The current options for monitoring data to help identify disease outbreaks like Ebola are not great. These are: 1) Monitoring chief complaint/reason for admission data in ADT data streams. Although this is a real-time approach, the data is not codified and would require some degree of NLP. 2) Monitoring coded data collected in EHRs. The most precise option available, but the data is not available until after the patient encounter is closed, which would be too late in most cases. And 3) Monitoring billing data. This approach has the same problems as the two listed above, but it’s better than nothing in the absence of an EMR. All of these weaknesses can be solved with the use of a data warehouse.

1. Disease Surveillance
Monitoring and Reacting
to Outbreaks (like Ebola)
with an Enterprise Data
Warehouse
Dale Sanders
Senior VP

2. Disease Surveillance in Healthcare
You don’t have to look much past
today’s headlines to understand the
importance of disease surveillance
among healthcare providers.
Here’s a summary of the current
options available for monitoring
healthcare data that could help
identify disease outbreaks.
Monitoring billing data.
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Monitoring chief complaint
/reason for admission data in
Admit, Discharge, and
Transfer (ADT) data streams.
Monitoring coded data
collected in Electronic Health
Records (EHRs).
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Data Quality and Data Profiles
One of the key concepts
underlying the discussion is
data quality. Poor data
quality translates into poor
outcomes for decision-making,
imprecise decision-making,
and imprecise
responses to a situation.
Data Quality = Completeness x Validity
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Data Quality and Data Profiles
The higher your data quality, the
more precise your understanding
of the situation at hand, and the
more precise your decisions and
reaction can be to a situation.
Just like photographs of a higher
resolution show more granular
detail, data “completeness” shows
more detail and is critical to the
patient diagnosis and treatment.
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Data Quality and Data Profiles
Data “Validity” is a little more
difficult to describe, but in short,
it relates to the context of the
situation in which accurate data
is collected.
It assumes the treatment team is
inputting accurate data into the
system. It also depends on the
timeliness of the data.
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Data Quality and Data Profiles
In addition to Data Quality, the
other key concept is the notion of
a “Data Profile” for a patient and
disease type.
A simple data profile for a patient
is pretty straightforward—name,
gender, age, height, weight,
address—those are the basics of
a first pass at a data profile.
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Data Quality and Data Profiles
Diseases also have a data
profile, based upon commonly
acknowledged symptoms and,
hopefully, very discrete lab
results or other diagnostics, such
as those from imaging.
Using a set of Boolean logical
determinations, based on
complete and valid data, we can
immediately see opportunities for
computer-assisted treatment
decision-making.
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8. Every healthcare system in the
U.S. should possess a
generalized data-profile-alerting
enterprise data warehouse,
that could also feed analytic
output to the EHR at the point
of care, as well as any number
of other downstream data
consumers, such as state and
federal governments.
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Data Profile Alert Engine
engine, fed by an
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Data Profile Alert Engine
Diagram of Conceptual Architecture:
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10. Disease Surveillance in the Near-Term
Option 1
Monitoring ADT messages:
This option would use the chief
complaint/reason for admission
data in an ADT message.
Advantage: This option is real-time, upon
presentation of the patient at a healthcare facility.
Disadvantage: Lacks codified, computable data
in the data stream, thus requiring some form of
natural language processing.
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11. Disease Surveillance in the Near-Term
Option 2
Analyzing Coded EHR and
Other Clinical Data:
Monitors coded data (SNOMED
or ICD) for diagnosis, labs tests
and results, and diagnostic
imaging.
Advantage: The most precise option available
however it is unlikely to ever be a real-time option
due to the inherent nature of healthcare delivery.
Disadvantage: Timeliness of treatment data will
lag the decision making process too late for
effective decision making.
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12. Disease Surveillance in the Near-Term
Option 3
Analyzing Coded Data From
Billing Systems:
This has all the problems of
option 2 and more. It’s not
unusual for revenue cycle
processes and systems to take
over 30 days to drop a bill.
But, in the absence of an EMR,
this data is certainly better than
nothing for profiling.
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13. Options are not great right
now, but with a well-designed
and flexible data warehouse,
at least healthcare delivery
organizations have the
beginnings of an option that
can improve in precision as
we integrate more and more
data, increasing the
completeness and resolution
of the picture for syndromic
surveillance.
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Conclusion
Disease Surveillance Needs a Data Warehouse
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14. Link to original article for a more in-depth discussion.
Disease Surveillance: Monitoring and Reacting to Outbreaks (like
Ebola) with an Enterprise Data Warehouse
Predictive Analytics: It’s the Intervention That Matters (webinar, slides, or transcript)
Dale Sanders, Senior Vice President and
David Crockett, PhD, Senior Director of Research and Predictive Analytics
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More about this topic
The Power of Maps to Improve Predictive Analytics in Healthcare
David Crockett, PhD, Senior Director of Research and Predictive Analytics
Defining Predictive Analytics in Healthcare
A primer on predictive analytics and what it means for healthcare
Three Problems with Comparative Analytics, Predictive Analytics, and NLP
Dale Sanders, Senior Vice President
In Healthcare Predictive Analytics, Big Data is Sometimes a Big Mess
David K. Crockett, Ph.D., Senior Director of Research and Predictive Analytics
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For more information:
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16. Other Clinical Quality Improvement Resources
Dale Sanders has been one of the most influential leaders in healthcare
analytics and data warehousing since his earliest days in the industry,
starting at Intermountain Healthcare from 1997-2005, where he was the
chief architect for the enterprise data warehouse (EDW) and regional
director of medical informatics at LDS Hospital. In 2001, he founded the
Healthcare Data Warehousing Association. From 2005-2009, he was the CIO for
Northwestern University’s physicians’ group and the chief architect of the Northwestern
Medical EDW. From 2009-2012, he served as the CIO for the national health system of
the Cayman Islands where he helped lead the implementation of new care delivery
processes that are now associated with accountable care in the US. Prior to his
healthcare experience, Dale had a diverse 14-year career that included duties as a CIO
on Looking Glass airborne command posts in the US Air Force; IT support for the
Reagan/Gorbachev summits; nuclear threat assessment for the National Security
Agency and START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data
Warehouse; and co-founder of Information Technology International. As a systems
engineer at TRW, Dale and his team developed the largest Oracle data warehouse in
the world at that time (1995), using an innovative design principle now known as a late
binding architecture. He holds a BS degree in chemistry and minor in biology from Ft.
Lewis College, Durango Colorado, and is a graduate of the US Air Force Information
Systems Engineering program.
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