Semantic Indexing of Patient Cases in a Boundary Infostructure for ehealth

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1 Semantic Indexing of Patient Cases in a Boundary Infostructure for ehealth Grace I. Paterson 1, MSc, Paul Fabry 2, MD, Andrew M. Grant 2, MB, PhD, Tuyet T. Thieu 1, BCS, Steven D. Soroka 3, MD, MSc, Hassan Diab 2, Andriy M. Moshyk 2, MD, MClinSci 1 Medical Informatics, Faculty of Medicine, Dalhousie University 5849 University Avenue, Halifax, Nova Scotia, Canada, B3H 4H7 grace.paterson@dal.ca 2 Centre for Research and Evaluation in Diagnostics, Centre hospitalier universitaire de Sherbrooke, Sherbrooke, Québec, Canada 3 Department of Medicine (Nephrology), Dalhousie University, Halifax, Nova Scotia, Canada Keywords: electronic health record, semantic indexing, chronic disease, nosology, clinical pragmatics Abstract This research explores semantic indexing using a boundary infostructure. The cases are about patients diagnosed with kidney disease secondary to hypertension or diabetes mellitus stored in electronic health records (EHR) from different jurisdictions, Quebec and Nova Scotia. The semantic foundation for case indexing is a three-layer ontology composed of subjects, structure and context. The subjects are the organizing principle for concepts chosen for completeness analysis. The completeness method uses semantic connections between concepts to focus its search for clinical pragmatic patterns in EHR instances. The concepts are the controlled vocabulary that can be represented with a unique concept identifier in UMLS with automated and manual mappings to nosology systems, SNOMED CT and ICD (versions 9, 9- CM and 10-CA). The structure is the information architecture and its expression in semantic classes, nosology systems, lexicons and HL7. The context is document-based communication and record-keeping skills. The semantic indexing of cases is based on a constrained set of subject terms drawn from clinical pragmatic patterns. The semantic indexing is stored in the Document Class, an HL7 Infrastructure class for Structured Documents. It supports retrieval of cases according to clinical pragmatic patterns that are embedded in the Chronic Kidney Disease Discharge Summary template for HL7 Clinical Document Architecture (CDA). The semantic indexing of patient cases is useful for bridging the gap between jurisdictions and leads to improvements in an HL7 Template functioning as a boundary object in the infostructure. 1. Introduction Clinical documentation is confounded by the way organizations store patient information in medical records. For patients diagnosed with a chronic condition, the clinical documentation tracks markers of disease progression. An infostructure may bridge the semantic gap caused by different ways of capturing clinical data in records, especially one composed of boundary objects. Boundary objects are pragmatic constructions that do the job required of them. They are implementations of medical language and information architecture standards used in electronic health records (EHR). Clinical pragmatic patterns are sense-making because they organize information in ways which correspond to how healthcare professionals expect them and in ways that facilitate their daily work [1]. They are assertions stated in actual discourse, such as discharge summaries and case writeups. The challenge is to retrieve Quebec cases that exhibit the clinical pragmatic patterns embedded in an HL7 Template designed for the Chronic Kidney Disease Discharge Summary for Nova Scotian cases. Both groups collect data differently making it difficult to determine if they mean the same thing. A boundary infostructure is pragmatically focused on finding a solution to how clinicians communicate meaning in documents. The boundary objects chosen for the infostructure are clinical terminology systems

2 (SNOMED CT, ICD-9, ICD-10-CA, UMLS) and a health information standard, HL7 CDA. Clinical terminology is defined as standardized terms and their synonyms which record patient findings, circumstances, events, and interventions with sufficient detail to support clinical care, decision support, outcomes research, and quality improvement; and can be efficiently mapped to broader classifications for administrative, regulatory, oversight and fiscal requirements [2]. The clinical pragmatics problem is a specialization of the terminology problem. 2. Methods Our cases are from the Centre Informatisé de Recherche Évaluative en Santé et Systèmes de Soins (CIRESSS), Université de Sherbrooke, Quebec (N=1006 visits by 762 patients); and from the Capital District Health Authority (CDHA), Halifax, Nova Scotia (N=2 visits by 1 patient). All patients have a diagnosis of chronic kidney disease (CKD) secondary to hypertension and/or diabetes mellitus. Each patient s information is transformed into a longitudinal EHR based on the HL7 CDA Release 2.0 standard and the CCD (Continuity of Care Document) specification. We manually mapped French terms in CIRESSS data to UMLS concept identifiers using bilingual personnel and UMLS Knowledge Sources. The patient data was loaded into an SQL database and rendered in English using our PatientRx style sheet. A subset of cases (N=17 visits for 5 patients in CIRESSS, 1 visit for 1 patient in CDHA) are filtered through the HL7 Template for Chronic Kidney Disease Discharge Summary to reduce the information to pertinent data for clinical communication [3]. These cases are put into a Clinical Document Repository. The semantic index for a patient case is stored in an HL7 Reference Information Model (RIM) Infrastructure class for structured documents.. The attribute, Document.bibliographicDesignationText, is defined as the citation for a cataloged document that permits its identification, location and/or retrieval from common collections [4]. While it is extremely unlikely that any two narrative references about the same patient will be strictly similar, it should be possible to produce a finite set of pattern expressions using boundary objects as mediators. A clinical pragmatic pattern can be described in language data, which Pratt defines as the data that the medical professional reduces or aggregates by logical inference and deduction to provide for the care of a patient or to communicate a medical concept to a student or colleague [5]. There is a strong association between the Problem-Based Learning (PBL) paradigm of medical education and the computational reasoning paradigm of Case-Based Reasoning (CBR) [6]. CBR cases provide medical students with an opportunity to learn how experts conceptualize knowledge in terms of constructs and also to leverage on experiential knowledge accumulated in the EHR which is then translated into a CBR case [2]. The process of transforming from EHR to CBR can serve as feedback to the student on their record-keeping practices and their ability to communicate clinically relevant information. The same process provides feedback to health informaticians on the infostructure s ability to support reuse from EHRs. A semantic index for a patient case is a tool for bridging between EHR and CBR representations of clinical data. A semantic foundation is required for case indexing to help ensure there is a single semantic interpretation for a statement. We created the semantic foundation for indexing using a 3-layer ontology model. The three-layer ontology is composed of subjects, structure and context. The subjects are the organizing principle for the clinical pragmatic patterns and are synthesized from facetted sources of information in the structure layer and expressed in XML for Topic Maps (XTM). The concepts are the controlled vocabulary that can be represented with a unique concept identifier in UMLS Version 2006AA with mappings to SNOMED CT and ICD (versions 9 and 10-CA). The structure is the information architecture and its expression in semantic classes, nosology systems, lexicons and health information standards. The relationship between subjects and structure supports semantic indexing of the examples in the Clinical Document Repository. The context is the use of subjects and structures to express a clinical pragmatic pattern in the production of an EHR or the production of an index for a CBR case.

3 Table 1 gives the clinical pragmatics citation from the literature and the associated EHR observations from the HL7 Template for Chronic Kidney Disease Discharge Summary [2] and CIRESSS Data Dictionary. The Topic Map for the controlled vocabulary serves as the semantic glue for integrating the subjects with their context of use. Table 1 Subjects Associated with a Clinical Pragmatic Pattern Object (HL7 Template) Object (CIRESSS) Pattern Subject Patient: BirthTime ECLE_ZZ_AGE DU_PT Abnormal GFR glomerular Patient: SEXE filtration rates are Stage of AdministrativeGenderCode used to classify the Chronic stages of chronic NOM_DE_LA_REQUETE= Kidney Créatinine sérique and kidney disease [7, Disease TITRE_CHAMP=Créatinine umol/l page 1553] Observation: serum creatinine umol/l with Method=Clinical Chemistry Observation: Ferritin ug/l with Method=Ferritin Measurement Observation: % Saturation with Method=Transferrin Saturation Observation: HGB g/l with Method=Complete Blood Count Observation: iron deficiency anaemia code D50* with Method=ICD- 10-CA Observation: Total Protein mg/l with Method=urine specimen collection, 24 hours Observation: Creatinine mmol/l with Method=urine specimen collection, 24 hours Observation: Proteinuria with Interpretation=stage Observation: Blood Pressure with Method=Vital Signs SubstanceAdministration: drug from Cardiovascular drug hierarchy NOM_DE_LA_REQUETE= Ferritine and TITRE_CHAMP= Ferritine NOM_DE_LA_REQUETE= Capacité de fixation du fer and TITRE_CHAMP= Saturation en fer NOM_DE_LA_REQUETE=FSC and TITRE_CHAMP=Hb g/l ECLE_ME_CIM9 DIAGNOSTIC3= NOM_DE_LA_REQUETE= Analyse d'urine macro/microscopie and TITRE_CHAMP=Pro NOM_DE_LA_REQUETE= Electrophorèse des protéines urinaires and TITRE_CHAMP= Albumine Drug data not in dataset Laboratory investigations for comorbidities and some reversible causes of chronic kidney disease: Ferritin, iron saturation (if patient is anemic) iron supplementation results in the return of iron stores and the elevation of hemoglobin concentration [7, pages ] a five or six point scale for proteinuria (from no proteinuria to full nephrotic syndrome) would be useful [8, page 914]. Treat Blood Pressure to Target (130/80 for low levels of protein excretion and less than 125/75 in those with high levels of protein excretion); ACEi or ARB; need 3-4 medications [9] Anemic Iron store category Iron supply category Stage of Proteinuria Hypertension Treatment Target Blood Pressure

4 3. Results The Document.BibliographicDesignationText entry for each case is constructed from a constrained set of subject terms. There are 18 entries in the Clinical Document Repository. Table 2 displays the semantic indexing using the subject terms from Table 1. In seven cases, the CIRESSS element, TITRE_CHAMP =Créatinine, was unavailable for calculating the GFR and CKD stage. Proteinuria is determined by three methods: 24-hour urine collection for protein and creatinine clearance, random urine test for albumin:creatinine ratio (mg/mmol) and albumin level by a dipstick [7]. The stages of proteinuria are negative, trace, 1+, 2+, 3+ and nephrotic syndrome [8]. When the method is 24-hour urine collection for protein, the stage 1+ corresponds to about mg/24 hours, a 2+ to gm/24 hours, a 3+ to 2-5 gm/24 hours and a 4+ represents 7 gm/24 hours or greater [10]. There were no cases where we could calculate the albumin:creatinine ratio and the albumin level by a dipstick was not recorded in lab results. Table 2 Semantic Case Indexing by Subject Case GFR CKD Stage Anemic (ICD_Dx, Ferritin, % Sat, Hgb) Proteinuria Q <ICD-9=285.9> <Hgb normal low> Q <Hgb low> Q <ICD-9=285.8,285.9> <Hgb low> 3+ Q <Hgb normal> Q <Ferritin normal> <Hgb low normal> Q <% saturation low> <Hbg normal> Q <Hgb low normal> Q <Ferritin high> <Hgb low> Q <Ferritin high> <% saturation low> <Hgb low> 3+ Q <Ferritin normal> <% saturation low> <Hgb low> Q <Ferritin high> <Hgb low> negative Q <% saturation low> <Hgb normal> 3+ Q <ICD-9=285.9> <ferritin normal high> <%saturation normal> <Hgb low> Q <Hgb normal low> Q <Hgb low> Q <ICD-9=285.9> <% saturation high> <Hgb low> Q < abnormal> <Hgb low> NS <ICD-10-CA=D50.9> <Ferritin normal> <% saturation low> <Hgb low> 3+ Different subjects were recruited to prepare a discharge summary for Case NS Their communication of the subject, Hypertension Treatment, showed variation in the Course in Hospital narration. An example narration is: Hypertension: the blood pressure was elevated during the admission in the range of systolic, diastolic so hytrin 2 mg and cardiazem SR 180 mg BID was added. The norvasc was discontinued. He continues with acebutolol 200 mg BID. One of the drug names was incorrectly spelled in the narration. The Medications portion of the HL7 Template for Chronic Kidney Disease Discharge Summary addresses the lexical variants issue through linkage to the Nova Scotia Drug Formulary. The semantic indexing enhances the narration by associating the different medications with their drug class. The source of the association between the brand name and generic name is the online Nova Scotia Drug Formulary. SNOMED CT is the source of the association between generic name and drug class. In the clinical pragmatics in Table 1, ARB refers to alpha-adrenergic blocking agent and ACEi to angiotensinconverting enzyme inhibitor agent. Hytrin has-generic-name terazosin, which is-a alpha 1 adrenergic blocking agent

5 Cardizem SR has-generic-name diltiazem, which is-a calcium channel blocking agent Norvasc has-generic-name amlodipine, which is-a calcium channel blocking agent Acebutolol is-a beta 1 blocking agent The HL7 Template gives the user a practical way to classify and communicate the kidney health status. The MDRD calculation for GFR does not need the patient s weight, just the age, sex and serum creatinine. The K/DOQI staging for Chronic Kidney Disease is a gold standard that has been adopted for use by Canadian clinicians [7]. Different versions of the International Classification of Disease (ICD) are used for coded attributes, and different languages, French and English, are used for field descriptions and free text. There are two version of ICD-9: the original ICD-9 which was published in 1975, and ICD-9-CM (Clinical Modification) which was published in 1986 by the National Center for Health Statistics (NCHS) and includes new categories compared to the original version. Both versions are available for download from the Center for Disease Control website [11]. A discrepancy between the two versions is due to the new category "diabetes with hyperosmolarity" added in ICD-9-CM. Table 3 Differences in ICD9 and ICD-9-CM Versions for Diabetes Mellitus Coding Code ICD-9 ICD-9-CM Number Diabetes mellitus without mention of complication Diabetes mellitus without mention of complication Diabetes with ketoacidosis Diabetes with ketoacidosis Diabetes with other coma Diabetes with hyperosmolarity Diabetes with renal manifestations Diabetes with other coma Diabetes with ophthalmic manifestations Diabetes with renal manifestations Diabetes with neurological manifestations Diabetes with ophthalmic manifestations Diabetes with peripheral circulatory disorders Diabetes with neurological manifestations Diabetes with other specified manifestations Diabetes with peripheral circulatory disorders Diabetes with other specified manifestations Diabetes with unspecified complication Diabetes with unspecified complication 4. Discussion The boundary infostructure recognizes the ontological knowledge that currently exists in widely accepted boundary objects drawn from clinical terminology systems (SNOMED, ICD, UMLS) and the HL7 health information standard. It creates a bridge between the different versions of ICD and makes visible maintenance issues arising from version changes. There is such a proliferation of information captured in hospital records in CIRESSS that it is pragmatic to filter the information for case indexing. The first filter, from CIRESSS to CCD, achieved a transformation from French to English and structured the information into HL7 classes. The second filter, from CCD to the HL7 Template for Chronic Kidney Disease Discharge Summary, reduced the information to clinically relevant data captured at different time intervals admission, course in hospital, discharge. There was a need to attend to unit differences, for example, the expression of Total Protein was mg/l in Nova Scotia and g/l in Quebec. The semantic indexing of cases was achieved based on clinical pragmatic patterns found in literature. The process of seeking clinical pragmatic patterns in another dataset helps us improve the HL7 Template design. The template captures data that is meaningful for chronic disease management. Medical educators teach about the markers of disease progression, such as the urinary albumin:creatinine ratio for staging proteinuria, and health informaticians need to ensure the data can be captured in an EHR. The boundary infostructure shows promise as a tool for learning at the boundary between different jurisdictions and

6 different communities of practice (clinicians, health informaticians, administrators, medical educators and patients). Acknowledgements We acknowledge scholarship funding from the Canadian Institutes of Health Research (CIHR) PhD/Postdoctoral Strategic Training Program in Health Informatics. References 1. A.L. Rector. Clinical Terminology: Why is it So Hard? Methods Inf.Med Dec; 38(4-5): C.G. Chute, et al. Clinical Classification and Terminology: Some History and Current Observations Journal of the American Medical Informatics Association 2000; 7: G.I. Paterson, S.S.R. Abidi, S.D. Soroka. HealthInfoCDA: Case Composition Using Electronic Health Record Data Sources Stud Health Technol Inform. 2005;116: G. Beeler, et al.. HL7 Reference Information Model. Version: V Available at: 5. A.W. Pratt. Medicine, Computers, and Linguistics. Advances in Biomedical Engineering. 1973;3: H. Eshach, H. Bitterman. From case-based reasoning to problem-based learning. Acad Med. 1994;69(12): C. Stigant, L. Stevens, A. Levin. Nephrology: 4. Strategies for the Care of Adults with Chronic Kidney Disease. Can. Med. Assoc. J. 2003; 168: Available at 8. C.M. Clase, A.X. Gary, B.A. Kiberd. Classifying kidney problems: can we avoid framing risks as diseases? BMJ 2004;329; S.D. Soroka. Diabetes and Kidney Disease. DCPNS Round Table. June 25, The Internet Pathology Laboratory for Medical Education, Florida State University College of Medicine. Available at Center for Disease Control website: ftp://ftp.cdc.gov/pub/health_statistics/nchs/publications/

Development of a Case Index for a Clinical Document Repository for Chronic Kidney Disease Management

Development of a Case Index for a Clinical Document Repository for Chronic Kidney Disease Management By Performed at Medical Informatics, Dalhousie University 5849 University Avenue Halifax, NS B3H 4H7