Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why: * Healthcare data is going from hard to nearly impossible to manage. * Applications come and go, data lives forever. * Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail. And, then talks about how new techniques are needed to store and manage healthcare data.

1. Revenue opportunities in the
management of healthcare data deluge
Healthcare data is hard to deal with and
getting even harder and more expensive
By Shahid N. Shah, CEO

2. NETSPECTIVE
Who is Shahid?
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20+ years of software engineering and multidiscipline complex IT implementations (Gov.,
defense, health, finance, insurance)
12+ years of healthcare IT and medical
devices experience (blog at
http://healthcareguy.com)
15+ years of technology management
experience (government, non-profit,
commercial)
10+ years as architect, engineer, and
implementation manager on various EMR
and EHR initiatives (commercial and nonprofit)
www.netspective.com
Author of Chapter 13, “You’re
the CIO of your Own Office”
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3. NETSPECTIVE
What’s this talk about?
Background
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Healthcare data is going from hard
to nearly impossible to manage.
Applications come and go, data lives
forever.
Data integration is notoriously
difficult, even in the best of
circumstances, and requires
sophisticated tools and attention to
detail.
www.netspective.com
Key takeaways
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New techniques are needed to store
and manage healthcare data.
He who has, integrates, and uses
data wins in the end.
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4. Users’ expectations about the availability of data are increasing
Without data, users can’t do their jobs

5. NETSPECTIVE
Data is in the news for good reason
Data matters more than ever
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Providers have lots of it
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6. NETSPECTIVE
What users want vs. what they’re offered
Data visualization requires integration and aggregation
What’s being offered to users
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What users really want
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7. NETSPECTIVE
Data is key for move from FFS to ACOs
Integrated and aggregated data is the only way to get to ACOs and PCMHs
The business needs
The technology strategy
• Quality and performance
metrics
• Patient stratification
• Care coordination
• Population management
• Surveys and other directfrom-patient data collection
• Evidence-based surveillance
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Aggregated patient registries
Data warehouse / repository
Rules engines
Expert systems
Reporting tools
Dashboarding engines
Remote monitoring
Social engagement portal for
patient/family
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8. NETSPECTIVE
Data is getting more sophisticated
Social Interactions
Biosensors
Economics
Phenotypics
Since 1970,
pennies per
patient
Since 1980s,
pennies per
patient
• Business focused data
• Retrospective
• Built on fee for service models
• Inward looking and not focused
on clinical benefits
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• Must be continuously collected
• Mostly Retrospective
• Useful for population health
• Part digital, mostly analog
• Family History is hard
Genomics
Since 2000s,
started at $100k
per patient, <$1k
soon
• Can be collected infrequently
• Personalized
• Prospective
• Potentially predictive
• Digital
• Family history is easy
Proteomics
Emerging
• Must be continuously collected
• Difficult today, easier tomorrow
• Super-personalized
• Prospective
• Predictive
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9. NETSPECTIVE
Data needs to be highly available
• Simplify & Unify: Create innovative techniques to capture clinical
data as a byproduct of care instead of specific documentation
entered by practitioners.
• Embrace, Adopt, Extend: Take data being created by vendors
systems (medical devices, labs, etc.), add value by repurposing
and aggregating it.
Operational
Operational
Systems
Systems
Data
Analytical
Systems
Feedback Loop:
Analytics must create new insight (such as patient value and safety prediction)
and feed it back to the operational systems (the applications)
www.netspective.com
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10. NETSPECTIVE
Data accessibility issues
Lack of Financial Data
Interchange
Lack of Clinical Data
Interchange
• Extended days sales outstanding
• Difficulty in following up with rejected
claims
• Reduced collections
• Inability to use data for patient care
improvements
• Difficulty using data for marketing
• Lag in regulatory or MU reporting
Lack of Document
Interchange
• Requires fax or other document
sharing
• Adds costs, reduces operational
efficiency
www.netspective.com
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11. Storing data long-term and keeping it accessible is not easy
Health data management is tough

12. NETSPECTIVE
Debunking data myths
Myth
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I already know how to acquire the data
I need
Extracting, transforming, and loading
(ETLing) data is a “solved” problem
I only have a few systems to integrate
I know all my data formats
I know where all my data is and most
of it is valid
Truth
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www.netspective.com
Data acquisition protocols are wide
and varied
ETL grows more and more difficult as
the number of systems to integrate
increases
There are actually hundreds of systems
There are dozens of formats you’re
not aware of
Lots of data is missing and data quality
is poor
Tons of undocumented databases and
sources
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13. NETSPECTIVE
Data is hidden everywhere
Patient Education,
Calculators, Widgets,
Content
Management
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Clinical trials data
(failed or successful)
Secure Social Patient
Relationship
Management (PRM)
Patient
Communications,
SMS, IM, E-mail,
Voice, and Telehealth
Blue Button, HL7,
X.12, HIEs, EHR, and
HealthVault
Integration
E-commerce, Ads,
Subscriptions, and
Activity-based Billing
Accountable Care,
Patient Care
Continuity and
Coordination
Patient Family and
Community
Engagement
Excel files, Word
documents, and
Access database
Patient Consent,
Permissions, and
Disclosure
Management
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14. NETSPECTIVE
System have different storage needs
Clinical systems
Consumer and
patient health
systems
Core transaction
systems
Decision
support systems
(DSS and CPOE)
Electronic
medical record
(EMR)
Managed care
systems
Medical
management
systems
Materials
management
systems
Clinical data
repository
Patient
relationship
management
Imaging
Integrated
medical devices
Clinical trials
systems
Telemedicine
systems
Workflow
technologies
Work force
enabling
technologies
www.netspective.com
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15. NETSPECTIVE
Unstructured patient data sources
Patient
Source
Self reported by
patient
Health
Professional
Observations by
HCP
Labs &
Diagnostics
Computed from
specimens
Errors
High
Medium
Slow
Slow
Low
Medium
Megabytes
Megabytes
Megabytes
Data type
PDFs, images
PDFs, images
PDFs, images
Availability
Common
Common
Common
Computed from
specimens
High
Data size
Computed realtime from patient
Medium
Reliability
Biomarkers /
Genetics
Low
Time
Medical Devices
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Uncommon
Uncommon
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16. NETSPECTIVE
Structured patient data sources
Patient
Source
Self reported by
patient
Health
Professional
Observations by
HCP
Labs &
Diagnostics
Specimens
Medical Devices
Real-time from
patient
Biomarkers /
Genetics
Specimens
Errors
High
Medium
Low
Low
Low
Time
Slow
Slow
Medium
Fast
Slow
Reliability
Low
Medium
High
High
High
Kilobytes
Kilobytes
Kilobytes
Megabytes
Gigabytes
Gigabytes
Gigabytes
Uncommon
Uncommon
Discrete size
Streaming size
Availability
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Uncommon
Common
Somewhat
Common
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17. NETSPECTIVE
Application focus is biggest mistake
Application-focused IT instead of Data-focused IT is causing business problems.
Silos of information exist across
groups (duplication, little sharing)
Clinical
Apps
Billing
Apps
Lab
Apps
Other
Apps
Healthcare Provider Systems
Patient
Apps
Partner Systems
Poor data integration across
application bases
www.netspective.com
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18. NETSPECTIVE
The Strategy: Modernize Integration
Need to get existing applications to share data through modern integration
techniques
Clinical
Apps
NCI
App
Billing
Apps
Lab
Other
Apps
Apps
NEI
App
Healthcare Provider Systems
Patient
Apps
NHLBI
App
Partner Systems
Master Data Management, Entity Resolution, and Data Integration
Improved integration by services
that can communicate between applications
www.netspective.com
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19. The Do’s and Don’ts of Data Storage

20. NETSPECTIVE
Don’t try to do it all in one step
Utilize and Enhance
Analyze & Predict
Match & Link
Transform
Transport
www.netspective.com
Once we have predictive and analytics available we can use
the information back within our applications or just for
dashboards/reports.
As soon as data has been matched and linked we can start using it
for analytics and prediction.
Depending on the complexity of information identifiers and other important
data may need to be matched and linked across applications. This is where
we manage data quality.
Once an application can send and receive information , it needs to transform it into a
manner it can understand. This means structural, format, and units may need to be
translated.
Getting the data from one application to another is the first problem to solve. SOA, ETL, huband-spoke and other mechanisms can be a good start.
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21. NETSPECTIVE
Ensure transport flexibility
Hospital or Cloud
Development
TCP, HTTPS, SOAP, REST
HTTP, SFTP, SCP, MLLP
SMTP, XMPP
Vendors & Partners
VPN
Services
Remote
Center
Apps
Apps
Registry
MQs
Services
HTTPS, REST, SOAP
SFTP, SCP, MLLP
SMTP, XMPP, TCP
Embeddable Integration Backbone
Central
DB
Security
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Service
DB
Management
Services
Firewall
App
DB
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22. NETSPECTIVE
Don’t limit the format types
HL7
HL7 RIM
CDISC
Excel, CSV
Access,
SQL
SEND
CCD
CCR
RDF, RDFa
www.netspective.com
ATOM Pub
X.12
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23. NETSPECTIVE
Choose tools that can do it all
Connect
Collect &
Cleanse
Exchange
Standardize
(Map & Link)
Federate
Store
Analyze
Report
Secure
www.netspective.com
Audit
Guarantee
HIPAA
Compliance
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24. NETSPECTIVE
Don’t start without a plan
Outcome
Gather Data
Interchange
Requirements
Select and
Deploy the
right tools
Create Data
Interchange
Connection
Points
Ability to connect
multiple systems
without each
system knowing
about each other
Allows you to reduce costs, increase
revenues, & improve care by having
faster and more comprehensive
access to data.
www.netspective.com
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25. NETSPECTIVE
Don’t move without success criteria
Goals
Senior executives finalize the definition of
the success criteria and list of target
financial and clinical systems that need to
be integrated.
Integration engineers analyze, gather, and
document the technical connection points.
25www.netspective.com
Requirements
Business analysts catalog the data
origination sources and destination sinks.
Result
PLAN
Create an
executable data
integration plan
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26. NETSPECTIVE
Choosing the right tool is the key
Senior integration engineers
install tools and experiment with
external systems.
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Result
Experiment
Senior integration engineers
install tools and experiment with
internal systems.
Goals
Senior architect uses the data
integration plan to select a vendor
and create a deployment strategy.
Tool Ready to Use
Begin using tool
for financial data
and when
successful move
to clinical data
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27. NETSPECTIVE
Decouple your systems
Formats
Senior architect uses data sources
catalog to decide on adapters, protocols,
and formats for data exchange
Result
Programmers write custom adapters for
non-standard protocols and formats
Code
Programmers start wiring up near-,
medium-, and long-term connection
points (following goals set by executives)
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Data Interoperability
A/R should
improve, care
coordination
should improve,
etc.
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28. NETSPECTIVE
Don’t limit your exchange models
Federated model with
shared repositories
Federated model with
peer-to-peer network +
real-time, request/delivery
of clinical data
Federated model with
peer-to-peer network +
clinical data pushed from
sending organization
Federated model with
peer-to-peer network–no
real-time clinical data
sharing
Non-federated peer-topeer network (co-op
model)
Centralized clinical
database or data
warehouse
Health data claims bank
Clinical data exchange
cooperative
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29. NETSPECTIVE
Build vs. Buy?
Build (or use
Open Source)
Buy
(commercial)
Start Immediately
Capabilities
Engineering Costs
License Costs
www.netspective.com
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30. NETSPECTIVE
Build vs. Buy Elaborated
Buy
(Commercial)
Build (or use
Open Source)
www.netspective.com
• Reasonable purchase cost, low maintenance cost
• Low engineering resources cost (less expertise
required)
• Easy to acquire and deploy
• High Performance, Reliable, Stable
• Excellent documentation and support
• No purchase cost, no license maintenance cost
• Low engineering resources cost (less expertise
required)
• Effort required to get high performance and stability
• Adequate documentation and paid support
Best choice if
you’re not
creating your own
interface engine
Recommended if
you want to build
and sell your own
interface engine
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31. NETSPECTIVE
Primary challenges
• Tooling strategy must be comprehensive. What hardware and
software tools are available to non-technical personnel to encourage
sharing?
• Formats matter. Are you using entity resolution, master data and
metadata schemas, documenting your data formats, and access
protocols?
• Incentivize data sharing. What are the rewards for sharing or penalties
for not sharing healthcare data?
• Distribute costs. How are you going to allow data users to contribute
to the storage, archiving, analysis, and management costs?
• Determine utilization. What metrics will you use determine what’s
working and what’s not?
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31

32. Visit
http://www.netspective.com
http://www.healthcareguy.com
E-mail shahid.shah@netspective.com
Follow @ShahidNShah
Call 202-713-5409
Thank You