interoperability and analytics february 29, 2016€¦ · interoperability and analytics february...

Interoperability and Analytics February 29, 2016

Matthew Hoffman MD, CMIO

Utah Health Information Network

Conflict of Interest

Matthew Hoffman, MD

Has no real or apparent conflicts of interest to report.

Agenda

• Electronic Secure Data

– Supplementary Clinical Data

– Patient Overlap Between Health Systems

– Diabetes Star Ratings Pilot

• Population Management

– Chronic Disease Populations

– Case Management Analyses

– Geomapping Tools

• Savings

– Encounter Notification Service and FHIR

– Risk Adjustment Analyses

Learning Objectives

• Describe how standards define use cases and parameters for health data

analytics

• Identify the ways in which data analytics can help to support the business

value of health information exchange

• Evaluate how new dimensions in standard definitions will shape data

analysis applications and use cases

The STEPS ™ Framework: Electronic Secure Data The Case for Interoperability -Supplementary Clinical/Hospital Data for

Clinicians

- Star Ratings Analysis

- Shared Identities Numbers

http://www.himss.org/ValueSuite

Supplementary Data from Health Information Exchange

EMR DW EMR DW EMR DW EMR DW

SMFM 23,535 100,315 105,849 236,460 - 501,652 109,608 705,304 6.46

CUC 713,480 1,990,063 585,269 5,369,244 1,301,722 13,425,165 2,240,747 28,182,616 10.11

DATA GAINMPI RECORDS LABS ADTS

*Numbers as of 01/13/2016

Patient Overlap Between Health Systems

0%

5%

10%

15%

20%

25%

30%

HS 1 HS 2 HS 3 HS 4 HS 5 HS 6 HS 7

22%

19%

29%

20%

15%

30%

13%

Diabetes Star Ratings Analysis

[VALUE]

[VALUE]

Data Related to Diabetic Measures

HIE Data

EHR Data

The STEPS ™ Framework: Population Management

Analytic Tools for the Front Line

- The Importance of Standards

- Chronic Disease Populations

- Case Management Analyses

- Geomapping Tools


UHIN Clinical Architecture

Using Standards to Combine Data


Cost to Value of Types of Analytics Models


Clinician Created Analyses


Custom Filtering Filter Settings

A1C

- Gender: (female, male)

- Age: (0 <= Age <= 124)

- A1C_Val: (1.30 <= A1C_Val <=

21.99)

MicroAlbumin

- Mic_Val: (0.00 <= Mic_Val <=

11.35)

BP BMI Cholesterol

- BP: (82 <= BP <= 205)

- BMI: (0.21 <= BMI <= 73.02)

- Cholesterol: (0 <= Cholesterol <=

251)


Care Management Tools: Diabetic Population


Geomapping


The STEPS ™ Framework: Savings For the CFO in All of Us - Encounter Notification Service and FHIR

- Risk Adjustment Analyses


FHIR ENS Solution Diagram


Results

• Anecdotal

– Post Surgery Re-admit reduction

– Decreased Asthma Hospital Admissions

• Documented

– 3 month pilot (Coaction):

• 54% reduction in hospitalization days

• 33% reduction in ED visits

• $178,547 estimated savings

– Medicare Patient Population (NY)

• 2.9% reduction in likelihood of 30 day readmission

• $1.24 million savings in admissions

Risk Adjustment Analysis Architecture


ICD-X from Clinical Note


STEP Summary


Exchange • 21% Avg Overlap Between Systems

• 17% Increase in Star Ratings Data

Population Health • Care Managers

• Physicians

• Public Health

Savings • Alerts: Decrease in Hospital Days,

Readmissions and ED Visits

• Risk Assessment:

• Increased Risk Reimbursement

Questions

• Matthew Hoffman, MD

• CMIO, Utah Health Information

Network

• [email protected]

mailto:[email protected]

Interoperability and Analytics February 29, 2016

Daniella Meeker, PhD

University of Southern California, Keck School of Medicine

Conflict of Interest

Daniella Meeker has no real or apparent conflicts of interest to report.

Funding

PCORI CDRN-1306-04819

Overview

• Multi-Institutional Learning Health Systems

• Data Standards and Standardization Process

• Computation Standards for Analysis

• Result Standards for Dissemination and Application

Learning Objectives

• Describe how standards define use cases and parameters for health data

analytics

• Identify the ways in which data analytics can help to support the business

value of health information exchange

• Evaluate how new dimensions in standard definitions will shape data

analysis applications and use cases

Standards for Data Exchange and Persistence are Necessary but Not

Sufficient to Obtain Value from Multisite Data Infrastructure.

Products of a Learning Health System

• Analysis models for causal inference and program evaluation

– Program X is 15% more effective at preventing readmission than Program Y

– Drug A has a greater risk for adverse events than Drug B

• Quality measurement reports

– Practice N is achieving 80% of quality indicators

– Practice L is achieving 60% of quality indicators

• Predictive models for patient-centered medicine

– For patients like John, Drug C is safer and more effective than Drug D.

– For patients with Debbie’s goals and comorbidity profile, Program Y is better than Program X

pSCANNER Network – 21M patients

• 5 University of California Medical Centers

• Cedar’s Sinai Hospital

• Pacific NW Rural Health Practice-Based

Research Network

• Los Angeles Department of Health

Services

• 5 multi-site FQHCs

• Children’s Hospital of Los Angeles

• Keck Medicine of USC

• Service Oriented Architecture for privacy-preserving analytics in distributed research networks

– Focus on map-reduce, iterative algorithms based on parallel-distributed processing algorithms

• Standardized Analytic Data Warehouse at every site

• Role based access control -> Attribute Based Access Control

• De-centralized study administration – no “coordinating center” peer-to-peer analytics.

• GUI for menu-driven queries for multiple activities

– Proposing collaboration partners, data sets, and protocols

– Specifying regression analytics

– Data set extractions

– Cohort discovery

Distributed Research Network Requirements (AHRQ 2010-2014)

• Methods repository for contributing and sharing analytic algorithms for classical statistics and machine learning

• Result repository for contributing and sharing analytic results –(e.g. causal models, predictive models, quality reports)

Distributed Research Network Requirements (AHRQ 2010-2014)

Need to select and implement standards for the entire analysis cycle:

• Data

• Data Processing Algorithms

• Data Analysis Algorithms

• Data Analysis Results

SCANNER Network Software (AHRQ 2010-2014)

Part I: Multisite Data Standardization

Health Economic Domains are

not represented in other research

and quality information models,

but value was So Cal Health

Leadership Priorities

Selecting a Data Warehousing Standard

Implementation

datamartist.com

Aside on Data Quality & Availability

Dx

R

x

Lab

Procedures

text

CCD

LIS

Finance

EHR QRDA

STANDARDIZED WAREHOUSE

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

SHARED

TRANSFORMATIO

N PROGRAMS

Quality

Model

Quality

Model

Quality

Model

Quality

Model

Quality

Model

Quality

Model

Quality

Model

Quality

Model

Quality

Model

CUSTOM

PROGRAMMING

QRDA

DISTRIBUTED

ANALYTICS

Standardizing 11 Health Systems

Aside on Data Quality & Availability

Dx

R

x

Lab

Procedures

text

CCD

LIS

Finance

EHR QRDA

STANDARDIZED WAREHOUSE

HIE?

Part II: Computation Standardization

Why do we need standards for computation if the data is already standardized?

• Reproducibility

• Robustness to innovation – update parts without starting from scratch

• Flexibility to local implementation

• Transparency

• Comparability

Implemented standards + process modularity

Direct and Quantifiable Comparisons

dataset A dataset

A

30 day readmission

Selecting a Standard for Computation Specification

• Sufficiently expressive to represent data processing concepts for transactional, time-series data (e.g. interval logic)

• Sufficiently expressive to represent data processing concepts that are specific to healthcare (e.g. time of administration, age of onset)

• Sufficiently expressive to represent basic statistics and data analysis algorithms

• Supported/Adopted

Quality Data

Model Data

Processing

Semantics

Predictive

Model Markup

Language

SQL

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

OMOP

DISTRIBUTED

ANALYTICS

PORTABLE DATA PROCESSING STANDARDS

PORTABLE DATA ANALYSIS STANDARDS

Part 3: Result Standardization

Products of a Learning Health System

• Analysis models for causal inference and program evaluation

– Program X is 15% more effective at preventing readmission than Program Y

– Drug A has a greater risk for adverse events than Drug B

• Quality measurement reports

– Practice N is achieving 80% of quality indicators

– Practice L is achieving 60% of quality indicators

• Predictive models for patient-centered medicine

– For patients like John, Drug C is safer and more effective than Drug D.

– For patients with Debbie’s goals and comorbidity profile, Program Y is better than Program X

Disseminating Products of a Learning Health System

OMOP

Reference

Data Model

Extracted

Data Set

(“Flat File”)

Data Set

Extraction

Program

Analysis

Program

Estimated

Predictive

Model

aka “Data Processing”

“Computable Phenotype”

“Cohort”

“Inclusion Criteria”

“Measure Denominator”

research

Patient

Record

Predictive

Model

Computation

Standardized

Extracted

Record

Patient

Centered

Prediction

Publish

care

Publish

Record

Processing

Program

CCD

Selecting Standards for Disseminating LHS Products

• Quality Indicators can be shared & Reported with QRDA

• Predictive Models can be shared & Reported with PMML (but not part of Health IT standards)

• Causal inference & program evaluation results are (still) disseminated on paper.

Summary: Standards for Exchanging Analysis Process and Products

Process we need to

represent

Standard Rationale

Data processing rules HQMF>CQL CMS, ONC, HL7 endorsed

Part of EHR certification process

New standards in trial use

Cohort definition rules HQMF>CQL 100’s of established data sets

1000s of cohort criteria

Data set description QRDA

PMML

QRDA – Quality Measurement EHR Certification & CMS

PMML – Data analysis

Data Analysis Methods PMML UCSD Data Mining Group

Extensible to support model specifications

Data Analysis Results

(Estimated Models,

Produce Predictions)

PMML Developed to represent results

Adopted by most stats packages

Process Workflow BPML? TBD

What Next?

• FHIR

• Clinical Quality Framework

• Data Access Framework

Questions?

• Daniella Meeker, PhD

Assistant Professor, USC Keck School of Medicine

Director, Clinical Research Informatics

Southern California Clinical Translational Sciences

Institute

[email protected]

interoperability and analytics february 29, 2016€¦ · interoperability and analytics february...

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