Decision Making by Emergency Room Physicians and Residents: Results From a Clinical Trial

Clinical decision-making is complex and uncertain and is dependent on accurate and timely information that is typically managed through Information Technology (IT) solutions. One particular class of IT that is becoming increasingly prevalent in the medical community is Clinical Decision Support Systems (CDSS). This paper will discuss results of the use of a CDSS that was developed for assisting triage decision making of pediatric abdominal pain in the Emergency department. We show how different user groups (staff physicians and residents) use the CDSS input variables in their triage decision making models

How Do Spinal Surgeons Perceive The Impact of Factors Used in Post-Surgical Complication Risk Scores?

When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing these functions enables the identification and discussion of differences among personal perceptions of risk factors. Our results show there exist differences in surgeons\u27 perceived factors including primary diagnosis, type of surgery, patient\u27s age, body mass index, or presence of comorbidities

APPLICATION OF ACTIVITY THEORY TO ELICITATION OF USER REQUIREMENTS FOR A COMPUTERIZED CLINICAL PRACTICE GUIDELINE: THE ACTCPG CONCEPTUAL FRAMEWORK

Clinical practice guidelines are knowledge uptake instrument that support decision making by the physicians. They are often implemented as computer-interpreted guidelines that are embedded in a hospital information system. We argue that computer-interpreted guidelines should be considered as regular information system, thus their development should follow all the steps of system analysis and design, starting with exploration and definition of user requirements. In this paper we propose the ActCPG conceptual framework to establish basic user requirements for implementing computer-interpreted guidelines. This framework relies on the Activity Theory to structure and decompose information coming from a clinical practice guideline and associated narrative so UML use cases can be developed. We illustrate operation of the ActCPG framework with an example of a practice guideline for a management of clinically obese children enrolled in some obesity program

Ideating Mobile Health Behavioral Support for Compliance to Therapy for Patients with Chronic Disease: A Case Study of Atrial Fibrillation Management

Poor patient compliance to therapy results in a worsening condition that often increases healthcare costs. In the MobiGuide project, we developed an evidence-based clinical decision-support system that delivered personalized reminders and recommendations to patients, helping to achieve higher therapy compliance. Yet compliance could still be improved and therefore building on the MobiGuide project experience, we designed a new component called the Motivational Patient Assistant (MPA) that is integrated within the MobiGuide architecture to further improve compliance. This component draws from psychological theories to provide behavioral support to improve patient engagement and thereby increasing patients\u27 compliance. Behavior modification interventions are delivered via mobile technology at patients\u27 home environments. Our approach was inspired by the IDEAS (Integrate, Design, Assess, and Share) framework for developing effective digital interventions to change health behavior; it goes beyond this approach by extending the Ideation phase\u27 concepts into concrete backend architectural components and graphical user-interface designs that implement behavioral interventions. We describe in detail our ideation approach and how it was applied to design the user interface of MPA for anticoagulation therapy for the atrial fibrillation patients. We report results of a preliminary evaluation involving patients and care providers that shows the potential usefulness of the MPA for improving compliance to anticoagulation therapy

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment

We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom’s taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients’ attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients’ understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners

Is There a Consensus when Physicians Evaluate the Relevance of Retrieved Systematic Reviews?

Background: A significant challenge associated with practicing evidence-based medicine is to provide physicians with relevant clinical information when it is needed. At the same time it appears that the notion of relevance is subjective and its perception is affected by a number of contextual factors. Objectives: To assess to what extent physicians agree on the relevance of evidence in the form of systematic reviews for a common set of patient cases, and to identify possible contextual factors that influence their perception of relevance. Methods: A web-based survey was used where pediatric emergency physicians from multiple academic centers across Canada were asked to evaluate the relevance of systematic reviews retrieved automatically for 14 written case vignettes (paper patients). The vignettes were derived from prospective data describing pediatric patients with asthma exacerbations presenting at the emergency department. To limit the cognitive burden on respondents, the number of reviews associated with each vignette was limited to three. Results: Twenty-two academic emergency physicians with varying years of clinical practice completed the survey. There was no consensus in their evaluation of relevance of the retrieved reviews and physicians’ assessments ranged from very relevant to irrelevant evidence, with the majority of evaluations being somewhere in the middle. This indicates that the study participants did not share a notion of relevance uniformly. Further analysis of commentaries provided by the physicians allowed identifying three possible contextual factors: expected specificity of evidence (acute vs chronic condition), the terminology used in the systematic reviews, and the micro environment of clinical setting. Conclusion: There is no consensus among physicians with regards to what constitutes relevant clinical evidence for a given patient case. Subsequently, this finding suggests that evidence retrieval systems should allow for deep customization with regards to physician’s preferences and contextual factors, including differences in the micro environment of each clinical setting

Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases.

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

Automatic indexing and retrieval of encounter-specific evidence for point-of-care support

Evidence-based medicine relies on repositories of empirical research evidence that can be used to support clinical decision making for improved patient care. However, retrieving evidence from such repositories at local sites presents many challenges. This paper describes a methodological framework for automatically indexing and retrieving empirical research evidence in the form of the systematic reviews and associated studies from The Cochrane Library, where retrieved documents are specific to a patient-physician encounter and thus can be used to support evidence-based decision making at the point of care. Such an encounter is defined by three pertinent groups of concepts - diagnosis, treatment, and patient, and the framework relies on these three groups to steer indexing and retrieval of reviews and associated studies. An evaluation of the indexing and retrieval components of the proposed framework was performed using documents relevant for the pediatric asthma domain. Precision and recall values for automatic indexing of systematic reviews and associated studies were 0.93 and 0.87, and 0.81 and 0.56, respectively. Moreover, precision and recall for the retrieval of relevant systematic reviews and associated studies were 0.89 and 0.81, and 0.92 and 0.89, respectively. With minor modifications, the proposed methodological framework can be customized for other evidence repositories

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Extending the MAD Portfolio Optimization Model to Incorporate Downside Risk Aversion

The mathematical model of portfolio optimization is usually represented as a bicriteria optimization problem where a reasonable trade–off between expected rate of return and risk is sought. In a classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. As an alternative, the MAD model was proposed where risk is measured by (mean) absolute deviation instead of a variance. The MAD model is computationally attractive, since it is transformed into an easy to solve linear programming program. In this paper we present an extension to the MAD model allowing to account for downside risk aversion of an investor, and at the same time preserving simplicity and linearity of th