Research is a complex yet vital component of improving care delivery, and it can be hindered by a variety of organizational and technical roadblocks: Insufficient tools and processes Poor infrastructure No single source of truth for data Health systems can overcome these common research roadblocks and turn analytics-powered research into care delivery improvements by using the Research Analytics Adoption model as a strategic roadmap. The model consists of 8 levels designed to align operations and research priorities: De-identified tools and data marts Delivery of customized data sets EDW-facilitated study recruitment Centralized, research-specific data collection Automated research operations reporting Biobank/genomic data integration Multi-site data sharing Translational Analytics

1. Turn Research Into Care Delivery
Improvements Using the Research
Analytics Adoption Model – Eric Just

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Healthcare Analytics Adoption Model
Health Catalyst introduced the
Healthcare Analytics Adoption Model
as a strategic guide for health
systems adopting analytics to drive
healthcare transformation in quality,
safety, and operations.
The Research Analytics Adoption
Model is a strategic roadmap
designed to help health systems
achieve analytics-driven research
capabilities.

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Organizational and Technical Roadblocks
The research process is complex
and involves many steps. Research
is often hindered by roadblocks that
arise from organizational and
technical inefficiencies:
• Lack of tools to support cohort
exploration
• No single source of truth for data
• Insufficient tools and processes to
release data to authorized
researchers
• Poor infrastructure for combining
clinical and experimental data

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Research Analytics Adoption Model:
A Strategic Roadmap
The Research Analytics Adoption Model consists of 8 levels
designed to remove research roadblocks, and improve
clinical research data management and care delivery:

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Level 1 – De-identified tools & data marts
Research Analytics Adoption Model:
A Strategic Roadmap
Provide access to de-identified
tools and data marts which
allows researchers to access
data for specific applications.
De-identified tools protect
patient identity and minimize
regulatory processes required
for accessing protected health
information (PHI).

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Research Analytics Adoption Model:
A Strategic Roadmap
Establish a research analytics team to develop
custom data sets to solve complex research.
Set clear guidelines for the data release
process.
Involve data stewards and dedicated research
analysts that understands both data and PHI.
Deliver data sets in an agile, consultative
manner.
Use a workflow tool that supports the data
request workflow.
Level 2 – Delivery of customized data sets, including clinical notes

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Research Analytics Adoption Model:
A Strategic Roadmap
Provide tools that allow researchers to define
a population of eligible patients.
Centrally manage recruitment contact lists to
prevent study fatigue.
Provide recruiters with updated scheduling
information via mobile devices.
Capture “do not contact” requests.
Provide an option for electronic consent.
Level 3 – Study recruitment facilitated by EDW

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Research Analytics Adoption Model:
A Strategic Roadmap
Avoid using Excel and Access as data
collection tools.
Use a data collection tool that collects and
stores data to and from the EDW.
Provide the infrastructure to support self-
service data collection form creation.
Apply appropriate security to data collection
and any data that ends up in the EDW.
Provide an option for electronic consent.
Level 4 – Research-specific data collection is centralized

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Research Analytics Adoption Model:
A Strategic Roadmap
Pull key research systems into the EDW
(clinical trials management system, patient
recruiting system, and electronic IRB system)
for deeper insight into research operations.
Level 5 – Automated reporting of research operations
Automatically generate key performance
metrics (i.e. number of active studies, number
of patients in trials, number of grants
submitted/funded).
Highlight areas for performance improvement.

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Research Analytics Adoption Model:
A Strategic Roadmap
Level 6 – Biobank/Genomic data integration
Provide a platform for genomic discovery
by integrating biobank and experimental
genomic data with the EDW.
Because biorepositories can be linked to
a more detailed clinical record, they are
no longer limited by data collected at the
time of sample acquisition.
Understand genomic data regulations
and apply appropriate security measures.

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Research Analytics Adoption Model:
A Strategic Roadmap
Level 7 – Multi-site data sharing
Provide the infrastructure needed
so coordinators can easily pull in
data from multiple sites.
Coordinating centers leverage
data warehousing techniques for
data collection, have automated
intake or federation of data from
participating sites, and provide
exploratory tools to analyze a
combined data set.

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Research Analytics Adoption Model:
A Strategic Roadmap
Learning health systems routinely transition
research data into care delivery guidelines.
Use care delivery analytics to power research.
Take discoveries made at the molecular level,
and incorporate them into clinical practice.
Leverage cohorts and outcome measures
from care improvement while adding data.
Level 8 – Translational analytics

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Conclusion
The Research Analytics
Adoption Model, when paired
with engaged, supportive
leaders, aligns research and
clinical priorities and
transitions data-powered
research discoveries into care
delivery improvements that
can be deployed system wide.
Powering Medical Research With Data:
The Research Analytics Adoption Model
Watch this Webinar:

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Eric Just – VP Technology
Link to original article for a more in-depth discussion.
Turn Research Into Care Delivery Improvements Using the Research
Analytics Adoption Model

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For more information:

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Edward Corbett, M.D. joined Health Catalyst in June 2014 as a medical officer. He
earned his medical degree at the University of Texas Health Science Center in San
Antonio where he also completed his residency in Internal Medicine. He is board certified
in Internal Medicine. He started his career as a physician at the Cooper Clinic in Dallas,
Texas specializing in preventive medicine. Prior to joining Health Catalyst he was a
physician partner at Central Utah Clinic, a large multispecialty clinic which was the first
Medicare ACO in the state of Utah. He has a special interest in improving patient care through the
better use of technology and has been actively involved in clinical IT throughout his career.
Other Clinical Quality Improvement Resources
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