An introduction to case finding and outcomes
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1 An introduction to case finding and outcomes Dr Harshana Liyanage Department of Clinical & Experimental Medicine University of Surrey Wednesday, 25 January
2 Objectives Problems with routine data Brief introduction to Coded data Ontologies Code list approach vs Ontological approach Three step ontological approach for identifying case Wednesday, 25 January
3 Problems with routine data Routinely collected datasets are: Incomplete Inconsistent Routine data often has errors: Miscoding Misclassification Misdiagnosis Sub optimal data undermines: measures of the quality of care and research Psychological and financial implications Wednesday, 25 January
4 Miscoding, misclassification and misdiagnosis Approximately 40% of patients identified by computer searches (5.8% of people with diabetes) had errors; misdiagnosis is commonest. Wednesday, 25 January
5 Ontological approaches to the rescue... Ontologies!! o Ontologies enable us to derive better outputs from sub-optimal data! Wednesday, 25 January
6 What are Ontologies? An ontology is a formal naming and definition of the types, properties, and interrelationships of the entities within a particular domain. Wednesday, 25 January
7 Clinical Coded data Weekly Bill of Mortality was established in 1634 WHO developed the International Classification of Diseases (ICD) in 1948 Primary care in UK uses Read codes: developed by James Read (1980s) Read ver2 (5 Byte) CTV3 (5 Byte) Secondary care in UK: ICD (disease classification) OPCS-4 (procedure classification) SNOMED CT Primary & secondary care (2020) Wednesday, 25 January
8 Getting data for research Code list approach Examine code dictionary/ code book for codes of interest Prepare lists of codes Perform data extraction (database administrator/developer) Clean dataset Analyse dataset Wednesday, 25 January
9 Getting data for research Code list approach Problems with the code list approach Code hierarchy may change ( newer versions) Utilisation of codes in code list cannot be guaranteed Data extraction specification is coding system dependent Limited scope for validating coding lists (dependency on code list creator/ reviewer) Coding systems have inherent issues Wednesday, 25 January
10 Read Code Hierarchy Clinical Terminology Browser C Endocrine or metabolic disease C1 Other endocrine disease C10 Diabetes mellitus Wednesday, 25 January
11 Read Code Hierarchy Clinical Terminology Browser C10F. C10F3 C10F7 C10F9 C10FJ Wednesday, 25 January
12 Ontological process Ontological approach starts with concepts NOT codes The ontological process should enable code lists used in research based on routine data to be constructed in a logical and open way. Interoperable Can be used to handle data coded in other coding systems Reusable This process will enable others to use the ontology and as is, update or modify it, or apply it to other coding systems. Use standards and tools to validate Wednesday, 25 January
13 Group task Group task Identify criteria for defining a Type 2 diabetes patient from routine data 10 minutes Wednesday, 25 January
14 Ontological approach for case finding Wednesday, 25 January
15 Identifying a case from routine computer data Wednesday, 25 January
16 Step 1: Constructing the ontology The ontological layer defines the relevant concepts. For an ontology that defines a diagnosis, this might include aetiology, diagnosis and other clinical features of the condition and its therapy. Reflects the requirements and purpose of the investigation. An ontology for diabetes would explicitly set out the criteria used in a study so that it is possible to understand how a particular prevalence might be defined. Ontological case definition might be restricted to one or more categories of data or require a combination E.g. a case of Type 1 diabetes must have a Type 1 diabetes diagnostic code AND currently prescribed insulin. Wednesday, 25 January
17 An ontological approach to case definition of diabetes Aetiology: Criteria that enable the validity of case identified in a population to be validated. The prevalence of most conditions is known For example, Type 2 diabetes is rare in people under 30 years old, more common with increasing age and in men compared with women World Health Organisation (WHO). Diabetes programme. Available from: Wednesday, 25 January
18 An ontological approach to case definition of diabetes Diagnostic criteria: Recording of a diagnostic code for diabetes, or we might stipulate classification as either Type 1 or Type 2 diabetes People with Type 1 diabetes mellitus require insulin for survival, whereas people with Type 2 have altered glucose metabolism and may or may not require insulin. Symptom codes: Thirst, polydipsia, polyuria, and describing weight loss might be diagnostic of diabetes. The World Health Organisation (WHO) criteria for diagnosis of diabetes include abnormal blood glucose plus symptoms of diabetes; however the latter are rarely looked for in database studies. World Health Organisation (WHO). Diabetes programme. Available from: Wednesday, 25 January
19 An ontological approach to case definition of diabetes Examination findings compatible with the diagnosis: Measured weight loss Smelling ketotic Pathology test criteria: Fasting or random blood test results showing a raised glucose meeting the diagnostic criteria set out by the WHO Glycated haemoglobin (HbA1c) levels compatible with diabetes Urine tests positive for glucose World Health Organisation (WHO). Diabetes programme. Available from: Wednesday, 25 January
20 An ontological approach to case definition of diabetes Medication and prescriptions: There are some medication and other prescribed items that imply a diagnosis of diabetes; others make the diagnosis unlikely. Some medicines, such as insulin, and some injectable and oral anti-diabetes drugs are used only in diabetes, whereas metformin is a medicine generally prescribed in diabetes but also used in other conditions. Prescriptions for testing for blood or urinary glucose or ketones make a diagnosis of diabetes more likely but not definite. For example, they may be prescribed in pregnancy or where there is impaired fasting glucose Bagheri A, Sadek A, Chan T, Khunti K and de Lusignan S. Using surrogate markers in primary electronic patient record systems to confirm or refute the diagnosis of diabetes. Informatics in Primary Care 2009;17(2): Wednesday, 25 January
21 An ontological approach to case definition of diabetes Process of care codes: There are a number of codes associated with the process of delivery of care, remuneration and administration of care which imply but do not make the diagnosis certain. Seen in diabetes clinic and Attending diabetes clinic. Most people with these codes in their records will have diabetes, but some people with gestational diabetes or impaired fasting glucose may also attend. Excepted from diabetes quality indicators: informed dissent declines to attend for review(removes them from the practice pay-for-performance target payment). DNA Did not attend diabetic clinic is an example of an administrative code. Stone MA, Camosso-Stefinovic J, Wilkinson J, de Lusignan S, Hattersley AT and Khunti K. Incorrect and incomplete coding and classification of diabetes: a systematic review. Diabetic Medicine 2010 May;27(5): doi: /j x. Wednesday, 25 January
22 An ontological approach to case definition of diabetes Treatment or procedure codes: There are very rare operations or other procedure codes related to diabetes. Surgery for very rare tumours can cure diabetes. Glucagonoma Phaeochromocytoma (This heading is included for ontological completeness) Wednesday, 25 January
23 Step 2: Coding layer creating a coding list from the ontology Each of the types of information included in the ontology should be included in the coding list. If you restrict your ontology to one or more categories of information (e.g. simply to diagnosis), then the same will apply to the coding list. Wednesday, 25 January
24 Step 2: Coding layer creating a coding list from the ontology Diabetes Mellitus Ontology Ontology Read Ver CTV ICD SNOMED Code Lists Wednesday, 25 January
25 Step 3: Logical data extract model Check that it is possible to extract the data you anticipate. Sometimes codes do not have sufficient granularity. Just because a code exists within a terminology, do not expect that clinicians or those involved in data entry will necessarily use it! Literature reviews, pilot searches of data sources and speaking to practitioners in the field about their data recording. Wednesday, 25 January
26 Advantages of the ontological approach The ontological process should enable code lists used in research based on routine data to be constructed in a logical and open way. This process will enable others to use the ontology and as is, update or modify it, or apply it to other coding systems. Make use of a wide range of tools to building, manage and query ontologies Wednesday, 25 January
27 Thank you for listening! Questions? Wednesday, 25 January
28 Clinical Ontologies Dr Harshana Liyanage Department of Clinical & Experimental Medicine University of Surrey Wednesday, 25 January
29 Objectives Explain the basic idea of an ontology Identify problems that can be resolved using ontologies Explain applications of ontologies Identify potential use cases for ontologies Ontology standards and tools Wednesday, 25 January
30 Ontology: onto- being; that which is logia - "science, study, theory" Parmenides ( B.C.) Wednesday, 25 January
31 What is an Ontology? An ontology is a formal, explicit specification of a shared conceptualization T. Gruber Gruber TR. The role of common ontology in achieving sharable, reusable knowledge bases. KR Apr 22;91: Wednesday, 25 January
32 The problem that ontologies try to address (example) What are the characteristics of a movie in a typical movie database? Wednesday, 25 January
33 Movie Database -A Movie Database -B Movie Title Movie Title Year Year of release Genre Genre List of actors Cast Director Director Producer Producer Country Screen Writer Alice Studio Age Rating Age Rating Official Website Bob Music Producer Runtime Special FX Producer Budget Distributor Gross Income Wednesday, 25 January
34 Alice s movie database Bob s movie Database Alice s movie website Bob s movie website Wednesday, 25 January
35 Alice s movie database Bob s movie Database Alice s movie website Bob s movie website Wednesday, 25 January
36 Building Ontologies Shared Conceptualization Movie Title Genre Director Music Producer Special FX Producer Country Age Rating Budget Distributor Movie Ontology Year of release Cast Producer Screen Writer Studio Official Website Runtime Gross Income Experts Wednesday, 25 January
37 Building Ontologies Formal, Explicit Specification Ontologies are expressed in computational logic-based languages so that knowledge can be exploited by computer programs. Machine readable Wednesday, 25 January
38 Building Ontologies Formal, Explicit Specification Human readable version Machine readable Wednesday, 25 January
39 What is an Ontology? An ontology is a formal, explicit specification of a shared conceptualization T. Gruber Gruber TR. The role of common ontology in achieving sharable, reusable knowledge bases. KR Apr 22;91: Wednesday, 25 January
40 Data sharing problems Possible use of ontologies Research Dataset - A Research Dataset - B Researcher - A Researcher - B Wednesday, 25 January
41 Gene Ontology Initiative to unify the representation of gene and gene product attributes across all species Wednesday, 25 January
42 Interoperability Problem Ideal Situation Hospital Database GP Database Pharmacy Database Wednesday, 25 January
43 Interoperability Problem Actual Situation Hospital Database GP Database Pharmacy Database Wednesday, 25 January
44 SNOMED -CT Systematized Nomenclature of Medicine Will be introduced to NHS UK in 2017 / 18 Facilitate integrated care across all health services in UK (GP, Hospitals etc) Wednesday, 25 January
45 Standards for Ontologies Ontology Web Language OWL Notation for building Ontologies (Machines can read OWL) Wednesday, 25 January
46 Tools for developing Ontologies ONTOLOGY AUTHORING TOOLS Protégé - Stanford Center for Biomedical Informatics Research Wednesday, 25 January
47 Standards for Ontologies SPARQL Language for querying Ontologies (Questions can be asked across multiple ontologies) Wednesday, 25 January
48 Final task Identify potential uses of ontologies in the domain you work in? Wednesday, 25 January
49 Summary Explain the basic idea of an ontology Identify problem that can be resolved using ontologies Explain applications of ontologies Identify potential uses of ontologies in other domains Wednesday, 25 January
50 Thank you for listening! Questions?? Wednesday, 25 January
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