Unlocking therapeutic symphonies:Innovations in clinical decision support for drug-disease interactions in kidney transplantation

Introduction: Drug-disease interactions (DDSIs) are associated with increasing morbidity, mortality, and healthcare costs. These interactions are preventable if recognized and managed properly. Medication safety is critical in kidney transplant patients due to polypharmacy, co-morbidities, and susceptibility to adverse events. Clinical decision support systems (CDSSs) can play a key role therein. Therefore, this study aims to report on the process of developing an innovative, patient-centered, context-aware CDSS for managing DDSIs in kidney recipients. Material and Methods: Clinically important DDSIs were identified in the medications of patients at a kidney transplant outpatient clinic. Subsequently, rules for their detection and management were extracted based on pharmacology references and clinical expertise. A CDSS was developed and piloted following recommendations on medication CDSS design principles. Results: The knowledge base for this CDSS was developed with clinical context sensitivity. We defined priority levels for alerts, established associated display rules, and determined necessary actions based on the transplantation clinical workflow. The DDSI-CDSS correctly detected 37 DDSIs and displayed nine warnings and 28 cautionary alerts for the medications of 113 study patients (32.7% DDSI rate). The system fired three warnings for diltiazem in bradyarrhythmia, and two for each of the following medications and underlying diseases: aspirin in asthma, erythropoietin alfa in hypertension, and gemfibrozil in gall bladder disease. The potential consequences of the identified DDSIs were GI complications (17%), deterioration of the existing disease/condition (6.1%), and an increased risk of arrhythmias (2.6%), thrombosis (2.6%), and hypertension (1.7%). Complying with system alerts and recommendations would potentially prevent all these DDSIs. Conclusion: This study delineates the process of developing an evidence-based DDSI-CDSS for kidney transplantation, laying the groundwork for future advancements. Our results underscore the clinical significance of these interactions and emphasize the imperative for their accurate and timely detection, particularly in these vulnerable patients.</p

MuCIGREF: multiple computer-interpretable guideline representation and execution framework for managing multimobidity care

Clinical Practice Guidelines (CPGs) supply evidence-based recommendations to healthcare professionals (HCPs) for the care of patients. Their use in clinical practice has many benefits for patients, HCPs and treating medical centres, such as enhancing the quality of care, and reducing unwanted care variations. However, there are many challenges limiting their implementations. Initially, CPGs predominantly consider a specific disease, and only few of them refer to multimorbidity (i.e. the presence of two or more health conditions in an individual) and they are not able to adapt to dynamic changes in patient health conditions. The manual management of guideline recommendations are also challenging since recommendations may adversely interact with each other due to their competing targets and/or they can be duplicated when multiple of them are concurrently applied to a multimorbid patient. These may result in undesired outcomes such as severe disability, increased hospitalisation costs and many others. Formalisation of CPGs into a Computer Interpretable Guideline (CIG) format, allows the guidelines to be interpreted and processed by computer applications, such as Clinical Decision Support Systems (CDSS). This enables provision of automated support to manage the limitations of guidelines. This thesis introduces a new approach for the problem of combining multiple concurrently implemented CIGs and their interrelations to manage multimorbidity care. MuCIGREF (Multiple Computer-Interpretable Guideline Representation and Execution Framework), is proposed whose specific objectives are to present (1) a novel multiple CIG representation language, MuCRL, where a generic ontology is developed to represent knowledge elements of CPGs and their interrelations, and to create the multimorbidity related associations between them. A systematic literature review is conducted to discover CPG representation requirements and gaps in multimorbidity care management. The ontology is built based on the synthesis of well-known ontology building lifecycle methodologies. Afterwards, the ontology is transformed to a metamodel to support the CIG execution phase; and (2) a novel real-time multiple CIG execution engine, MuCEE, where CIG models are dynamically combined to generate consistent and personalised care plans for multimorbid patients. MuCEE involves three modules as (i) CIG acquisition module, transfers CIGs to the personal care plan based on the patient’s health conditions and to supply CIG version control; (ii) parallel CIG execution module, combines concurrently implemented multiple CIGs by performing concurrency management, time-based synchronisation (e.g., multi-activity merging), modification, and timebased optimisation of clinical activities; and (iii) CIG verification module, checks missing information, and inconsistencies to support CIG execution phases. Rulebased execution algorithms are presented for each module. Afterwards, a set of verification and validation analyses are performed involving real-world multimorbidity cases studies and comparative analyses with existing works. The results show that the proposed framework can combine multiple CIGs and dynamically merge, optimise and modify multiple clinical activities of them involving patient data. This framework can be used to support HCPs in a CDSS setting to generate unified and personalised care recommendations for multimorbid patients while merging multiple guideline actions and eliminating care duplications to maintain their safety and supplying optimised health resource management, which may improve operational and cost efficiency in real world-cases, as well

CDSSs for CVD Risk Management: An Integrative Review

Cardiovascular disease (CVD) is a preventable disease affecting almost half of adults in the United States (U.S.) and can have significant negative outcomes such as stroke and myocardial infarction, which can be fatal. Utilizing clinical decision support systems (CDSSs) in the primary care and community health setting can improve primary prevention of CVD by supporting evidence-based decision making at the point of care. This integrative review synthesizes the most up-to-date literature on the use of clinical decision support (CDS) tools to support guideline-based management of CVD risk. Using Whittemore and Knafl’s framework for integrative reviews, a systematic search of CINAHL, Cochrane, and Medline and ancestry search yielded 492 results; 17 articles were included in the final review after applying inclusion and exclusion criteria. Evidence-based CDSSs for CVD prevention improved guideline-based initiation and intensification of pharmacological treatment, increased frequency and accuracy of CVD risk screening, and facilitated shared decision-making discussions with patients about CVD risk; however, they were not effective in promoting smoking cessation and only sometimes effective in improving blood pressure (BP) control. This integrative review supports future evidence-based practice projects implementing CDSSs designed to improve guideline-based primary prevention of CVD as an, albeit partial, solution to improving prevention of CVD in the U.S. and globally

Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.info:eu-repo/semantics/publishedVersio

Automation bias and prescribing decision support – rates, mediators and mitigators

Purpose: Computerised clinical decision support systems (CDSS) are implemented within healthcare settings as a method to improve clinical decision quality, safety and effectiveness, and ultimately patient outcomes. Though CDSSs tend to improve practitioner performance and clinical outcomes, relatively little is known about specific impact of inaccurate CDSS output on clinicians. Although there is high heterogeneity between CDSS types and studies, reviews of the ability of CDSS to prevent medication errors through incorrect decisions have generally been consistently positive, working by improving clinical judgement and decision making. However, it is known that the occasional incorrect advice given may tempt users to reverse a correct decision, and thus introduce new errors. These systematic errors can stem from Automation Bias (AB), an effect which has had little investigation within the healthcare field, where users have a tendency to use automated advice heuristically. Research is required to assess the rate of AB, identify factors and situations involved in overreliance and propose says to mitigate risk and refine the appropriate usage of CDSS; this can provide information to promote awareness of the effect, and ensure the maximisation of the impact of benefits gained from the implementation of CDSS. Background: A broader literature review was carried out coupled with a systematic review of studies investigating the impact of automated decision support on user decisions over various clinical and non-clinical domains. This aimed to identify gaps in the literature and build an evidence-based model of reliance on Decision Support Systems (DSS), particularly a bias towards over-using automation. The literature review and systematic review revealed a number of postulates - that CDSS are socio-technical systems, and that factors involved in CDSS misuse can vary from overarching social or cultural factors, individual cognitive variables to more specific technology design issues. However, the systematic review revealed there is a paucity of deliberate empirical evidence for this effect. The reviews identified the variables involved in automation bias to develop a conceptual model of overreliance, the initial development of an ontology for AB, and ultimately inform an empirical study to investigate persuasive potential factors involved: task difficulty, time pressure, CDSS trust, decision confidence, CDSS experience and clinical experience. The domain of primary care prescribing was chosen within which to carry out an empirical study, due to the evidence supporting CDSS usefulness in prescribing, and the high rate of prescribing error. Empirical Study Methodology: Twenty simulated prescribing scenarios with associated correct and incorrect answers were developed and validated by prescribing experts. An online Clinical Decision Support Simulator was used to display scenarios to users. NHS General Practitioners (GPs) were contacted via emails through associates of the Centre for Health Informatics, and through a healthcare mailing list company. Twenty-six GPs participated in the empirical study. The study was designed so each participant viewed and gave prescriptions for 20 prescribing scenarios, 10 coded as “hard” and 10 coded as “medium” prescribing scenarios (N = 520 prescribing cases were answered overall). Scenarios were accompanied by correct advice 70% of the time, and incorrect advice 30% of the time (in equal proportions in either task difficulty condition). Both the order of scenario presentation and the correct/incorrect nature of advice were randomised to prevent order effects. The planned time pressure condition was dropped due to low response rate. Results: To compare with previous literature which took overall decisions into account, taking individual cases into account (N=520), the pre advice accuracy rate of the clinicians was 50.4%, which improved to 58.3% post advice. The CDSS improved the decision accuracy in 13.1% of prescribing cases. The rate of AB, as measured by decision switches from correct pre advice, to incorrect post advice was 5.2% of all cases at a CDSS accuracy rate of 70% - leading to a net improvement of 8%. However, the above by-case type of analysis may not enable generalisation of results (but illustrates rates in this specific situation); individual participant differences must be taken into account. By participant (N = 26) when advice was correct, decisions were more likely to be switched to a correct prescription, when advice was incorrect decisions were more likely to be switched to an incorrect prescription. There was a significant correlation between decision switching and AB error. By participant, more immediate factors such as trust in the specific CDSS, decision confidence, and task difficulty influenced rate of decision switching. Lower clinical experience was associated with more decision switching (but not higher AB rate). The rate of AB was somewhat problematic to analyse due to low number of instances – the effect could potentially have been greater. The between subjects effect of time pressure could not be investigated due to low response rate. Age, DSS experience and trust in CDSS generally were not significantly associated with decision switching. Conclusion: There is a gap in the current literature investigating inappropriate CDSS use, but the general literature supports an interactive multi-factorial aetiology for automation misuse. Automation bias is a consistent effect with various potential direct and indirect causal factors. It may be mitigated by altering advice characteristics to aid clinicians’ awareness of advice correctness and support their own informed judgement – this needs further empirical investigation. Users’ own clinical judgement must always be maintained, and systems should not be followed unquestioningly

Mobile Clinical Decision Support Systems – A Systematic Review

In this review article, we provide a descriptive analysis of the current state of mobile decision support systems in the healthcare domain based on studies published in the following databases: Business Source Complete, CINAHL, Cochrane library, MEDLINE, PsycINFO, PubMed, ScienceDirect and Web of Science databases. A total of 29 studies were identified and analyzed to understand the current state of development, evaluation efforts, usability and challenges to adoption by patients and care providers. Our aim is to evaluate these systems and identify the key challenges which hinders their widespread adoption. Although, mobile based decision support systems in healthcare context have the potential to improve clinical decision making, the current state with low adoption rate and early stage of development need to be addressed for successful health outcomes

The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions : a review

Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS