the impact of rapidly developed health data tools final

5/12/2021

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EMPOWERING PEOPLE TO IMPACT HEALTH

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©2021 | AHIMA.ORG

The Impact of Rapidly Developed Health Data Tools on Public Health Information

Bala Hota, MD MPHCasey Frankenberger, PhD


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DisclaimerThis webinar is informational and for educational purposes only. It is not to be construed as providing legal advice. The views and opinions expressed herein are those of the authors and do not necessarily represent the views of AHIMA. Every effort has been taken to ensure that the educational information provided in today’s presentation is accurate and useful as of date this presentation was produced. Applying best practice solutions and achieving results will vary in each hospital/facility situation.


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Objectives• Recognize the gaps in current public health reporting in the

COVID-19 era• Describe how informatics standards like CCDA and ELR reporting

can be leveraged to improve public health COVID-19 surveillance• Illustrate how the FHIR standard can support future, modern,

infrastructure for public health surveillance of vaccine administration and molecular epidemiology of SARS-COV2

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Responding to COVID PUI Data Gaps

Source: https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm?s_cid=mm6912e2_w; OSTP.gov

CDPH Data Limitations Experience:

Key Missing data variables:Race / ethnicity(29%)Hospitalization data (40%)Underlying conditionsLength of hospital stayReporting:‐‐78% of data reported thru ELR submission

‐‐very limited data fields‐‐Only 22% with provider report‐‐not all testing sites connected to ELR‐‐critical data elements to conduct investigations notbeing captured in timely manner

INITIAL CRITICAL NEEDS:‐Timely reporting of essential basic data elements to lead case investigations‐Efficient mechanism to collect more complete epi and clinical information, including other data sources‐coordinated mechanism for hospital utilization monitoring

“No data on serious underlying health conditions were available. Data…are preliminary and are missing for some key characteristics of interest, including hospitalization status(1,514), ICU admission (2,253), death (2,001), and age (386).” –as of March 16, a total of 4,226 COVID-19 cases in the United States had been reported to CDC


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Standardized COVID-19 data

• Can we auto-generate a Table 1 and Table 2?

• What are the characteristics of COVID-19?

•Who is getting sick

• Who has highest mortality• Are there race/ethnicity

differences in outcomes?• Does hydroxychloroquine work?• Do Ace Inhibitors help? Do they

hurt?

DOI: 10.1056/NEJMoa2002032


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Real world evidence from a single site

Clarity & Caboodle

Electronic Health Record System

Reporting and Research Modules

Staging Data

Concept Mapping

Data in OMOP Common Data Model

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Gaps or inconsistencies in Research Databases Shared or Common definitions

The Complexity of Multi‐site Data Sharing

Clarity & Caboodle

Reporting and Research Modules

Electronic Health Record System

Currently the solution is focused within Epic based members

Organizations can have variable levels of implementation and various levels of familiarity with common data models (OMOP)

Other EHRs exist and other common data models are being used in other initiatives competing for under resourced projects

How do you define what constitutes a Covid-19 patient?

What are appropriate time points to share data?

How to count the number of hours on ventilation?

How to standardize definitions of race/ethnicity?

Data governance and legal review

Each site has specific processes for data governance policies, committee reviews for data sharing and processes for legal review and contract negotiations.


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Many initiatives running in parallel IP issues related to underlying vendors

Barriers to Turnkey Solutions

Had to post Caboodle queries to Epic message boardNational

Regional


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Original Goals of CDPH Data Hub

Data aggregator/”catcher’s mitt”Analytics

COVID‐19 patient registryBed capacity

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Open Source Public Health Data Hub

• AWS Based Data Hub

• Leveraging AWS HealthLake

• Goal of open sourced by end of February


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CCDA Race/Ethnicity by Age

Excludes missing/unknown lines to reduce clutter

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0 2 4 6 8 101214161820222426283032343638404244464850525456586062646668707274767880828486889092949698100

age

FREQ

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AGE (YEARS)

RACE/ETHNICITY BY AGE (YEARS)

WHITE

BLACK

HISPANIC

ASIAN

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Adult ICU Bed Capacity By Hospital –12/1/20

• Many Chicago hospitals reported Adult ICU Bed Capacity the same between EMResource(bars) and HHS Protect (line).

• Three of the largest Chicago hospitals reported more Adult ICU Capacity to HHS Protect than EMResource.

• A few hospitals reported less Adult ICU Bed Capacity to HHS Protect than EMResource


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Fast moving concerns related to COVID-19- Shifting needs for bed capacity data

- Where should reporting occur? CDC vs HHS- Adoption by sites and the source of truth- How to achieve real time, automated bed capacity data

- Role of longitudinal data in ongoing surveillance for late sequelae- From a public health perspective, monitoring and addressing long hauler syndrome- Architecture of a data ecosystem for data sharing, scalability of public health orders

- Vaccine scheduling and administration- Regional scheduling, one-stop shop for scheduling- Vaccine administration equity- Vaccine inventories- Documentation of vaccination

- Molecular epidemiology of SARS-CoV2- Regional strain typing, surveillance, and reporting

- Gathering real time real world evidence- COVID-19 and beyond- Lollapalooza, Chicago Marathon

COVID-19 and agility in platforms


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Data Science in the CloudSASCloud data workflowsData Science toolkits

Public Health data repositoryExploring the options generated by scalable data storage

Master Patient Index and HashingMPI- for linking ELR and CCDA; reusable for additional

projects if neededHashing – deidentify data and share

https://linkja.github.io – privacy protecting record linkage

Data Hub as a means to modernize public health infrastructure

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Data Hub as foundational public health infrastructure

Current State

HHS Protect

EMResource

ICAREZocdoc

Scheduling

COVID‐19 Registry

In Development

RIPHL

Planned

FHIR Enablement

FHIR pulls vs CCDA pushes

Form data via FHIR submission(e.g. eCR Now)

ADT for real time data, or FHIR surrogate

Data Release

HIV/STI Program Data

Other Program Data

API or other mechanism for release of data

DeidentificationData submission to state/federal

agencies


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Regional Innovative Public Health Lab

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RIPHL Data Flows


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The agility of cloud based services has compelling value for novel use cases in public health

Themes of data integration, patient matching, and ease of reporting have been recurring over the COVID-19 era

Ideal scenario is to combine standards, reporting needs, with desire to generate real world evidence

Public health is a trusted partner in the data ecosystem and could serve a vital role in data aggregation

Conclusions


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Today’s Speakers

Bala Hota MD, MPH

Vice President and Chief Analytics Officer

Rush University Medical Center

Casey Frankenberger PhDChief Research Informatics Officer

Rush University Medical Center

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the impact of rapidly developed health data tools final

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