Customer journey analytics uses machine learning and big data to track and analyze when and through what channels customers interact with an organization, with an aim to influence behavior (e.g., buying behaviors among retail customers). Similarly, healthcare organizations want to influence health-related behaviors, such a taking medication as prescribed and not smoking, to improve outcomes and lower the cost of care. In a partnership with an analytics services provider, a payer organization is leveraging customer journey analytics among healthcare consumers to identify the best opportunities and channels for patient outreach. With this analytics-driven engagement strategy, the payer has found an opportunity to significantly improve patient engagement—a predicted overall increase from 18 percent to 31 percent.

1. Customer Journey Analytics: Cracking the
Patient Engagement Challenge for Payers

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Customer Journey Analytics
This presentation is based on a 2018 Healthcare Analytics Summit
presentation given by Christer A. Johnson, Principal, EY Analytics, and
Alexander (Alex) Marano, Customer Analytics Lead, Cigna Information
Management and Analytics, entitled, “Using Machine Learning and Big Data
to Drive Patient Engagement and Better Health Outcomes.”
Christer A. Johnson
Principal, EY Analytics
Alexander (Alex) Marano
Customer Analytics Lead
Cigna Information Management and Analytics

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There are moments in every patient
journey in which a well-informed, well-
timed intervention (e.g., phone call,
personal visit, etc.) can effectively
engage patients and positively
influence their health-related behavior.
Random engagement, however, is far
less effective than analytics-driven
strategic engagement.
To know when to reach out to and
which patients to focus on, payers need
an approach that leverages machine
learning and big data.
Customer Journey Analytics

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Other industries have used analytics-driven
engagement strategies, or customer
journey analytics, to analyze real-time data
on customer interactions, demographics,
and lifestyle events and use these insights
to influence customer behavior.
The healthcare industry is now working to
leverage a similar approach to engage
effectively with healthcare consumers to
help them avoid risky health behaviors
in favor of behaviors that improve their
health and lower healthcare costs.
Customer Journey Analytics

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This presentation describes a collaboration
between a global analytics services
provider and a large payer organization
to leverage customer journey analytics
among healthcare consumers.
Customer Journey Analytics

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Patient engagement solutions support outcomes
improvement and cost reduction by targeting
behaviors that have negative health consequences
but are largely avoidable.
These behaviors have a significant
impact on patients with chronic and
multiple health conditions, who are
among the costliest populations.
Patient Engagement Solutions Target
Avoidable Risky Behaviors

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Considering these examples of unacceptable high rates of avoidable risky
behaviors, improving patient engagement must become an industry priority:
Patient Engagement Solutions Target
Avoidable Risky Behaviors
Twenty-four percent of
adults with diabetes
don’t know they have it.
About 13 million adults
with high blood pressure
don’t know they have it.
One in five deaths
is caused by
cigarette smoking.

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Addressing avoidable health concerns above
and their like presents a sizeable healthcare
improvement opportunity.
But to impact these statistics and the
patients who comprise them, health
services providers must understand
behavioral patterns and factors
most linked to engagement.
Patient Engagement Solutions Target
Avoidable Risky Behaviors

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Customer journey analytics follows patient
behavior to identify patterns most likely
associated with engagement.
Analysis may show behaviors and
characteristics linked with likelihood to
engage and key times to reach out to
those patients.
Patient Journey Analytics for
Strategic Engagement

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For example, the period following a specialist
visit can be an engagement opportunity as
patients tend to seek information at this time.
If a case manager reaches out to the patient
after the specialist appointment, she may
have the answers the patient is looking for.
To leverage journey analytics to improve
patient engagement, payers create the
journey data, follow a framework for and
execute the four phases of journey analytics,
and operationalize the analytics.
Patient Journey Analytics for
Strategic Engagement

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Patient journey data creation (Figure 1)
starts with a reference library that holds
events, population criteria, outcomes, steps,
attributes, and respective SQL logic.
From that library come the patient
attributes, time-sequenced journey,
and journey step attributes.
These datasets allow for:
Ongoing applications
Future applications
Creating Patient Journey Data
>
>

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Creating Patient Journey Data
Figure 1: Patient journey data creation

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The framework for patient journey analysis (Figure 2) asks questions about
the patient journey in three key categories:
Understanding value:
What is the total value of the opportunity to move customers from
undesired to desired outcomes?
Understanding negative outcomes:
What kind of customers are having negative outcomes?
What kind of providers may be influencing negative outcomes?
Understanding the relationship between journeys and positive outcomes:
Which are the most frequent journeys?
Which journeys lead to the most positive outcomes?
The Patient Journey Analysis Framework
>
>
>
>
>

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The Patient Journey Analysis Framework
Figure 2: The framework for patient journey analysis

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The framework also considers patient
and provider attributes and patterns most
associated with engagement or the lack
of engagement.
Steps may include internet searches, in-
network primary care physician visits, in-
network specialist visits, care gaps, ED
visits, and more.
These insights can help payers improve
the timing, channel, and content they use
to engage members with chronic and
complex conditions.
The Patient Journey Analysis Framework

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Journey analytics also helps payers
identify the most impactable candidates
for engagement using likelihood-to-
engage score leverages.
When the payer mentioned in this report
compared using the likelihood-to-engage
score with a traditional approach to
outreach, predicted overall engagement
increased from 18 percent to 31 percent.
The Patient Journey Analysis Framework

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Payers have previously based engagement
strategies on claims data.
As a layered approach to data, a time-
sequenced customer journey data lake
(Figure 3), combines claims and EMR
data, negative events, physical and
verbal signals, and digital signals.
Payers can apply data to the journey to
find moments when interventions will
increase the probability that a patient
will choose a better path.
The Patient Journey Analysis Framework

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These insights identify paths that help
create alerts for good times to take action
to improve outcomes and reduce cost.
Actions include:
Predict future events and behaviors.
Identify signals suggesting changes
to expected behaviors.
Prescribe interventions to shape the
right outcomes.
The Patient Journey Analysis Framework
>
>
>

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The Patient Journey Analysis Framework
Figure 3: A layered approach to data

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Patient journey analysis occurs in four phases.
Each phase yields detailed journey data for
patients identified for case management, a
list of the most important journey steps by
case management category, and potential
engagement and engagement lift by case
management category:
1. Data collection and journey data creation
2. Journey steps importance test
3. Rules selection
4. Results
The Four Phases of Patient Journey Analysis

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Data collection and journey data creation
Identify selected patients for case management.
Query multiple sources to design a time-
sequenced journey dataset.
The Four Phases of Patient Journey Analysis
>
>

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Journey steps importance test
Calculate importance of and rank each
journey step.
Use above results to select the most
important journey steps.
The Four Phases of Patient Journey Analysis
>
>

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Rules selection
Modify journeys to include only
the most important journey steps.
Identify the most prevalent rules
(combinations of journey steps).
Calculate score for each rule
as total engaged/total outreached.
Create rules to prioritize certain
rulesets over others.
The Four Phases of Patient Journey Analysis
>
>
>
>

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Results
Use the selected rules to score patients
by case management category.
Combine scores at case management level
to determine overall engagement rate.
The Four Phases of Patient Journey Analysis
>
>
The payer who implemented journey
analytics identified the opportunity for a 30
percent increase in engagement [Figure 4].

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The Four Phases of Patient Journey Analysis
Figure 4: Example of increasing overall engagement rate

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To be effective, payers must operationalize
customer journey analytics.
The key differentiators between basic
analytics and operationalized analytics is that
basic analytics only analyzes and reports
(informs), whereas operationalized analytics,
anticipates, engages, senses, and responds
(the middle orange box in Figure 5).
With the added capabilities of operationalized
analytics, payers can better sense opportunity,
engage in real time, and personalize these
interventions over time.
Operationalizing Patient Journey Analytics

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Operationalizing Patient Journey Analytics
Figure 5: Operationalizing customer journey analytics

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Healthcare organizations can’t help patients
if they can’t engage them effectively, and in
an increasingly complex health landscape,
better patient outreach is an imperative.
Engagement improves significantly with an
analytics-driven strategy that identifies
whom to engage, when, and how.
With patient journey analytics, payers
and other healthcare organizations can
influence better health behaviors to
improve outcomes, customer satisfaction,
and lower healthcare costs.
Anticipate, Sense, and Respond

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

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More about this topic
Link to original article for a more in-depth discussion.
Customer Journey Analytics: Cracking the Patient Engagement Challenge for Payers
Six Challenges to Becoming a Data-Driven Payer Organization
Luke Skelley, Sales, VP; Matt Denison, Payer Solutions, VP; Rob McCrory , Strategic Advisor
A Guide to Governing Healthcare Claims Data Successfully: Lessons from OSF HealthCare
Brian Eliason, MIS; Roopa Foulger
The Secret to Patient Compliance: An Application of The Four Tendencies Framework
Health Catalyst Editors
Healthcare Payers and Providers: The Best System for Process Improvement
Bobbi Brown, SVP
Improve Patient Engagement with Five Public Health-Inspired Principles
Maggie O'Keefe, Product Line Director, Operations and Performance Management; Marie Dunn

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Other Clinical Quality Improvement Resources
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