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TRANSCRIPT
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Data-driven medicine: Actionable insights from patient data
Session #2, February 20, 2017
Turner Billingsley, MD, CMO, InterSystems
Randy Pallotta, Manager, InterSystems
Charlie Harp, CEO, Clinical Architecture
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Speaker Introduction
• M. Turner Billingsley, MD, FACEP Chief Medical Officer, InterSystems
• Charlie Harp CEO, Clinical Architecture
• Randy Pallotta Manager End User Healthcare Sales Engineering, InterSystems
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Conflict of Interest
• M. Turner Billingsley, MD, FACEP
• Charlie Harp
• Randy Pallotta
Have no real or apparent conflicts of interest to report.
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Agenda
• Longitudinal Patient Health and Care Record
• Ontologies
• Innovative Point of Care Provider Tools, Actionable Insight
• Data Normalization
• Inferencing and Logical Reasoning
• Pilot Overview
• Q & A
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Learning Objectives
• Describe how presenting medically relevant information in an innovative CliniGraphic is of high value to providers
• Discuss how state-of-the art inferencing technology can synthesize complex & disparate patient information
• State how a large HCO identified 4800+ previously unrecognized high risk patient conditions in 6 months
• Discuss the ways connected health records can enhance care delivery, improve patient outcomes, and manage population health and risk more effectively
• Identify the role both structured and unstructured data can take in providing clues for completing and correcting patient information
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STEPS™ Value Category
• HCO with an advanced integrated medical record used clinical inferencing technology to reason over medical records to:
– Advance clinical awareness
– Identify 95 patients with undocumented high risk conditions on day one
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Data Driven Medicine
• Today – well beyond tipping point of EHR installation
• Challenge: risk of data overload
• How do we…
– Get to what matters?
– Extract and deliver value from the electronic health records and systems?
– Keep promise to clinicians - “it will be worth it”
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Payer
Physician
Researcher
Government
Pharma/device
company
Medical school
Child protective
services
Social service agency
Prison
Senior center
Family
Nursing
home
Laboratory
Home care
agency
Ambulance
Pharmacy
Rehab
Hospital
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Data Driven Medicine: Data, Data and More Data
• Disparate data sources
• Structured and unstructured data
• Information overload vs. “What am I missing?”
• Expanding access to patient records
• Clinicians must consider increasing volumes of data from clinical research
• Important information may be unstructured
“The volume of unstructured
data present in most clinic-
based systems is estimated
at 80 percent and growing.”
Source: FY16 HIE inPractice Task Force (2016). Blending
Structured and Unstructured Data to Develop Healthcare Insights.
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Where can data be leveraged to make a difference?
• Providers and healthcare organizations need
– Right information
– At the right time
– In the right format
• Provide relevant knowledge at the point of care
• Improve patient care delivery, increase efficiency
• Meet organizational goals and regulatory requirements
• Support population health initiatives
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How do we enable providers to achieve these goals?
• Make it part of their normal workflow
• Within a comprehensive care record
• Provide relevant, actionable insight and value
• “Tell me something I didn’t know / need to know”
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Partnership = Remarkable Results
Large Health
System
3 Hospitals &
1 Million+ Patients
InterSystems
HealthShare
Information
Exchange
Clinical
Architecture
Symedical &
Advanced Clinical
Awareness Suite
+ +
Six Months
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Introducing Ontologies
What is an ontology?
• An ontology is a collection of relationships specific to a domain
• For instance, we could have the following ontologies defined as
subsets of the “Type II Diabetes” ontology:
– Type 2 Diabetes Medications
– Type 2 Diabetes Comorbidities
– Type II Diabetes Related Lab Results
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Leveraging Ontologies
• Consolidated views of clinical data
• Building out clinical alerts (for gaps in care, missed procedures, vaccinations, labs, missing diagnoses, etc.)
– Send alert if patient is on a diabetes medication, has a high glucose OR a high A1C, and has at least one diabetes comorbidity
As opposed to:
– Send alert if patient.medication contains ('12345', '54331', '4455'....) AND patient.labs contains ('556677','554433', '332211'...) and lab. result > 6 OR patient.labs contains ('83838','02020','20020', ...) and patient.diagnoses contains ('83838', '92929', '01010',...)
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HealthShare with Embedded Extensible Data Model
Connected Health Solutions
Normalization
Legacy Standards
Emerging Standards
Proprietary formats
Unstructured
Legacy Standards
Emerging Standards
Proprietary formats
Unstructured
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Consolidated Views of Patient Data
Why is this important?
• With longitudinal patient record, we solve the missing data problem; how do we make it efficient for providers?
• Ontologies allow aggregated data - from multiple clinical/financial/claims sources - to be displayed in a way that is meaningful to clinicians
- Normalized
- Consolidated
- De-duplicated
• With one-click, real-time in the provider workflow…
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Pilot CliniGraphics
• Four CliniGraphics currently deployed at a customer site:
– Hypertension
– COPD
– CHF
– Type II Diabetes
• More to follow:
– High Risk Pregnancy, Renal Failure, etc.
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• InterSystems’ role– Aggregate data across multiple clinical sources and messaging formats
– Uniquely identify each patient
– Provide a normalized composite health record for each patient at the point of care
– Apply patient consent policies
– Allow for secure clinical messaging
• Clinical Architecture’s role – Semantic Operating System– Provide standard terminologies and ontologies
– Support interoperability and normalization
– Support unstructured text processing
– Support complex ontological reasoning (CliniGraphic)
– Support clinical inferencing
The Pilot
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Advanced Clinical Awareness
• Summarize patient information relative to a particular condition
• Alert providers of potential issues
• Proactively look for gaps in patient information
• Improve outcomes with proactive quality interventions
• Identify patient cohorts for disease management
• Identify patient cohorts for clinical trial recruitment
• The potential is limitless
Leverage Encapsulated Knowledge to Improve Provider Awareness
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Good advice requires good information
• Build the most complete picture of the patient as possible
• Aggregate information from all available sources
• Normalize structured data to reduce dissonance
• Make the most of unstructured data where necessary
• Summarize, remove noise and fills gaps in data where possible
Chaos = Uncertainty
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Subscribe
Manage
Interoperate
Normalize
Problems
LabsMeds
Observations
Aggregate
Normalize
Patient
PRACTICES
HOSPITALS
CLAIMS
LABS
The only people who see the
whole picture are the ones who
step outside the frame.
First, build a solid information foundation
Aggregation and Normalization
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PRACTICES
Unstructured Information
Observation
Code System: SNOMEDCT
Code : 250908004
Term: Left Ventricle Ejection Fraction
Result Value: 55
Result Unit: %
Then scour all sources for critical insights
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Patient
Problems
LabsMeds
Observations
Congestive heart failure
Acute congestive heart failureCongestive heart failure
rolls up to
Ejection Fraction
Measured by
Atrial Flutter
Has comorbidity
Losartan
Treated with
Ontologies allow software to summarize data and understand how the different pieces relate
to one another
Ontological Reasoning
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Patient
Problems
LabsMeds
Observations
Inferences leverage patient information,
ontologies and logical reasoning to look for patterns
of interest
is not present
is present
is greater than 7 %
Sulfonylurea
Member of Class
Hemoglobin A1c
Has rollup
Type 2 Diabetes Mellitus
Has rollup
AND
OR
IF
Logical Reasoning
THENPatient may be an undocumented diabetic!
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Logical Reasoning
I agree clinically with the above concern.
I DO NOT agree clinically with the above concern.
I am aware of the concern and am monitoring the patient.
I have not seen this patient before.
This patient is no longer under my care.
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• Team of three RN clinical informaticists- Research best practices for standards of care
- Identification of rules, exceptions, logic flow
• First builds were completed by Clinical Architecture- Reviewed, tested, and validated by informaticists and QA team
- Discovered a small number of false positives/negatives
- Tuned the rules and algorithms
• Training for self service- 2 days training of clinical informaticists
- Built 2 Conditions with CA supervision
- Built the remaining independently
Pilot: Staffing & Build Approach
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• Ontologies and logical reasoning must be localizable and portable
• Encapsulated reasoning must incorporate all relevant information,
including unstructured text
• Encapsulated reasoning should support complex time and longitudinal
reasoning
• Encapsulated reasoning must collect relevant evidence and dynamically
build a narrative that support the assertion
What We Learned in the Process
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Pilot Use Case - Results
Three hospitals, ~100 clinics, 1 clinical lab, 14 diagnostic imaging groups
Over a period of six months, involving 1 million+ patients
Undocumented DiagnosisIdentified Patients
5 Moderate COPD
6 Severe COPD
36 Hypertensive Disorder
~1300 Congestive Heart Failure
~3500 Diabetes Mellitus Type II
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Data-driven medicine: Actionable insight from patient data
• Uncover undiagnosed patient conditions / undocumented diagnoses
• Broaden the circle of knowledge
• Improve the information available to other care providers
• Expand the information available for population health efforts
– Quality improvement, gaps in care, etc.
– Disease registries
– Care coordination
• Avoid unintended consequences
1. National diabetes statistics report: estimates of diabetes and its burden in the United States, 2014
Atlanta: US Centers for Disease Control and Prevention; 2014. Available:
https://www.cdc.gov/diabetes/pdfs/data/2014-report-estimates-of-diabetes-and-its-burden-in-the-
united-states.pdf (accessed 2017 Jan. 25).
2. Zhang Y1, Dall TM, Mann SE, Chen Y, Martin J, Moore V, Baldwin A, Reidel VA,
Quick WW. “The economic costs of undiagnosed diabetes.” Population Health
Management. 2009 Apr;12(2):95-101.
27.8% US diabetics
undiagnosed1
~cost of $2864/pp/yr.2 $
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Wrap Up
• Power in collaboration / partnership
• Longitudinal, extensible source - neutral community-wide health and care record
• Clinigraphic, Clinical Inference – tools available real-time, in provider workflow
• Added value
– Providers - actionable, relevant information at point of care
– Organization - manage risk, PH strategies, quality initiatives, etc.
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A Summary of How Benefits Were Realized for the Value of Health IT
Clinical Inferencing and the
CliniGraphic address all
5 STEPS™
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Contact Info / Questions
• Turner Billingsley, [email protected]
@InterSystems
https://www.linkedin.com/company/intersystems
• Charlie [email protected]
@ClinicalArch
• Randy [email protected]
• Please complete the online session evaluation
Booth #1561
Booth #3171