Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"

Frequency of somatic and germ-line mosaicism in retinoblastoma: implications for genetic counseling.

Although mosaicism can have important implications for genetic counseling of families with hereditary disorders, information regarding the incidence of mosaicism is available for only a few genetic diseases. Here we describe an evaluation of 156 families with retinoblastoma; the initial oncogenic mutation in the retinoblastoma gene had been identified in these families. In 15 ( approximately 10%) families, we were able to document mosaicism for the initial mutation in the retinoblastoma gene, either in the proband or in one of the proband's parents. The true incidence of mosaicism in this group of 156 families is probably higher than our findings indicate; in some additional families beyond the 15 we identified, mosaicism was likely but could not be proven, because somatic or germ-line DNA from key family members was unavailable. Germ-line DNA from two mosaic fathers was analyzed: in one of these, the mutation was detected in both sperm and leukocyte DNA; in the other, the mutation was detected only in sperm DNA. Our data suggest that mosaicism is more common than is generally appreciated, especially in disorders such as retinoblastoma, in which a high proportion of cases represent new mutations. The possibility of mosaicism should always be considered during the genetic counseling of newly identified families with retinoblastoma. As demonstrated here, genetic tests of germ-line DNA can provide valuable information that is not available through analysis of somatic (leukocyte) DNA