An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients

Background: Patients with multiple conditions have complex needs and are increasing in number as populations age. This multimorbidity is one of the greatest challenges facing health care. Having more than 1 condition generates (1) interactions between pathologies, (2) duplication of tests, (3) difficulties in adhering to often conflicting clinical practice guidelines, (4) obstacles in the continuity of care, (5) confusing self-management information, and (6) medication errors. In this context, clinical decision support (CDS) systems need to be able to handle realistic complexity and minimize iatrogenic risks. Objective: The aim of this review was to identify to what extent CDS is adopted in multimorbidity. Methods: This review followed PRISMA guidance and adopted a multidisciplinary approach. Scopus and PubMed searches were performed by combining terms from 3 different thesauri containing synonyms for (1) multimorbidity and comorbidity, (2) polypharmacy, and (3) CDS. The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth. For articles appropriate for this review, data were collected on clinical tasks, diseases, decision maker, methods, data input context, user interface considerations, and evaluation of effectiveness. Results: A total of 50 articles were selected for the full in-depth analysis and 20 studies were included in the final review. Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines. A total of 17 articles reported their CDS systems were knowledge-based. Most articles reviewed considered patients’ clinical records (n=19), clinical practice guidelines (n=12), and clinicians’ knowledge (n=10) as contextual input data. The most frequent diseases mentioned were cardiovascular (n=9) and diabetes mellitus (n=5). In all, 12 articles mentioned generalist doctor(s) as the decision maker(s). For articles reviewed, there were no studies referring to the active involvement of the patient in the decision-making process or to patient self-management. None of the articles reviewed adopted mobile technologies. There were no rigorous evaluations of usability or effectiveness of the CDS systems reported. Conclusions: This review shows that multimorbidity is underinvestigated in the informatics of supporting clinical decisions. CDS interventions that systematize clinical practice guidelines without considering the interactions of different conditions and care processes may lead to unhelpful or harmful clinical actions. To improve patient safety in multimorbidity, there is a need for more evidence about how both conditions and care processes interact. The data needed to build this evidence base exist in many electronic health record systems and are underused.

Adoption of clinical decision support in multimorbidity: a systematic review.

Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines

Analyzing interactions on combining multiple clinical guidelines.

The prevalence of chronic diseases is growing year after year. This implies that health care systems must deal with an increasing number of patients with several simultaneous pathologies (i.e., comorbid patients), which involves interventions combining primary, specialist, and hospital cares. Clinical practice guidelines provide evidence-based information on these interventions, but only on individual pathologies. This sets up the urgent need of developing ways of merging multiple single-disease interventions to provide professional assistance to comorbid patients. Here, we propose an integrated care model formalizing the treatment of chronic comorbid patients across primary, specialist and hospital cares. The model establishes the baseline of a divide-and-conquer approach to the complex task of multiple therapy combination that was tested on the comorbidity of hypertension and chronic heart failure.

Model-Based Combination of Treatments for the Management of Chronic Comorbid Patients

The treatment of patients with several chronic diseases (comorbidities) has become a frequent actuation of health-care professionals in their daily practice. As different treatments are needed for each disease, there is a risk of undesired drug interactions that must be detected and solved using evidence-based medical knowledge. In this paper we have extracted part of this knowledge for the comorbidities of hypertension, diabetes mellitus and heart failure, and we have represented it by means of combination rules. A rule execution system has been developed which is able to combine treatments of different diseases into a unique comorbid treatment avoiding undesired drug interactions. The system has been checked by health-care professionals of the SAGESSA Health-care group in 20 medical cases.

Rule-Based combination of comorbid treatments for chronic diseases applied to hypertension, diabetes mellitus and heart failure

MPM: A knowledge-based functional model of medical practice.

There are several domains, such as health-care, in which the decision process usually has a background knowledge that must be considered. We need to maximize the accuracy of the models, but we also need them to be meaningful. Otherwise it will lead to the problem that the expert finds the obtained models incomprehensible. We propose a way for representing the knowledge of the experts in order to modify the C 4.5 algorithm to produce decision trees which are more comprehensible to medical doctors without losing accuracy.

Increasing Acceptability of Decision Trees with Domain Attributes Partial Orders

Medical interventions can be compared attending to their health benefits and costs but also considering the similarity of the clinical actions involved. An intervention is a dominant alternative with respect to another intervention if it is better and cheaper. In this paper we introduce a hierarchy of medical actions that provides the semantics required by a methodology to detect dominant alternative interventions. After a formal introduction of this methodology, it is applied to analyze the data about the long term treatment of hypertension in the health care center ABS Vandellos-l'Hospitalet de l'Infant (Spain) in the years 2005-2009 in order to analyze feasible cost reductions after replacing medical interventions by their corresponding optimal, observed, dominant alternatives. This study shows that the use of this methodology reduces the average cost of each clinical encounter in €1.37.

Antoni Collado

Papers

Model-Based Combination of Treatments for the Management of Chronic Comorbid Patients

Rule-Based combination of comorbid treatments for chronic diseases applied to hypertension, diabetes mellitus and heart failure

MPM: A knowledge-based functional model of medical practice.

Increasing Acceptability of Decision Trees with Domain Attributes Partial Orders

Detecting dominant alternative interventions to reduce treatment costs