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KCOM Kaiser Clinical Ontology Modeling Peter Hendler and Michael Rossman With some copyrighted material from Matthew Horridge.

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Presentation on theme: "KCOM Kaiser Clinical Ontology Modeling Peter Hendler and Michael Rossman With some copyrighted material from Matthew Horridge."— Presentation transcript:

1 KCOM Kaiser Clinical Ontology Modeling Peter Hendler and Michael Rossman With some copyrighted material from Matthew Horridge

2 Why Do This? Last year we stressed the cost savings and simplicity added if different healthcare systems use a similar (canonical) base model This year we will show significant additional advantages if the model is created using Web Ontology Language OWL and Description Logics (DL)

3 Quick Review Why Canonical Models

4 4 Why Do We Need Them? Clinical Models Electronic health care systems have evolved separately over the decades Most were created in isolation to solve one particular domain problem (Pharmacy, Lab, Radiology, Clinical Notes, Scheduling, Billing, Admissions Discharges and Transfers or Clinical Decision Support) As a result they all have their own models, and they can not share clinical data without complex expensive interfaces being built

5 5 How Do These Systems Interoperate? Clinical Models All systems have a data model whether it is explicitly designed or is just the result of how the systems store data You must map the data model from one system to the data model for the other system if they are to share data. This requires too many expensive interfaces that goes up by N squared for N systems. Every mapping or interface results in the loss of some meaning

6 6 Current state of information modeling at KP Current Information Modeling in KP All applications are proprietary or legacy ad hoc one-off Each system has a unique persistence layer and data model Each new project generates a new relational database and new analytics Projects require the creation of unique interfaces with all the other programs and systems Interfacing and integrating programs and systems is both expensive and time consuming

7 7 A standard representation of clinical data and the implied mapping back from each application to that (in common) representation Canonical Information Modeling implies Interoperability is inherently built into all clinical systems that are based on a canonical model At a minimum, if each legacy system can import and export the canonical data model, interfacing becomes much simpler (just N instead of N(N-1)/2) CANONICAL: conforming to a general rule or acceptable procedure : orthodox []orthodox

8 Why Do This in OWL? How are relevant research and outcome studies done now?

9 Some Example Questions Do patients on NSAIDs get more GI bleeds? Do RA patients on biologic DMARDs get more non pulmonary TB? Do RA patients on non biologic DMARDs do as well as patients on any DMARDs plus biologic DMARDS?

10 And how are these questions answered today?


12 By Manual Chart Review Kat This does not change with an Electronic Health Record of unstructured data. Whether paper or electronic, non structured text and non Ontological terminologies (like ICD) require individual reading and evaluation by a reviewer

13 By using OWL in KCOM, these queries can be automated!


15 Outline Three kinds of modeling kats Why use SNOMED / CMT, and OWL? What happens when you model the HL7 RIM backbone in OWL? Very Short Intro to OWL and Protege

16 Outline How does KCOM address these problems? The generalizable part of the model valid for all sub specialty domains The specialized parts of the Rheumatoid Arthritis Assessment Model (RAAM) The Clinical Stories used to create KCOM Walk through one semantic query

17 Three Kinds of Modelers

18 This is often the cause of communication problems between IT people with different training backgrounds and different ways of looking at things.

19 19 Knows about RDBMSKind of comfortable Database Kitteh

20 20 Thinks in Unified Modeling Language (UML). Has lots of friends. Object Oriented Kat 8

21 21 Is lonely, and misunderstood. But very powerful. He made SNOMED Ontology Kat 9

22 Why Use SNOMED / CMT, OWL?

23 Medical Terminology SNOMED Ontology Description Logic Concerned with clinical meaning, not billing Fine grained enough to be clinically meaningful Can be used for Outcomes measurements Can be used by machines to make inferences

24 Inferences possible with SNOMED Strep throat is caused by streptococcus Pneumococcal pneumonia is caused by pneumococcus Streptococcus and pneumococcus are both sub types of gram positive cocci Therefore both pneumococcal pneumonia and strep throat are gram positive cocci infections.

25 25 First Example Question Do patients on NSAIDs get more GI bleeds? Without SNOMED or Ontology, clinical experts have to know the names and codes of all medications that are a kind of NSAID. They have to know all the names of the hundreds of ICD9 codes that are a kind of GI bleed. This requires Chart Review Kat and is error prone

26 26 Second Example Question Do RA patients on biologic DMARDs get more non pulmonary TB? How many ICD9/10 codes are a kind of RA How many ICD9/10 codes are a kind of DMARD? How many ICD9/10 codes are a kind of non pulmonary TB? Very difficult to do manually. Automatically done by SNOMED semantic search!

27 What happens when you model the HL7 RIM backbone in OWL?

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29 A Very Short Intro To OWL and Protege Its all about triples

30 Protege Three main views Taxonomy: Only Is A OWL-Viz: Only Is A Definition: Where the triplets are defined

31 Taxonomy View

32 OWL Viz View

33 Definition View

34 OWL is all about Triplets

35 Domain and Range

36 Subclasses

37 Define CheeseyPizza

38 Define MargheritaPizza

39 Define SohoPizza

40 A Stated Taxonomy View

41 A Stated OWL-Viz View

42 An Inferred OWL-Viz View

43 Stated and Inferred Taxonomies

44 IsA How It Looks To The Reasoner

45 IsA How It Looks To the Reasoner

46 46 They could be Myocardial Infarction and Acute Myocardial Infarction The right side is the child of (subsumed by) the left side Or they could be Pneumonitis and Infectious Pneumonitis To the Reasoner it doesnt matter, as long as it can keep track of all the symbols. It is manipulating symbols but the result makes perfect sense and results in clinically useful inferences 46

47 What Does RAAM Model?

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49 49 What Goes In and Out of The Brain Not trying to model the rules, or what happens in the brain of the expert who makes the decisions Only modeling the data that a human expert clinician specialist brain needs to make its own assessment Once the brain has made the assessment then we model the decision This is Decision Support in a new way, no rules or suggested solutions, just support the decision maker with data

50 50 4/8/2013Kaiser Permanente © 2013 The Reasoner Completely Understands the Entire Model Semantics Detects Inconsistencies Makes Logical Inferences Classifies Clinical Data Automatically

51 51 4/8/2013Kaiser Permanente © 2013 The Reasoner Knows All About The Whole Model

52 The generalizable part of the model valid for all sub specialty domains Some example views into the model

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58 The Medical Specialty Domain Specific Part

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65 How KCOM is bound to SNOMED

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67 The Clinical Stories Used to Design KCOM

68 Based on clinical cases When KCOM was first designed, we took six examples of clinical notes from Rheumatoid Arthritis Assessments They covered various clinical scenarios We will select six specific clinical statements from case number one and explore them in depth We will look at them in English, UML and finally in KCOM OWL

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88 And One Semantic Query Example

89 2 Compare the number of gastrointestinal bleeds in RA patients in the following groups. Those who have and have not taken NSAIDs

90 Break this up into steps. First find Patients with RA PersonAsPatient and subjectOf Observation hasAssociatedFinding Rheumatoid Arthritis (we will be using this basic query in all the other examples) Now using this limited cohort of RA patients continue to query as follows to find the sub groups.

91 Now we find two sub groups, those who are not on NSAIDs and those that are. (we can do this for current use or past use which ever we choose) PersonAsPatient and subjectOf Observation has MedicationAdministration has AssociatedMedication some NSAID. (note the subsumption here is very useful. Otherwise you have to accumulate all the meds that are NSAIDs with the help of a clinical/pharmacy expert. In the KCOM case this clinical knowledge is part of the model itself)

92 PersonAsPatient and subjectOf Observation has MedicationAdministration has AssociatedMedication ONLY NOT NSAID (we will not explain the difference between some and only but this query gets those NOT on any kind of NSAID. Now we have these two groups and we need to find in each one who has had GI bleed. The ICD9 or ICD10 has many different diagnosis that are all some kind of GI bleed. Not being able to use SNOMED subsumption is a fatal drawback. Because we are using SNOMED and because we are using OWL in our base clinical model we can simplify this complex query into.

93 PersonAsPatient and SubjectOf Observation hasAssociatedFinding some > (the latter part is a post coordinated SNOMED expression I used just for demonstration We could also use instead the pre-defined SNOMED term :GastroIntestinal Hemorrhage (disorder)

94 It is important to point out. There are too many ICD9 and ICD10 codes that are all a kind of Gastrointestinal Hemorrhage and unless you happen to know all of them, you will miss some patients. This SNOMED Description Logic Subsumption query will catch all of them even if you dont know what they are called. Even a clinical expert could not be expected to recall every possible kind of ICD9 or 10 term that is some kind of gastrointestinal bleed.

95 Conclusions Last year we stressed the advantages (in time money and simplicity) of using standard (canonical) models to integrate clinical systems

96 Conclusions This year we show that by using models based on Description Logic (OWL) and SNOMED we are able to use Semantic Searching to automate important and complex queries that would otherwise need manual chart reviewers and take much more time and expense.

97 Does Ontology Kat Work Well With OWL?


99 Abbreviations RAAM: Rheumatoid Arthritis Assessment Model RDBMS: Relational DataBase Management System OO: Object Oriented KCOM: Kaiser Clinical Ontology/OWL Model DMARD: Disease Modifying AntiRheumatic Drug NSAID: Non Steroidal AntiInflammatory Drug OWL: Web Ontology Language DL: Description Logics

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