Interoperability and Analytics February 29, 2016

Interoperability and Analytics

February 29, 2016

Matthew Hoffman MD, CMIO Utah Health Information Network

Conflict of Interest

Matthew Hoffman, MD

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

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 GAIN

MPI RECORDS LABS ADTS

Patient Overlap Between Health

Systems

0%

5%

10%

15%

20%

25%

30%

22%

19%

29%

20%

15%

30%

13%

Diabetes Star Ratings Analysis

[VALUE]

[VALUE]

Data Related to Diabetic Measures

HIE Data

The STEPS ™

Framework:

Population Management

Analytic Tools for the Front Line

- The Importance of Standards - Chronic Disease Populations - Case Management Analyses - Geomapping Tools

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

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:

Questions

• Matthew Hoffman, MD

• CMIO, Utah Health Information

Network

Interoperability and Analytics

February 29, 2016

Daniella Meeker, PhD

Conflict of Interest

Funding

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

• 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

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

• Data

• Data Processing Algorithms • Data Analysis Algorithms • Data Analysis Results

SCANNER Network Software

Health Economic Domains are

not represented in other research and quality information models,

Implementation

Aside on Data Quality & Availability

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

Aside on Data Quality & Availability

QRDA STANDARDIZED WAREHOUSE

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

Direct and Quantifiable Comparisons

dataset A dataset

A

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 DISTRIBUTED ANALYTICS PORTABLE DATA PROCESSING STANDARDS

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

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

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