Achieve Data-Informed Healthcare in Eight Steps

Achieve Data-
Informed
Healthcare in
Eight Steps

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Achieve Data-Informed Healthcare in Eight Steps
“Data driven” and “data informed” are current
buzzwords in healthcare—but what do these
terms actually mean for a health system?
The process of becoming data informed is
complex; it requires a health system to
take data—in its raw form—and transform
it into intelligence that should lead to action.
Transforming data into intelligence is
invaluable in healthcare because it is a
reliable and repeatable way to identify
areas for opportunity (clinical, financial, etc.)
and improve outcomes.

A robust analytics platform is critical for
providers to deliver data-informed
healthcare that leads to better outcomes.
However, if healthcare leaders and
organizations do not approach the
platform with the correct understanding
of how to transform data to intelligence,
process improvement measures will not
reach their full potential and outcomes
improvement may fall short.

The Analytics Ascension Model:
Drive Data-Informed Healthcare
One example of the process to turn data
into intelligence is an analytics ascension
model (Figure 1)—an eight-step hierarchy
that guides health systems through the
process of taking raw data and turning it
into meaningful action, or intelligence.
Figure 1. The Analytics Ascension Model

The process enables data analysts to
develop muscle memory they can rely on
as they drive data-informed healthcare
decisions in everyday projects or when
working as part of a larger framework, such
as the 8-Step Improvement Science Model
or the Healthcare Analytics Adoption Model.

For example, an improvement team may
become blocked in the first phase—
attempting to agree on an accurate
definition of their population—but the
ascension model helps team members
progress by reminding them of the
purpose of the current step and allowing
them to see the next step.
The model provides a conceptual
framework for tracking progress from
data to intelligence and can serve as a
common language for all team members.

Step 1: Population Definition
In step 1, improvement teams need to
identify and define the patient population
they are trying to influence.
Typically, health systems will develop
populations based on similar diagnoses
(e.g., diabetes patients, heart failure) or
procedures (e.g., hip surgery, orders
placed, medications administered).
Regardless of the target population,
teams must define who or what they
want to impact or change.

Agreeing on a population definition at the start of
project increases project buy-in and makes it easier
to gain insights from brainstorming and problem-
solving meetings because every team member is
focused on the same population definition

Teams will also avoid wasting time in subsequent
steps by not having to revisit the population
definition each time they review a new
measure or result.
Having a shared and thorough understanding
of the target population will pay dividends as
the work progresses.

Step 2: Measurement
In step 2, measurement, as teams work to
discover and define data elements of interest
for the population, they must understand what
they can currently know about the population.
Understanding which data elements are
available in various source systems will allow
teams to quantify any gaps they may have for
measurement.
If teams cannot measure or make reliable
observations of needed data elements, the
improvement work may not be meaningful.

Step 2: Measurement
If a project starts without a defined under-
standing of the specific data elements
needed to develop measures of interest
(a common scenario), project teams can
create a thorough documentation of the
data exploratory process.
This can help the team understand what
knowledge they can track about the patient
population defined in step 1.

Step 2: Measurement
It is also important to make provisions for
missing data within the defined population.
Patient populations may not have certain
data elements available to them because
of different hospital workflows, regulatory
requirements, etc.

Step 2: Measurement
For example, Hospital Consumer
Assessment of Healthcare Providers and
Systems (HCAHPS) are patient surveys
that rely on volunteer participation.
If only half of the defined population has
replied to the HCAHPS survey, the team
may lack sufficient HCAHPS data to
understand their full population.

Step 2: Measurement
The improvement team should discuss
examples like HCAHPS because these
scores can be easily overlooked and
impact the accuracy of the data as teams
progress through the ascension model.
In some cases, discussions about
measurement and the data available
may prompt adjustments to the
patient population (step 1) or
selected measures.

Step 3: Data
In step 3, teams start to retrieve data
elements they defined in step 2 and
evaluate quality against other known
data sources (internal or external).
There may be times when comparing
directly to benchmarks is not possible.
Therefore, teams may substitute their
own measures in place of similar
benchmark values where appropriate.

Step 3: Data
Teams can now start to validate actual data to
assess quality (e.g., outliers, missing data, or
use of free text fields), then clean the data to
ensure it is accurate and reliable.
It is important for teams to condition the
data because any statistical testing that
has too many outliers or empty fields can
result in inconsistent, misleading, or
erroneous information.

Step 4: Information
In step 4, teams aggregate, stratify, and trend
the trusted data to compare performance across
categories. This is the first time teams would
perform a statistical test on the data elements.
For example, it would be the first time a team
member would see the median length of stay for
their patient population or the average time
between two workflow timestamps.
Access and exposure to the real information
allows teams to check anecdotal understanding
against the verified information from their own
dataset, setting the stage to adjust expectations.

Step 4: Information
Often, the information may differ from the
team’s expectations resulting in concerns
with the population definition.
Team members may think: “Where did we
go wrong in our process? Who’s to blame?.”
However, if team members were properly
engaged in the data retrieval and validation
process (steps 2 and 3), they will be ready
to entertain and accept information that is
different from what they expected, having
a posture of learning from the information
rather than projecting onto it.

Step 5: Knowledge
In step 5, teams compare their outcome
measures against a set of balancing
measures, process measures, and
potential drivers, such as risk or acuity.
This allows teams to achieve a broader
awareness of their organization and
make connections between other
projects happening in the organization.

Step 5: Knowledge
Team members should ask, “How does this
give me new context for other work
experiences and how can I incorporate them?.”
For example, a team focused on reducing
readmissions may also consider length of
stay as a correlated component in the system
and will want to understand the work and
priorities happening in that adjacent space.

Step 5: Knowledge
The “Knowledge” step is also known as the
information layer because team members
are starting to see possible explanations of
the why behind given performance or certain
challenges in the organization (e.g., poor
patient satisfaction).
Having a robust set of project measures,
including outcome, process, balancing,
component, and explanatory measures,
will help this step be successful.

Step 5: Knowledge
Team members learn to trust the information
in this step, weighing the new knowledge
against what they already believe to be true,
and have the opportunity to receive
additional context that will provide a more
robust set of perspectives on the project.

Step 6: Insight
In step 6, improvement team members
view the results in the context of
benchmarks, literature, and other
considerations within the organization.
The leader who is accountable for the
performance the specific project is
targeting should have a vested interest
in knowing how other health systems
perform.

Step 6: Insight
In particular, if a project is meant to support
an ACO network or address specific CMS
recommendations, then having an
understanding of how other systems
perform will help establish the magnitude of
potential penalties or shared savings.

Step 6: Insight
When leaders fail to review the results
in proper context, they miss out on
understanding that other organizations
are experiencing the same challenges
and have the same objectives.
Discovering shared challenges among
peers can be encouraging for leaders
because they understand they are not
alone when facing this problem.

Step 6: Insight
Leaders also gain a community to
work with and share knowledge,
frustrations, and wins.
Since health systems generally
share the same vision of improving
patient outcomes in one form or
another, applying insight from one
organization to the next is possible
more often than not.

Step 7: Wisdom
In step 7, teams assess whether actionable
opportunity exists in the context of operational
complexity and readiness, with consideration to
prior stories of success or failure.
This is where team members prepare for
the actions the organization needs to take
to see improvements.

Step 7: Wisdom
Wisdom would suggest that improvement
teams find areas where there is an
appetite for change and begin efforts
in those areas.
For example, if multiple improvement
efforts are simultaneous, the team may
find more success by identifying
different parts of the organization that
are receptive to piloting the changes.

Step 8: Action
In step 8, teams create and implement
the improvement plan.
This will require components of the work
to be prioritized, where each component
will be assigned accountability.
If not already in place, the team will
need to develop a robust communication
structure and cadence.

Step 8: Action
In the action step, the goal(s) should be
explicitly stated and agreed on by all team
members and executives who are
supporting the initiative.
There will often be competing perspectives
on what the goal thresholds should be,
which is why finding a balance between
motivating, but achievable, goals will
be essential.

Step 8: Action
After everyone has agreed on the goal to a
reasonable degree, the team can now work
to implement the change.
If the goals have been set properly,
meeting a goal would mean the improve-
ment in outcomes from baseline has
passed both a statistical and practical
significance test.
It would be best practice to revisit goals
on an annual basis to recalibrate against
system obstacles or opportunities.

Data-Informed Healthcare Drives
Outcomes Improvement
Health systems can benefit greatly
from following the analytics ascension
model, but only if improvement teams
follow each step.
It can be tempting to skip steps or
rush through a step in order to more
quickly start implementation (step 8),
but this risks misguided efforts and
wasted resources.

Following the model hierarchy and giving
each step the time it deserves creates a
culture of data literacy and sets teams up
for success in the often-challenging field
of outcomes improvement, delivering
data-informed healthcare.
It also empowers teams to achieve
cultural alignment and illustrates how to
approach data in the context of
improvement projects.

Rather than leaning on buzzwords like “data
informed” or “data driven,” the Analytics
Ascension Model gives team members the
approach and vocabulary to take data from its
raw state all the way to an action that results in
meaningful patient outcome improvements.

For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

More about this topic
Link to original article for a more in-depth discussion.
Using Improvement Science in Healthcare to Create True Change
Heather Schoonover, MN, ARNP-CNS, PHCNS-BC, FCNS, VP
Healthcare Data Literacy: A Must-Have for Becoming a Data-Driven Organization
Anna Kleckner, PhD, MPH, Business Consultant
Three Must-Haves for a Successful Healthcare Data Strategy
David Grauer, MBA, MHSA, SVP, Professional Services
Five Practical Steps Towards Healthcare Data Governance
Stephen Hess
Four Steps to Effective Opportunity Analysis
Health Catalyst Editors

Sam joined Health Catalyst in February of 2015 as a Data Analyst. Prior to coming to
Health Catalyst, he spent 4.5 years working for 3M as a Lab Research Physicist,
which built upon the 4 years of internship experience he gained there while he
pursued a degree in Applied Physics from UW-River Falls. Always looking for
opportunities to learn and grow, he has more recently taken masters courses in
Predictive Analytics from Northwestern University, and is excited by the promise of
big data in healthcare.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Sam McCutcheon

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement
company that helps healthcare organizations of all sizes improve clinical, financial, and operational
outcomes needed to improve population health and accountable care. Our proven enterprise data
warehouse (EDW) and analytics platform helps improve quality, add efficiency and lower costs in
support of more than 65 million patients for organizations ranging from the largest US health system
to forward-thinking physician practices.
Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS, and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
Millenials, and a “Best Perks for Women.”

Achieve Data-Informed Healthcare in Eight Steps

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