OpenEHR Pratical Approach: From Idea to Application (tutorial) MEDINFO 2015@S. Paulo/Brasil

Samuel Frade Borut Fabjan Ricardo Cruz-Correia © 2014 Marand

Contents •

Introduction (1 hour) − Information in HealthCare − General vision of the openEHR use

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Break (5minutes)

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Hands on (2hours)

INFORMATION IN HEALTHCARE

Information in health •

Healthcare is information and knowledge driven

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Good healthcare depends on taking decisions at the right time and place, according to the right patient data and applicable knowledge

Wyatt, J.C. and P. Wright, Design should help use of patients' data. Lancet, 1998. 352: p. 1375-8.

Questions •

What is currently collected health data used for? − Documenting a person’s delivery of care − Communicate with other systems / institutions − Management − Legal purposes − Teaching − Research (eg. in retrospective studies)

The problem •

According to the Healthcare Information and Management Systems Society (HIMSS), quality problems are connected to missed drug interactions, inadequate access to medical records, poor communication, and equipment failure.

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All of these contribute to medical errors that kill as many as 98,000 people in U.S. hospitals each year, according to a famous 1999 report by the Institute of Medicine.

Where Do the Errors Come From? • Data entry accounts for the overwhelming majority (76 percent) of all data errors, regardless of setting according to The Data Warehousing Institute. In health care, high stakes data entry points include claim filing, procedure coding, and medical records. •

Bringing together disparate systems that must integrate data from multiple sources from both within and outside their organizations. Repetitive or incompatible data from these systems can be a significant source of data quality problems.

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Translation problems between codes. For example, consider that many organizations translate standard data into home-grown code to fit the requirements of their legacy systems.

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Finally, almost all data quality problems are rooted in a lack of organizational awareness. If people don’t know about the implications of bad data, how can they improve the way they manage it?

Who is responsible for data quality in our Healthcare Institutions?

COMMUNICATION IN HEALTHCARE

Patient Record Information Flow

NECESSITY FOR STANDARDIZATION

What are clinical data standards •

Data standards are an agreed upon set of rules that allow information the be shared and processed in a uniform and consistant manner.

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Clinical data standards apply this concept to clinical delivery care. − Which information from patient’s medical history should be communicated to the pshician, and how? − If a patient has a life-threatening allergy, who ought to know, and when? − Should a patient’s gender be documented as “M” or “male”?

Baseado em: California Healthcare Foundation. Clinical Data Standards Explained. November 2004

Coding the gender • M - Male • F – Female • T – Transgender • U – Undifferenciated • ? - Unknown

System A

• F – Female • M – Male • O – Other

• F – Female • M – Male • O – Other • U – Unknown • A – Ambiguous • N – Not applicable

DICOM

HL7

• 1 – Man • 2 – Women • 3 – Transgender

System B

Data comprehension Changes in [coding] protocol

ICD-9-CM to code Acute ischemic stroke

Questions •

For how long do you expect to live?

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Do you expect your health data to survive that long?

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If the data did survive, will it be understandable by future systems & users?

SEMANTIC INTEROPERABILITY

Gerard Freriks. The EHR Standards an Overview. (PDF). Out 2006

WHY OPENEHR?

Motivation

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Motivation

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Motivation

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Motivation

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Motivation

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Motivation

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Motivation •

EHR structured data − compute health information • • • • • • •

Clinical Decision Support Patient Safety Registries Population Health Business intelligence for payers Medical research Personalized-medicine

− historically heated debate (data standards problem) • HL7 RIMv3, ISO13606, OpenEHR • Data normalization

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Simple question... •

What is the percentage of patients with high BMI?

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How many diabetes patients are controlling their sugar?

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How many patients have been diagnosed with Crohn’s disease last year?

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The Quest for the Holy Grail

Part of The Mythical Quest - In search of adventure, romance and enlightenment.

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Motivation •

EHR structured data − compute health information • • • • • • •

Clinical Decision Support Patient Safety Registries Population Health Business intelligence for payers Medical research Personalized-medicine

− historically heated debate (data standards problem) • HL7 RIMv3, ISO13606, OpenEHR • Data normalization

28

Simple question... •

What is the percentage of patients with high BMI?

•

How many diabetes patients are controlling their sugar?

•

How many patients have been diagnosed with Crohn’s disease last year?

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Semantic underpinning •

OpenEHR framework

Use-case specific data-set definitions Templates

All possible item definitions for health

1:N

Terminology interface

Defined connection to terminology

Terminologies Archetypes Snomed CT

Portable, model-based queries

ICDx

Reference Model

Querying

ICPC

1:N

Defines all data 30

OPENEHR BASICS

Clinical Content: Archetypes •

Maximum data set

Use Case Specific: Templates •

Use case specific / based on archetypes

Elements of EHR/Composition

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Vertical semantic framework from GUI to Storage

Model-based querying •

The openEHR community has defined a query language spec based on archetypes called AQL – Archetype Query Language

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Compositions (records) are based on templated archetypes

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Archetypes are hierarchical in structure, and every node can be addressed by its path (locatable)

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Query based on clinical models, independent of persistence / storage model

AQL in a nutshell •

SQL + path syntax to locate nodes or data values within archetypes

SELECT data elements to be returned

FROM query data source CONTAINS Containment (matches context)

WHERE set filtering criteria on archetypes or any node within the archetypes

ORDER BY result ordering

OPENEHR IN THE BATTLEFIELD

AQL in EMR – Lines, Tubes, Drains

AQL in EMR – Labs

AQL in EMR – body fluids

AQL in EMR – Medication Administration

AQL in EMR – Clinical Decision Support

Health Data Analytics

Think!EHR Explorer AQL Query Editor

Think!EHR Explorer AQL Query Builder (QBE)

Think!EHR Explorer AdHoc Input Form Generator

Think!EHR AdHoc Form Generator

Think!EHR AdHoc Form Generator

EhrScape.com

USAGE EXAMPLES

Nation wide EHRs using openEHR •

Slovenia’s national eHealth Infrastructure − Scale: 2 mio. population − IHE / OpenEHR ecosystem

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Moscow City EHR Project − − − −

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Scale: 12 million population, 1B documents Many applications, vendors, one CDR eHealth platform for the future Short time-to-delivery

UNIMED Minas Gerais/Brazil EHR Project − Scale: 18,9 mio. Population − Many applications, vendors, one CDR − Data integration, longitudinal structured patient EHR

City of Moscow eHealth Moscow city - 780 medical facilities, including: •

149 hospitals, 76 health centers, 428 policlinic institutions

Volume: •

Patients- 12 million, Beds in hospitals – 83,000

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Physicians – 45,000, all users – 130,000

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Patient visits/year - 161 million

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Documents/year - 1 Billion, 25TB

Want to learn more?

Want to learn more? Portuguese online course on introduction to openEHR •

http://www.cides.med.up.pt/openehr/

HANDS-ON

Hands on • • • • •

Using the CKM Using and creating Archetypes Using and creating Templates Creating a form based on an openEHR template openEHR platform: − REST API overview, − examples

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Getting started the REST API example Rendering a functional form EHR data exploration: using AQL Integrating Clinical Decision Support services

Form example

Using CKM •

http://openehr.org/ckm

Form example

Using and creating Archetypes

Form example

Using and creating Templates

Form example

Creating a form based on template •

https://www.ehrscape.com/explorer/

openEHR platform: REST API overview

openEHR platform: Examples

Getting started the REST API example

Rendering a functional form

EHR data exploration: using AQL

Integrating Clinical Decision Support services