Dokument/Ersteller: E. Flamme / Salzburgerstrasse 56, A-4600 Wels

A user story from behind

the CONTENT covered

mountains and the deep

BIG DATA forest

Elmar Flamme, sCIO, Wels, Austria, 2014

• Learning Outcomes:

– Implementing an enterprise wide information & technology strategy to support the business

– Lessons learnt integrating data from across a health economy

– How to make more data relevant; giving data intelligence through the use of meta-data

– Utilising meta-data and healthcare to deliver Big Data

Dokument/Ersteller: E. Flamme / Salzburgerstrasse 56, A-4600 Wels / 25.08.2014

Elmar Flamme

s(trategic) CIO / s(enior)

Consultant

I started working in healthcare as a nurse, spending 15

years on intensive care and emergency units

From 1998 I held the position of CIO at two German

Hospitals, where i was responsible for delivery projects

such as HIS development and the Electronic Healthcare

Card for Germany, working with major healthcare

stakeholders across the country.

Currently I’m the strategic CIO for Klinikum Wels

Grieskirchen, the biggest convent Hospital in Austria.

In that role, I was responsible for selecting the Hitachi

Clinical Repository as a strategic component for an

Enterprise Wide administrative and clinical information

archive.

Population County Districts Wels / Grieskirchen = 300.000

Population Upper Austria Federal State = 1.400.000

Population Austria = 8.000.000

LinzWels

• Since January 2008: One hospital on four sites

• 1000 bed Hospital in Wels merged with 260 bed

Hospital in Grieskirchen and (60) bed

Psychiatric Hospital in Wels

• 1260 beds with over 75.000 inpatients per year

• 28 departments (different specialties)

• 28 outpatient departments with approx. 240.000

outpatient visits/yr

• 30.000 operations and 2.300 births per year

• Number of staff is 3.500 (including 500 doctors

and 1200 nurses)

• Budget: approx. 304 million Euro

Psychiatric Clinic

Location WelsMain Clinic Wels-Grieskirchen

Location Wels

Main Clinic

Wels-Grieskirchen

Location Grieskirchen

The largest Convent Hospital in Austria / 5th largest Hospital in

Austria / Part of the Fraternity Enterprises (Schools, Kindergartens,

Healthcare- and Technical Services)

Take Care that the Healthcare Processes

supported by IT still workingTake Care that the

necessary

Infrastructure is

present for the

services

„Be ready for the Future

and take care of customer

satisfaction“

Data Management

Strategy

Main Inpatient / Outpatient Services

Specialized Centers / Departments

Genetic-/ Molecularbiology

Referrals, AmbulatoryHome Care, Elder Home Care

Life Science, Wellness, Fitness, Agility

Treatment

Maintenance of

good Health (..)

Healing

One Repository / One True

Mobility / Knowledge Management

Interoperability

Analyzing / Data Mining

Patient Expectations: Faster, Secure, Transparent, Equality, Quality of Life Generation 60+Customer Expectations: Make Things Faster, Easier, Usable, Support Work ProcessesManagement Expectations: Save Costs, Flexible and Dynamic Enterprise in HealthcareGovernment Expectations: Save Costs, Achieving Synergies with CollaborationTechnology Expectations: Work assist- & Information Delivery, Guideline- & Knowledge Support

Data Exchange on Standards

from „Birth“ to „Death“

Out of Big Data ……

only a specific single information is important

for the

consumer

in a specific

Situation

Physician fears the overlook of a single

important result in a „nightmare“ of

Information

• Main Data Suppliers in Healthcare are GPs, Hospitals, Nursing Homes and

Home Care

• Main Consumers of Healthcare Information's are Healthcare Providers,

Healthcare Insurances, Pharmacy, Government, Science and Research

Institutions

• and a NEW PLAYER appears: The Patient himself will be OWNER

DELIVERER and CONSUMER of Healthcare Data (PHR)

Every Healthcare Employees and every Healthcare Consumer needs

Applications which offers him information:

• At the right time

• At the right place

• At the right device

• In the right context

• In the right role

between Healthcare

Provider

between Diagnostic and Site

Location

between IT Infrastructure and Site

Location

In Focus:

DATA

CONTENT

INFORMATION

KNOWLEDGE

Changing Roles and Responsibilities: From „Data Defender“ to „Data Enabler“ From „Content Depositor“ to „Content Manager“ From „Silo Operator“ to „Repository Manager“

• A cultural shift in how data is perceived and managed is

required.

– Enabling the access on Data and Information

• Data management could change the way Healthcare IT

work

– Data must be accessible every time, everywhere, from

everything

• (Every) Data becomes Valuable

– No more Data Graveyards

– No more Data Silos

• Status Quo: Starting with PACS and a non DICOM Archive we needed additional archive solutions for SAP Digital Receipt Management, eHealth Repository, Email, Share Portal Server …..

• CEO Order: Look for a vendor who supports most of the our requirements !

• Considerations: Which vendor delivers most of our requirements and can help migrate from our previous archives?

• Concern: Who I will do the migration of DATA and how long it will take ?

What happens in five years ?

Will we do this again, again and again ?

• Accessing and Presenting data in different context cases for patient treatment, science and education

• Make clinical Data accessible for decision support and knowledge Management Systems to support the clinician with event triggered summaries

• Find new ways to present data on new Display Technologies and Devices

• Consolidation of different archives,

archive technologies, archive vendors

• Transfer from „Data“ to „Content“

Archiving in one platform for common

access

• Data “Independence” – avoid migrations – break the chains of application dependencies

• Change “unstructured Data into

structured Data” using international

Standards (CDA, LOINC, ICD, ….)

• Make Data Accessible and combinable

while using the created META DATA for

Search and Analyze additional to the

OBJECT Information

• Considering Commercial interests

(prevention rather than cure / medical

trial evaluations / managerial decisions)

• Storing all Data in One Repository

• Financial Data

• Clinical Data

• One Repository (One True) delivers content to different Applications

• Electronically Medical Record

• DWH (Financial / Medical)

• Quality- / Risk Management Systems

• Decision Assistant or Knowledge Management

• Readiness for a “digital memory” of a hospital and a regional healthcare record

• Be Ready to Change on Time Applications without Data Migration in the Background

• Fulfill legal and Compliance Requirements for a revisions save Archive Compliance with meetings legal retention periods for data

• Fulfill technical Requirements

• Automated Tiering

• Archiving instead of Backup

• Fulfill Standard Archiving Requirements from Third Party Software without Custom META DATA Requirements

• SAP/OpenText

• LogFiles

• SharePoint

• Email Archiving

• ………

• Seperate Data from the Application by storing every

Information as Object in his Orgin and made the Content

accessable by Meta Data

• Make the Data accesable for Predict- / Correlation- / Data

Ware House Systems using the Meta Data as Information

Source

• Offer knowledge Management Systems a wide Data

Collection with the possibility to combine every object Type

by his Meta Data

Adm. / ERP

System(HR, FI,

CO, DW, MM)

eHealth

GP Port

al

Home Care

Portal

Upper Austria

n eHealth Connec

t

Clinicals / SchedulingClinical Information Systems

EHR / CPOE / PoC / ED

PACSLaboratory 3rd Party Departmental’ / Subsystems- DCIOM /

- Digitalized Documents- ECG

- Ultrasonic- Pathology

- Microbiology- Maternity

- ……..

Self-develop-ment

products

MedicationCoding

„META DATA ROBOT“

Automatic Extraction“Meta Data Robot”

Master Index Specialised Indices

PACS

Kernel

Documents

IHE

HCP Lucene IHE-RegistryNon DicomMetadata

PACS-MD

DICOM-Header-Data

eMind-Metadata

IHE-Metadata

Documents

PACS-ApplicationIHE-

ApplicationKIS ISH

Non Dicom

HDDS

Standard

Connectors

Specia

lised

Connecto

rs

Whatever

WardApplication

Whatever˅

Powered by

• Meta Data Structure

• Analyzing all enterprise document types (clinical/ office

documents)

• Analyzing the content of all documents

• Analyzing the possible different levels for information

(equal content / different content)

• Applications

• Selecting application which

provides extracting Meta Data

otherwhile we use the HCP

Standard Features

• Classification

• Defining document categories

• Defining rules and regulations

• Choosing standards for automatic

classification of documents types and

automatic content filtering

Custom Meta Data:Department / Speciality Information

Custom Meta DataSections

Custom Meta Data:Diagnosis / Coding

Custom Meta DataDICOM Header + RIS Result Text

Example: Phrase Search: „Duodenalschleimhautbiopsien“

Expected Result: Pathology Results / Documents

Clinicals / SchedulingClinical Information Systems

EHR / CPOE / PoC / ED

Clinical User

Other Source Systems

Output Input

HIS EHR Portal

Synedra View

Microbiology

Radiology

Pathology

…………..

• Key technology: a scalable predictive analytic software engine for massive amounts of data

• Seamlessly combine and enrich data from many sources in nearly any form, both structured and unstructured, streaming

data, and time series data from connected devices and

sensors

• REST API: enables development of web-based interactive discovery tools (such as the one built for this POC) and easy

integration with existing tools, systems, and work flows

• Simularity’s Predictive Archetypes are easy to create and understand, without writing any code or needing special

statistics expertise• Incorporated in 2011

• Finalist at Strata’s Startup Showcase

• Named to the 25 Coolest Emerging Vendors by CRN in 2013

• Named as an Emerging Big Data vendor by CRN in 2014

• Finalist at Strata RX’s Startup Showcase

• Finalist at Dataweek’s Startup Challenge

• Hitachi Data Systems Technology Alliance Partner

• Won the Innocentive challenge for “Establishing a business value for data”

simularity© Copyright 2014, Simularity. All rights reserved.

simularitygives everyone the power to do data-driven

decision making with intuitive predictive analytics

• Implementation Time: 3 Month

• Direct Access on Data and Meta Data

• Defined Use Case:

– All Patients with Gender Female

– Older then fifty years

– With Diagnosis Breast Cancer

– Radiology Exposure in the last 6 Month

– Show the Visits

– Drill Down in Objects of the Visits

• Presented on a Live DEMO System at HIMSS 2014 • DEMO Aviable by Request

Content Search / Forensic Search

- Search over all (Custom) Meta Data (not limited on Object /

Document Content) Search for Phrases / Expressions

Clinical Search

- Real Time Search (depends from Transfer Time)

- Search over PID (all Documents)

- Including Merges

- Custom Meta Data Changes

- (including SAP Patient Receipts PID)

Administrative Search

- SAP ERP / HR Documents (DEMO)

Storing

- Every (TextBased) Information is stored as CDA Level 2

Document

- Every Information is stored as Original Information (HL7, TXT,

…..)

- Every (TextBased Information is stored as PDF-A

- Meta Data can be changed / updated (without impact to

original object)

Display

- Every Stored Information can be displayed by ONE Viewer

- XML Viewer (CDA-L2)

- DICOM / NonDICOM Picture Viewer

- PDF Viewer

• Restoring

- Every Information which is needed for Restoring is saved

inside the Custom Meta Data

- No DataBase is needed – Only XML Tools for Reading

Custom

• Analyzing

– Offering DATA via META DATA to Analyical Software (Proof

of Concept with Simularity)

Present:

1.400 diff. Objects / Document Types

2.5 Mill. Documents / Files

10 Mill. Objects

25TB DICOM Data Up to 60TB 31.12.2014

• Separate Data from the Application by storing every

Information as Object in his Origin and made the Content

accessible by Meta Data

• Make the Data accessible for Predict- / Correlation- / Data

Ware House Systems using the Meta Data as Information

Source

• Offer knowledge Management Systems a wide Data

Collection with the possibility to combine every object Type

by his Meta Data

• Looking for new Device / -technologies display Data

Proofed

Online

Developing

Searching

• Building an enterprise-wide meta data repository

needs a lot of preparation inside the enterprise

organizations (Document Management,

Standardization).

• Consider short-term requirements: A meta data

repository can not substitute viewer functionalities

and most of the existing clinical and administration

software is simply not ready for meta data yet.

• Standard / Structure and future intelligent (Semantic

Networks) Engines are the key components in

handling BIG DATA (structured / unstructured).

• Greater flexibility and cost effectiveness: Having

stored data in a generic way you are independent

from migration timelines and costs. Change

applications as and when needed for user

acceptance and improving your processes.

Building an Archiv is like building a House :

Two Steps forward one Step Back and everytime you are waiting for the craftsman !

“The farther backward you can look, the

farther forward you can see”

Sir Winston Churchill

Questions ?