Integrating Healthcare Delivery through the Innovative Use of Information & Technology - A user story from behind the CONTENT covered mountains and the deep BIG DATA forest

1. Elmar Flamme, sCIO, Wels, Austria, 2014
A user story from behind
the CONTENT covered
mountains and the deep
BIG DATA forest
Dokument/Ersteller: E. Flamme / Salzburgerstrasse 56, A-4600 Wels

2. • 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

3. 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.
Dokument/Ersteller: E. Flamme / Salzburgerstrasse 56, A-4600 Wels / 25.08.2014

4. Wels Linz
Population County Districts Wels / Grieskirchen = 300.000
Population Upper Austria Federal State = 1.400.000
Population Austria = 8.000.000

5. • 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 Wels
Main 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)

6. Take Care that the Healthcare Processes
supported by IT still working
Take Care that the
necessary
Infrastructure is
present for the
services
Data Management
Strategy
„Be ready for the Future
and take care of customer
satisfaction“

7. Specialized Centers / Departments
Main Inpatient / Outpatient Services
Genetic-/ Molecularbiology
Referrals, Ambulatory
Home Care, Elder Home Care
Life Science,
Wellness, Fitness,
Agility
Treatment
Maintenance of
good Health (..)
Healing

8. One Repository / One True
Mobility / Knowledge Management
Data Exchange on Standards
Interoperability
Analyzing / Data Mining
Patient Expectations: Faster, Secure, Transparent, Equality, Quality of Life Generation 60+
Customer Expectations: Make Things Faster, Easier, Usable, Support Work Processes
Management Expectations: Save Costs, Flexible and Dynamic Enterprise in Healthcare
Government Expectations: Save Costs, Achieving Synergies with Collaboration
Technology Expectations: Work assist- & Information Delivery, Guideline- & Knowledge Support

9. from „Birth“ to „Death“

10. 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

11. • 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

12. 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“

13. • 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

14. • 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 ?

15. • 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

16. • 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

17. • 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)

18. • 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

19. • 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
• ………

20. • 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

21. Adm. /
ERP
System
(HR, FI,
CO, DW,
MM)
eHealth
GP
Port
al
Home
Care
Portal
Upper
Austria
n
eHealth
Connec
t
Clinicals / Scheduling
Clinical Information Systems
EHR / CPOE / PoC / ED
Laboratory PACS 3rd Party Departmental’ / Subsystems
- DCIOM /
- Digitalized Documents
- ECG
- Ultrasonic
- Pathology
- Microbiology
- Maternity
- ……..
Self-develop-ment
products
Medication
Coding
„META DATA ROBOT“

22. Automatic Extraction
“Meta Data Robot”

23. PACS
Standard
Connectors
Kernel
Documents
IHE
DICOM-Header-Data
Master Index Specialised Indices
HCP Lucene IHE-Registry
Non Dicom
Metadata
PACS-MD
eMind-Metadata
IHE-Metadata
Documents
PACS-Application
IHE-Application
KIS ISH
Non Dicom
HDDS
Specialised
Connectors
Whatever
Ward
Application
˅ Whatever
Powered by

25. • 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)
• Classification
• Defining document categories
• Defining rules and regulations
• Choosing standards for automatic
classification of documents types and
automatic content filtering
• Applications
• Selecting application which
provides extracting Meta Data 
otherwhile we use the HCP
Standard Features

26. Custom Meta Data:
Department / Speciality Information
Custom Meta Data
Sections
Custom Meta Data:
Diagnosis / Coding

27. Custom Meta Data
DICOM Header +
RIS Result Text

28. Example: Phrase Search: „Duodenalschleimhautbiopsien“
Expected Result: Pathology Results / Documents

29. Clinicals / Scheduling
Clinical Information Systems
EHR / CPOE / PoC / ED
Clinical User
Other Source
Systems
Output Input

30. HIS EHR Portal
Synedra View
Microbiology
Radiology
Pathology
…………..

32. simularitygives everyone the power to do data-driven
decision making with intuitive predictive analytics
• 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”
© Copyright 2014, Simularity. All rights reserved. simularity

36. • 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

37. 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)

38. 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

39. • 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

40. • Separate Data from the Application by storing every
Information as Object in his Origin and made the Content
accessible by Meta Data
Online
• Make the Data accessible for Predict- / Correlation- / Data
Ware House Systems using the Meta Data as Information
Source
Proofed
• Offer knowledge Management Systems a wide Data
Collection with the possibility to combine every object Type
by his Meta Data
Developing
• Looking for new Device / -technologies display Data
Searching

41. • 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 !

42. “The farther backward you can look, the
farther forward you can see”
Sir Winston Churchill
Questions ?