Making Electronic Health Records a Reality for Underwriting

Scott Brammell

Executive Director, Underwriting

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Electronic Health Records (EHRs)Topics we will cover today

HIT landscape in the U.S.

EHR data-acquisition models

Content, structure, and format of EHRs

Data challenges related to EHRs

Potential impact on the life insurance

underwriting process

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HIT LandscapeEveryone wants a piece of the action…

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Catalysts for EMR Adoption and Maturity in the U.S.

Government involvement and funding

Patients as consumers (engagement)

Physician comfort with technology

Maturing technologies

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Adoption Rates: Nearly 87% of Doctors Use EHRs

Source: ONC Health IT Dashboard

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Acquiring EHR DataAggregators and Data Sharing Models

Consumer

Patient

Portal

Consumer

Vendor

Patient

Portal

Consumer

Vendor

Provider

EHR

Life Ins CoLife Ins Co

Life Ins Co

HIE

1 2 3

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Sources of Healthcare Data (Where the Data Comes From)

1. Payers

2. Electronic health records

3. Healthcare clearinghouses**

4. Pharmacy benefit managers (PBMs)**(includes pharmacies)

5. Health information exchanges (HIEs)**

6. Other vendors (mostly aggregators)

** Aggregators

Individual Records

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1) Payers (aka: Third-Party Payers)

Source: BRI Benefit Resource, Inc.

Examples include: United Healthcare, CIGNA, etc.

Third-party Payers

Reimburse the cost of health services

• Healthcare claims data (think EOBs)

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Advantages as Compared to EHRs

Assessing medication compliance

• Claims data includes important details about medications

• Every fill/refill of a prescription, complete with date of that event,

shows up

Good reflection of tests, procedures, and services provided

• From the example described, we can see that EMR data might not

capture the fact that Steve had an eye exam

• Similarly, any services provided by a provider not using an EMR will

fail to be reflected in EMR data

• The claims data, however, will contain evidence of them because all

of these services need to be reimbursed

11Source: Optum White Paper, ‘The Benefit of Using Both Claims Data and Electronic Medical Record Data in Health Care Analysis’ 2012.

Claims Data EMR Data

Scope of DataBroad: Captures information from all doctors

and providers caring for a patient

Limited: Captures only the portion of care

provided by doctors using the EMR

Scope of Patients Insured patients only All patients (including uninsured)

Prescription DataAccurate record of all prescriptions that were

filled, including dates of refills

Contains only that a physician prescribed a drug

but not whether it was filled/refilled

Non-Prescription

DrugsNot present Present

Data Richness Limited: Diagnosis, proceduresRich: Lab results, vital signs, patient surveys,

habits (e.g., smoking, etc.), problem list, etc.

What Does EHR Data Add?Comparison: Claims Data vs. EMR Data

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2. Electronic Health Records (EHRs)Examples include: Cerner, EPIC, Allscripts, NextGen, McKesson, etc.

Section Standardized Codes

Demographics HITSP harmonized code sets for gender, marital status

Problem List ICD-9/10 or SNOMED-CT

Procedures CPT-4 or ICD-9/10

Medications RxNORM

AllergiesUNII for foods and substances, NDF-RT for medication

class, RxNorm for medications

Immunizations HL7 CVX

Vital Signs (height, weight, blood pressure, BMI) SNOMED-CT or LOINC

Progress notes and other narrative documents (history & physical, operative

notes, discharge summary)CDA templates

Departmental reports (pathology/cytology, GI, pulmonary, cardiology, etc.) SNOMED-CT

Lab Orders and ResultsLOINC for lab name, UCUM for units of measure,

SNOMED-CT for test ordering reason

Microbiology LOINC for lab name/observation

Administrative transactions (benefits, referrals, claims) X12, CAQH CORE

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Content of EHR and Data Sources

SOURCE: Office of the National Coordinator for Health Information Technology (ONC)

HL7 CCDA Templates

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Content of EHR and Data Sources (cont.)

EMRs are arranged in chronological sequences of events

and contain the following groups of data

• Patient Demographics

• Problems/Diagnoses (current, past)

• Treatment Plans

• Results

• Communications

Combinations of structured and unstructured data, and images

(in varying degrees) across vendors and healthcare providers

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Unstructured Data: The Other 80% of All Data

Clinician notes and plans

Attachments (e.g., forms)

Images: historical data, EKGs,

radiology, etc.

SOURCE: http://insights.datamark.net/white-papers/unstructured-data-in-electronic-health-record-systems-challenges-and-solutions

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Assessing Structured Data

*Actual fully underwritten decision based on APS: decline

Applicant Demographics* Problem List

Use case for life underwriting

Male, Age 42; Married

Height 6’0”, Weight 184

Average BP 120/83

All standard

Rx CUIs (RxNORM)

All

Standard

320864 Crestor 3

224920 Synthroid 3

700402 Tekturna 2

All

Standard

I45.10 Unspecified right bundle-branch block 3

I10 Essential (primary) hypertension 3

K21.9Gastro-esophageal reflux disease without

esophagitis2

K92.9 Disease of digestive system, unspecified 5

E03.9 Hypothyroidism, unspecified 3

N18.9 Chronic kidney disease, unspecified 5

Q61.5 Medullary cystic kidney 10

N04.9Nephrotic syndrome with unspecified

morphologic changes9

E89.5 Postprocedural testicular hypofunction 3

Decline

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3. Healthcare Clearinghouses

Companies that function as intermediaries that forward claims information

from healthcare providers to insurance companies

Claims scrubbing: check the claim for errors (e.g., eligibility verification, etc.)

and verify compatibility with payer software

Ensure procedural and diagnosis codes submitted are valid and that each

procedure code is appropriate for the diagnosis code submitted with it

Each provider chooses which clearinghouse it wants to use for submitting

claims (most clearinghouse companies charge the providers for each claim

submitted)

Examples include: Change HC, RelayHealth, Availity

For our purposes, healthcare clearinghouses are

great aggregators and normalizers of healthcare claims data.

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4. Pharmacy Benefit Managers (PBMs)

Third-party administrators of prescription-drug programs for payers

Think of PBMs as a middleman in the healthcare process

• Negotiating rebates to get the most affordable options

• Operate mail order so medications are delivered directly to patients' doors

• Process claims from patients and pharmacies

• Manage formularies so individuals know which medications are covered via

their health plans

• Manage distribution among a network of pharmacies

Examples include: Express Scripts, CVS Caremark, Argus, Envision, ProCare Rx

For our purposes, PBMs are great aggregators and normalizers of Rx data.

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5. Health Information Exchanges (HIEs)

Office-based physicians with

electronic health record systems

who shared patient health

information electronically with

other providers, by certified

electronic health record system

status: United States, 2014

* Not to be confused with Health Insurance Exchanges used for government healthcare.

Aggregators of EHRs from healthcare providers (e.g., physicians, etc.)

Source: CDC/NCHS, National Electronic Health Records Survey, 2014.

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Paths to Interoperability

Commonwell Health Alliance

The Sequoia Project

Blue Button

The Direct Project

Fast Healthcare Interoperability

Resources (FHIR)

Apple, Amazon, etc.

Small successes

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Electronic Health Records

Good News

Good News – More Data

• Lifetime medical history

• Multiple sources

• Discrete data

• Records on demand

Bad News

Bad News – More Data

• Volume of data (thousands of pages)

• Disparate formats, hybrid records

• New standards, vocabularies, etc.

• Access, sharing, matching

Be careful what you wish for….

Records are exploding!

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EHR Challenges for the Life Industry

Challenges

• Accessibility

• Aggregation

• Data normalization

Yes, the majority of records are digital and ‘available’ – but …

Initially, we may actually have to

learn to do more with less

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Accessibility - Challenges

Data Privacy

Cost

Cumbersome

SOURCE: https://sites.duke.edu/rethinkingclinicaltrials/acquiring-and-using-electronic-health-record-data/

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Aggregation - Challenges

“The ability of different information technology systems and software

applications to communicate, exchange data, and use the information that

has been exchanged”

A nationwide set of traffic rules that enable specific pieces of health

information to travel when and where they are needed

Technical rules and standards—that allow systems to “talk to” each other

Policies on how to handle information– that build trust

Model contractual language—that holds it all together

Interoperability

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Normalization - Challenges

1,000+ health IT systems today –

many customized by providers

Record matching– can we

unambiguously identify individuals?

Technical hurdles (languages,

standards, artifacts)

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The Future: No Crystal Ball Here

What can we expect over the next

12-24 months?

Five years out?

Is there room for underwriters in

an EHR world?

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Questions

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Thank You