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Abstract
The rationalization of the healthcare processes and organizations is a task of fundamental importance 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 widely used to provide decision support to physicians, supplying them with evidence-based predictive and prescriptive information about patients’ status and treatments, but usually on individual pathologies. This sets up the urgent need for developing decision support methodologies to assist physicians and healthcare managers in the detection of interactions between guidelines, to help them to devise appropriate patterns of treatment for comorbid patients (i.e., patients affected by multiple diseases). We 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. We then propose a general methodology (data/knowledge structures and reasoning algorithms operating on them) supporting user-driven and flexible interaction detection over the multiple levels of abstraction. Finally, we discuss the impact of the adoption of computerized clinical guidelines in general, and of our methodology in particular, for patients (quality of the received healthcare services), physicians (decision support and quality of provided services), and healthcare managers and organizations (quality and optimization of provided services).
Highlights
Given its social and economic relevance, the healthcare system is the object of continuous studies, aiming at optimizing it, both in terms of costs, and of the quality of the supplied services
The treatment of patients affected by multiple diseases is one of the main challenges for modern healthcare, due to population aging and the increase of chronic diseases
Patients and managers we discuss the impact of Clinical Practice Guidelines (CPGs) and CIGs in general, and the impact of the approach described in this paper
Summary
Given its social and economic relevance, the healthcare system is the object of continuous studies, aiming at optimizing it, both in terms of costs, and of the quality of the supplied services. See e.g. the systems Asbru (Shahar et al 1998), EON (Shahar et al 1996), GEM (Shiffman et al 2000) GLARE (Terenziani et al 2001) (Terenziani et al 2003), GLIF (Peleg et al 2000), GUIDE (Quaglini et al 2001), PROforma (Fox et al 1998), and the collections (Gordon and Christensen 1995) (Fridsma 2001) (Ten Teije et al 2008) (Peleg 2013) One of such approaches is GLARE (Guideline Acquisition, Representation and Execution), which started from 1997 in a long-term cooperation between the Department of Computer Science of the University of Eastern Piedmont 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 Database formal techniques to provide advanced supports for different tasks, including reasoning about temporal constraints (Anselma et al 2006), the treatment of periodic data (Stantic et al 2012), guideline versioning (Anselma et al 2013), model-checking verification (Bottrighi et al 2010), decision support (based on Decision Theory) (Montani and Terenziani 2006) (Anselma et al 2011), contextualization (Terenziani et al 2004)
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