Chapter 20 - Governance and implementation

Does Clinical Decision Support Reduce Unwarranted Variation in Yield of CT Pulmonary Angiogram?

Association of Clinical Guidelines and Decision Support with Computed Tomography Use in Pediatric Mild Traumatic Brain Injury

Clinical Decision Support Tools

Design and implementation of a standards-based interoperable clinical decision support architecture in the context of the Korean EHR

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part II. Clinical implementation and evaluation

Clinical Decision Support Systems (CDSSs) signify the framework shift in the medical sector in the modern age. CDSSs are utilized in augmenting healthcare facilities in the process of making complex clinical decisions. Since the first application of CDSSs in the 80s, the framework has witnessed significant transformation. The frameworks are now administered through electronic healthcare records with complex capacities. Irrespective of these complex advancements, there are existing questions concerning the impacts of CDSSs on service providers, healthcare costs, and patients’ records. There are many published texts concerning the success stories of CDSSs, but significant setbacks have proved that CDSSs are not without any potential risks. In this research, we provide critical analysis on the application of CDSSs in clinical setting, integrating various forms, present application cases with proven effectiveness, potential harms and common pitfalls. We therefore conclude with evidence-centered recommendation for mitigating the issues of CDSSs maintainability, evaluation, implementation and designing.

BackgroundIn silico evaluation (ISE) methods create a digital twin or a computer simulation of actual care pathways, enabling a broader assessment of the potential impact of algorithm-based clinical decision support systems (CDSS) before implementation. A programmatic search of several academic research databases showed at least 886 CDSS development and evaluation studies in the past 3 decades. However, fewer than 3% applied ISE to evaluate the potential impact on broader clinical care pathways.ObjectiveThis study aims to review the scope of proposed ISE methods to evaluate CDSS, with a focus on simulation modeling approaches, care pathway parameters considered, and outcomes evaluated within the ISE methodological domain.MethodsThis review followed the established scoping review methodological guidelines. We conceptualized a tailored search framework and conducted a 2-stage screening process on studies identified through automated searches of selected databases. Relevant information on CDSS study characteristics and the application of care pathway simulation modeling in CDSS evaluation was subsequently extracted.ResultsA small subset of studies on CDSS development conducted ISE. Most ISE studies were published after 2019, reflecting a more recent increase in the application of ISE. These studies frequently emphasized patient, process, and cost-effectiveness outcomes. Notably, the evaluation of outcomes directly related to care providers’ well-being is lacking, highlighting a critical gap in current ISE applications. Among the studies included in this review, various simulation modeling paradigms were used, including dynamic simulations and state-based models. Three themes were found among the different motivations and objectives for using ISE: (1) outcome comparison, (2) outcome comparison with sensitivity analysis, and (3) simulation-based optimization of the proposed CDSS. The first two approaches considered a decoupled CDSS model training followed by simulation-based evaluation of the trained CDSS model. The third approach iteratively improved the CDSS model’s decision-support capabilities through optimization based on care pathway simulations. These approaches can be broadly categorized as decoupled (1 and 2), where CDSS models are evaluated within seperately designed clinical workflow simulations, and integrated (3), where simulation iteratively optimizes CDSS performance.ConclusionsThe growing body of algorithm-based CDSS research underscores the need for evaluation approaches that are resource-efficient and account for systems-level workflow implications. This review highlights both the gaps and the potential of ISE, particularly care pathway simulation-based approaches, as a preimplementation strategy to strengthen evidence before significant resource allocations to pilot or full-scale implementations. ISE presents as a promising intermediary evaluation approach that bridges model-level performance and clinical workflow impact, allowing more contextualized and resource-efficient assessments prior to implementation.

Technology readiness assessment: Case of clinical decision support systems in healthcare

Clinical decision support software (CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes. A literature search of articles on CDSS relevant to community pharmacy and published between 1 January 2005 and 21 October 2013 was undertaken. Articles were included if the healthcare setting was community pharmacy and the article indicated that pharmacy use of CDSS was part of the study intervention. Eight studies were found which assessed counselling, selected drug interactions, inappropriate prescribing and under-prescribing. One study was halted due to insufficient data collection. Six studies showed statistically significant improvements in the measured outcomes: increased patient counselling, 31% reduced frequency of drug-drug interactions (DDIs), reduced frequency of inappropriate medications in the elderly (2·2-1·8% patients) and in pregnant women (5·5-2·9% patients), and increased pharmacists' interventions for under-prescribed low-dose aspirin (1·74 vs. 0·91 per 100 patients with type 2 diabetes) and over-prescribed high-dose proton-pump inhibitors (PPIs) (1·67 vs. 0·17 interventions per 100 high-dose PPI prescriptions). Most studies showed improved prescribing practice, via direct communication between pharmacists and doctors or indirectly via patient education. Factors limiting the impact of improved prescribing included alert fatigue and clinical inertia. No study investigated patient outcomes and little investigation had been undertaken on how CDSS could be best implemented. Few studies have been undertaken in community pharmacy practice, and based on the positive findings reported, further research should be directed in this area, including investigation of patient outcomes.

Clinical decision support (CDS) has the potential to improve clinical decision-making consistent with evidence-based care. CDS can be designed to save health care providers time and help them provide safe and personalized analgesic prescribing. The aim of this report is to describe the development of a CDS system designed to provide dentists with personalized pain management recommendations to reduce opioid prescribing following extractions. The use of CDS is also examined. This study was conducted in HealthPartners, which uses an electronic health record (EHR) system that integrates both medical and dental information upon which the CDS application was developed based on SMART (Substitutable Medical Applications and Reusable Technologies) on FHIR (Fast Healthcare Interoperability Resources). The various tools used to bring relevant medical conditions, medications, patient history, and other relevant data into the CDS interface are described. The CDS application runs a drug interaction algorithm developed by our organization and provides patient-specific recommendations. The CDS included access to the state Prescription Monitoring Program database. The pain management CDS was implemented as part of a study examining opioid prescribing among patients undergoing dental extraction procedures from February 17, 2020, to May 14, 2021. Provider-level use of CDS at extraction encounters ranged from 0% to 87.4% with 12.1% of providers opening the CDS for no encounters, 39.4% opening the CDS for 1%-20% of encounters, 36.4% opening it for 21%-50% of encounters, and 12.1% opening it for 51%-87% of encounters. The pain management CDS is an EHR-embedded, provider-facing tool to help dentists make personalized pain management recommendations following dental extractions. The SMART on FHIR-based pain management CDS adapted well to the point-of-care dental setting and led to the design of a scalable CDS tool that is EHR vendor agnostic. ClinicalTrials.gov NCT03584789; https://clinicaltrials.gov/study/NCT03584789.

Multimorbidity, the presence of more than one condition in a single individual, is a global health issue in primary care. Multimorbid patients tend to have a poor quality of life and suffer from a complicated care process. Clinical decision support systems (CDSSs) and telemedicine are the common information and communication technologies that have been used to reduce the complexity of patient management. However, each element of telemedicine and CDSSs is often examined separately and with great variability. Telemedicine has been used for simple patient education as well as more complex consultations and case management. For CDSSs, there is variability in data inputs, intended users, and outputs. Thus, there are several gaps in knowledge about how to integrate CDSSs into telemedicine and to what extent these integrated technological interventions can help improve patient outcomes for those with multimorbidity. Our aims were to (1) broadly review system designs for CDSSs that have been integrated into each function of telemedicine for multimorbid patients in primary care, (2) summarize the effectiveness of the interventions, and (3) identify gaps in the literature. An online search for literature was conducted up to November 2021 on PubMed, Embase, CINAHL, and Cochrane. Searching from the reference lists was done to find additional potential studies. The eligibility criterion was that the study focused on the use of CDSSs in telemedicine for patients with multimorbidity in primary care. The system design for the CDSS was extracted based on its software and hardware, source of input, input, tasks, output, and users. Each component was grouped by telemedicine functions: telemonitoring, teleconsultation, tele-case management, and tele-education. Seven experimental studies were included in this review: 3 randomized controlled trials (RCTs) and 4 non-RCTs. The interventions were designed to manage patients with diabetes mellitus, hypertension, polypharmacy, and gestational diabetes mellitus. CDSSs can be used for various telemedicine functions: telemonitoring (eg, feedback), teleconsultation (eg, guideline suggestions, advisory material provisions, and responses to simple queries), tele-case management (eg, sharing information across facilities and teams), and tele-education (eg, patient self-management). However, the structure of CDSSs, such as data input, tasks, output, and intended users or decision-makers, varied. With limited studies examining varying clinical outcomes, there was inconsistent evidence of the clinical effectiveness of the interventions. Telemedicine and CDSSs have a role in supporting patients with multimorbidity. CDSSs can likely be integrated into telehealth services to improve the quality and accessibility of care. However, issues surrounding such interventions need to be further explored. These issues include expanding the spectrum of medical conditions examined; examining tasks of CDSSs, particularly for screening and diagnosis of multiple conditions; and exploring the role of the patient as the direct user of the CDSS.

Background: Errors in prescribing remain a major challenge to patient safety across the globe, particularly in economically challenged health systems. Clinical Decision Support Systems (CDSS) powered by artificial intelligence (AI) are being developed to attempt to improve clinical decision making, reduce errors, and enhance patient outcomes. However, the impact of AI-assisted CDSS systems in the context of healthcare in Pakistan, and particularly among nurses, is largely unknown. Objective: This study aims to assess the relationship between the use of AI-assisted Clinical Decision Support Systems (CDSS) and the rates of errors in medication prescribing, the clinical judgment of nurses and the safety of patients. Methods: An analytical correlational study was conducted with 84 registered nurses. The sample was determined using the G*Power software and convenience sampling was used to select the participants. A validated and structured questionnaire on CDSS, clinical judgement and patient safety outcomes was used to collect the data. For the analysis of descriptive and inferential statistics, the Statistical Package for the Social Sciences (SPSS) version 27 was used. For the Pearson correlation, it was set at p ≤ 0.05 for statistically significant results. For the regression analysis it was set p ≤ 0.05. Results: Participants scored CDSS utilization to be at moderate to high levels, and supportive outcomes. There were significant positive correlations with CDSS clinical judgement and patient safety (p < 0.001) and there was a significant negative correlation with medication errors. CDSS utilization was determined to be a statistically significant predictor for positive outcomes in the regression analysis. Conclusion: The study showed that CDSS are a major improvement to the nursing and patient safety practices. Integration, training and evaluation are recommended to continue optimal use of CDSS.

Older people are particularly vulnerable to medication errors and adverse drug events (ADEs) due to polypharmacy and age-related physiological changes. Clinical decision support systems (CDSS) presents itself as a potential solution for clinicians, but their effectiveness in care for older people remains uncertain. To evaluate the impact of CDSS on reducing potentially inappropriate medications (PIMs) and improving medication safety amongst older people. This was a systematic review of randomized controlled trials (RCTs) evaluating CDSS interventions in older populations (aged 65 or older). A comprehensive search was conducted in OVID Medline, Embase, and Cochrane Library from inception to April 2025. Eligible studies were RCTs assessing CDSS in reducing PIM use and ADEs amongst older people. Data on medication safety outcomes (PIM, deprescription and ADEs) were extracted. The findings were narratively synthesized, and the certainty of evidence was evaluated using the GRADE framework. The Cochrane Risk-of-Bias 2 tool was also applied. A total of 16 RCTs (comprising 135,108 participants) were included. CDSS significantly reduced PIM initiation by up to 18% (moderate certainty) and improved deprescription rates, with intervention groups achieving 55.4% PIM discontinuation. However, CDSS had inconsistent effects on ADEs (low certainty). CDSS improved prescribing practices by reducing inappropriate medication use in older people. However, its impact on ADEs was less evident. Implementation challenges such as clinician adherence, alert fatigue and system usability must be addressed to optimize CDSS effectiveness in care for older people. Further research is needed for long-term evaluation and refinement of CDSS.

Objective: This review aims to provide a brief introduction of clinical decision support systems (CDSSs) and discuss the role of psychology for the integration of CDSSs for child mental health into pediatric subspecialty care. Methods: A review of literature regarding CDSSs used for physical and mental health services was conducted to lay the foundation for our recommendations. Results: Children with chronic physical illness are twice as likely to suffer from mental illness as compared to healthy controls. These mental health problems are often discovered in pediatric subspecialty care. Yet their mental health needs are often unaddressed when the sections of pediatric subspecialty care and mental health are not well coordinated. CDSSs could help subspecialty providers with evidence-based decision-making regarding mental health screening and referrals. That is, CDSSs could enhance the access to most effective mental health treatment for children. However, the implementation of CDSSs in pediatric subspecialty care is very limited. Conclusions: Psychologists could assist subspecialty providers with advocating CDSSs to administrative and information technology staff, as well as build, customize, evaluate, and update CDSSs. Implications for Impact Statement This review discusses a need for integrating clinical decision support systems (CDSSs) into pediatric subspecialty care to improve care coordination between pediatric subspecialty care and mental health sections. A role of pediatric psychology is proposed.

The importance of Medical Information Systems (MIS) for medical practice is emphasized. The wide usage of the Electronic Medical Records (EMR) software is displayed. The importance and alternative approaches to implementation of the MIS in the Ukraine healthcare system are discussed. The benefits of the open-source MIS usage are shown. Effectiveness of the Clinical Decision Support System (CDSS) application in the medical decision making process is emphasized.<p>The open-source MIS OpenMRS developer tools and software API are reviewed. The results of code refactoring of the dialog subsystem of the CDSS platform which is made as module for the open-source MIS OpenMRS are presented. The structure of information model of database of the CDSS dialog subsystem was updated according with MIS OpenMRS requirements. The Model-View-Controller (MVC) based approach to the CDSS dialog subsystem architecture was re-implemented with Java programming language using Spring and Hibernate frameworks. The MIS OpenMRS Encounter portlet form for the CDSS dialog subsystem integration is developed as an extension. The administrative module of the CDSS platform is recreated. The data exchanging formats and methods for interaction of OpenMRS CDSS dialog subsystem module and DecisionTree GAE service are re-implemented with help of AJAX technology via jQuery library</p>