INNOVATION IN HEALTHCARE IT STANDARDS: THE PATH TO BIG DATA INTERCHANGE

LUCIANA TRICAI CAVALINI, MD, PHDTIMOTHY WAYNE COOK, MSC

BIG DATA IN HEALTHCAREMYTHS (AND FACTS)

MYTH #1: "BIG DATA" HAS A UNIVERSALLY ACCEPTED, CLEAR DEFINITION

MYTH #2: BIG DATA IS NEWCollecting, processing and analyzing sheer amounts of data is not a new activity in mankind Example: Middle Age monks and their concordances (correlations of every single word in the Bible)

What is new is the volume size and the speed it can be processed and analyzed

MYTH #3: BIGGER DATA IS BETTERIn biomedical science, this is partially fact: the bigger the

sample size, the more precise the

estimates are

However, large sample

sizes with bad quality data

are dangerously misleading

In healthcare, precision and reliability are both equally

important

MYTH #4: BIG DATA MEANS BIG MARKETING

There is no evidence that analyzing Big Data

increases the number of customers

Big Data is useful when it helps emerging actionable insights

(e.g., an unknown relationship between a gene and a disease)

That has little relevance in healthcare, especially in universal healthcare

systems

HOW TO GET RELIABLE BIG DATA?TRADITIONAL STANDARDS X INNOVATION

THE TRADITIONAL HEALTHCARE IT STANDARDS

HL7, openEHR, ISO 13606

Primary focus on message exchange

among EMRs

All of them precede in history the emergence of

Big Data and the Semantic Web

Top-down data modeling approach: not

prepared to deal with the 3V of Big Data

SNOMED-CT, LOINC, ICD

Controlled vocabularies

Also preceding Big Data and

Semantic Web

Main focus on pre-coordination (top-down approach)

In other words: the traditional healthcare IT standards are not prepared to deal with Big Data

A DEVELOPMENT ABOUT OPENEHR

The current version of the Archetype

Definition Language is 1.4

It requires an archetype to be the maximal data set for

a given concept

By the book, it means that there can be just one

archetype for each single concept in the whole

globe

There are several archetypes being developed

in isolation, not being submitted to the proper

governance tool (the CKM)

Now

BIG DATA IS BEING PRODUCED:

A BIG DATA-AWARE HEALTHCARE IT STANDARD IS:

Compliant to Semantic Web Technologies

Respectful to the different points of view coming from different medical schools

Welcoming to all healthcare professionals (and their concepts)

Not limited to EMR data modeling

Prepared to deal with the emerging mHealth and the Internet of Things

MULTILEVEL HEALTHCARE INFORMATION MODELING (MLHIM)

AN INNOVATION IN HEALTHCARE IT STANDARDS

THE BACKGROUND - 1

The typical application design locks up semantics in the database structure and application source code

Different use cases in different scenarios often interpret seemingly similar data, differently when the semantics are missing

Multilevel modelling provides a way to share semantics about any medical (healthcare) concept between distributed and independent applications

THE BACKGROUND - 2MLHIM is based on the core modelling concepts of openEHR to provide semantics external from applications

From openEHR, MLHIM inherited the multilevel model principles

MLHIM also uses certain conceptual principles from HL7 v3

From HL7, MLHIM inherited the XML-based implementation

THE IMPLEMENTATION

MLHIM simplifies the openEHR Reference Model

It is called a ‘minimalistic’ multilevel model

A NOTE ON ADL X XML

There is a loss of information when moving between an object model (AOM) and XML Schema

dADL is the proper instance serialization for

the AOM

However, in practice implementers are

serializing openEHR/ISO13606 data

in XML

ADL X XML: A COMPARISONADL XML

The openEHR test suite includes approximately 1600 total files, with known independent validations of its files

The XML Schema test suite contains more than 40,000 independently validated tests

OpenEHR tools are developed by one company and there is one open source reference model

There are more than 30 XML editors, open source and proprietary from as many companies. There are additional tools in the XML family, XSLT, Xquery, Xlink and Xproc

The FOSS Java RM has not been thoroughly tested and validated

There are at least 3 widely used, XML parser/validators, open source and proprietary from different companies and communities

The only ADL courses are from Ocean Informatics and a few startup course taught by non-experts

XML is taught in all computer science courses as well as online

There are zero books on ADL O'Reilly has 54 books on XML, Amazon has 11,890 results for Books: "xml"

QUESTION BREAK

MODELING CLINICAL MODELS IN MLHIM

THE HEART OF HEALTHCARE IT STANDARDIZATION

CLINICAL KNOWLEDGE MODELING: FUNDAMENTALS

Modeling clinical data is a complex taskRequires deep

knowledge of the specific clinical

domain

Requires at least an intermediate understanding of

data types

Modeling clinical data is a core

activity in healthcare IT

It is the only way to produce

Big Data in healthcare with

responsibility

Even well designed clinical

data modes in conventional

software are not interoperable

Multilevel model software is

interoperable and it requires

thoughtful clinical knowledge modeling

CLINICAL MODELS IN MULTILEVEL MODELING

•The Reference Model: generic information model shared by the ecosystem•The Domain Model: definition of constraints to the Reference Model for each medical concept

In multilevel modeling, the information

ecosystem is structured in (at least) two levels:

Multilevel Model openEHR MLHIM

Domain Model Archetype Concept Constraint Definition (CCD)

Language ADL XML Schema 1.1

# of DM/concept 1 n

Governance Top down, consensus Bottom-up, merit

CONCEPT CONSTRAINT DEFINITION (CCD)

In MLHIM, CCDs are XML Schemas that define

constraints to the Reference Model, in order to model

clinical concepts

CCDs can be validated to the correspondent MLHIM Reference Model by third-

party applications

The CCD Schema informs the application developer of the

structure of a valid data instance for each concept modeled for that system

If the CCD is made public, any receptor of a data instance

coming from this application can store, validate, query etc

that data instance

CCD HIGH LEVEL STRUCTURE

CCD

Care, Demographic or AdminEntry

Cluster

DvAdapter (or Cluster)

DataType

MLHIM DATATYPES FOR CCDS

Ordered

Quantified

DvCount

DvQuantity

DvRatio

DvOrdinal DvTemporal

Unordered

DvString

(with enumeration)

(without enumeration)

DvCodedString DvMedia DvParsable

DvInterval

RerefenceRange

MLHIM ELEMENTS: PRINCIPLES

The elements of a CCD do not carry any semantics

Since element names are structural identifiers, this is in keeping with the best practices of healthcare knowledge artifact identifiers, as first proposed by Dr. James Cimino (circa 1988)

Characteristic #3 - Dumb Identifiers

An identifier itself should not have meaning. If an identifier is comprised of other identifiers that have been combined, then the composite identifier is inherently unstable. If the circumstances that related the composite identifiers together in the first place change, the resulting identifier must also change.

MLHIM CCDS: TECHNICAL ASPECTS

CCDs are the equivalent of an archetype in CEN13606 and openEHR

With the exceptions:

• They may be defined at any level, for any application use• complexType definitions may be reused in multiple CCDs• CCDs persist for all time and are not versioned, this is essential for data integrity across time• All element names are unique identifiers (Type 4 UUIDs)

CCD GOVERNANCE MODEL

Artifact governance in

MLHIM consists of

maintaining a copy of the CCDs and Reference

Models

This can be on the web at the

specified location or locally and referenced using the

standard XML Catalog tools

Because of the naming

conventions, changes to the

MLHIM reference

model does not impact previously

defined CCDs or data

This maintains accurate

semantics for all time

MLHIM RESOURCESPUTTING INNOVATION INTO PRACTICE

MLHIM REFERENCE MODEL

The release version is availble at www.mlhim.org

The development version is available at www.github.com/mlhim

CCD GENERATOR (CCD-GEN)

CCD editor maintained by the MLHIM Laboratory at www.ccdgen.com

Produces CCDs according to the correspondent MLHIM Reference Model

CCDs are automatically validated

Other products include:

A sample data instanceJSON serialization of the data instanceA sample HTML formModules for the R programming language to pull MLHIM data into R data frames for processing and analysis

OTHER MLHIM TOOLS

MLHIM Application Platform & Learning Environment (MAPLE)

• A MLHIM repository using an SQL DB for persistence with a browser and a REST interface

MLHIM XML Instance Converter (MXIC)

• Utility to convert MLHIM CCD XML instances to use shortuuids and to convert to JSON and back again to XML• It is intended to demonstrate how mobile apps can use smaller data files to pass over the wire to an API that expects these formats and can convert them back to full XML instances for validation

Form2CCD

• Web application to build a form and create a CCD from it (work in progress)

Constraint Definition Designer (CDD)

• FOSS CCD editor (work in progress)

IN BRIEFCONCLUSIONS AND THE VIEW TO THE FUTURE

MLHIM IS BIG DATA READY

MLHIM uses standard XML technologies and

embedded RDF to define the syntax and semantics

The semantics are in the CCD and can be easily exchanged or

referenced via the web

Their RDF can be queried, analyzed and linked using standard

tools

MLHIM data can be stored in SQL or NoSQL databases

Examples are on GitHub for eXist-DB (XML) and SQLite3 (can easily be ported to use PostgreSQL, MySQL, Oracle,

etc.)

OUR VISION OF THE FUTURE

There are intuitions inside the healthcare IT world already about the inadequacy of conventional EMRs to collect reliable data at the point of care

The real Big Data in healthcare will come from purpose-specific applications modeled by the domain experts

The hardware support of choice for those apps is the mobile computing

The other source of Big Data in healthcare will come from the Internet of Things

All that data which is MLHIM compliant will participate in a semantically interoperable health information ecosystem

lutricav@mlhim.org

tim@mlhim.org

THANK YOU!

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@mlhim2

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