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Creative Solutions To Common ProblemsINT5, March 5th, 2018

Jodi Daniel, Partner, Crowell & Moring

Timothy Pletcher, DHA, Executive Director,Michigan Health Information Network (MiHIN)

Mousumi Sengupta, Director, Healthcare Knowledge Development, Nuance Communications

2

Timothy A Pletcher

Jodi Daniel, Partner

Mousumi Sengupta

Has no real or apparent conflicts of interest to report.

Conflict of Interest

3

Learning Objectives

• Identify key barriers to “connect and collect” solutions in health information exchange

• Evaluate new approaches to supporting coordination and cooperation in exchange

• Examine how new approaches to exchange processes can support improved data quality and the effective use of all forms of data

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Challenges in Setting National Interoperability Policy

• Interoperability is not just a technical problem –

– Must focus on business, policy, and cultural factors

• Federal government has made many attempts at governance

– RFI on Nationwide Health Information Network (2012)

– Governance Framework (May 2013)

– 21st Century Cures Act (December 2017)

– ONC Trusted Exchange Framework and Common Agreement (January 2018)

• Meanwhile, HIEs face their own challenges with sustainability, and new requirements can be burdensome

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21st Century Cures Act & Trusted Exchange

• Trusted Exchange Framework and Common Agreement (TEFCA)

• Legislative mandate:

– “…for the purpose of ensuring full network-to-network exchange of health information, convene public-private and public-public partnerships to build consensus and develop or support a trusted exchange framework, including a common agreement among health information networks nationally.” (Sec. 4003)

• The common agreement may include:

– Authentication

– Set of rules for trusted exchange

– Organizational and operational policies to enable exchange of health information among networks (minimum conditions)

– Process for filing and adjudicating non-compliance with terms

TEFCA is

voluntary –

But…

Federal agencies

may require

compliance with

TEFCA provisions for

procurement or

grants/cooperative

agreements

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Part A: Principles for trusted exchange Part B: Minimum required terms and

conditions for trusted exchange

• Standardization

• Transparency

• Cooperation and Non-Discrimination

• Privacy, Security, Safety

• Individual/Caregiver Access

• Data-driven Accountability

• Six broad permitted purposes

• Cannot limit exchange/aggregation of

EHI within the permitted purposes

• Non-Discrimination Policy

• Fee limiting based on attributable cost

• Privacy, security, consent requirements

• Mandatory updating of data format/API to align

with USCDI

• Mandatory updating of participant agreements

• Compliance with updated standards

How it Works

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• Broad Permitted Purposes

• Non-Discrimination Policy

• Fees Based on Attributable Costs

• Impact on Participation Agreements

• Compliance and Enforcement - RCE

What Does it Mean for HIEs?

8

ContactJodi G. DanielPartner, Crowell & Moring

202.624.2908

jdaniel@crowell.com

@jodidaniel

Areas of Practice

Healthcare

Digital Health

Privacy and Cybersecurity

Government Affairs

Regulatory and Policy

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Creative Solutions to Common ProblemsSession INT5, March 5, 2018

Timothy Pletcher, DHA, Executive Director

Michigan Health Information Network (MiHIN)

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Agenda• Michigan Health Information Network Shared Services (MiHIN) snapshot

• Common barriers and challenges in health information exchange

• Use-case-focused policy & incentives to drive data quality & foster cooperation

• Power of the Active Care Relationship Service approach

• Using prior knowledge to harness shared analytics for data enrichment

• Cross-organizational Clinical Decision Support and sharing computable biomedical knowledge

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MiHIN

Statewide

Shared ServicesMDHHS Data Hub

Medicaid

MSSS

State

Labs

Providers

&

Health

Systems

Health Information

Exchanges (HIEs)

Data

Warehouse

Health Plans

Single point of

entry/exit for state

Pharmacies

(more coming)

Immunizations

MI

Syndromic

Surveillance

System

MI Disease

Surveillance

System

Consumer-facing Organizations

PIHPs (10)

Other Data Sharing Orgs

MyHealthPortal

MyHealthButton

Chronic

Diseases

State

Innovation

Models (SIM)

---

43 Provider

Organizations

Federal

12

(218)

(118)

(1686)

(5481)

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New Interoperability Maturity Model

Raw data

• Push

• Pull

• Identity

Context

• Linkages

• Prior knowledge

• Data enrichment

• Consent

Precision

• Just-in-time intelligence

• Automation

• Targeted actions

Data

Access

Situational

Awareness

Computable

Biomedical

Knowledge

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Common Barriers 1 & 2

1. Lack of motivation to share data among unaffiliated organizations

2. Limited recourse when HIE participants send low-quality data

15This Photo by Unknown Author is licensed under CC BY-

SA

Use

Case

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An Upward Spiral

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Use

Case

Provider agrees to use

case terms and sends

data to MiHIN based

on use case

requirements

MiHIN

Statewide

Shared Services

Health plan ties

incentives to use case

participation

MiHIN shares

conformance report

$

17

1

ADT data quality: April 2015Fields

populate

d

Fields

mappedEnhanced

fields

ADT data quality: December 2015Fields

populated

Fields

mappedEnhanced

fields Hospital

System

Conformance

December 2015 snapshot shows

one health system by individual

hospitals resulting in additional

rows

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Common Barriers 3-6

3) Matching patients across organizations

4) Maintaining the provider directory (in Michigan, the Health Directory)

5) Figuring out which providers and patients go together

6) Tracking electronic endpoints: where to send or query for information

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Active Care Relationships

19

Provider

Linkage

Patient

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Data for the Common Key Service

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Patient

Patient

Demographics

Used by Common

Key Service

Primary Care

Provider

LinkageFirst

Name

Last

Name

Date of

BirthGender

Last 4

SS#

Local

MRN#

(plus other demographic data)

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Improving Patient Matching

Active Care

Relationship

Service

Common

Key

Service

Data clean-up

services

Patient Matching

Accelerator (very

high performance

MPI)

Patient

Demographics

+ Common Key

Common key returned with

cleaned demographics

Patient

Demographics

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Provider & Affiliation Data for Health Directory

22

Primary Care

ProviderPatientPractice

Unit

Physician

Group

PracticeDirect

Address or

ESI

Used for the Health

Directory

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Tracking All Linkages

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Primary Care

Provider

Patient

Specialist

Hospital or SNF

Pharmacist

Care Coordinator

Health Plans

Physician

Organizations &

ACOs

Community

Based Services

Foster

Care

ProgramTelehealth &

Consumer Services Clinical TrialResearch Study

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Basic Access

AActive Care

Relationship

Service

Provider

Info

Send Data for Use Case to MiHIN

Declare linkages

Commo

n Key

Service

Health

Director

y

Re

ce

ive

Data

Fro

m M

iHIN

Via

AC

RS

& H

D

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Streamlines Quality Reporting

NPO

WSUPG

UP

OSP

One format and one location for:• PO’s to submit supplemental data

• Payers to submit Gaps in Care

• PO’s to close Gaps in Care

MiHIN

ACRSCKS

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Common Barriers 7 & 8

7) Knowing where to find records

8) Reducing information overload

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27

Active Care

Relationship

Service

Specialist

Hospital & SNF

ACRS for Raw Data Query

Primary Care

Physician

Health PlanPharmacist

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Raw data

Analysis

Predictive model

or decision rule

𝑥 + 𝑎 𝑛

=

𝑘=0

𝑛𝑛

𝑘𝑥𝑘𝑎𝑛−𝑘

Analytics Pipeline

Determine

Attribute

IndicatorClassification

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Active Care Relationship Attributes

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Attributes

Linkage

Person

Indicator

Classification

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Use of Prior KnowledgePatient A: High Utilizer

Patient B: High Utilizer

Patient C: High Utilizer

List of Patients

Patient A: Patient Activation Level 1

Patient B: Patient Activation Level 2

Patient C: Patient Activation Level 2

Patient D: Patient Activation Level 4

List of Patients

Patient A: High Risk of Readmission

Patient B: Medium Risk of Readmission

Patient C: Low Risk of Readmission

List of Patients

Patient A: Lead Exposure

Patient B: Medium Risk Food Security

Patient C: Communicable Disease Flag

List of Patients

Health Plans

Physician

Organizations &

ACOs

Health System

Public

Health

Active Care

Relationship

Service

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Attribute Data

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AttributePerso

n

Attribute

Information

Source

Organization

Information

End Point

URL

Direct Address

Used for the Health

Directory

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ACRS Attribute Lists

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Person

Social Determinants

Risks

High Utilizer

Risk Scores

Opioid

Registry

Level of

Engagement

Chronic

Disease

Registry

Communicable

Disease

Exposure

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ACRS SituationalAwareness

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AttributesLinkages

Patient

Social Determinants

Risks

High Utilizer

Risk Scores

Opioid

Registry

Level of

Engagement

Chronic

Disease

Registry

Communicable

Disease

Exposure

Primary Care

Provider

Specialist

Health System

Pharmacist

Care Coordinator

Health Plans

Physician

Organizations &

ACOs

Community

Based Services

Government

Programs

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Situational Awareness

&Minimum

Necessary Principle

Patient

Social Determinants

Risks

High Utilizer

Risk Scores

Opioid

Registry

Level of

Engagement

Chronic Disease

Registry

Communicable

Disease

Exposure

Primary Care

Provider

Specialist

Hospital or SNF

Pharmacist

Care Coordinator

Health Plans

Physician

Organizations &

ACOs

Community

Based Services

Government

Programs

Primary Care

Provider

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Enrichment Example

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ACRS

• ADT Notifications or Regular ACRS

• Care Summary & Results

• Cat1 Quality Measure

StandardizedEnriched

Registries

Appended

Info

Scored Analytics

Data

High Utilizer

Database Patient

ActivationSocial

Determinants

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The Power of Data Enrichment

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Enriched Standard ADT message

{Utilization} , {Public Health}, {Engagement}, {ACRS},

{URLS (end points)},{Risk Scores}

GEORGE TULLISON; 62 yo black male admitted to

Windward Hospital on January 18, 2017 with Diagnosis

Codes (ICD-10) I50.43 and E1010, DRGs 291 and 637

Health Plan High Utilizer

Program

Chronic: Diabetes, CHF

PAM Score = Level 2

UMHS Epic Portal (http:xxx)

PCMH Contact:

jones@direct.clinic.com

LACE = 14

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Common Barriers 9 & 10

9) Cross-organization clinical decision support

10)Transparent and fair use of analytics

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Raw data

IndicatorClassification

Computable Biomedical Knowledge (CBK)*

𝑥 + 𝑎 𝑛 =

𝑘=0

𝑛𝑛

𝑘𝑥𝑘𝑎𝑛−𝑘

Predictive Models

Surveillance

Algorithms

Attributes

* http://hdl.handle.net/2027.42/140789

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Shared Computable Biomedical Knowledge (CBK)

Health Plans

Physician

Organizations &

ACOs

Health System

Public

Health

𝑥 + 𝑎 𝑛 =

𝑘=0

𝑛𝑛

𝑘𝑥𝑘𝑎𝑛−𝑘

Predictive Models

Surveillance

Algorithms

40

Health Plans

Physician

Organizations &

ACOs

Health System

Public

Health

41

Shared Intelligent Processing of Message Content (Filters)

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Indicator

Classification

Patient Specific Data

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Opioid Filter Example

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MedicationsEnriched

Simple classifier to

identify a high-risk,

first-time opioid

user

Send to providers

or generate alert

based on ACRS

Auto enroll

member in

education

program

Auto update

statewide monitoring

system

Notify

prescriber of

nonstandard

prescription

Append the classification

information to message and

send out to ACRS distribution

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Questions & Thank You!

Tim Pletcher

Executive Director

pletcher@mihin.org

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Creative Solutions To Common Problems

INT5, March 5th, 2018

Mousumi Sengupta, Director, Healthcare Knowledge Development, Nuance Communications

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Agenda• The Landscape

• The Knowledge of all things is possible

• The Data, Information, Knowledge Continuum

• The Knowledge Ecosystem

• Packaging and Repackaging

• Architecting a solution

• Use Case: Sepsis

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The Landscape

• Data, Information and Knowledge exist throughout an organization

• Combining these sources can lead to actionable insights

• These insights can be assets to an organization

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The Data, Information, Knowledge Continuum

Data Information Knowledge

Relationships Context

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The Knowledge of all things is possible

• A Centralized Knowledge Resource

• Providing data, information and knowledge at the point of need

• A living, breathing, entity

• Meet needs of stakeholders and customers, internal and external

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The Knowledge Ecosystem

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Components

• Databases

• PHI/EHR

• Apps

• Knowledge Artifacts

• NLP

• Cloud

• Clinical Practice Guidelines/Quality Indicators

• AI

• Machine Learning

• Image Exchange

• Speech

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Types of Knowledge

• Referential Knowledge

• Explicit Knowledge

• Tacit Knowledge

• Declarative Knowledge

• Procedural Knowledge

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Translating to the Clinical WorldReferential

Terminologies

Explicit

Databases

Documents

Chronological Documents

Tacit

Machine Learning

Deep Learning

AI

Declarative

Patient Workflows

Clinical Workflows

HL7

Clinical Practice Guidelines

Procedural

Procedures

Methods

Real-Life

Experience/Clinical

Knowhow

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Packaging and Repackaging

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Packaging: Knowledge Containers• Different types

• Spreadsheets, tables, XML files, Knowledge Artifacts

• What can it do?

• What it can’t do?

• How can strengths be exploited?

• How can strengths be combined across different containers?

• How can weaknesses be reduced or eliminated?

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Repackaging

• Data

• Information

• Knowledge

• Quality Metric

• Training/Testing Set

• App

• Product

• Alert

• Clinical Decision

• Annotation

• Analytics

• CDS Hook

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Knowledge Reuse

• Existing knowledge assets

• How can they be leveraged?

• What value do they currently bring?

• Does that value fit the future vision?

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Architecting a Solution

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The Vision

• How does one develop a Knowledge Vision?

• Articulation

• Consider inputs and outputs to the system

• Synergy of multiple use-cases, look for commonalities and gaps

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Requirements Analysis

• Very difficult to articulate requirements

• Often easier to study problems and complaints

• Consider inputs and outputs to the system

• What’s not working is often easier to gauge than what is

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Putting the pieces together

• Back to Basics approach

• What is needed, how and when

• Talk in general terms, irrespective of workflow, tools, data

• Only then match technology to the story

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Context

• Very important

• A knowledge system will not work without it

• The same data, information and knowledge can have different connotations depending on context

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Use Case: Sepsis

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Sepsis and its progression

INFECTION

SIRShigh/low body temperature

HR>90

RR>20

WBC>12,000 or <4000 or >10%Bands

SEPSISSIRS + infection

SEVERE SEPSISsepsis + organ dysfunction

SEPTIC SHOCKSepsis

lactate>4 mmol/L

Mean arterial pressure < 65mmHg

Sys BP decrease >40mmHg

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Sepsis: Explicit Info & Knowledge

SEPSIS

SIRS

ORGAN DYSFUNCTION SEVERE SEPSIS

HEALTH

DISORDER

IS-A

SEPTIC SHOCK HAS-EVIDENCE

INFECTION PROCESS

HAS-EVIDENCE

IS-A IS-A

HAS-EVIDENCE

HAS-EVIDENCE

VERY HIGH

LACTATE LEVEL

LOW BLOOD PRESSURE

HAS-ASSESSMENT

HIGH BILIRUBIN LEVEL

LOW SYSTOLIC BP

LOW URINE OUTPUT

HIGH CREATININE LEVEL

LOW PLATELET COUNT

HAS-ASSESSMENT

HAS-ASSESSMENTHIGH TEMPERATURE

LOW TEMPERATURE

HIGH PULSE RATE

HIGH RESPIRATORY RATE

HIGH WBC

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Sepsis: Referential Knowledge

SEPSISSIRS

SEVERE SEPSIS

INFECTION PROCESS

ICD10 A41 SEPSIS

SNOMED 91302008 SEPSIS (DISORDER)

MEDDRA 10040047 SEPSISSNOMED 238149007 SIRS (DISORDER)

MEDDRA 10051379 SIRS

ICD10 R65.2 SEVERE SEPSIS

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Sepsis: Tacit Knowledge

Artificial IntelligencePatient reports Implicit sepsis knowledge

revealprocessing

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Sepsis: Tacit Knowledge

SEPSIS

CELLULITIS

OLD-AGE

HAS-EVIDENCE DIABETES MELLITUS

DEMENTIA

PNEUMONIA source of infection

source of infection

higher susceptibility to infection

higher susceptibility to infection

higher susceptibility to infection

RISKS for sepsis

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SEPSIS

SIRS

ORGAN DYSFUNCTION SEVERE SEPSIS

HEALTH

DISORDER

IS-A

SEPTIC SHOCK HAS-EVIDENCE

INFECTION PROCESS

HAS-EVIDENCE

IS-A IS-A

HAS-EVIDENCE

HAS-EVIDENCE

VERY HIGH

LACTATE LEVEL

LOW BLOOD PRESSURE

HAS-ASSESSMENT

HIGH BILIRUBIN LEVEL

LOW SYSTOLIC BP

LOW URINE OUTPUT

HIGH CREATININE LEVEL

LOW PLATELET COUNT

HAS-ASSESSMENT

HAS-ASSESSMENTHIGH TEMPERATURE

LOW TEMPERATURE

HIGH PULSE RATE

HIGH RESPIRATORY RATE

HIGH WBC

CELLULITIS

OLD-AGE

DIABETES MELLITUS

DEMENTIA

PNEUMONIA

RISK FACTOR

Sepsis: Tacit Knowledge

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Sepsis: Declarative Knowledge • Surviving Sepsis Campaign:

International Guidelines for Management of Sepsis and Septic Shock

• Includes recommendations for treatment of sepsis

o Administration of broad spectrum therapy

o Use of Norepinephrine as the first choice vasopressor for septic shock

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SEPSIS

SIRS

ORGAN DYSFUNCTION SEVERE SEPSIS

IS-A

SEPTIC SHOCK HAS-EVIDENCE

INFECTION PROCESS

HAS-EVIDENCE

HAS-TREATMENT

HAS-EVIDENCE

HAS-EVIDENCE

VERY HIGH

LACTATE LEVEL

LOW BLOOD PRESSURE

HAS-ASSESSMENT

HIGH BILIRUBIN LEVEL

LOW SYSTOLIC BP

LOW URINE OUTPUT

HIGH CREATININE LEVEL

LOW PLATELET COUNT

HAS-ASSESSMENT

HAS-ASSESSMENTHIGH TEMPERATURE

LOW TEMPERATURE

HIGH PULSE RATE

HIGH RESPIRATORY RATE

HIGH WBC

CELLULITIS

OLD-AGE

DIABETES MELLITUS

DEMENTIA

PNEUMONIA

RISK FACTORBROAD SPECTRUM ANTIBIOTICS

NOREPINEPHRINE

Sepsis: Declarative Knowledge

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• Methods and processes used to implement sepsis guidelines

• Added to the relevant entities in the Knowledge Structure

Sepsis: Procedural Knowledge

SEPSIS

HAS-TREATMENT

BROAD SPECTRUM ANTIBIOTICS

NOREPINEPHRINE

Clinical procedure for IV drug administration

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SEPSIS

SIRS

ORGAN DYSFUNCTION SEVERE SEPSIS

IS-A

SEPTIC SHOCK HAS-EVIDENCE

INFECTION PROCESS

HAS-EVIDENCE

HAS-TREATMENT

HAS-EVIDENCE

HAS-EVIDENCE

VERY HIGH

LACTATE LEVEL

LOW BLOOD PRESSURE

HAS-ASSESSMENT

HIGH BILIRUBIN LEVEL

LOW SYSTOLIC BP

LOW URINE OUTPUT

HIGH CREATININE LEVEL

LOW PLATELET COUNT

HAS-ASSESSMENT

HAS-ASSESSMENTHIGH TEMPERATURE

LOW TEMPERATURE

HIGH PULSE RATE

HIGH RESPIRATORY RATE

HIGH WBC

CELLULITIS

OLD-AGE

DIABETES MELLITUS

DEMENTIA

PNEUMONIA

RISK FACTORBROAD SPECTRUM ANTIBIOTICS

NOREPINEPHRINE

Sepsis: Procedural Knowledge Methodology for IV

drug administration

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Questions to think about…

• How can technologies such as speech recognition help enhance interoperability?

• How does one fit a data governance strategy around this?

• Can knowledge artifacts become interoperable in themselves?

• How can interoperability strategies consider integration of context?

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Questions?

Mousumi Sengupta, Nuance HealthcareMousumi.Sengupta@Nuance.com

https://www.linkedin.com/in/mousumisengupta/