Supporting Physicians in the Detection of the Interactions between Treatments of Co-Morbid Patients

Supporting Physicians in the Detection of the Interactions between Treatments of Co-Morbid Patients

Luca Piovesan (Università degli Studi di Torino, Italy), Gianpaolo Molino (Azienda Ospedaliera San Giovanni Battista, Italy) and Paolo Terenziani (Università del Piemonte Orientale “Amedeo Avogadro”, Italy)
Copyright: © 2015 |Pages: 29
DOI: 10.4018/978-1-4666-6316-9.ch009
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Abstract

The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for modern healthcare. Clinical practice guidelines are widely used to support physicians, providing them evidence-based information of interventions, but only on individual pathologies. This sets up the urgent need of developing methodologies to support physicians in the detection of interactions between guidelines, to help them in the treatment of comorbid patients. In this chapter, the authors identify different levels of abstractions in the analysis of interactions, based on both the hierarchical organization of clinical guidelines (in which composite actions are refined into their components) and the hierarchy of drug categories. They then propose a general methodology (data/knowledge structures and reasoning algorithms operating on them) supporting user-driven and flexible interaction detection over multiple levels of abstraction.
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Introduction

Background

Nowadays, the rationalization of the healthcare systems is a task of fundamental importance in order to grant both the quality and the standardization of healthcare services, and the minimization of costs. Clinical Practice Guidelines (CPGs) are one of the major tools that have been introduced to achieve such a challenging task. CPGs are, in the definition of the USA Institute of Medicine, “systematically developed statements to assist practitioner and patient decisions about appropriate health care in specific clinical circumstances” (Institute of Medicine, Committee on Quality Health Care in America, 2001). Thousands of CPGs have been devised in the last years. For instance, the Guideline International Network (http://www.g-i-n.net) groups 77 organizations of 4 continents, and provides a library of more than 5000 CPGs. CPGs are commonly recognized as a tool to encode and support the practical adoption of evidence-based medicine (EBM).

The adoption of computerized approaches to acquire, represent, execute and reason with CPGs can further increase the advantages of CPGs, providing crucial advantages to:

  • 1.

    Patients, granting them that they will receive the best quality medical treatments (since CPGs are actually a way of putting EBM into practice);

  • 2.

    Physicians, providing them with a standard reference which they may consult, with a way of certifying the quality of their activity (e.g., for insurance or legal purposes), as well as with advanced support to their decision-making activity;

  • 3.

    Hospitals and health-care centers, providing them with tools to grant the quality and the standardization of their services, as well as with a means to evaluate quality, and to optimize costs and resources.

In recent years, the research about computerized guidelines has reached a relevant role within the Medical Informatics community, and many different approaches and projects have been developed to create domain-independent computer-assisted tools for managing, acquiring, representing and executing computer-interpretable clinical guidelines (henceforth CIGs). See e.g. the systems Asbru (Shahar, Miksch, & Johnson, 1998), EON (Shahar, Musen, & Tu, 1996), GEM (Shiffman, et al., 2000) GLARE (Terenziani, Molino, & Torchio, 2001) (Terenziani, et al., 2003), GLIF (Peleg, Boxwala, & Ogunyemi, 2000), GUIDE (Quaglini, Stefanelli, Lanzola, Caporusso, & Panzarasa, 2001), PROforma (Fox, Johns, & Rahmanzadeh, 1998), and the collections (Gordon & Christensen, 1995) (Fridsma, 2001) (Lucas & Ten Teije, 2008). One of such approaches is GLARE (Guideline Acquisition, Representation and Execution), which has been built starting from 1997 in a long-term cooperation between the Department of Computer Science of the University of Piemonte Orientale, Alessandria, Italy, and the Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy). Besides supporting CIG acquisition, representation, storage and execution, GLARE is characterized by the adoption of advanced Artificial Intelligence and Temporal Databases formal techniques to provide advanced supports for different tasks, including reasoning about temporal constraints (Anselma, Terenziani, Montani, & Bottrighi, 2006), the treatment of periodic data (Stantic, Terenziani, Governatori G., Bottrighi, & Sattar, 2012), guideline versioning (Anselma, Bottrighi, Montani, & Terenziani, 2013), model-checking verification (Bottrighi, et al., 2010), decision support (based on Decision Theory) (Montani & Terenziani, 2006) (Anselma, et al., 2011), contextualization (Terenziani, et al., 2004).

By definition, clinical guidelines address specific clinical circumstances (i.e., specific diseases). However, unfortunately, specific patients may be affected by more than one disease. The treatment of patient affected by multiple diseases (comorbid patients) is one of the main challenges for the modern health care, also due to the aging of population, and the increase of chronic diseases. Just as an example, the prevalence of chronic diseases such as hypertension, diabetes mellitus or heart failure in society has grown during the last decades as the number of elderly people increased (Robert Wood Johnson Foundation and Partnership for Solutions, 2004). Moreover, most of the patients suffering from such chronic diseases, are affected by two or more of them (Gijsen, Hoeymans, & Schellevis, 2001)

Thus, “health care systems must deal with an increasing number of patients with several simultaneous pathologies (i.e., comorbid patients). … Clinical practice guidelines provide evidence-based information of interventions, but only on individual pathologies. This sets up the urgent need of developing ways of merging multiple single-disease interventions to provide professionals’ assistance to comorbid patients.” (Riaño & Collado, 2013).

However, though some CPGs covering frequently occurring comorbidities might be devised, the approach of considering all the possible combinations of pathologies does not scale up:

“Developing Clinical Practice Guidelines that explicitly address all potential comorbid diseases is not only difficult, but also impractical, and there is a need for formal methods that would allow combining several disease-specific clinical practice guidelines in order to customize them to a patient”. (Michalowski, et al., 2013).

Several studies show that the lack of such methods is one of the obstacles in the adoption of CPGs in the clinical practice, and the development of these methods has been identified as one of the “grand challenges” for clinical decision support (Sittig, et al., 2008).

In the very last years, some computer-based approaches have started to face this problem, aiming at providing physicians with different forms of support for managing multiple CIGs. Notably, in the last Artificial Intelligence in Medicine Europe (AIME '13) Conference, held in Murcia, Spain, from 29/5/2013 to 1/6/2013, a whole session (4 papers) has been devoted to such an issue (Sánchez-Garzón, et al., 2013) (Jafarpour & Abidi, 2013) (Riaño & Collado, 2013) (Michalowski, et al., 2013). The state of the art in the computer-based treatment of comorbidities is presented in section “Related Works”. Despite the high quality of the recent research approaches in the literature, the treatment of comorbidities is an extremely difficult and challenging problem, so that substantial forward steps need to be achieved. In the rest of the paper, we address our contribution.

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