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Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework.

Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious and burdensome task, even using CIG tools and languages designed to improve the process. Natural language understanding (NLU) systems perform automated reading comprehension, parsing text and using reasoning to convert syntactic information from unstructured text into semantic information. Influenced by successful systems used in other domains, we present the architecture for a system which uses NLU approaches to create semantic representations of entire CPGs. In the future, these representations may be used to generate CIGs.

Computer-interpretable guidelines (CIGs) are based on clinical practice guidelines, which typically address a single morbidity. However, most of the aging population suffers from multiple morbidities. Currently, there is no demonstrated effective mechanism that integrates recommendations from multiple CIGs. We are developing a goal-based method that utilizes knowledge of drugs’ physiological effects and therapeutic usage to combine knowledge from CIGs. It incrementally detects interactions and plans non-contradicting therapies. Our algorithm uses patterns to check consistency and respond to events, including data enquiries, diagnoses, adverse events, recommended medications, tests, and goals. Our method utilizes existing standards and CIG tools, including the Fast Healthcare Interoperability Resources (FHIR) patient data model, SNOMED-CT, and the PROforma CIG formalism with its Alium knowledge-engineering environment and PROforma enactment engine. We demonstrate our approach using a case study involving two clinical guidelines with templates for responding to a new goal and to a medication request that causes an inconsistency which can be automatically detected and resolved based on the knowledge of the two CIGs.

This paper describes our approach for fostering and facilitating communication among patients and caregivers in the context of shared decision making, i.e., when decisions must be taken not only on the basis of scientific evidence but also of the patient’s preferences and context. This happens because clinical practice guidelines cannot provide recommendations for every possible situation, and cannot foresee every change in a patient’s context, which might imply the deviation from a previously acknowledged recommendation. Within the EU-funded project MobiGuide (www.mobiguide-project.eu), supporting remote patient management, we propose decision theory as a methodological framework for a tool that, during face to face encounters, is used to tailor pre-defined, generic decision models to the individual patient, by involving the patient himself in the customization of the model parameters. Although this approach is not appropriate for all patients, it leads, in well-chosen cases, to a more informed choice, with potentially better treatment compliance.

Clinical guidelines are usually written as text documents that are meant for human consumption. Implementing clinical guidelines as decision-support systems that deliver patient-specific advice at the point of care could increase the effectiveness of clinical guidelines. Several researchers studied the transition from narrative guidelines to computer-interpretable guidelines and have identified specific barriers to guideline implementation. GuideLine Implementability Appraisal (GLIA) is a comprehensive instrument for identifying such barriers, such that they could be revised. We used the GLIA instrument to appraise a historic thyroid nodule guideline that is now being reviewed by the American Association of Clinical Endocrinologists. Our analysis uncovered new guideline implementation barriers related to control-flow that we integrated into GLIA.

There is an increasing interest in preserving patients' privacy while accessing Electronic Health Record (EHR) data. Two models that support representation of data-request authorization policies are the Contextual Role-Based Access Control (Context) model [1] and the Situation-Based Access Control (SitBAC) model [2]. We conducted a controlled experiment that compared the two models with respect to model-understanding and model-synthesis.

Several data types (symptoms, signs, and tests) are gathered and used in the process of investigating a clinical problem. In this study, we aimed to quantitatively evaluate how often the knowledge contained in clinical practice guidelines refer to these data types, and to what extent evidence based medicine principles are applied to them. To this end, we analyzed the knowledge contained in diagnostic problem-oriented guidelines using a set of relevant characteristics that we developed. We believe that the results of this study may be helpful for developers of clinical decision-support systems.