Health Information Exchanges:Overview, Architectures, and Business Models

Indranil Bardhan, UT DallasKirk Kirksey, UTSW Medical Center

South African ATM Here

Hmmmmmmm?

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A female patient involved in a serious auto accident is brought to a local emergency room. She is unconscious. Her driver’s

license indicates she has a local address. A quick name search on hospital computer

systems shows she has never been a patient in this facility; however, ER

physicians need to quickly determine her medical status, and identify any medication

allergies. This could be accomplished if physicians had quick access to the patient’s medical information at other local institutions.

They do not.

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The Idea and History

Requirements – Digital Records, Standards, Models of Interoperability

HIW Architectural Models

HIE Predictions

Discussion

Roadmap

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Definition: A Health information exchange is defined as the exchange of healthcare information electronically across organizations within a region, community or hospital system.

Goal: To facilitate access to and retrieval of clinical data to provide safer, more timely, efficient, effective, equitable, patient-centered care

Health Information Exchange

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Healthcare is regional. Know where a patient has been seen

(inpatient, outpatient, ancillary service).

Locate critical demographic and clinical information

medications,allergies,clinical laboratory results,radiology based images

Flag duplicative, non-necessaryprocedures.

Increased patient safety – not sure about lowering costs.

Technology is a no brainer.

Health Information Exchanges – “Not Rocket Surgery”

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Potential Benefits to Providers

• Outpatient docs do not know what happened in the hospital to one of their patients– Medication Lists, Lab results, Diagnoses, Problems, Discharge Summary

• The ER does not know the history of a patient being seen by a PCP– Clinic Notes, Medication Lists, Diagnoses and Problems

• Specialist does not know what tests were done on referred patient– Referral Question – i.e. why were they referred?– Lab and test results, Radiology and Nuclear Medicine data– Medication Lists, Diagnoses

• PCP does not know what a specialist did– Specialty care clinic notes, Follow-up recommendations

• Other questions :– Was the patient seen in other clinics or in other ERs recently and for what and

what was done?– What appointments does the patient have that are upcoming or which

appointments were missed?• Prevention and Surveillance

– Immunization and Disease Outbreaks• Home Health Care

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Imaging Duplication Rates for CHF Outpatients in North Texas

Switching Event Stat Duplication (%)

1. Admitted to same hospital

Avg 14.24Std 34.02N 8484

2. Admitted to different hospital within the same health system

Avg 27.29Std 43.93N 473

3. Admitted to different health system

Avg 23.84Std 40.82N 446

Mean t-test for 1 - 2 Pr>|t| 0.000Mean t-test for 1 - 3 Pr>|t| 0.000Mean t-test for 2 - 3 Pr>|t| 0.218

Potential Benefits to Institutions

• Depends on types of data shared– Financial ROI is still unclear – different estimates!– Infection Control is a major burden

• MRSA, VRE mainly but Hep B/C and HIV as well• Sharing data could help streamline hosp. costs

• Supports Practice of Medicine– Clinically relevant data is immediately accessible– Improves inter-provider communication– Helps with bio-surveillance (eg: PHIN)

• Promotes Standardization of Care– Reduces disparities in care– Helps detect undocumented issues

• Improves Safety and Quality of Care– Enhances patient satisfaction– Reduces need for repeat testing– Helps with medication reconciliation

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Health Information Exchanges - NOT NEW

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Evolution of Health Information Exchanges

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Health Information Exchanges – in Texas

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HIE Governance Model

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Health Information Exchange - Requirements

Digital patient information. Financial incentive. Widespread regional use in ambulatory, inpatient, and

ancillary organizations. Adequate data communication infrastructure. A sustainable HIE architecture. Security Sustainable business model

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Health Information Exchange - Incentive

Meaningful Use Requirement

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EMR Adoption Trend

% of Hospitals InstalledMeanin

gful

2007 2008 2009 2010 2011 Q4 2012 Q2 UseStage 8 (rumored): HIExchange in place

Stage 7 MR Fully Electronic; Data Warehouse in use   0.00% 0.30% 0.70% 1.00% 1.20% 1.70%  2015

Stage 6: Physician Documentation; Full R-PACS 0.30% 0.50% 1.60% 3.20% 5.20% 6.50%

Stage 5: Closed loop medication   1.90% 2.50% 3.80% 4.50% 8.40% 11.50%  2013

Stage 4: CPOE. Clinical Decision Support   2.20% 2.50% 7.40% 10.50% 13.20% 13.30%  2011

Stage3: Nursing/clinical Documentation 25.10% 35.70% 50.9% 49.00% 44.90% 42.40%

Stage 2: CDR. Medical Vocabulary; HIE Capable 37.20% 31.40% 16.9% 14.60% 12.40% 11.70%

Stage 1: Lab/Rad/Rx all installed 14.00% 11.50% 7.20% 7.10% 5.70% 5.10%

Stage 0: No ancillaries installed 19.30% 15.60% 11.5% 10.10% 9.00% 7.90%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

Stage 4: CPOE. Clinical Decision Support

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

Stage 5: Closed loop medication

0.00%0.20%0.40%0.60%0.80%1.00%1.20%1.40%1.60%1.80%

Stage 7 MR Fully Electronic; Data Warehouse in use

EMR Hospital Adoption Rate2007 – Q2 2012

Source: HIMSS Analytics

HIMSS Eight Stage EMR Adoption Model for Ambulatory Operations

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60,000 access codes assigned

30,000 users

Call reduction verified in Ambulatory Practice Clinics

MyChart at UT Southwestern

Institution selects which data is exposed

EMPI and Patient Locator Services finds location of patient records.

All participants must feed demographic transactions to EMPI.

Visual Integration – patient information never physically leaves owner institution.

EMPI contains only location pointer to information

Internal ElectronicMedical Record

HIEEMPI

Edge Server

Hospital A

Internal LaboratoryInfo System

Edge Server

Reference Lab

Internal PracticeMgt System

Edge Server

Doctors Office

Federated

HIE IntegrationSoftware

Reconciles Patient IdentityLocates Patient Records

Retrieves Patient Records from Source Systems

Prepares Integration forPresentation to User

Health Information Exchanges – Federated Model

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Records physically retrieved from target systems. Duplicate stored locally.

One query. One retrieval.

No patient locator service. Must know where patient has been seen.

Currently singlevendor (Epic).

Exchanges limited record set.

Internal EMR

Hospital A

Internal LabInfo System

Reference Lab

Internal PracticeMgt System

Doctors Office

Hospital BInternal EMR

Record Request

Record Return

ExternalRecord Request

Health Information Exchanges – Record Exchange

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Internal Clinical Systems

Hospital A

Internal Clinical Systems

Reference Lab

Internal Clinical Systems

Doctors Office

Serious security concerns

Viable for single delivery organization

Multiple systems. Multiple problems

Query only. Integration Limited.

Health Information Exchanges – Trusted Direct Access

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Clinical data leaves the institution. Higher levels of breach risk.

EMPI and Patient Locator Services finds location of patient records.

Reconciliation of duplicates is expensive.

Operations analysis possible.

Time series studies possible

Internal ElectronicMedical Record

HIEEMPI

Hospital A

Internal LaboratoryInfo System

Reference Lab

Internal PracticeMgt System

Doctors Office

ClinicalData Warehouse

Integration Layer

Reconciles Patient IdentityLocates Patient Records

Imposes Data ModelRationalizes Data

Vocabularies

Health Information Exchanges – Clinical Data Warehouse

Real TimeDemographics &Clinical Results

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HIE Architecture Components

1. Clinical Data Warehouse: To identify patients for clinical studies or provide clinical care

It contains: Patient demographics Diagnoses and problem lists Medication history Clinical reports Lab results Visit history Orders

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Clinical Data Warehouse

• The database can be centralized or distributed.– Centralized Models

• Aggregate data in one location (either in real time or in batch mode)

• Need to normalize the data to a common vocabulary, units etc.• Need a master-patient index to aggregate data• Runtime is fast if the connection to a central server is fast• Data storage can be secure and be audited• In a federated model, data providers have access to servers

– Distributed (or Switch) Models• Use a Record Locator Service, which is a “yellow pages” for

data• At runtime, two processes occur, one to get the location and

another to get the data

HIE Architecture Components

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• Clinical Applications sit on top of this database to make higher-level functions available to stakeholders:– Data Retrieval– Provider Order Entry– Decision Support– Electronic Prescribing– Electronic Patient Registration– Research and Data Mining– Clinical Messaging– Public Health– Accreditation and Compliance

HIE Architecture Components

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HIE Architecture Components

2. Clinical Vocabulary: Used to understand the content of the database. It contains many data type definitions,

Test names Medication names Diagnoses Procedure names

– General clinical terms– Anatomic Locations– Complaints and Problems– Institution-specific names (biox = pulseox)

Many “ways” to represent and define disease in systems: For example, diabetes can be defined in any of the following ways:

fasting BS > 126 random BS > 200 person on insulin or other diabetic drug person with a diagnosis (ICD 250.XX) of diabetes person with a Hgb A1c > 8 or other value

Representing data in the right way using the right codes is key to being able to get data out easily

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HIE Architecture Components

3. Master Patient Index: Accurately identifies the patient Cross-references patient identifiers for each data

source location Employs deterministic and probabilistic algorithms to

adjudicate patient identity Indexes & reconciles patient demographics so

relevant patient data can be identified by the Record Locator Service (RLS)

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Name: Bob SmithSex: MaleAddrs: 4141 GilbertDallas 75219DOB: 8/27/52Patient ID: 464-98-7628

Name: Robert SmithSex: MaleAddrs: 4141 GilbertDallas 75214DOB: 8/27/52ID: 464-98-7627

Master PersonIndex

P=80%Patient Match

P<80%No Patient Match

Master Patient Index

Methods: - Probabilistic Matching- Weighting of Criteria- Suspense Queue for Human Intervention- Reduce cost of duplicate record processing

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Problems with Data Sources

Data is stored across multiple repositories in various institutions

Difficult to bridge and combine data Data are stored at different levels of granularity Each uses a different code to identify the same

information

Many institutions do not capture all data of interest to clinicians

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Role of Standards

• The solution to the problem of bridging these data systems lies in the implementation of Standards for Data Communication.

• These standards permit data to be easily translated from one database system to another

• There are many standards, each for a different purpose– Lab Data Communication– General Clinical Messaging– Radiology Image Transmittals– Diagnostic Coding– Procedure Coding

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Role of StandardsTwo types of standards:• Messaging Standards:

– Communicate actual patient data– Combine a data element and a

concept code in the same stream– Messages contain identifiers for

patients, date and time, transaction type, service provider etc.

– Examples: HL7, DICOM

• Coding Standards:– Represent clinical knowledge using

codes– Contain NO patient data– Examples: LOINC, Snomed, ICD9,

CPT, UMLS– These codes are attached to data

elements to represent the semantics (meaning) of the message

HL7 Message

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Source: Dpt of Health and Human Services. Health Buzzhttp://www.healthit.gov/buzz-blog/meaningful-use/meaningful-use-stage-2/

Health Information Exchanges – Maturing Standards

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HIE Business Models

Among state-run HIEs, two revenue models are prevalent:

1. Transaction Fee Model

2. Subscription Fee Model

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Transaction Fee Model

HIE charges for each set of data that is sent or received

Benefit: With increased transactions, there is a corresponding increase in revenue

Drawback: More transactions may require more monitoring, which increases the administrative burden of tracking and recordkeeping.

High volume users may balk at the prospect of paying on a per-transaction basis

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Subscription Fee Model

The subscription fee model offers a predetermined level of access to data, which is fixed for those providers and other users of the system

A weekly, monthly, or annual subscription rate helps to maintain a consistent revenue stream for the HIE

Benefit: The subscription model can lower costs if more participants use the HIE's service

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Hybrid HIE Models

Some HIEs, such as the Utah Health Information Network, HealthBridge, and the Community Health Information Collaborative, are using a combination of the two models.

Future trend: Cloud Service Models in HIE. This is already being adopted by Chief Information Officers in many state governments.

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Types of HIEs

Public HIEs: Typically encompass a specific region and involve multiple hospital-based organizations. Majority of funding and governance comes from government agencies.

Private HIEs: Typically based around one or two integrated delivery networks (IDNs) or hospital organizations, with the majority of funding and governance from private sponsoring entities

Eg., Sandlot Connect, subsidiary of North Texas Specialty Physicians

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Why This Might Not Work

No self sustaining funding model

The local politics of healthcare Can’t get to payback soon

enough Security and authentication No bipartisan HIE support from

the Feds (What happens in 2016)

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HIE Sustainability Challenges

• Core Competencies– Data Model / Data Repository /

Clinical Vocabulary• Minimum Data Set to be

shared?– Data sharing architecture

• Central vs. Switch Model?• Federated or Non-Federated?

– Messaging and Coding Standards

• Mapping Effort – who, how?• Message Processors – interface

engines?– Patient and Provider

Identification• Enterprise Master

Patient/Provider Index?

• Business and Legal Components– Security and

Authentication?• Patient authorization

– Data Sharing and Data Use Agreements

• Minimum Data Sets• Terms of Use• Arbitration and

Grievance Processing• Security

– Cost and Sustainability Model: Who pays?

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Top 5 Random HIE Predictions

1. Economically HIEs cannot be justified in most regional environments.

2. Reimbursement reform will drive the formation of HIE as a collaborative tool to better serve a growing population of under-insured.

3. ACOs and similar care models will drive the formation of HIEs between partnered healthcare organizations.

4. Meaningful Use HIE requirements will not be a major driver of a functional HIE.

5. Federated HIE model will give way to the repository model as organizations recognize the need for quality improvement analytics.

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Questions, Comments, Criticisms, Bets?

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