 37.3%; P=.03) and stool culture testing (54.6% to 66.9%; P=.005); the latter is a test used to distinguish a flare from an infectious disease. There were no significant slope or level changes in ITS analyses in Implementation Period 2. The overall provider adoption rate was moderate at approximately 25%, with greater adoption by nurse providers (used in 30.5% of flare encounters) compared to physicians (used in 6.7% of flare encounters). This is one of the first studies to investigate the implementation of a CDSS for IBD, designed with a leading EMR software (Epic Systems), providing initial evidence of an improvement over routine care. Several areas for future research were identified, notably the effect of CDSSs on outcomes and how to design a CDSS with greater utility for physicians. CDSSs for IBD should also be evaluated on a larger scale; this can be facilitated by regional and national centralized EMR systems.

CDSS Implementation Processes in the East Malaysian Government Hospitals During COVID-19 Pandemic

Clinical Decision Support Systems (CDSS) have been shown to reduce medical mistakes, but their implementation can sometimes be demanding. This exploratory study aimed to pinpoint key characteristics for implementing CDSS in government hospitals in East Malaysia. Through in-depth case studies and focus group interviews, essential determinants were identified, including technical support, communication and collaboration, and user readiness for new technology. The analysis revealed these determinants as crucial for CDSS implementation. The findings and proposed framework are valuable in assisting the hospitals in establishing guidelines for implementing CDSS, particularly in the context of the COVID-19 era. By considering these insights, hospitals may leverage CDSS to reduce medical errors and enhance patient care.

Using Clinical Decision Support Systems to Decrease Intravenous Acetaminophen Use: Implementation and Lessons Learned.

Clinical decision support systems (CDSS) can enhance medical decision-making by providing targeted information to providers. While they have the potential to improve quality of care and reduce costs, they are not universally effective and can lead to unintended harm. To describe the implementation of an unsuccessful interruptive CDSS that aimed to promote appropriate use of intravenous (IV) acetaminophen at an academic pediatric hospital, with an emphasis on lessons learned. Quality improvement methodology was used to study the effect of an interruptive CDSS, which set a mandatory expiry time of 24 hours for all IV acetaminophen orders. This CDSS was implemented on April 5, 2021. The primary outcome measure was number of IV acetaminophen administrations per 1,000 patient days, measured pre- and postimplementation. Process measures were the number of IV acetaminophen orders placed per 1,000 patient days. Balancing measures were collected via survey data and included provider and nursing acceptability and unintended consequences of the CDSS. There was no special cause variation in hospital-wide IV acetaminophen administrations and orders after CDSS implementation, nor when the CDSS was removed. A total of 88 participants completed the survey. Nearly half (40/88) of respondents reported negative issues with the CDSS, with the majority stating that this affected patient care (39/40). Respondents cited delays in patient care and reduced efficiency as the most common negative effects. This study underscores the significance of monitoring CDSS implementations and including end user acceptability as an outcome measure. Teams should be prepared to modify or remove CDSS that do not achieve their intended goal or are associated with low end user acceptability. CDSS holds promise for improving clinical practice, but careful implementation and ongoing evaluation are crucial for maximizing their benefits and minimizing potential harm.

Clinical decision support system for clinical nurses’ decision-making on nurse-to-patient assignment: a scoping review protocol

IntroductionOptimal nurse-to-patient assignment plays a crucial role in healthcare delivery, with direct implications for patient outcomes and the workloads of nursing staff. However, this process is highly intricate, involving a...

Acceptance and use of a clinical decision support system in musculoskeletal pain disorders – the SupportPrim project

BackgroundWe have developed a clinical decision support system (CDSS) based on methods from artificial intelligence to support physiotherapists and patients in the decision-making process of managing musculoskeletal (MSK) pain disorders in primary care. The CDSS finds the most similar successful patients from the past to give treatment recommendations for a new patient. Using previous similar patients with successful outcomes to advise treatment moves management of MSK pain patients from one-size fits all recommendations to more individually tailored treatment. This study aimed to summarise the development and explore the acceptance and use of the CDSS for MSK pain patients.MethodsThis qualitative study was carried out in the Norwegian physiotherapy primary healthcare sector between October and November 2020, ahead of a randomised controlled trial. We included four physiotherapists and three of their patients, in total 12 patients, with musculoskeletal pain in the neck, shoulder, back, hip, knee or complex pain. We conducted semi-structured telephone interviews with all participants. The interviews were analysed using the Framework Method.ResultsOverall, both the physiotherapists and patients found the system acceptable and usable. Important findings from the analysis of the interviews were that the CDSS was valued as a preparatory and exploratory tool, facilitating the therapeutic relationship. However, the physiotherapists used the system mainly to support their previous and current practice rather than involving patients to a greater extent in decisions and learning from previous successful patients.ConclusionsThe CDSS was acceptable and usable to both the patients and physiotherapists. However, the system appeared not to considerably influence the physiotherapists' clinical reasoning and choice of treatment based on information from most similar successful patients. This could be due to a smaller than optimal number of previous patients in the CDSS or insufficient clinical implementation. Extensive training of physiotherapists should not be underestimated to build understanding and trust in CDSSs.

Expectation of clinical decision support systems: a survey study among nephrologist end-users

BackgroundChronic kidney disease (CKD), a major public health problem with differing disease etiologies, leads to complications, comorbidities, polypharmacy, and mortality. Monitoring disease progression and personalized treatment efforts are crucial for long-term patient outcomes. Physicians need to integrate different data levels, e.g., clinical parameters, biomarkers, and drug information, with medical knowledge. Clinical decision support systems (CDSS) can tackle these issues and improve patient management. Knowledge about the awareness and implementation of CDSS in Germany within the field of nephrology is scarce.PurposeNephrologists’ attitude towards any CDSS and potential CDSS features of interest, like adverse event prediction algorithms, is important for a successful implementation. This survey investigates nephrologists’ experiences with and expectations towards a useful CDSS for daily medical routine in the outpatient setting.MethodsThe 38-item questionnaire survey was conducted either by telephone or as a do-it-yourself online interview amongst nephrologists across all of Germany. Answers were collected and analysed using the Electronic Data Capture System REDCap, as well as Stata SE 15.1, and Excel. The survey consisted of four modules: experiences with CDSS (M1), expectations towards a helpful CDSS (M2), evaluation of adverse event prediction algorithms (M3), and ethical aspects of CDSS (M4). Descriptive statistical analyses of all questions were conducted.ResultsThe study population comprised 54 physicians, with a response rate of about 80–100% per question. Most participants were aged between 51–60 years (45.1%), 64% were male, and most participants had been working in nephrology out-patient clinics for a median of 10.5 years. Overall, CDSS use was poor (81.2%), often due to lack of knowledge about existing CDSS. Most participants (79%) believed CDSS to be helpful in the management of CKD patients with a high willingness to try out a CDSS. Of all adverse event prediction algorithms, prediction of CKD progression (97.8%) and in-silico simulations of disease progression when changing, e. g., lifestyle or medication (97.7%) were rated most important. The spectrum of answers on ethical aspects of CDSS was diverse.ConclusionThis survey provides insights into experience with and expectations of out-patient nephrologists on CDSS. Despite the current lack of knowledge on CDSS, the willingness to integrate CDSS into daily patient care, and the need for adverse event prediction algorithms was high.

Improving computerized decision support system interventions: a qualitative study combining the theoretical domains framework with the GUIDES Checklist

BackgroundComputerized clinical decision support systems (CDSSs) can improve care by bridging knowledge to practice gaps. However, the real-world uptake of such systems in health care settings has been suboptimal. We sought to: (1) use the Theoretical Domains Framework (TDF) to identify determinants (barriers/enablers) of uptake of the Electronic Asthma Management System (eAMS) CDSS; (2) match identified TDF belief statements to elements in the Guideline Implementation with Decision Support (GUIDES) Checklist; and (3) explore the relationship between the TDF and GUIDES frameworks and the usefulness of this sequential approach for identifying opportunities to improve CDSS uptake.MethodsIn Phase 1, we conducted semistructured interviews with primary care physicians in Toronto, Canada regarding the uptake of the eAMS CDSS. Using content analysis, two coders independently analyzed interview transcripts guided by the TDF to generate themes representing barriers and enablers to CDSS uptake. In Phase 2, the same reviewers independently mapped each belief statement to a GUIDES domain and factor. We calculated the proportion of TDF belief statements that linked to each GUIDES domain and the proportion of TDF domains that linked to GUIDES factors (and vice-versa) and domains.ResultsWe interviewed 10 participants before data saturation. In Phase 1, we identified 53 belief statements covering 12 TDF domains; 18 (34.0%) were barriers, and 35 (66.0%) were enablers. In Phase 2, 41 statements (77.4%) linked to at least one GUIDES factor, while 12 (22.6%) did not link to any specific factor. The GUIDES Context Domain was linked to the largest number of belief statements (19/53; 35.8%). Each TDF domain linked to one or more GUIDES factor, with 6 TDF domains linking to more than 1 factor and 8 TDF domains linking to more than 1 GUIDES domain.ConclusionsThe TDF provides unique insights into barriers and enablers to CDSS uptake, which can then be mapped to GUIDES domains and factors to identify required changes to CDSS context, content, and system. This can be followed by conventional mapping of TDF domains to behaviour change techniques to optimize CDSS implementation. This novel step-wise approach combines two established frameworks to optimize CDSS interventions, and requires prospective validation.

Evaluating the Feasibility and Acceptance of a Mobile Clinical Decision Support System in a Resource-Limited Country: Exploratory Study.

In resource-limited countries, access to specialized health care services such as dermatology is limited. Clinical decision support systems (CDSSs) offer innovative solutions to address this challenge. However, the implementation of CDSSs is commonly associated with unique challenges. VisualDx-an exemplar CDSS-was recently implemented in Botswana to provide reference materials in support of the diagnosis and management of dermatological conditions. To inform the sustainable implementation of VisualDx in Botswana, it is important to evaluate the intended users' perceptions about the technology. This study aims to determine health care workers' acceptance of VisualDx to gauge the feasibility of future adoption in Botswana and other similar health care systems. The study's design was informed by constructs of the Technology Acceptance Model. An explanatory, sequential, mixed methods study involving surveys and semistructured interviews was conducted. The REDCap (Research Electronic Data Capture; Vanderbilt University) platform supported web-based data capture from March 2021 through August 2021. In total, 28 health care workers participated in the study. Descriptive statistics were generated and analyzed using Excel (Microsoft Corp), and thematic analysis of interview transcripts was performed using Delve software. All survey respondents (N=28) expressed interest in using mobile health technology to support their work. Before VisualDx, participants referenced textbooks, journal articles, and Google search engines. Overall, participants' survey responses showed their confidence in VisualDx (18/19, 95%); however, some barriers were noted. Frequently used VisualDx features included generating a differential diagnosis through manual entry of patient symptoms (330/681, 48.5% of total uses) or using the artificial intelligence feature to analyze skin conditions (150/681, 22% of total uses). Overall, 61% (17/28) of the survey respondents were also interviewed, and 4 thematic areas were derived. Participants' responses indicated their willingness to accept VisualDx. The ability to access information quickly without internet connection is crucial in resource-constrained environments. Selected enhancements to VisualDx may further increase its feasibility in Botswana. Study findings can serve as the basis for improving future CDSS studies and innovations in Botswana and similar resource-limited countries.

Complex decision making in an intensive care environment: Perceived practice versus observed reality.

Advancing our understanding of how decisions are made in cognitively, sociallyand technologically complex hospital environments may reveal opportunities to improve healthcare delivery, medical educationand the experience of patients, familiesand clinicians. Explore factors impacting clinician decision making in the Boston Children's Hospital Cardiac Intensive Care Unit. A convergent mixed methods design was used. Quantitative and qualitative data sources consisted of a faculty survey, direct observations of clinical rounds in a specific patient population identified by a clinical decision support system (CDSS)and semistructured interviews (SSIs). Deductive and inductive coding was used for qualitative data. Qualitative data were translated into images using social network analysis which illustrate the frequency and connectivity of the codes in each data set. A total of 25 observations of eightfaculty-led interprofessional teams were performed between 12 Februaryand 31 March 2021. Individual patient characteristics were noted by faculty in SSIs to be the most important factor in their decision making, yet ethnographic observations suggested faculty cognitive traits, team expertiseand value-based decisions were more heavily weighted. The development of expertise was impacted by role modeling. Decisions were perceived to be influenced by the system and environment. Clinician perception of decision making was not congruent with the observed behaviours in a complicated and dynamic system. This study identifies important considerations in clinical curricula as well as the design and implementation of CDSS. Our method of using social network analysis to visualize components of decision making could be adopted to explore other complex environments.

ACO based Clinical Decision Support System for Better Medical Care

In the realm of healthcare, the utilization of clinical decision support systems (CDSSs) has become increasingly prevalent as a means of providing medical professionals with a computer-based tool that grants them access to pertinent data and expertise, thereby aiding in their ability to make informed clinical decisions. The potential applications of a CDSS are numerous, ranging from disease diagnosis and the creation of treatment programs, to patient progress monitoring. A crucial component of a CDSS is its knowledge base, which comprises the data utilized by the system to generate recommendations and provide feedback to healthcare providers. In an effort to enhance the knowledge base of a CDSS for a particular clinical condition, metaheuristic methods such as Ant Colony Optimization (ACO) can be employed to select the most suitable and applicable data. ACO facilitates the identification of the portion of a CDSS's knowledge base that is most likely to result in the optimal clinical decision, from among the vast array of data that it may contain. This study aims to explore the potential benefits of utilizing ACO methods in CDSSs for the betterment of patient care. The paper outlines the design and implementation of an ACO-based CDSS, which can offer tailored treatment plans for patients based on their medical histories and current condition.

Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning-Based Clinical Decision Support Systems.

Machine learning (ML)-based clinical decision support systems (CDSS) are popular in clinical practice settings but are often criticized for being limited in usability, interpretability, and effectiveness. Evaluating the implementation of ML-based CDSS is critical to ensure CDSS is acceptable and useful to clinicians and helps them deliver high-quality health care. Malnutrition is a common and underdiagnosed condition among hospital patients, which can have serious adverse impacts. Early identification and treatment of malnutrition are important. This study aims to evaluate the implementation of an ML tool, Malnutrition Universal Screening Tool (MUST)-Plus, that predicts hospital patients at high risk for malnutrition and identify best implementation practices applicable to this and other ML-based CDSS. We conducted a qualitative postimplementation evaluation using in-depth interviews with registered dietitians (RDs) who use MUST-Plus output in their everyday work. After coding the data, we mapped emergent themes onto select domains of the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. We interviewed 17 of the 24 RDs approached (71%), representing 37% of those who use MUST-Plus output. Several themes emerged: (1) enhancements to the tool were made to improve accuracy and usability; (2) MUST-Plus helped identify patients that would not otherwise be seen; perceived usefulness was highest in the original site; (3) perceived accuracy varied by respondent and site; (4) RDs valued autonomy in prioritizing patients; (5) depth of tool understanding varied by hospital and level; (6) MUST-Plus was integrated into workflows and electronic health records; and (7) RDs expressed a desire to eventually have 1 automated screener. Our findings suggest that continuous involvement of stakeholders at new sites given staff turnover is vital to ensure buy-in. Qualitative research can help identify the potential bias of ML tools and should be widely used to ensure health equity. Ongoing collaboration among CDSS developers, data scientists, and clinical providers may help refine CDSS for optimal use and improve the acceptability of CDSS in the clinical context.

Premières journées francophones des utilisateurs de système d’aide à la décision en pharmacie clinique : retour d’expériences et perspectives

Clinical decision support systems (CDSS) are tools that have been used for several years by clinical pharmacy teams to support pharmaceutical analysis, with a perspective of contributing to the quality of care in collaboration with the other health care team members. These tools require both technical, logistical and human resources. The growing use of these systems in different establishments in France and in Europe gave birth to the idea of meeting to share our experiences. The days organized in Lille in September 2021 aimed at proposing a time of exchange and reflection on the use of these CDSS in clinical pharmacy. A first session was devoted to feedback from each establishment. These tools are essentially used to optimize pharmaceutical analysis and to secure patient medication management. This session outlined the clear advantages and common limitations of these CDSS. Two research projects were also presented to put the use of these tools into perspective. The second session of these days, in the form of workshops, addressed 4themes that surround the implementation of CDSS: their usability, the legal aspect, the creation of rules and their possible valorization. Common problems were raised, the resolution of which requires close collaboration. This is a first step proposing a beginning of harmonization and sharing that should be deepened in order not to lose the dynamics created between the different centers. This event ended with the proposal to set up two working groups around these systems: the creation and structuring of rules for the detection of risk situations and the common valorization of the work.

Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review

IntroductionArtificial intelligence (AI) technologies are increasingly applied to empower clinical decision support systems (CDSS), providing patient-specific recommendations to improve clinical work. Equally important to technical advancement is human, social, and contextual factors that impact the successful implementation and user adoption of AI-empowered CDSS (AI-CDSS). With the growing interest in human-centered design and evaluation of such tools, it is critical to synthesize the knowledge and experiences reported in prior work and shed light on future work.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review to gain an in-depth understanding of how AI-empowered CDSS was used, designed, and evaluated, and how clinician users perceived such systems. We performed literature search in five databases for articles published between the years 2011 and 2022. A total of 19874 articles were retrieved and screened, with 20 articles included for in-depth analysis.ResultsThe reviewed studies assessed different aspects of AI-CDSS, including effectiveness (e.g., improved patient evaluation and work efficiency), user needs (e.g., informational and technological needs), user experience (e.g., satisfaction, trust, usability, workload, and understandability), and other dimensions (e.g., the impact of AI-CDSS on workflow and patient-provider relationship). Despite the promising nature of AI-CDSS, our findings highlighted six major challenges of implementing such systems, including technical limitation, workflow misalignment, attitudinal barriers, informational barriers, usability issues, and environmental barriers. These sociotechnical challenges prevent the effective use of AI-based CDSS interventions in clinical settings.DiscussionOur study highlights the paucity of studies examining the user needs, perceptions, and experiences of AI-CDSS. Based on the findings, we discuss design implications and future research directions.

Accuracy of a clinical decision support system in identifying patients at risk of cancer in primary care.

1562 Background: 85% of patients diagnosed with cancer are symptomatic at presentation. Yet a delay in diagnosis (e.g. missing a presentation) can result in late stage detection and poorer outcomes. Clinical decision support systems (CDSS) have been used in other fields to calculate risk and support standardization of care. However, little has been reported about successful implementation of CDSS with determining cancer risk. C the Signs is a CDSS which uses artificial intelligence, mapped with the latest evidence and guidance, to assist healthcare professionals in identifying patients at risk of cancer and determining the most appropriate pathway for diagnosis. The aim of this study is to assess the accuracy of C the Signs in a real-world setting. Methods: A retrospective observational study was conducted in the National Health Service in England utilizing all patients risk assessed for cancer through the Electronic Healthcare Record (EHR) integrated C the Signs platform between 1st January 2021 and 31st August 2022. A total of 118,677 patients were risk assessed. All patients were followed up for 6 months post risk assessment to determine outcome. A sub-analysis was performed on the patients who were flagged at risk of cancer through C the Signs and subsequently diagnosed with cancer to establish the CDSS’s cancer origin accuracy (the ability for the system to accurately predict which cancer the patient is at risk of). Results: 7,295 patients were diagnosed with cancer within 6 months of a C the Signs risk assessment. 7,056 patients were successfully identified at risk of cancer by the CDSS with a sensitivity of 96.7%. There were 103,168 false positives with a specificity of 7.37%. Of the 8,453 patients who were identified as not at risk of cancer using the CDSS, 239 had a confirmed cancer diagnosis within 6 months of risk assessment (with a negative predictive value 97.2%). The cancer origin accuracy of C the Signs was 85.6% across all cancers. Conclusions: A cancer CDSS like C the Signs can be used to accurately identify patients at risk of cancer. More research is needed to understand the specificity and false positive rate, and the impact of identifying these patients with non-cancer disease.

Abstract P4-07-22: Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: an implementation model based on a scoping review

Abstract Background Clinical decision-making by multidisciplinary teams (MDTs) is getting more complex as treatment advice for the individual patient must be based on an increasing amount of patient and tumor characteristics, and scientific evidence on treatment efficacy. Clinical decision support systems (CDSSs) can make an important contribution to assist but also optimize MDT decision-making. However, implementation of CDSSs in clinical practice is challenging. Aim &amp; methods The aim of our study is to set up a CDSSs implementation model for multidisciplinary decision-making in solid cancer. It is based on a scoping review of the currently reported CDSSs for MDT decision-making in solid cancers with identification of reported barriers and facilitators for implementation of these CDSSs. For this we systematically searched the Cochrane Library, MEDLINE (accessed through PubMed) and Scopus up to September 1st 2021. Results Of the 710 screened abstracts, 38 papers met the inclusion criteria (table 1). Sixteen different CDSSs were identified. For implementation of CDSSs, 87 barriers and 73 facilitators were reported. The reported barriers could be categorized in the same categories as those of the facilitators (a factor can be reported as a barrier if the factor is not addressed well, and as a facilitator if the factor is properly addressed). The most frequently reported barriers for CDSS implementation for MDT decision making mainly concerned CDSS maintenance (e.g. not incorporating guideline updates), loco-regional feasibility of the CDDS recommendation (e.g. no access to diagnostics or treatment), validity, not incorporating patient preference in decision making, data accuracy, noncoverage of certain patient subpopulations, lack of an information standard, usability, data availability and no interoperability of the CDSS with the electronic health record. The most frequently reported facilitators included, besides the categories as mentioned above, the category shared decision making (reporting of alternative treatment options) and technical skills (involvement of a computer scientist). Table 2 shows the most frequently reported categories of barriers and facilitators, and scores for each included study the number of reported barriers (B) and facilitators (F) in each category. Conclusion Based on the identified barriers and facilitators, we developed a CDSS implementation model to guide more successful CDSS integration in the clinical workflow to support MDTs (the model will be shown at the congress). The usability of this theoretical model should be explored in future studies. Table 1. Characteristics of 38 included articles Table 2. Overview of the most frequently reported categories of barriers and facilitators. For each included study the number of reported barriers (B) and facilitatiors (F) are scored for each category. Citation Format: Mathijs P. Hendriks, Kees C. Ebben, Janine A. Van Til, Agnes Jager, Sabine Siesling. Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: an implementation model based on a scoping review [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-07-22.

Low Vision Rehabilitation Service Utilization Before and After Implementation of a Clinical Decision Support System in Ophthalmology

Electronic clinical decision support systems apply clinical guidelines in real time and offer a new approach to improve referral and utilization of low vision rehabilitation (LVR) care. To characterize patients and factors associated with LVR service utilization with and without the use of an electronic health record (EHR) clinical decision support system (CDSS) alert. Quality improvement study using EHR data to compare patients who did and did not utilize LVR service after referral between November 6, 2017, and October 5, 2019, (primary) and to assess overall service utilization rate from September 1, 2016, to April 2, 2021, regardless of referral status (secondary). Participants in the primary analysis were patients at a large ophthalmology department in an academic medical center in the US who received an LVR referral recommendation from their ophthalmologist according to the CDSS alert. The secondary analysis included patients with best documented visual acuity (BDVA) worse than 20/40 before, during, and after the CDSS implementation. Data were analyzed from August 2021 to April 2022. Number and locations of referral recommendations for LVR service according to the CDSS alert in the primary analysis; active CDSS implementation in the secondary analysis. LVR service utilization rate was defined as the number of patients who accessed service among those who were referred (primary) and among those with BDVA worse than 20/40 (secondary). EHR data on patient demographics (age, sex, race, ethnicity) and ophthalmology encounter characteristics (numbers of referral recommendations, encounter location, and BDVA) were extracted. Of the 429 patients (median [IQR] age, 71 [53 to 83] years; 233 female [54%]) who received a CDSS-based referral recommendation, 184 (42.9%) utilized LVR service. Compared with nonusers of LVR, users were more likely to have received at least 2 referral recommendations (12.5% vs 6.1%; χ21 = 5.29; P = .02) and at an ophthalmology location with onsite LVR service (87.5% vs 78.0%; χ21 = 6.50; P = .01). Onsite LVR service (odds ratio, 2.06; 95% CI, 1.18-3.61) persisted as the only statistically significant factor after adjusting for patient demographics and other referral characteristics. Among patients whose BDVA was worse than 20/40 before, during, and after the CDSS implementation regardless of referral status, the LVR service utilization rate was 6.1%, 13.8%, and 7.5%, respectively. In this quality improvement study, ophthalmologist referral recommendations and onsite LVR services at the location where patients receive other ophthalmic care were significantly associated with service utilization. Ophthalmology CDSSs are promising tools to apply clinical guidelines in real time to improve connection to care.

Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools

BackgroundDengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation.MethodsWe utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers.ResultsKey clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools.ConclusionsThe study highlights the contemporary priorities in delivering clinical care to patients with dengue in an endemic setting. Key decision-making processes and the sources of information that were of the greatest utility were identified. These findings serve as a foundation for future clinical interventions and improvements in healthcare. Understanding the decision-making process in greater detail also allows for development and implementation of CDSS which are suited to the local context.

951. Implementation and Rapid Uptake of an Antimicrobial Stewardship Clinical Decision Support System Across 19 UPMC Hospitals

Abstract Background UPMC Antimicrobial stewardship (AS) includes independently functioning AS programs and a central tele antimicrobial stewardship (TASP) (1 ID pharmacist and 1 ID physician) that supports smaller hospitals with few on-site AS resources. We describe implementation of an AS clinical decision support system (CDSS) at 19 UPMC hospitals (12-695 staffed beds). Figure 1 is an example of the CDSS. Figure 1Representative Example of CDSS Patient Overview Page (not real patient data) Methods The CDSS extracts clinical data to provide real-time alerts to AS teams. It also supports asynchronous communications between AS team members, including between a local AS and our TASP. CDSS utilization and intervention data from Oct 21 to Mar 22 are reported. Figure 2 shows hospital locations and the timeline for CDSS implementation. Table 1 shows implementation steps. Figure 2:Implementation Timeline and Geographic Location of HospitalsTable 1:Implementation Steps Results Table 2 summarizes category alerts provided by the CDSS. Category 1 alerts were independently reviewed by local pharmacists. Category 2 alerts were reviewed either by local pharmacists or central TASP team. Figure 3 shows high CDSS usage over time with numerous unique users and frequent logins. Most users were pharmacists. CDSS usage was steady and increased in the final month included here. Communications between users were frequent and increased 46%. On average, 7033 CDSS generated alerts per month (70-798 per hospital) were reviewed from Oct 21 to Mar22. Table 3 groups the alerts into successful, unsuccessful, or nonactionable interventions. 93% of actionable alerts (959 of 1029 per month) produced a successful outcome. Table 2:CDSS Alert CategoriesFigure 3:CDSS Utilization SummaryTable 3:Summary and Alert Statuses and Intervention Outcomes Conclusion After a rapid 5-month deployment across 19 hospitals this CDSS was successfully adopted as evidenced by sustained monthly use, increasing communications, and a high rate of accepted interventions on actionable alerts. A high rate of nonactionable alerts in the first 6 months was a limitation. Although this did not appear to limit CDSS use, sites implementing a CDSS should monitor for nonactionable alerts during early utilization and adjust as needed. CDSS has the potential to improve efficiency and consistency of AS within a health system. After a successful implementation, evaluation of impact on antimicrobial usage and clinical outcomes are important future metrics. Disclosures J Ryan Bariola, MD, Infectious Disease Connect: Salary support|Merck: Grant/Research Support Tina Khadem, PharmD, Infectious Disease Connect: Salary support|Merck: Grant/Research Support Caley Yakemowicz, n/a, Infectious Disease Connect: Employee Courtney Simonick, n/a, Infectious Disease Connect: Stocks/Bonds Erin K. McCreary, PharmD, AbbVie: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|Ferring: Advisor/Consultant|Merck: Advisor/Consultant|Shionogi: Advisor/Consultant|Summit: Advisor/Consultant Christina Andrzejewski, n/a, Melinta Therapeutics: employee John W. Mellors, MD, Abound Bio: Multiple|Abound Bio: Ownership Interest|Gilead Sciences: Advisor/Consultant|Infectious Disease Connect: Advisor/Consultant|Infectious Disease Connect: Ownership Interest Rima Abdel-Massih, MD, Infectious Disease Connect: Co founder and Chief Medical Officer|Infectious Disease Connect: Ownership Interest.

PP151 VALIDATE Methodology On A Medication-Related Clinical Decision Support System: Holistic Assessment For Optimal Technology Adoption

IntroductionIn the past decade, health technology assessment (HTA) has narrowed its scope to analyses of mainly clinical and economic benefits. Technology challenges in the 21st century emphasize the need for holistic assessments to obtain accurate recommendations for decision-making, as in HTA’s foundations. Using the VALues In Doing Assessments of health TEchnologies (VALIDATE) methodology for complex technologies provides a deeper understanding of problems through analysis of stakeholders’ views, allowing for more comprehensive HTAs. This study aimed to assess a pharmaceutical clinical decision support system (CDSS) using VALIDATE.MethodsSemi-structured interviews with different stakeholders were conducted in the following domains: problem definition (medication error [ME] occurrence and prevention); judgement of solution (existing preventive methods and previous experiences of the CDSS); background theories (future impact and personal beliefs); and barriers to and facilitators of implementation. The following individuals were interviewed: medical informatic specialists (n=3), pharmacists (n=2), nurses (n=2), physicians (n=2), CDSS company representatives (n=1), electronic health record developer (n=1), and health consultancy firm representatives (n=1). Content analysis was used to integrate and analyze the data.ResultsThe multistakeholder interviews identified various barriers to the acceptance and implementation of a pharmaceutical CDSS that were different from those reported in the literature. These included: (i) occurrence of ME (no traceability of medication taken or poor patient medication empowerment); (ii) perception of current level of MEs (huge improvement from ten years ago); (iii) perception of technology as a tool to prevent ME (not enough if only implemented at one point of care); (iv) previous experiences with a CDSS (low rates of development of CDSSs are due to medication prescriptions being digitalized last in hospitals); (v) CDSS metrics (input data should be measured to control CDSS performance); and (vi) other barriers.ConclusionsIncluding facts and stakeholders’ values in problem definition and the scoping of health technologies is essential for the proper conduct of HTAs. Incorporating views from multiple stakeholders when scoping the assessment of health technologies brings additional values to literature findings, resulting in a more holistic evaluation. The lack of multistakeholder scoping can lead to inaccurate information and result in wrong decisions about if, when, and how to adopt a CDSS.