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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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Smarter Systems using Ontologies

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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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Integration at Point-Of-Care

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Clinical Inference: CHF

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Clinical Inference Workflow: CHF

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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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CliniGraphic Available

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CliniGraphic Presentation: CHF

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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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The Pilot

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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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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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Summary / Wrap Up

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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, MDturner.billingsley@intersystems.com

@InterSystems

https://www.linkedin.com/company/intersystems

• Charlie Harpcharlie_harp@clinicalarchitecture.com

@ClinicalArch

• Randy Pallottarandy.pallotta@intersystems.com

• Please complete the online session evaluation

Booth #1561

Booth #3171