Quality Checking of Medical Guidelines through Logical Abduction

Abstract

Formal methods have been used in the past for the verification of the correctness of formalised versions of medical guidelines. In this paper a second possible application of the use of formal methods is proposed: checking whether a guideline conforms to global medical quality requirements. It is argued that this allows spotting design errors in medical guidelines, which is seen as a useful application for formal methods in medicine. However, this type of verification may require medical knowledge currently not available within the guidelines, i.e. medical background knowledge. In this paper, we propose a method for checking the quality of a treatment for a disorder, based on the theory of abductive diagnosis. We also examine the medical background knowledge required to be able to quality check a guideline. The method is illustrated by the formal analysis of an actual guideline for the management of diabetes mellitus type 2.

Checking the quality of clinical guidelines using automated reasoning tools

“…We have made use of a logical meta-level characterisation of such requirements, and with respect to the requirements use was made of the theory of abductive, diagnostic reasoning, i.e., to diagnose potential problems with a guideline (Lucas 1997;Lucas 2003;Poole 1990). In particular, what was diagnosed were problems in the relationship between medical knowledge, and suggested treatment actions in the guideline text and treatment effects; this is different from traditional abductive diagnosis, where observed findings are explained in terms of diagnostic hypotheses.…”

“…Finding the best possible explanation given a number of findings is called abductive reasoning Poole 1990). We say that a set of drugs T is a treatment according to the theory of abductive reasoning if (Lucas 2003):…”

A Knowledge Representation and Reasoning System for Multimodal Neuroimaging Studies

“…Under this setting, another metric has to be considered, which will be denoted as DoC (Degree-of-Confidence), that stands for one's confidence that the argument values or attributes of the terms that make the extension of a given predicate, having into consideration their domains, are in a given interval [14]. The DoC is figured using = 1 − ∆ | , where ∆ stands for the argument interval length, which was set to the interval [0, 1] (Figure 4).…”

“…where ⋃, m and l stand, respectively, for set union, the cardinality of the extension of predicate i and the number of attributes of each clause [14]. …”

Combining task execution and background knowledge for the verification of medical guidelines

“…where Good ≤ (T, P atient) denotes that T is a treatment according to good practice medicine for P atient, as defined in [8]. To prove this, the following axiom was added to the system:…”