Clinical Data and Public Health Surveillance: Improving Accuracy - - PowerPoint PPT Presentation

Clinical Data and Public Health Surveillance: Improving Accuracy Using a Statewide Master Patient Index Rachel Zucker, MPH September 13, 2016

What is CHORDS?

• The Colorado Health Observation Regional

Data Service

• Facilitates electronic health record (EHR)

data sharing and aggregation from 11 participating healthcare sites representing

• ver 3 million registered patients in the

Denver Metro region.

• Early projects have focused on public

health – Tobacco use and exposure – Obesity – Cardiovascular risk

Our Technology

• Data Sharing

Application:

– PopMedNet

• Common Data Model:

– Virtual Data Warehouse

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

Data Mart (VDW) Data Customer with Question PMN Data Mart Client Firewall

CHORDS Federated Query Overview

UC Anschutz Data Partners

PMN Client Administrator Query Query PopMedNet (PMN) Query Portal

VVVVV

Data Mart Administrator ETL from Electronic Health Record

VDW = Virtual Data Warehouse ETL = extract transform and load

May include researcher, public health department/ agency, etc.

The problem?

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

• Duplicate records for patients across

institutions

– Leads to artificially inflated counts for record # and prevalence estimates

Patient 1 is an overweight man whose height and weight are recorded in his regular provider’s EHR. Patient 1 is later seen at the ER for difficulty

• breathing. His height and

weight are recorded.

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

CHORDS will overestimate the number of patients Query: How many patients does CHORDS have data for?

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

CHORDS counts Patient 1 as two overweight people Query: Return count of patients with BMI>25 in Denver Metro region

The problem?

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

• Scattered data for case identification

– Leads to artificially deflated counts

Patient 1 is an obese man who sees his regular primary care physician Patient 1 is later seen at the ER for an asthma attack

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

The relevant criteria are in two different records; Patient 1 is not counted Query: Return patients with BMI>30 who have had an asthma attack

The problem?

ACCORDS – ADULT AND CHILD CONSORTIUM FOR HEALTH OUTCOMES RESEARCH AND DELIVERY SCIENCE

University of Colorado Denver | Anschutz Medical Campus

• Both lead to inaccurate estimates for

public health monitoring and inaccurate results for research

• We need to link patient records to mitigate

these problems

Potential solutions?

Manual Linkage PPRL MPI

Most resource intensive Less resource intensive Most data exposure Additional governance required “Delegating”

demand Heavy computational demand

Manual steps

Defining Requirements

• No centralization of records within CHORDS
• Minimal modification to CHORDS VDW
• Limited resources (staff time, funding, technical) available

to support record linkage – especially at some sites

• Timeliness of data

Solution: MPI

• Share existing statewide HIE MPI (CORHIO)
• Best fit for existing CHORDS infrastructure and resources
• Early efforts:

– Obtaining required data elements – 2 site match – Use cases – Preliminary data

Getting the MPI – Ad Hoc

Getting the MPI – Periodic

Using the MPI

20

Matching Across Institutions

100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 KP DH UCH TCH TCH UCH DH KP No auto match

Considerations

• Ensuring Data Security
• Defining Needed Fields to link MPI #s
• Defining Data Transfer Processes
• Will all partners work with HIE?
• HIE’s purpose vs. CHORDS’ purpose

Questions?

Thank you to the CHORDS team:

• Dr. Art Davidson and Emily McCormick at Denver Health

and Jessica Bondy, Bryant Doyle and Dr. Lisa Schilling at Anschutz Medical Campus. Additional thanks to Dr. Michael Kahn at Children’s Hospital CO, and Dr. John Steiner and David Tabano at Kaiser Permanente CO. We would like to thank the Colorado Health Institute for supporting this presentation.