What if We Really Had a Silver Bullet

What if We Really Had a Silver Bullet to Deal with Health Information? 1 Dec 2011, COMPASS Seminar Koray Atalag, MD, Ph. D, FACHI

What’s the Problem with Health Information? • We capture heaps of data - sit in silos • Partly structured and coded – eg ICD 10, ICD-O, READ, LOINC etc. • Coding is not easy / expensive – Depends on context, purpose, or just coder’s mood! – Automated coding is not reliable • Difficult to code from free text after capturing – Usually context is lost – Best at the time and place of data capture • Still wealth of valuable information in free text • We cannot link, aggregate and reuse!

What are the Implications? • Apart from: – Safety, quality, effectiveness and equity in healthcare – New knowledge discovery and advances in Science • Cost of not sharing health information: – In US could sum up to a net value of $77. 8 billion/yr (Walker J. The Value Of Health Care Information Exchange And Interoperability. Health Affairs 2005 Jan) – In Australia well over AUD 2 billion (Sprivulis, P. , Walker, J. , Johnston, D. et al. , "The Economic Benefits of Health Information Exchange Interoperability for Australia, " Australian Health Review, Nov. 2007 31(4): 531– 39. )

If the Banks Can Do It, Why Can’t Health? • Clinical data is wicked: – Breadth, depth and complexity • >600, 000 concepts, 1. 2 m relationships in SNOMED – – – – Variability of practice Diversity in concepts and language Conflicting evidence Long term coverage Links to others (e. g. family) Peculiarities in privacy and security Medico-legal issues It IS critical…

Wickedness: Medication timing Dose frequency Examples every time period …every 4 hours n times per time period …three times per day n per time period … 2 per day … 6 per week every time period range …every 4 -6 hours, … 2 -3 times per day Maximum interval …not less than every 8 hours Maximum per time period …to a maximum of 4 times per day Acknowledgement: Sam Heard

Wickedness: Medication timing Time specific Examples Morning and/or lunch and/or evening …take after breakfast and lunch Specific times of day 06: 00, 12: 00, 20: 00 Dose duration Time period …via a syringe driver over 4 hours Acknowledgement: Sam Heard

Wickedness: Medication timing Event related Examples After/Before event …after meals …before lying down …after each loose stool …after each nappy change n time period before/after event … 3 days before travel Duration n time period before/after event …on days 5 -10 after menstruation begins Acknowledgement: Sam Heard

Wickedness: Medication timing Treatment duration Examples Date/time to date/time 1 -7 January 2005 Now and then repeat after n time period/s …start, repeat in 14 days n time period/s …for 5 days n doses …Take every 2 hours for 5 doses Acknowledgement: Sam Heard

Wickedness: Medication timing Triggers/Outco mes Examples If condition is true …if pulse is greater than 80 …until bleeding stops Start event …Start 3 days before travel Finish event …Apply daily until day 21 of menstrual cycle Acknowledgement: Sam Heard

How Do We Model Now? Complex techy stuff

A New Approach: Open source specifications for representing health information and person-centric records – Based on 20+ years of international experience including Good European Health Record Project – Superset of ISO/CEN 13606 EHR standard Not-for-profit organisation - established in 2001 www. open. EHR. org Separation of clinical and technical worlds* • Big international community and research

Clinicians in the Driver’s Seat!

Key Innovation “Multi-level Modelling” separation of health information representation into layers 1) Reference Model: Technical building blocks (generic) 2) Content Model: Archetypes (domain-specific) 3) Terminology: ICD, CDISC/CDASH, SNOMED etc. è Data exchange and software development based on first layer è Archetypes provide ‘semantics’ + behaviour and GUI è Terminology provides linkage to knowledge sources (e. g. Publications, knowledge bases, ontologies)

Multi-Level Modelling in open. EHR

Date and Time Handling in open. EHR

Archetypes: Models of Health Information • Puts together RM building blocks to define clinically meaningful information (e. g. Blood pressure) • Configures RM blocks • • Structural constraints (List, table, tree) What labels can be used What data types can be used What values are allowed for these data types How many times a data item can exist? Whether a particular data item is mandatory Whether a selection is involved from a number of items/values • They are maximal datasets–contain every possible item • Modelled by domain experts using visual tools

Content Example: Blood Pressure Measurement

Blood Pressure Measurement Meta-Data

Blood Pressure Measurement Data

Blood Pressure Measurement Patient State

Blood Pressure Measurement Protocol

Open Source Archetype Editor

Content Modelling in Action Back in 2009 – GP view of BP WHAT HAVE WE MISSED? Acknowledgement: Heather Leslie & Ian Mc. Nicoll

Blood pressure: CKM review Acknowledgement: Heather Leslie & Ian Mc. Nicoll

Blood Pressure v 2 …additional input from other clinical settings Acknowledgement: Heather Leslie & Ian Mc. Nicoll

Blood Pressure v 3 …and researchers Acknowledgement: Heather Leslie & Ian Mc. Nicoll

CKM: Versioning Acknowledgement: Heather Leslie & Ian Mc. Nicoll

CKM: Discussions

Blood Pressure: Translation Acknowledgement: Heather Leslie & Ian Mc. Nicoll

How Do They All Fit Together? • Common RM blocks ensure data compatibility – No need for type conversions, enumerations, coding etc. • Common Archetypes ensure semantic consistency – when a data exchange contains blood pressure measurement data or lab result etc. it is guaranteed to mean the same thing. – Additional consistency through terminology linkage • Common health information patterns and organisation provide a ‘canonical’ representation – All similar bits of information go into right buckets – Easy & accurate querying + aggregation for secondary use • Addresses provenance and medico-legal issues

A Simple Health Information Organisation EHR Folders Compositions Sections Entries Clusters Elements Data values

Patterns in Health Information Clinician Published evidence base Observations measurable or observable Subject Actions Personal knowledge Recording data for each activity Evaluation clinically interpreted findings Administrative Entry Instructions order or initiation of a workflow process Investigator’s agents (e. g. Nurses, technicians, other physicians or automated devices)

Specialisation of Archetypes l l l Data conforms %100 to parent archetype International -> regional -> local Generalist -> specialist -> subspecialist Problem Text or Term • Clinical description • Date of onset • Date of resolution • Side • No of occurrences Diagnosis Term + • Grading • Diagnostic criteria • Stage Diabetes diagnosis Term + • Diagnostic criteria • Fasting > 6. 1 • GTT 2 hr > 11. 1 • Random > 11. 1

Specific blood test Urine culture Genomic assay Retinography N/A Routine N/A Rx N/A Fluid Tx Insuline inj Infection Tx Psychologic Detailed DNA Pedigree Chronic Foot and Seq. eyes Assays Each finding usually depends on other – clinical context matters! Person-Centric Record Organisation Low sugar Exercise etc. Diabetes Dx -Type -Severity -Course etc. Life Style Rx B Dispense Administer Genetics BP 120/70 (24 hour avg) HR 70 T: 37 C Physical Exam Hospital adm. Diabetes Priv insurance Past History Subject B USAddress State Next of kin Routine Blood Urine X-Ray Family History Dx 1 Dx 2 etc. Interventions Rx A Dispense Administer Diagnostic Tests BP 130/90 HR 90 T: 38. 5 C Diagnoses GP visit Flu-like PHO enrolm. Medications Clinical Encounter Subject A NZ Address Ethicity 1, 2. Whanau Shared Archetypes Vital Signs Demographics Providing a Canonical Representation

Can Clinicians Agree on Single Definitions of Concepts? • “What is a heart attack? ” – 5 clinicians: ~2 -3 answers – probably more! • “What is an issue vs. problem vs. diagnosis? ” – No consensus for conceptual definition for years! BUT • There is generally agreement on the structure and attributes of information to be captured Problem/Diagnosis name Status Date of initial onset Age at initial onset Severity Clinical description Date clinically recognised Anatomical location Aetiology Occurrences Exacerbations Related problems Date of Resolution Age at resolution Diagnostic criteria Acknowledgement: Sam Heard

Achievable? • 10 -20 archetypes core clinical information to ‘save a life’ • 100 archetypes primary care • 2000 archetypes secondary care – [compared to >600, 000 concepts in SNOMED]

Achievable? – cont. • Initial core clinical content is common to all disciplines and will be re-used by other specialist colleges and groups – – Online archetype consensus in CKM Achieved in weeks/archetype Minimises need for F 2 F meetings Multiple archetype reviews run in parallel • Leverage existing and ongoing international work Acknowledgement: Sam Heard

NZ Interoperability Architecture is underpinned by open. EHR

Thanks. . . Questions? Not a silver bullet, but definitely a good shot! Visit: www. openehr. org k. atalag@auckland. ac. nz