HETT Conference Olympic Central 2014 Integrating Healthcare Delivery
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Transcript of HETT Conference Olympic Central 2014 Integrating Healthcare Delivery
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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
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• 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
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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.
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Population County Districts Wels / Grieskirchen = 300.000
Population Upper Austria Federal State = 1.400.000
Population Austria = 8.000.000
LinzWels
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• 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)
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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
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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
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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
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from „Birth“ to „Death“
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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
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• 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
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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“
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• 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
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• 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 ?
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• 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
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• 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
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• 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)
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• 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
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• 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
• ………
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• 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
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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“
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Automatic Extraction“Meta Data Robot”
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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
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• 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
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Custom Meta Data:Department / Speciality Information
Custom Meta DataSections
Custom Meta Data:Diagnosis / Coding
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Custom Meta DataDICOM Header + RIS Result Text
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Example: Phrase Search: „Duodenalschleimhautbiopsien“
Expected Result: Pathology Results / Documents
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Clinicals / SchedulingClinical Information Systems
EHR / CPOE / PoC / ED
Clinical User
Other Source Systems
Output Input
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HIS EHR Portal
Synedra View
Microbiology
Radiology
Pathology
…………..
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• 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
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• 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
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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)
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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
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• 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
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• 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
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• 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 !
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“The farther backward you can look, the
farther forward you can see”
Sir Winston Churchill
Questions ?