The 3 Challenges of Translational and Clinical Research Data Management and a Strategy to Succeed

The 3 Challenges of
Translational and Clinical
Research Data Management
and a Strategy to Succeed

© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Challenges Facing Clinical Research
Clinical research is a
critical part of creating a
learning health system
that routinely incorporates
the latest treatment
guidelines into its clinical
care.
Increasingly, researchers have been tasked with not only
identifying new interventions to create better clinical
outcomes, but also collaborating with healthcare delivery
systems to implement those new discoveries.

Challenges Facing Clinical Research
Researchers are facing
problems with their clinical
research data management.
These issues include accessing
clinical data, the inability to
efficiently use time and
resources when dealing with
that data, and translating that
research data into everyday
clinical practice.

Challenge One: Access to Clinical Data
One critical problem facing
researchers is an inability to
access the data they need.
The main barriers to access
include:
• Technology barriers
• Regulatory barriers
• Organizational barriers

Technology Barriers
With healthcare data housed in
a wide variety of siloed
systems there is no single
source of truth to rely rely on.
Most health system’s data isn’t
aggregated or easily accessed.
Furthermore, health
researchers frequently require
access to data beyond the
health system in order to gain
patient population insight.

Regulatory Barriers
Once researchers receive
funding, they want to get
started digging into the data
but regulatory barriers can slow
them down.
These regulatory barriers
(specifically HIPAA, which
requires an institutional review
board [IRB] to approve data
use) are necessary and
surmountable, but they add a
layer of process and complexity
to research.

Organizational Barriers
Maintaining the privacy and
security of health data is
absolutely essential.
When the data goes out to
researchers at another
academic institution the
transfer must be secure.
It must also cross-check that
the data request is consistent
with federal and local
regulations.

Challenge Two: Time & Resource Issues
Lack of needed infrastructure
can lead to major inefficiencies
of both time and resources
when researchers attempt to
access or use healthcare data.
These inefficiencies come in
the form of
• Inefficient Study Recruitment
• Data Cobbling
• Materials Waste

Inefficient Study Recruitment
A quick glance at
clinicaltrials.gov** shows how
many research studies fail due
to the inability to recruit enough
participants.
A good recruitment program
needs data from multiple
systems: EMRs and adminis-
trative data for cohort
identification, physician,
hospital ADT and ambulatory
scheduling data.
**A registry and results database of global, publicly and privately supported clinical studies.

Data cobbling
Data cobbling happens when
researchers must piece
together disparate data sources
manually using Excel, Access,
or other consumer database
software programs.
The right data collection tool
should also interface directly
with an enterprise data
warehouse so when the
researcher wants to analyze
the data, no time is wasted to
integrate data.

Materials Waste
Shadow systems like Excel
spreadsheets also contribute to
materials waste.
Consider the example of bio
banks which are often stored in
disparate systems and with
limited amounts of clinical data
associated with the samples.
Researchers need a
repository where all samples
are catalogued and available
for querying along with
detailed clinical data.

Challenge Three: Translating Research
Discovery into Clinical Practice
The third challenge is making
the transition between research
and clinical practice—moving
from bench to bedside.
It takes on average 17 years
for discoveries about best
practices to become part of
everyday clinical care.
If a health system has trouble
deploying established best
practices, they will also struggle
to deploy more-recently
discovered guidelines.

What Does a Research Program Need
to Be Successful?
Many health systems have a
research mission–not just
academic medical centers.
This type of mission is important
because it is driving the better
care of tomorrow.
Organizations that innovate
ways to deliver healthcare will
differentiate themselves in an
increasingly competitive market.

to Be Successful?
Sharing these innovations and
discoveries with other providers
helps the healthcare industry as
a whole become more effective
and improve global health.
That is why removing
roadblocks for research is so
imperative.

to Be Successful?
Many of the problems outlined
above are solvable at a strategic
level by creating a synergy
between research and care
improvement teams.
Those who master clinical quality
improvement can apply those
techniques to deploy the latest
research-driven guides.
A high level of cooperation
between health systems and
their academic affiliates is crucial.

More about this topic
How to Develop a Robust Clinical Content System
Holly Rimmasch, Chief Clinical Officer
Knowledge Management in Healthcare: It’s More Important Than You Realize
Dr. John Haughom, Senior Advisor
The Content System: Making the Best Medical Knowledge Standard Practice (Webinar)
Dr. John Haughom, Senior Advisor
Best Solution to Aggregate Healthcare Data Including Clinical, Financial, Research,
Population Health, and More – ric Just, Vice President of Technology
6 Steps for Implementing Successful Performance Improvement Initiatives in Healthcare
Bobbi Brown, Vice President of Financial Engagement
Leslie Falk, Vice President, Executive Engagement
Link to original article for a more in-depth discussion.
The 3 Challenges of Translational and Clinical Research Data Management
and a Strategy to Succeed

For more information:

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Eric Just joined the Health Catalyst family in August of 2011 as Vice President of
Technology, bringing over 10 years of biomedical informatics experience. Prior to Catalyst,
he managed the research arm of the Northwestern Medical Data Warehouse at
Northwestern University's Feinberg School of Medicine. In this role, he led the
development of technology, processes, and teams to leverage the clinical data
warehouse. Previously, as a senior data architect, he helped create the data warehouse technical
foundation and innovated new ways to extract and load medical data. In addition, he led the
development effort for a genome database. Eric holds a Master of Science in Chemistry from
Northwestern University and a Bachelors of Science in Chemistry from the College of William and Mary.
Sean Whitaker joined Health Catalyst in January 2015 as an Engagement Lead. He
comes to Catalyst from Valence Health where he worked as a Solutions Delivery Manager
and then as Director of Program Management. Prior to Valence, Sean worked for
Northwestern University Medical School in project and program management with a
primary focus on creating tools to support clinical research.

The 3 Challenges of Translational and Clinical Research Data Management and a Strategy to Succeed

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (11)

More from Health Catalyst

More from Health Catalyst (20)

Recently uploaded

Recently uploaded (20)

The 3 Challenges of Translational and Clinical Research Data Management and a Strategy to Succeed