Computing Drug Order Compliance with Guidelines Using an OWL2 - PowerPoint PPT Presentation

Context and objectives Material and method Experiment and results Discussion and conclusion Computing Drug Order Compliance with Guidelines Using an OWL2 Reasoner and Standard Drug Ressources Joseph Noussa Yao a , Brigitte S´ eroussi b , Jacques Bouaud a , c a INSERM, UMR S 872, eq. 20, CRC, Paris, France. b Universit´ e Paris 6, UFR de M´ edecine, Paris ; AP-HP, Hˆ opital Tenon, D´ epartement de Sant´ e Publique, Paris ; LIM&BIO, Bobigny ; APREC, Paris. c AP-HP, STIM, Paris. Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 1/15

Context and objectives Material and method Experiment and results Discussion and conclusion Guideline compliance Fact: medical practice variability Effort to promote “best practice” - quality, safety, and costs Clinical practices guidelines (CPGs) and recommendations Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 2/15

Context and objectives Material and method Experiment and results Discussion and conclusion Guideline compliance Fact: medical practice variability Effort to promote “best practice” - quality, safety, and costs Clinical practices guidelines (CPGs) and recommendations Guideline compliance measured by the rate to which clinicians follow guideline recommendations (performance measures) (More) “easily” measured at a global level Relative measure - rarely baseline rates Impact measured through change (time, location...) Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 2/15

Context and objectives Material and method Experiment and results Discussion and conclusion Guideline compliance Fact: medical practice variability Effort to promote “best practice” - quality, safety, and costs Clinical practices guidelines (CPGs) and recommendations Guideline compliance measured by the rate to which clinicians follow guideline recommendations (performance measures) (More) “easily” measured at a global level Relative measure - rarely baseline rates Impact measured through change (time, location...) Guideline compliance at the level of individual decisions More difficult to establish Especially in the domain of chronic diseases Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 2/15

Context and objectives Material and method Experiment and results Discussion and conclusion Guideline compliance Fact: medical practice variability Effort to promote “best practice” - quality, safety, and costs Clinical practices guidelines (CPGs) and recommendations Guideline compliance measured by the rate to which clinicians follow guideline recommendations (performance measures) (More) “easily” measured at a global level Relative measure - rarely baseline rates Impact measured through change (time, location...) Guideline compliance at the level of individual decisions More difficult to establish Especially in the domain of chronic diseases ➠ Focus on medical treatments Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 2/15

Context and objectives Material and method Experiment and results Discussion and conclusion The drug order A set of drugs (+ posology) Using commercial names For several patient health problems eg. P2: tareg  160 ; lipanthyl  160 1 cp/j Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 3/15

Context and objectives Material and method Experiment and results Discussion and conclusion The drug order A set of drugs (+ posology) Using commercial names For several patient health problems eg. P2: tareg  160 ; lipanthyl  160 1 cp/j Computerized physician order entry (CPOE) → Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 3/15

Context and objectives Material and method Experiment and results Discussion and conclusion The prescription recommendation(s) The recommended treatment according to guidelines for THE patient A set a drug Using drug classes (eg. ARBs, Th, BB, ACEi, CCBs...) For one given health problem (eg. AHT) eg. bitherapy of “ARBs and Th” Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 4/15

Context and objectives Material and method Experiment and results Discussion and conclusion The prescription recommendation(s) The recommended treatment according to guidelines for THE patient A set a drug Using drug classes (eg. ARBs, Th, BB, ACEi, CCBs...) For one given health problem (eg. AHT) eg. bitherapy of “ARBs and Th” Guideline-based clinical decision support systems (CDSSs) Quick reference guide Issue date: June 2006 Hypertension: management of hypertension in adults in primary care NICE clinical guideline 34 (Partial update of NICE clinical guideline 18) Hypertension Society and the National Collaborating Centre for Chronic Conditions → This clinical guideline was developed by the Newcastle Guideline Development and Research Unit; the section on prescribing drugs has been updated by the British Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 4/15

Context and objectives Material and method Experiment and results Discussion and conclusion Problems Problem statement Does a patient’s drug order comply with the patient’s recommended treatment for a given pathology? Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 5/15

Context and objectives Material and method Experiment and results Discussion and conclusion Problems Problem statement Does a patient’s drug order comply with the patient’s recommended treatment for a given pathology? Eg. Does “tareg  160 ; lipanthyl  160 1 cp/j” comply with the AHT guidelines? ie. is an antihypertensive bitherapy of “ARBs and Th”? Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 5/15

Context and objectives Material and method Experiment and results Discussion and conclusion Problems Problem statement Does a patient’s drug order comply with the patient’s recommended treatment for a given pathology? Eg. Does “tareg  160 ; lipanthyl  160 1 cp/j” comply with the AHT guidelines? ie. is an antihypertensive bitherapy of “ARBs and Th”? Orders may address multiple pathologies whereas CPGs don’t Orders and recommendations refer to drugs at different levels of abstraction Some drugs are combinations of drug classes (bitherapies) Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 5/15

Context and objectives Material and method Experiment and results Discussion and conclusion Objectives The semantic web community has produced Standard syntaxes and associated tools (OWL2) Representing knowledge and reasoning with ontologies Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 6/15

Context and objectives Material and method Experiment and results Discussion and conclusion Objectives The semantic web community has produced Standard syntaxes and associated tools (OWL2) Representing knowledge and reasoning with ontologies The WHO produced the ATC drug classification standard Internationally widespread Available for every commercialized drug Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 6/15

Context and objectives Material and method Experiment and results Discussion and conclusion Objectives The semantic web community has produced Standard syntaxes and associated tools (OWL2) Representing knowledge and reasoning with ontologies The WHO produced the ATC drug classification standard Internationally widespread Available for every commercialized drug Objectives Propose a generic model to calculate the conformity relationship between a drug prescription P and a prescription recommendation R a as a subsumption relationship between their representations using: 1 the OWL2 syntax and ontological reasoners 2 the ATC as a standard drug ressource and as a “quasi”-ontology a In the domain of hypertensive patient management. Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 6/15

Context and objectives Material and method Experiment and results Discussion and conclusion Tools and ressources OWL2 – Web Ontology Language Standardized language for representing hierarchical structures and defining logical concept SHOINQ interpretation to handle quantified cardinality restriction (QCR) (to identify levels of drug associations) Appropriate reasoners (eg. HermiT) Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 7/15

Context and objectives Material and method Experiment and results Discussion and conclusion Tools and ressources OWL2 – Web Ontology Language Standardized language for representing hierarchical structures and defining logical concept SHOINQ interpretation to handle quantified cardinality restriction (QCR) (to identify levels of drug associations) Appropriate reasoners (eg. HermiT) The WHO ATC classification as a “quasi”-ontology Hierarchical drug classes, ARBs and valsartan Plain drug subclasses can be considered as ontologies Each commercial drug has an ATC code per indication Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 7/15

Context and objectives Material and method Experiment and results Discussion and conclusion Tools and ressources OWL2 – Web Ontology Language Standardized language for representing hierarchical structures and defining logical concept SHOINQ interpretation to handle quantified cardinality restriction (QCR) (to identify levels of drug associations) Appropriate reasoners (eg. HermiT) The WHO ATC classification as a “quasi”-ontology Hierarchical drug classes, ARBs and valsartan Plain drug subclasses can be considered as ontologies Each commercial drug has an ATC code per indication Noussa Yao, S´ eroussi, Bouaud MIE 2011, Oslo, Norway 7/15