Computerized Decision Support System Research Articles

Computerized Decision Support Systems Informing Community-Acquired Pneumonia Surveillance, France, 2017-2023.

We show the value of real-time data generated by a computerized decision support system in primary care in strengthening pneumonia surveillance. The system showed a 66% (95% CI 64%-67%) increase in community-acquired pneumonia from 2018 to 2023 for the population of France, 1 month before a national alert was issued.

An inherently interpretable deep learning model for local explanations using visual concepts.

Over the past decade, deep learning has become the leading approach for various computer vision tasks and decision support systems. However, the opaque nature of deep learning models raises significant concerns about their fairness, reliability, and the underlying inferences they make. Many existing methods attempt to approximate the relationship between low-level input features and outcomes. However, humans tend to understand and reason based on high-level concepts rather than low-level input features. To bridge this gap, several concept-based interpretable methods have been developed. Most of these methods compute the importance of each discovered concept for a specific class. However, they often fail to provide local explanations. Additionally, these approaches typically rely on labeled concepts or learn directly from datasets, leading to the extraction of irrelevant concepts. They also tend to overlook the potential of these concepts to interpret model predictions effectively. This research proposes a two-stream model called the Cross-Attentional Fast/Slow Thinking Network (CA-SoftNet) to address these issues. The model is inspired by dual-process theory and integrates two key components: a shallow convolutional neural network (sCNN) as System-I for rapid, implicit pattern recognition and a cross-attentional concept memory network as System-II for transparent, controllable, and logical reasoning. Our evaluation across diverse datasets demonstrates the model's competitive accuracy, achieving 85.6%, 83.7%, 93.6%, and 90.3% on CUB 200-2011, Stanford Cars, ISIC 2016, and ISIC 2017, respectively. This performance outperforms existing interpretable models and is comparable to non-interpretable counterparts. Furthermore, our novel concept extraction method facilitates identifying and selecting salient concepts. These concepts are then used to generate concept-based local explanations that align with human thinking. Additionally, the model's ability to share similar concepts across distinct classes, such as in fine-grained classification, enhances its scalability for large datasets. This feature also induces human-like cognition and reasoning within the proposed framework.

The Role of Language in Remote Healthcare Triage: AMeta-Aggregative Review.

To provide an overview of evidence on the role of language in remote healthcare services prioritisation, from now on termed triage. This study synthesises literature, to better understand how language affects triage interactions, aiming to improve these processes. We conducted a meta-aggregative review. A systematic search in Scopus from inception to September 2023 identified 437 studies on language in remote healthcare triage, of which 23 were included. Information was selected using both inductive and deductive coding, focusing on six linguistic features of interaction that have been described in the literature on studies using conversation analysis: turn-taking organisation, overall structure, sequence organisation, turn design, lexical choice, and epistemological and other forms of asymmetry. The process followed the RAMESES Publication Standards for Meta-narrative Reviews. Two main findings emerged. First, all six linguistic features are present in triage conversations, indicating that language involves more than just what is said. It also matters, for example, how and when a question is asked. Second, computerised decision support systems (CDSS) significantly affect conversation flow and dynamics. Language in triage involves more than literal speech and is heavily influenced by CDSS. Our study suggests that quality assessments of triage conversations should consider not only what is said but cover all relevant aspects of language. The influence of computerised decision support systems (CDSS) on linguistic features highlights the need for systems to be adaptable, to improve conversation quality and better addressing caller needs rather than focusing on one-size-fits-all questions. This review highlights the complex role of language in triage conversations and its impact on interaction. It calls for a broader view of language in quality assessments, recognising that both call-takers and callers contribute to call quality. Insights from this review can help developers enhance question types, sequence, and delivery methods of computerised decision support systems. Finally, education for call-takers in healthcare sectors may be improved based on our findings. No patient or public contribution.

Efficiency of computerized clinical decision support systems involving anticoagulants: A flashmob study in Dutch hospital pharmacies.

Computerized decision support systems (CDSSs) aim to prevent adverse drug events. However, these systems generate an overload of alerts that are not always clinically relevant. Anticoagulants are frequently involved in these alerts. The aim of this study was to investigate the efficiency of CDSS alerts on anticoagulants in Dutch hospital pharmacies. A multicentre, single-day, cross-sectional study was conducted using a flashmob design in Dutch hospital pharmacies, which have CDSSs that operate on both a national medication surveillance database and on self-developed clinical rules. Hospital pharmacists and pharmacy technicians collected data on the number and type of alerts and time needed for assessing these alerts. The primary outcome was the CDSS efficiency on anticoagulants, defined as the percentage of alerts on anticoagulants that led to an intervention. Secondary outcomes where among other CDSSs efficiency related to any medications and the time expenditure. Descriptive data-analysis was used. Of the 69 hospital pharmacies invited, 42 (61%) participated. The efficiency of CDSS alerts on anticoagulants was 4.0% (interquartile range [IQR] 14.0%) for the national medication surveillance database alerts and 14.3% (IQR 40.0%) for alerts from clinical rules. For any medication, the efficiency was lower: 1.8% (IQR 7.5%) and 13.4% (IQR 21.5%) respectively. The median time for assessing the relevance of all alerts was 2 (IQR 1:21) h/day for pharmacists and 6 (IQR 5:01) h/day for pharmacy technicians. CDSS efficiency is generally low, both for anticoagulants and any medication, while the time investment is high. Optimization of CDSSs is needed.

An Institutional Febrile Neutropenia Protocol Improved the Antibacterial Treatment and Encouraged the Development of a Computerized Clinical Decision Support System.

We investigated the influence of a local guideline on the quality of febrile neutropenia (FN) management and the applicability of a computerized decision support system (CDSS) using real-life data. The study included 227 FN patients between April 2016 and January 2019. The primary outcome measure was the achievement of a 20% increase in the rate of appropriate empirical treatment of FN in bacteremic patients. The compatibility of the CDSS (the development of which was completed in November 2021) with local protocols was tested using standard patient scenarios and empirical antibiotic recommendations for bacteremic FN patients. In total, 91 patients were evaluated before (P1: between April 2016 and May 2017) and 136 after (P2: between May 2017 and January 2019) the guideline's release (May 2017). The demographic characteristics were similar. Appropriate empirical antibacterial treatment was achieved in 58.3% of P1 and 88.1% of P2 patients (p = 0.006). The need for escalation of antibacterial treatment was significantly lower in P2 (49.5% vs. 35.3%; p = 0.03). In P2, the performance of the CDSS and consulting physicians was similar (CDSS 88.8% vs. physician 88.83%; p = 1) regarding appropriate empirical antibacterial treatment. The introduction of the local guideline improved the appropriateness of initial empirical treatment and reduced escalation rates in FN patients. The high rate of compliance of the CDSS with the local guideline-based decisions in P2 highlights the usefulness of the CDSS for these patients.

Identifying system adaptations to overcome technology-based workflow challenges in a telephone triage organization

Call-center-based telephone triage is an example of a complex sociotechnical system relying on successful interactions between patients, callers, and the integration of many digital technologies. Digital technologies such as computer decision support systems are used to standardize triage outcomes with little consideration of how these unique healthcare systems adapt to maintain functionality in response to real-world operating challenges. Using structured observations of call handlers in two call centers and guided by usability heuristics and the concept of ‘workarounds’, this paper aims to investigate the effects of technology design on workflow and system adaptations. Opportunities for improvement are highlighted, particularly, assessment prompts, and updating software to reflect dynamic real-world situations. Interactions between system components, especially technological and organizational processes affected workflow, making adaptations at the individual and organizational levels necessary to ensure callers could be triaged safely. System designers could consider these findings to improve systems and procedures during challenging periods.

‘Male cystitis does not exist’: A qualitative study of general practitioners’ experiences and management of male urinary tract infections in France

Background Male urinary tract infections (mUTIs) are rare in primary care. The definition of mUTIs varies across countries. The therapeutic management of mUTIs in France is based on a 14-day course of fluoroquinolones despite a high risk of antimicrobial resistance. Objectives The objective of this qualitative study was to explore general practitioners’ (GPs) experiences and behaviours regarding the diagnostic and therapeutic management of mUTIs. Methods GPs were recruited by convenience sampling in Haute Normandie (France) and interviewed individually with semi-structured guides. GPs’ experiences and behaviours were recorded and analysed using an interpretive phenomenological approach. Results From March 2021 to May 2022, 20 GPs were included in the study. Defining a mUTI was perceived as a diagnostic challenge. A diagnosis based on clinical evidence alone was insufficient and complementary tests were required. For GPs: ‘male cystitis does not exist’. A mUTI was considered an unusual disease that could reveal an underlying condition. GPs considered fluoroquinolones to be ‘potent’ antibiotics and treated all patients with the same 14-day course. GPs implemented improvement strategies for antibiotic stewardship and followed the guidelines using a computerised decision support system. Conclusions GPs’ experiences of mUTIs are limited due to low exposure and variable clinical presentations in primary care, representing a diagnostic and therapeutic challenge. In order to modify GPs’ antibiotic prescribing behaviours, a paradigm shift in the guidelines will need to be proposed. KEY MESSAGES Defining a male urinary tract infection represents a diagnostic challenge for GPs. A diagnosis based on clinical evidence alone is insufficient and complementary tests are required. A male urinary tract infection is an unusual disease in primary care and suggests a more serious underlying condition.

Neural network architectures in data sequence analysis: diagnosis of Reinke's oedema and Laryngeal polyps

In this review paper, we focus our attention on presenting selected neural network architectures dedicated to the analysis of sequential data, in particular to support the diagnosis of Reinke's oedema and laryngeal polyps. The research discussed here is located in the area of clinical computer decision support systems (CDS) based on the use of artificial recurrent neural networks (RNNs) for speech signal analysis. RNNs are able to predict time series due to their memory and local recurrent connections. In the experimental part, Elman-Jordan artificial neural networks are used, whose characteristics are speed and accuracy in pattern learning allowing real-time decision-making. In the review presented here, one important theme is the use of Bezier curves for preprocessing the speech signal, leading to data reduction and noise elimination. Elman-Jordan networks significantly speed up the learning process and show high classification accuracy in laryngopathy diagnosis.

Appropriateness of intended antibiotic prescribing using clinical case vignettes in primary care, and related factors

Background Factors associated with the appropriateness of antibiotic prescribing in primary care have been poorly explored. In particular, the impact of computerised decision-support systems (CDSS) remains unknown. Objectives We aim at investigating the uptake of CDSS and its association with physician characteristics and professional activity. Methods Since May 2022, users of a CDSS for antibiotic prescribing in primary care in France have been invited, when registering, to complete three case vignettes assessing clinical situations frequently encountered in general practice and identified as at risk of antibiotic misuse. Appropriateness of antibiotic prescribing was defined as the rate of answers in line with the current guidelines, computed by individuals and by specific questions. Physician’s characteristics associated with individual appropriate antibiotic prescribing (< 50%, 50–75% and > 75% appropriateness) were identified by multivariate ordinal logistic regression. Results In June 2023, 60,067 physicians had registered on the CDSS. Among the 13,851 physicians who answered all case vignettes, the median individual appropriateness level of antibiotic prescribing was 77.8% [Interquartile range, 66.7%–88.9%], and was < 50% for 1,353 physicians (10%). In the multivariate analysis, physicians’ characteristics associated with appropriateness were prior use of the CDSS (OR = 1.71, 95% CI 1.56–1.87), being a general practitioner vs. other specialist (OR = 1.34, 95% CI 1.20–1.49), working in primary care (OR = 1.14, 95% CI 1.02–1.27), mentoring students (OR = 1.12, 95% CI 1.04–1.21) age (OR = 0.69 per 10 years increase, 95% CI 0.67–0.71). Conclusion Individual appropriateness for antibiotic prescribing was high among CDSS users, with a higher rate in young general practitioners, previously using the system. CDSS could improve antibiotic prescribing in primary care.

Correlation between antifungal clinical practices and a new clinical decision support system ANTIFON-CLIC® for the treatment of invasive candidiasis: a retrospective multicentre study.

Invasive candidiasis is still recognized as a major cause of morbidity and mortality. To support clinicians in the optimal use of antifungals for the treatment of invasive candidiasis, a computerized decision support system (CDSS) was developed based on institutional guidelines. To evaluate the correlation of this newly developed CDSS with clinical practices, we set-up a retrospective multicentre cohort study with the aim of providing the concordance rate between the CDSS recommendation and the medical prescription (NCT05656157). Adult patients who received caspofungin or fluconazole for the treatment of an invasive candidiasis were included. The analysis of factors associated with concordance was performed using mixed logistic regression models with department as a random effect. From March to November 2022, 190 patients were included from three centres and eight departments: 70 patients from centre A, 84 from centre B and 36 from centre C. Overall, 100 patients received caspofungin and 90 received fluconazole, mostly (59%; 112/190) for empirical/pre-emptive treatment. The overall percentage of concordance between the CDSS and medical prescriptions was 91% (173/190) (confidence interval 95%: 82%-96%). No significant difference in concordance was observed considering the centres (P > 0.99), the department of inclusion (P = 0.968), the antifungal treatment (P = 0.656) or the indication of treatment (P = 0.997). In most cases of discordance (n = 13/17, 76%), the CDSS recommended fluconazole whereas caspofungin was prescribed. The clinical usability evaluated by five clinicians was satisfactory. Our results demonstrated the high correlation between current antifungal clinical practice and this user-friendly and institutional guidelines-based CDSS.

Implementation of the Profile Matching Algorithm to Identify Outstanding Students at Pesantren Modern Misbahul Ulum

This research aims to design a decision support system to identify outstanding students at the Misbahul Ulum Modern Islamic Boarding School. Although determining outstanding students is done by taking the average score from all aspects of the criteria, the evaluation process which is still carried out manually takes quite a long time. Therefore, a computerized decision support system is needed to help make decisions more efficiently and accurately. This system was developed using the PHP programming language. The aim of this research is to overcome obstacles in recognizing outstanding students at the Misbahul Ulum Modern Islamic Boarding School by applying the Profile Matching Algorithm. This method can provide the best data by comparing alternative values and predetermined criteria. This research contributes to the development of educational technology and facilitates the introduction of outstanding students at the Misbahul Ulum Modern Islamic Boarding School. By determining the criteria aspects, determining the weight value of each aspect, finding the GAP value, and carrying out a ranking process, this research produces objective results. Involving 252 students from grades 7-11, this research produced a final grade for each student. The calculation results show that Zikril got the highest score, while Asma Biwi got the lowest score.

On the Development of State-of-the-Art Computational Decision Support Systems for Efficient Water Quality Management: Prospects and Opportunities in a Climate Changing World

On the Development of State-of-the-Art Computational Decision Support Systems for Efficient Water Quality Management: Prospects and Opportunities in a Climate Changing World

Allergy recording in clinical systems: comparing general practice and hospital records

Abstract Introduction Healthcare organisations may have different clinical and administrative electronic systems, meaning there may be gaps in how important information about a patient’s care is communicated effectively between organisations.1 Our current hospital discharge summary does not include a record of patients’ allergies and sensitivities despite this being one of the national standards.2 Furthermore, the electronic transfer of allergy/intolerance data must be classed as machine-readable through coded entries using SNOMED CT or dm+d terminology.3 We wanted to determine whether allergy/sensitivity records in primary and secondary care are transferred accurately and if our hospital discharge summaries should be amended to include allergy information for improved flow of data. Aim To record and compare the number of allergies/sensitivities matched on both the hospital electronic prescribing system (EPS, CareFlow Medicines Management) and a GP practice system (SystmOne), and to determine whether inconsistencies within allergy recording between primary and secondary care are significant, and if there is scope to add allergy recording to eDischarges. Methods A random sample of 150 patients registered with a GP practice who had been admitted to hospital and had undergone recent medicines reconciliations (during 2022/23) and had at least one allergy recorded on the hospital EPS was analysed. Allergy entries (medication or class of medication or other) were collated and analysed using Microsoft Excel. As this project falls under the definition of a service evaluation, according to UK NHS Research Ethics Committees, formal ethical approval was not required. This project was registered on the hospital’s clinical audit database. Results The 150 patients (61% female, mean age 63 years, range 1-97) recorded a total of 350 allergy entries in the hospital EPS system and 294 entries in primary care SystmOne. Only 71 (47%) patients had matching records across both systems. Fifty-two (35%) patients had one or more allergies not recorded on SystmOne compared to EPS, whereas only 16 (11%) patients had one or more allergies not recorded on EPS compared to SystmOne. A total of 11 (7%) patients had one or more inconsistencies across both systems. There were significant issues with how medication names were recorded e.g. generic/brand name for the same medicine. The majority of inconsistencies in records were related to penicillin allergies. Discussion/Conclusion Whilst a small-scale study only, we observed that allergy data is often not transferred accurately between primary and secondary care, resulting in unmatched records and discrepancies, particularly in transfer from secondary to primary care. This limits the potential of computerised decision support systems to help alert clinicians to the risk of further reactions. This problem may be due to a lack of allergy information on the current hospital eDischarge summaries; hence any new allergies identified through medicines reconciliation completed during the admission, or reactions to newly prescribed drugs in hospital, are not transferred. One patient safety improvement would be improved interoperability such that allergies on EPS are included on hospital eDischarge summaries in accordance with PRSB Standards.2 Ideally, this information should be coded in a suitable format to ensure effective transfer onto SystmOne.

Summary of the Best Evidence for Nutritional Support in Patients with Acute Gastrointestinal Graft-Versus-Host Disease

[Abstract] Objective: To retrieve, evaluate and integrate the best evidence summary of nutritional support for patients with acute gastrointestinal graft-versus-host disease after hematopoietic stem cell transplantation at home and abroad. Methods: According to the “6S” model, domestic and foreign computer decision support systems, guideline networks, websites of relevant professional societies or institutions, professional books, and comprehensive databases were systematically searched to screen literatures related to nutritional support for patients with acute gastrointestinal graft-versus-host disease, including guidelines, clinical decision-making, evidence summary, expert consensus, expert opinion, systematic review, and original research. The search time limit was from the establishment of the database to March 2023. Five researchers evaluated the quality of the literature, extracted evidence and integrated it. Results: A total of 12 articles were included, including 1 clinical decision, 4 guidelines, 1 evidence summary, 1 expert consensus, 3 systematic reviews, 1 cohort study, 1 expert opinion,. A total of 14 best evidences were summarized from 4 aspects, including scientific diet and tolerance assessment, nutrient selection, nutritional support method selection, and multidisciplinary cooperation. Conclusions: This study systematically integrates the relevant evidence of nutritional support in patients with acute gastrointestinal graft-versus-host disease, and can provide evidence-based evidence for nutritional support in patients with acute gastrointestinal graft-versus-host disease. When applying evidence, it is recommended that clinical nurses comprehensively consider the actual clinical situation and the wishes of patients, and carefully select evidence. Keywords acute gastrointestinal graft-versus-host disease，nutritional support，malnutrition，evidence-based nursing

Impact of a Decision Support System on Fall-Prevention Nursing Practices.

The aim of this study was to develop a computerized decision support system (CDSS) that could automatically calculate the risk of falls using electronic medical record data and provide evidence-based fall-prevention recommendations based on risk factors. Furthermore, we analyzed the usability and effect of the system on fall-prevention nursing practices. A computerized fall-prevention system was developed according to the system development life cycle, and implemented between March and August 2019 in a single medical unit with a high prevalence of falls. The usability was evaluated 1 month after CDSS implementation. In terms of time and frequency, changes in fall-prevention nursing practices were analyzed using survey data and nursing documentation, respectively. Finally, the incidence of falls before and after system implementation was compared to examine the clinical effectiveness of the CDSS. According to the usability test, the average ease of learning score (5.083 of 7) was the highest among 4 dimensions. The time spent engaged in fall-prevention nursing care per patient per shift increased, particularly for nursing diagnoses and planning. Moreover, the mean frequency of daily documented fall-prevention interventions per patient also increased. Particularly, nursing statements related to nonspecific interventions such as environmental modifications increased. However, the incidence of falls did not decrease after implementation of the CDSS. Although adoption of the computerized system increased the time spent and number of records created in terms of fall-prevention practices in nurses, no improvement in clinical outcomes was observed, particularly in terms of fall rate reduction.

Artificial intelligence systems in surgery: A review of opportunities, limitations, and prospects

Artificial intelligence technologies are increasingly being applied in a variety of medical disciplines. After reviewing 278 publications from 1985 to 2023, 99 articles were selected from the databases elibrary, PubMed, Medline, WoS, Nature, Springer, and Wiley J Database to present the main approaches and a modern picture of the application of artificial intelligence methods and technologies in pediatric surgery and intensive care. The article examines many facets of artificial intelligence systems for medical uses, namely, computer decision support systems or supporting the surgeon throughout the surgical intervention procedure. Computer analysis of 3D visualization and 3D anatomical modeling of images obtained from computed tomography and magnetic resonance imaging investigations can be used to plan operations. Because of the possibilities of sufficiently accurate 3D models and methods for organs and pathological processes, various methodologies and software tools for preoperative planning and intraoperative support of surgical intervention have been developed. Computer (technical) vision analyzes high-quality medical images and interprets them in multimodal three-dimensional images for computer diagnoses and operations under visual control, including augmented reality methods. Robotic surgery involves manipulators, including remotely controlled ones, and intellectualized complexes that independently perform specific actions of the second assistant surgeon. In intensive care, artificial intelligence technologies are being investigated to merge data from bedside monitors and other information about patients conditions to identify critical situations and control mechanical ventilation. Simultaneously, several obstacles impede the adoption of artificial intelligence in surgery. The nature and standardization of the initial data required for their integration, taking into consideration atypical cases, the possibility of bias in the sample used, and the transparency of the decision-making process in machine learning models are examples. The explanation of solutions presented in machine learning models and the transition to full-fledged validation of the systems being built define the prospects for developing and using artificial intelligence systems.

Adoption of cognitive computing decision support system in the assessment of health-care policymaking

Purpose Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model. Design/methodology/approach A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data. Findings The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly. Research limitations/implications Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking. Originality/value This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

Resource use and cost associated with computerized decision support system and usual care in managing patients with atrial fibrillation: analysis of IMPACT-AF randomized trial data

BackgroundIMPACT-AF is a prospective, randomized, cluster design trial comparing atrial fibrillation (AF) management with a computerized decision support system (CDS) to usual care (control) in the primary care setting of Nova Scotia, Canada. The objective of this analysis was to compare the resource use and costs between CDS and usual care groups.MethodsCase costing data, 12-month self-administered questionnaires, and monthly diaries from IMPACT-AF were used in this analysis. Descriptive statistics were used to compare costs and resource use between groups. All costs are presented in 2021 Canadian dollars and cover the 12-month period of participation in the study.ResultsA total of 1,145 patients enrolled in the trial. Case costing data were available for 466 participants (41.1%), 12-month self-administered questionnaire data for 635 participants (56.0%) and monthly diary data for 223 participants (19.7%). Emergency department visits and hospitalizations comprised the most expensive component of AF care. Across all three datasets, there were no statistically significant differences in costs or resource use between CDS and usual care groups.ConclusionsAlthough there were no significant differences in resource use or costs among CDS and usual care groups in the IMPACT-AF trial, this study provides insight into the methodology and practical challenges of collecting economic data alongside a trial.RegistrationClinicaltrials.gov (registration number: NCT01927367, date of registration: 2013-08-20).

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.

Implementation of a Best Practice Advisory to Improve Infection Screening Prior to New Prescriptions of Biologics and Targeted Synthetic Drugs.

Use of biologic and targeted synthetic DMARDs (b/tsDMARDs) in patients with pre-existing tuberculosis (TB), hepatitis B virus (HBV), or hepatitis C virus (HCV) infection can have serious consequences. Although various society guidelines recommend routine screening for these infections prior to initiating certain b/tsDMARDs adherence to these recommendations varies widely. This quality improvement initiative evaluated local compliance with screening and assessed if automated computerized decision support system (CDSS) in the form of a best practice advisory (BPA) in the electronic health record could improve patient screening. Established patients with autoimmune rheumatic disease (ARD) aged 18 years or older with at least one visit to our rheumatology practice between 10/1/2017 and 3/3/2022 were included. When prescribing a new b/tsDMARD, clinicians were alerted via a BPA that showed the most recent results for TB, HBV, and HCV. Screening proportions for TB, HBV, and HCV before BPA initiation were compared with those of eligible patients after the BPA implementation. A total of 711 patients pre-BPA and 257 patients post-BPA implementation were included in the study. The BPA implementation was associated with statistically significant improvement in screening for TB from 66% to 82% (p=<0.001), HCV from 60 to 79% (p=<0.001), hepatitis B core antibody (HBcAb) 32% to 51% (p=<0.001), and hepatitis B surface antigen (HBsAg) from 51% to 70% (p=<0.001). Implementation of a BPA can improve infectious disease screening for patients with ARD who are started on b/tsDMARD and has potential to improve patient safety. This article is protected by copyright. All rights reserved.