Distributed Data Analytics & Annual Community Health ... Query Health, ... Ross Lazarus Emma...

Distributed Data Analytics &

Querying: EHR Value to Health

Centers & Public Health

Moderator:

Rick Shoup, PhD

Chief Technology Officer, Massachusetts eHealth Institute

Jeffrey Brown, PhD Assistant Professor, Department of Population Medicine, Harvard Medical School & the Harvard Pilgrim Health Care Institute

Richard Elmore Coordinator, Query Health, Office of the National Coordinator for Health Information, US Department of Health & Human Services

Michael Klompas, MD, MPH Department of Population Medicine, Harvard Medical School & the Harvard Pilgrim Health Care Institute

Joshua Vogel, MPH Epidemiologist/Data Analyst, Massachusetts Department of Public Health

Annual Community Health Institute

May 9-11, 2012

Resort & Conference Center of Hyannis

Hyannis, MA

PHIConnect CDC Center of Excellence in Public Health Informatics

Michael Klompas MD, MPH, FRCPC CDC Center of Excellence in Public Health Informatics

Harvard Medical School and Harvard Pilgrim Health Care Institute Boston, MA

Community Health Institute May 9, 2012

Electronic Support for Public Health Public Health Surveillance Platform


Outline

Background on ESP

Case identification strategies

Reportable diseases

Syndromic surveillance

Chronic diseases


“No health department, State or local, can effectively prevent or

control disease without knowledge of when, where, and under what conditions cases are occurring”

Introductory statement printed each week in Public Health Reports, 1913-1951


Software and architecture to extract, analyze, and transmit electronic health information from providers to public health.

Surveys codified electronic health record data for patients with conditions of public health interest

Generates secure electronic reports for the state health department

Designed to be compatible with any EHR system

JAMIA 2009;16:18-24 MMWR 2008;57:372-375

Advances Disease Surveillance 2007;3:3

Electronic Support for Public Health (ESP)


Current ESP installations

Northern Berkshires, MA Health Info Exchange 14 sites • 50,000 patients

Atrius Health 27 Sites • 700,000 pts

© Google Maps

Cambridge Health Alliance 20 sites • 400,000 patients

MetroHealth Cleveland, OH 250,000 patients


esphealth.org

Source code and documentation available

free of charge from esphealth.org


Practice EMR’s ESP Server

D P H

Health Department

HL7 electronic

case reports or aggregate summaries

ESP: Automated disease detection and reporting for public health

diagnoses

lab results

meds

demographics

vital signs

JAMIA 2009;16:18-24


Decoupled architecture

ESP decoupled from host electronic medical record

Implications

Allows system to be agnostic to the source EMR

(local codes translated to common nomenclature) Universal

Offloads computing burden from clinical systems

(and keeps ESP invisible to clinicians) Unobtrusive

Can still remain within host practice’s firewall

Secure

EMR ESP


CASE IDENTIFICATION


ICD9’s

chlamydia type 1 diabetes

active tuberculosis

ESP


Case Identification Limitations of ICD9’s

Condition Sensitivity

Positive

Predictive

Value

Acute hepatitis C 63% 22%

Tuberculosis 100% 17%

Postherpetic neuralgia 59% 84%

Gestational diabetes 91% 53%


Case Identification Limitations of lab tests

Blind to purely clinical diagnoses

e.g. culture negative TB, early Lyme, PID

Multiple reports for same episode

e.g. hepatitis C

Poor discriminator between active & resolved, acute & chronic disease

e.g. acute vs chronic hep B, current vs remote Lyme


Solution

Integrate multiple streams of data from the EMR to increase sensitivity and specificity

Lab orders

Lab results (present and past)

ICD9 diagnoses (present and past)

Medication prescriptions


Case Identification Logic

Active Tuberculosis

Any of the following: Prescription for pyrazinamide

OR

Order for (AFB smear or AFB culture) followed by ICD9 code for TB within 60 days

OR

Order for 2 or more anti-tuberculous medications followed by an ICD9 code for TB within 60 days

Performance (Atrius Health, 2006-2009)

20 patients identified, all with clinician-suspected TB

14 cases confirmed (2 culture-negative disease, 1 previously unreported to health department)

No known missed cases

Public Health Reports 2010:125:843


Case Identification Logic:

Acute Hepatitis B

Both of the following:

ICD9 for jaundice OR liver function tests > 5x normal

IgM to core antigen

OR

All four of the following:

ICD9 for jaundice OR liver function tests > 5x normal

Hep B surface antigen or ‘e’ antigen present

No prior positive Hep B specific lab tests

No present or prior ICD9 code for chronic hepatitis B

PLoS ONE 2008:3:e2626

Sensitivity: 99% PPV: 97%


Sorting through positive Hep B Results - ESP versus ELR

601 distinct patients

8 acute

593 chronic cases

2648 positive test results for hepatitis B


INFECTIOUS DISEASE CASE REPORTING


ESP Case Reporting Atrius Health and MetroHealth, June 2006-July 2011

Condition Total Cases

Chlamydia 10,406

Gonorrhea 2,056

Pelvic inflammatory disease 122

Acute hepatitis A 21

Acute hepatitis B 56

Acute hepatitis C 74

Tuberculosis 168

Syphilis 313


Manual versus electronic reporting Atrius Health (variable time periods)

Manual

Reports* ESP Change

Chlamydia 545 758 39%

Gonorrhea 62 95 53%

Pelvic Inflammatory Disease 0 25

Acute Hepatitis B 3 8 140%

Acute Hepatitis C 14 38 150%

Tuberculosis 13 14 8%

MMWR 2008;57:372-375 PLoS ONE 2008;e2626

Public Health Reports 2010;125:843


SYNDROMIC SURVEILLANCE


Syndromic Surveillance Influenza-Like Illness, Atrius Health, 2009-2011

0.0

0.5

1.0

1.5

2.0

2.5

3.0

10/10/2009 2/10/2010 6/10/2010 10/10/2010 2/10/2011 6/10/2011 10/10/2011 2/10/2012

% V

isit

s w

ith

ILI

Week ending date


CHRONIC DISEASE SURVEILLANCE


Criteria for Frank Diabetes

Hemoglobin A1C ≥ 6.5

Fasting glucose ≥126

Random glucose ≥200 on two or more occasions

Prescription for INSULIN outside of pregnancy

ICD9 code 250.x (DM) on two or more occasions

Prescription for any of the following:

GLYBURIDE, GLICLAZIDE, GLIPIZIDE, GLIMEPIRIDE

PIOGLITAZONE, ROSIGLITAZONE

REPAGLINIDE, NATEGLINIDE, MEGLITINIDE

SITAGLIPTIN

EXENATIDE, PRAMLINTIDE


Summarizing and sharing data: The RiskScape

Web-based interface that automatically displays and analyze chronic disease surveillance data derived from electronic health records

Helps users easily visualize and manipulate surveillance data in order to rapidly identify outliers and trends

Designed to be commensurate with the unique strengths of the data

Electronic and analysis ready

Timely (updated daily)

Clinically rich – includes clinician-measured demographics, vitals (height, weight, blood pressure), lab tests, medications, vaccines, and outcomes


Select an Outcome


Add Filters (optional)


Automatically Map Prevalence by Zip


Automatically stratify by age, sex, race, BMI, BP, etc.

Prevalence of type 2 diabetes remains correlated with age in young people

Type 2 diabetes more prevalent in males versus females in people under age 40


Hone in on Areas of Particular Interest


Hone in on Areas of Particular Interest

Prevalence of type 2 diabetes amongst people under age 40 is 1.0% in this

zipcode versus 0.26% in the full state


Stratify & compare disease prevalence in this zip to the full state

Obese people in this zip (blue) have higher prevalence of diabetes compared to obese

people in the rest of MA (orange)

Hypertensive people in this zip (blue) have higher prevalence of diabetes compared to hypertensives in the rest of MA (orange)


Evaluate whether patients are meeting clinical targets

57% of people with type 2 diabetes have high blood pressure

51% of people with type 2 diabetes have a hemoglobin A1C above 6.5


Obesity (BMI >30) in Eastern Massachusetts


Hypertension in Eastern Massachusetts


Summary

Electronic health record systems can facilitate real-time, automated public health surveillance

Automated mapping and stratification can identify geographic areas and subpopulations at increased risk of disease

Potential populations for targeted health interventions

Future Steps:

Increase the number of outcomes and clinical parameters available for display in RiskScape

Add in automated cluster detection using SatScan

Create a distributed network of ESP sites…


ESP Team

Harvard Dept of Population Medicine

Richard Platt

Ross Lazarus

Emma Eggleston

Julie Lankiewicz

Michael Murphy

Massachusetts Dept of Public Health Patricia Daly Paul Oppedisano Gillian Haney Josh Vogel

Ohio Department of Health

Lilith Tatham

MetroHealth, OH

David Kaelber

Guptha Baskaran Atrius Health Ben Kruskal Mike Lee

Cambridge Health Alliance Michelle Weiss Brian Herrick Northern Berkshires eHealth Collaborative Don LeBreux

Commonwealth Informatics

Contact: [email protected]


MDPHnet Distributed Data Approach

39

Jeffrey Brown, PhD May 9, 2012

Overview

• Background: Approach to Distributed Querying

• Distributed Networks

• Implementing MDPHNet

• MDPHNet Demonstration

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Approach to Distributed Querying

41

• Data partners maintain control of their data

• Data partners’ ongoing involvement is needed to interpret findings

• Little or no exchange of person-level data is needed

• Secondary use can’t interfere with primary use

• Few data elements are needed to answer most questions

• Sharing computer programs rather than protocols reduces effort and improves consistency

Distributed Querying Guiding Principles

42

• Data partners keep and analyze their own data

• Standardize the data using a common data model

• Distribute code to partners for local execution

• Provide results, not data, to requestor

43

Distributed Data/ Distributed Analysis

PopMedNet Architecture Overview

Internet

Archive Data Vault

Repository

Database

Web Services

Document Manager

IRB Manager

Model Manager

Workflow Manager

Project Organization User DataMart

Archive Manager

Search Manager

Audit Manager

Request Manager

Schema Results

Manager Meta Data

Business Objects

Security Manager

Roles Manager

Rights Manager

Access Control

Data Source

Data Access

Portal Database

Network Portal

Presentation Layer

Content Manager

Security Manager

Search Manager

Network

Models

Public Admin DataMart Application

Business Objects

Web Services

Connection Manager

Security Manager

Results Manager

Request Manager

Presentation Layer

DataMart Administrator

Data Manager

Data Source Manager

Data Source Database

Data Partner

Data Partner Host

Data Source

Common Data Model

EMR Database

DataMart Administrator

Network Administrators

Researchers

Internet

Technical Protocols and Overview - 1

• Network Portal

– Multi-tiered .NET MS SQL Server application

– Access via HTTPS

– Communications via Web Services

– Role / rights based access

• Hosting and support

– Hosted in a Tier III SAS 70 FISMA certified data center

– Redundant infrastructure

– 99.9% Application uptime SLA

– SAN based data storage & backup

– Remote data replication

– Secure VPN administration via RSA token ID

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Technical Protocols and Overview - 2

• DataMart Client application

– Small footprint

– Easy to install on desktop or server

– Developed in C#

– Connects to any ODBC compliant data source

– Software updates downloaded from network portal

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PopMedNet Design Features

• Any data model from any source

• Flexible and secure distributed querying

– Execution of custom analytic code

– Menu-driven queries

• Role-based access control

• Data partner autonomy

• Query execution options range from fully automated to manual

• Auditing

• Software-enabled governance

47

• Secure, private multi-center research network

• Open source application

• Data partners maintain control of their data

• Flexible governance, access control, permissions, auditing

• Secure FISMA-compliant platform

• Mature documentation and set-up procedures

• Scalable: easy to add new data, new partners

• Interoperable with other PopMedNet networks

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PopMedNet Implementation Features

49

Security Features • FISMA compliant tier III data center

• 3rd-party secure audit completed – Enhanced system procedures

• Securely store credentials as Salted Hashes

• No maximum password length, require expiration, enforce history

• Use cryptographically secure random values for session IDs (.Net Type 4 GUID)

• Cookies marked as ‘SECURE’, ‘SESSION’ & ‘HTTPONLY’ and the cookie domain

– Transmission

• Require/force Secure Socket layer (SSL) for all communications

• Enable strongest cipher suites and Transport Layer Security (TLS) versions

– Web Service and Portal Authorization

• Ensure all submissions are performed via POST method

• Do not publish WSDL

• Limit the number and size of file submissions

• Passed multiple independent security audits and penetration tests

Existing Networks

50

• SPAN: Scalable PArtnering Network for CER (AHRQ) – ADHD, Obesity

• PEAL: Population-Based Effectiveness in Asthma and Lung Diseases Network (AHRQ)

• Mini-Sentinel (FDA) – Safety of marketed medical products

• HMO Research Network

• MDPHNet (ONC): MA Department of Public Health

• ONC QueryHealth Pilots (planned)

PopMedNet Networks

51

PopMedNet and National Standards

• PMN is a key component of the ONC’s QueryHealth Initiative

• ONC national standard for distributed querying

– QueryHealth Initiative uses PMN as the distributed querying platform for policy and governance

• Standards & Interoperability (S&I) Framework: http://wiki.siframework.org/Home

52

http://wiki.siframework.org/Home

QueryHealth – Query Lifecycle

53

PopMedNet Website

54

www.popmednet.org

Implementing MDPHNet

55

Implementing MDPHnet 1. Implement the common data model (ESP)

2. Store files is relational DB (need ODBC connection)

3. Establish settings on the Network secure portal

– Notifications

– Permissions

4. Install the PopMedNet DataMart Client on local system

5. Establish settings on the DM Client software (logins, data connection)

6. Respond to queries via the DM Client

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MDPHnet Features

1. Menu Driven Querying of ESP Data Model

2. Project Based Network Management

3. Granular Access Control

4. Scheduled Reports

57

1. Menu Driven Query Capability

• Customizable reporting template (menu-driven)

• Menu-driven query interface can identify cohorts of interest based on inclusion and exclusion criteria and Boolean operators

– Time window

– Demographics

– Diagnoses

• Query interface generates standardized executable code for distribution to sites

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EXAMPLE: Identify and characterize a cohort of patients with a diagnosis of asthma in 2010

2. Project Based Network Management

• Organizational entity “Projects” will be developed

• Combined with users, organizations, and query types, “projects” give more precise query authorization control and role-based access control

• Assigns roles based on projects, thus allowing a user to have multiple roles under a single login credential.

EXAMPLE

Establish projects for ILI and diabetes, assigning specific users, query types, and queries to each project. Users create and distribute queries based on their role within a project, and query response determined based on those factors

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3. Granular Access Control

Fine-grained control of query authorization that allows data partners to give permission to execute queries to individual users for specific query types and projects

EXAMPLE

Data partner gives specific MDPH staff permission for automated execution of queries for a specified project (e.g., ILI), but would require manual review of the same query coming from a different user, or the same user for a different project

60

4. Scheduled Reports

Capability to

– Store a query

– Schedule routine distribution of the query

– Data partners to specify how they will be processed

EXAMPLE

As part of the ILI project, MDPH develops a query for distribution every Monday morning. Data partners determine how to process the query (manually or automatically) based on the project, user, and query.

61

MDPHnet Demonstration

62

PopMedNet™ Login

63

PopMedNet™ Demonstration Portal

http://fdaquerytool.lincolnpeak.com/

http://drnquerytool.lincolnpeak.com/Pages/Login.aspx

http://drnquerytool.lincolnpeak.com/Pages/Login.aspx

MDPHnet Login

64

http://mdphnetquerytool.lincolnpeak.com

http://mdphnetquerytool.lincolnpeak.com/

MDPHnet Home Page

65

Request Summary Page

66

Request Selector Dialog Box

67

Request Detail Page

68

ICD-9 Code Selector

69

Request DataMart Routing

70

DataMart Client Application

71

DataMart Client Request Detail

72

DataMart Client Request Detail

73

Response Detail/Results Page

74


75


76

Thank You

77

Distributed Population Queries – National Strategies

Rich Elmore, Coordinator – Query Health

May 2012

Vision

Enable a learning health system to understand population measures of health, performance, disease and quality, while respecting patient privacy, to improve patient and population health and reduce costs.

Distributed queries unambiguously define a population from a larger set

Questions about

disease outbreaks,

prevention activities,

health research,

quality measures, etc.

Distributed Query Networks Voluntary, No Central Planning

Community of participants that voluntarily

agree to interact with each other. There will be

many networks; requestors and responders may

participate in multiple networks.

Requestors Participating

Responders

Query

Query Health Pilots

Pilot Focus RI Queries

RI Policy Layer

Data Sources Kickoff

NYC & NYS Depts. of Public Health

Diabetes (NYC) Hypertension (NYS)

i2b2 PMN RHIOs (Intersystems & Axolotl) EHRs (eCW to start)

May 2012

FDA Mini-Sentinel

Use of clinical data sources for FDA questions

PMN PMN i2b2/Beth Israel June 2012

CDC Front-end to Bio-Sense 2

TBD TBD Bio-Sense 2 June 2012

Mass. Dept. of Public Health

Diabetes PMN PMN MDPHNet July 2012

CQM Quality Measures

hQuery PMN EHR Vendor July 2012

NYS DOH

NYC PCIP

Information Requestors

Data Sources

Axolotl RHIO

Inter-systems

RHIO

eCWEHR

Sends Query to Data Sources

Distributes Query Results to Information Requestor

New York City / New York State

Pilot

Sends Query to Data Sources

Distributes Query Results to Information Requestor

Vocabulary &

Code Sets

Develop modular, testable portfolio of Query Health standards and specifications that

can adopted by the industry, and support key HITECH and govt. priorities

Content

Structure

Queries &

Responses

Privacy &

Security

Foundation:

Distributed

Query

Solutions

SNOMED-CT

Clinical Element

Data Dictionary

i2b2

The Results

New QRDA 2 & 3

PopMedNet

LOINC ICD-10 RxNorm

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Query Health Standards and Reference Implementation Stack

Reference

Implementation

Stack

The Question New HQMF

Query Envelope Privacy Policy

Enablement

hQuery

The Query New HQMF

• Health Quality Measure Format

• HQMF newly modified to support the needs for dynamic population queries:

– More executable

– Simplified

• Advantages for query

– Avoids “yet another standard”

– Secure (vs procedural approach)

– Works across diverse platforms

• Benefits – Speed and Cost

The Query Envelope

• Query agnostic

• Content agnostic

• Metadata facilitates privacy guidance from HIT Policy Committee

• RESTful interface specification

The Data Clinical Element Data Dictionary

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– Demographic

– Patient Contact Information

– Payer Information

– Healthcare Provider

– Allergies & Adverse Reactions

– Encounter

– Surgery

– Diagnosis

– Medication

– Procedure

– Immunization

– Advance Directive

– Vital Signs

– Physical Exam

– Family History

– Social History

– Order

– Result

– Medical Equipment

– Care Setting

– Enrollment

– Facility

• ONC S&I Framework deliverable

• Standards independent dictionary

The Results New QRDA

• Quality Reporting Document Architecture

– Category I – Patient Level

– Category II – Patient Populations

– Category III – Population Measures

Operational Guidance

U.S. Health and Human Services Office of the National Coordinator for Health IT

Standards & Interoperability Framework Query Health Technical Approach Operational Guidelines March 26, 2012

Query Health How it works together

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The path to critical mass

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• Today, distributed queries are generally limited to organizations with large IT & research budgets

• Missing: Primary Care, FQHCs, CAHs, HIEs, etc… In other words, most places where clinical care is delivered and recorded

• MDPHNet is strategically important

Distributed Data Analytics & Annual Community Health ... Query Health, ... Ross Lazarus Emma...

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Transcript of Distributed Data Analytics & Annual Community Health ... Query Health, ... Ross Lazarus Emma...