Automated Reasoning for Applica4on of Clinical Guidelines
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1 Computa(onal Thinking to Support Clinicians and Biomedical Scien(sts June 21 22, 2011 Automated Reasoning for Applica4on of Clinical Guidelines Mark A. Musen, M.D., Ph.D. Mary K. Goldstein, M.D., M.Sc. Samson W. Tu, M.S.
2 GLINDA: GuideLine INterac4on Detec4on Architecture Computa4onal methods for reasoning about evidence- based prac4ce Mechanisms for dealing with the messiness of clinical situa4ons Applica4on of mul4ple clinical- prac4ce guidelines Adjustments for pa4ent co- morbidi4es Adjustments for interac4ons among interven4ons
3 GLINDA Project Team Mark Musen, M.D., Ph.D. 1 Mary Goldstein, M.D., M.Sc. 1,2 Samson Tu, M.S. 1 Susana Mar4ns, M.D., M.Sc. 2 Csongor Nyulas, M.S. 1 Hyunggu Jung, M.S. 1 Pamela Kum 2 1 Stanford University, Stanford, CA 2 VA Palo Alto Health Care System, Palo Alto, CA
4 Clinical Context of our Work Popula4ons are aging worldwide Older adults tend to have mul4ple chronic condi4ons 75 million people in the US have two or more concurrent chronic condi4ons Management of mul4ple co- morbidi4es presents challenging problems Compe4ng therapeu4c goals Interven4ons that interact Difficulty achieving parsimonious treatment plans
5 Role of Clinical Prac4ce Guidelines Clinical prac4ce guidelines define evidence- based best prac4ces Lots of work on automa4ng CPGs EON, InterMed (GLIF), SAGE, PROforma, Asbru, Almost all CPGs and all systems to automate treatment in accordance with CPGs focus on single diseases
6 6 SYNTHETIC PATIENT DATA
7 Simplified ATHENA Architecture CPRS VISTA Electronic Hierarchical Medical Record System Database Patient in M Data Treatment Recommendation Guideline Interpreter SQL Server: Relational database Data Mediator ATHENA HTN Guideline Knowledge Base 7
8 ATHENA HTN KB
9 ATHENA HTN Knowledge Base
10 Goals Messages Ac4on Choices SYNTHETIC PATIENT DATA ONLY
11 ATHENA- HTN Evalua4on Studies
12 Encoded Guidelines ATHENA Hypertension ATHENA Heart Failure ATHENA Hyperlipidemia ATHENA Diabetes ATHENA Kidney Disease ATHENA Opioid Therapy
13 Limita4ons of Single- Disease Guidelines [Boyd et al. JAMA 2005] Simultaneous applica4on of mul4ple guidelines leads to subop4mal care Hypothe4cal 79- year- old woman with chronic obstruc4ve pulmonary disease, Type 2 diabetes, osteoporosis, hypertension, and osteoarthri4s If the relevant CPGs were followed, the hypothe4cal pa4ent would be prescribed 12 medica4ons and a complicated, pharmacologically inappropriate regimen Applica4on of CPGs needs to Detect and repair conflic4ng interac4ons Priori4ze recommenda4ons
14 Recommenda4ons for Hypertension 14
15 Recommenda4ons for Hyperlipidemia 15
16 Recommenda4ons for Diabetes 16
17 Overview of GLINDA Approach Incorporate our extensive experience with ATHENA CDS in an agent- oriented architecture Use task method decomposi4on to create agent- oriented model of procedural elements Develop ontology of guideline interac4ons Develop agents for detec4ng conflicts, repairing conflicts, priori4zing and integra4ng treatment recommenda4ons
18 GLINDA Task Method Decomposi4on Mul4- guideline CDS Get Data Select Guideline Apply Guideline Consolidate Advisories Detect Interac4ons Repair Priori4ze DB query Manual selec4on Goal sa4sfied? ATHENA ATHENA w/ Addi4onal Knowledge Source Heuris4c Rules based on Interac4on Ontology Interac4on- Specific Strategy Weight of Support Get KS Apply Guideline ATHENA
19 Modeling tasks and methods in Protégé
20 Modeling tasks and methods in Protégé
21 Modeling tasks and methods in Protégé
22
23 Knowledge Sources Implementa4on of Tasks and Methods in Task 3 an Agent- Oriented Architecture Task 2 Task 1 System agent Task method decomposi4on Data Sources GLINDA workflow Task 4 Blackboard Agent System agent Task 5 Task 6 Task 7 Task 8
24 Running GLINDA Ini4alizing ATHENA HTN KB Configurator agent Task method decomposi4on Pa4ent Data GLINDA agent configura4on Blackboard Agent Controller agent
25 Running GLINDA Crea4ng Agents ATHENA HTN KB ATHENA HTN agent Select GL agent... ATHENA agent #n Configurator agent Task method decomposi4on Pa4ent Data GLINDA agent configura4on Get Data agent Blackboard Agent Controller agent Consolidator agent GL Interac4on agent Repair agent Priori4za4on agent
26 Running GLINDA Configuring Agents ATHENA HTN KB ATHENA HTN agent Select GL agent... ATHENA agent #n Configurator agent Task method decomposi4on Pa4ent Data GLINDA agent configura4on Get Data agent Blackboard Agent Controller agent Consolidator agent GL Interac4on agent Repair agent Priori4za4on agent
27 Running GLINDA Ac4va4ng Agents ATHENA HTN KB ATHENA HTN agent Select GL agent... ATHENA agent #n Configurator agent Task method decomposi4on Pa4ent Data GLINDA agent configura4on Get Data agent YES/NO HF, HTN, CKD Blackboard Agent Controller agent Consolidator agent GL Interac4on agent Repair agent Priori4za4on agent
28 Running GLINDA Get Data ATHENA HTN KB ATHENA HTN agent Select GL agent... ATHENA agent #n Configurator agent Task method decomposi4on Pa4ent Data GLINDA agent configura4on Get Data agent <XML> Blackboard Agent Controller agent Consolidator agent GL Interac4on agent Repair agent Priori4za4on agent
29 Running GLINDA Select Guideline ATHENA HTN KB ATHENA HTN agent Select GL agent... ATHENA agent #n Configurator agent Task method decomposi4on Pa4ent Data Get Data agent YES/NO YES/NO YES/NO GLINDA agent configura4on Consolidator agent Blackboard Agent Controller agent GL Interac4on agent Repair agent Priori4za4on agent
30 Running GLINDA Run ATHENA Agents ATHENA HTN KB ATHENA Lipid agent ATHENA CKD agent ATHENA HF agent ATHENA HTN agent Configurator agent Task method decomposi4on ATHENA DM agent Consolidator agent pa(ent data [labs, probs.] goal pa(ent classifica(on ac(on choices Blackboard Agent pa(ent data [labs, messages probs.] goal pa(ent classifica(on ac(on choices messages pa(ent data [labs, probs.] goal pa(ent classifica(on pa(ent data [labs, ac(on probs.] choices goal messages pa(ent classifica(on ac(on choices pa(ent data [labs, messages probs.] goal pa(ent classifica(on ac(on choices messages GLINDA agent configura4on Controller agent GL Interac4on agent Repair agent Priori4za4on agent
31 Running GLINDA Consolidate Advisories ATHENA HTN KB ATHENA Lipid agent pa(ent data [labs, probs.] ATHENA CKD agent goal ATHENA pa(ent HF agent classifica(on ATHENA HTN agent Configurator agent ac(on choices Task method decomposi4on ATHENA DM agent Consolidator agent messages pa(ent data [labs, probs.] goal pa(ent classifica(on ac(on choices Blackboard Agent pa(ent data [labs, messages probs.] goal pa(ent classifica(on ac(on choices messages pa(ent data [labs, probs.] goal pa(ent classifica(on pa(ent data [labs, ac(on probs.] choices goal messages pa(ent classifica(on ac(on choices pa(ent data [labs, messages probs.] goal pa(ent classifica(on ac(on choices messages GLINDA agent configura4on Controller agent GL Interac4on agent Repair agent Priori4za4on agent
32 Running GLINDA Calculate Interac4ons ATHENA HTN KB ATHENA Lipid agent pa(ent data [labs, probs.] ATHENA CKD agent goal ATHENA pa(ent HF agent classifica(on ATHENA HTN agent Configurator agent ac(on choices pa(ent data [labs, probs.] Task method decomposi4on messages! goal GLINDA agent configura4on ATHENA DM agent! pa(ent classifica(on! ac(on choices Blackboard Agent Controller agent Consolidator agent messages GL Interac4on agent Repair agent Priori4za4on agent
33 Running GLINDA Repair and Priori4ze ATHENA HTN KB ATHENA Lipid agent ATHENA CKD agent ATHENA HF agent ATHENA HTN agent Configurator agent pa(ent data [labs, probs.] Task method decomposi4on ATHENA DM agent pa(ent data [labs, probs.] goal pa(ent classifica(on !! goal pa(ent classifica(on GLINDA agent configura4on ac(on choices messages! ac(on choices Blackboard Agent Controller agent Consolidator agent messages GL Interac4on agent Repair agent Priori4za4on agent
34 Ontology of Cross- Guideline Interac4ons Among Recommenda4ons
35 Example 1: Contradictory Recommenda4ons ATHENA HTN agent ATHENA HF agent GL Interac4on agent Repair agent Contra- indica4on X
36 Example 2: Inconsistent Pa4ent Characteriza4ons ATHENA HTN agent ATHENA CKD agent Conclude CKD if abnormal egfr Conclude CKD if abnormal egfrs > 3 months apart GL Interac4on agent Repair agent Inconsistency Message
37 Example 3: Cumula4ve Number of Interven4ons ATHENA HTN agent ATHENA Lipid agent GL Interac4on agent Repair agent Constraint Viola4on Adjust strength of support for drug addi4on
38
39 Use of pa4ent data to drive our work We extracted 2455 complex, deiden4fied pa4ent cases from the Stanford Transla4onal Research Integrated Database Environment (STRIDE) We are applying our method for interac4on detec4on to 226 selected cases selected for their combina4on of diseases and number of drugs Forma4ve evalua4on of system performance drives knowledge- base evolu4on hqps://clinicalinforma4cs.stanford.edu/research/stride.html
40 Conclusions Systems that assist with guideline- based care need to address the messiness of actual clinical situa4ons An agent- oriented architecture allows for Reasoning about comorbidi4es, applica4on of mul4ple guidelines, and situa4on- specific interac4ons Flexibility in experimen4ng with alterna4ve computa4onal workflows Crea4ng GLINDA will drive development of formal models for computa4onal thinking about Guideline interac4ons Repair mechanisms Priori4za4on of interven4ons
41 GLINDA This work has been supported by the Na(onal Library of Medicine Any opinions expressed here are not necessarily those of the NLM or of the Department of Veterans Affairs
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