Quality Progress - September 2017 - 38

F E AT U R E

DATA AND IT
attract members from a competitor that are expressing their frustration on social media.
+ Preventing customer churn.13 A health insurance
organization may use social media information and call
center call log and speech log information to identify
potential attrition from employer groups or individual members and take appropriate action to prevent
churn.
+ Identifying added value for existing customers. A
health insurance organization may analyze the information gathered through advanced analytics to offer
value-added products and services-such as bundled
services for members. Bundled services may include a
20% discount at local gyms or pharmacies.
Hospitals and insurance organizations can partner with local and national information exchanges to
share data in real-time. They can also combine internal
data with consumer self-reported real-time data from
wellness applications, wearable devices and sensors
using advanced analytics to prevent diseases, hospital
acquired infections and hospital readmissions, improve
care coordination, and improve health and well-being
of the total population by empowering consumers and
providers.
+ Reducing fraudulent behavior.14 A health insurance
organization may use advanced analytics that integrate member eligibility and claims systems, provider
information systems, fraud and abuse detection
systems, customer call center systems and complaint
management systems to reduce fraudulent behavior.
+ Reducing costs. A health insurance organization can
link member eligibility and claims data with information gathered from the Centers for Disease Control
and Prevention and local social media to recommend
appropriate preventive measures to members. The
organization can make use of advanced analytics to
transform healthcare from volume-based payment
models to new value-based payment models, such as
replacing traditional fee-for-service models and introducing performance-based payments to healthcare
providers. A health insurance company also could use
advanced analytics to identify an emergent, high-cost
and high-risk population and implement appropriate
interventions and monitoring.
+ Making recommendations, such as personalized
treatment plans and prevention actions. A health
insurance organization can recommend an appropriate health insurance policy or healthcare provider, or
appropriate preventive measures, by linking available
data from multiple data sources, such as member
eligibility, claims, electronic health records, survey

38 QP

September 2017 ❘ qualityprogress.com

feedback, customer feedback and customer service
telephone calls. HR departments can use advanced
analytics that combine skills data, certification data
and job description data to identify the best candidates for jobs.
+ Making data-driven decisions. A health insurance
organization can make underwriting decisions using
advanced analytics that combine data from current
and anticipated claims data, business risks, stock market risks and anticipated reserve.

QMS to integrate advanced
quality analytics

Organizations typically use lean Six Sigma methods for
improving productivity, reducing costs and improving
stakeholder satisfaction. These organizations use traditional statistical tools such as control charts, design of
experiments, linear regression, analysis of variance and
response surface methodology.
Organizations can gain a competitive advantage by
leveraging a lean Six Sigma culture and the define, measure, analyze, improve and control (DMAIC) approach
to supplement traditional statistical tools with advanced
data analytics.
Lean Six Sigma DMAIC methods support analytical
thinking and tools for making fact-based decisions,
and these can provide a roadmap to organizations to
leverage these new opportunities. Without a structured
approach such as lean Six Sigma DMAIC, implementation
of advanced analytics would be overwhelming. Lean Six
Sigma's continuous improvement culture allows change
management, knowledge management and management support infrastructures to focus on their goals.
Define: This stage starts with defining concise and
clear problem areas or improvement opportunities
aligned with the organization's vision and mission. This
includes identifying stakeholders, selecting a team,
creating a schedule, defining outcomes, providing
justification, obtaining management sponsorship and
launching the project in which advanced analytics
implementation can have an impact. A clear business
case on how advanced analytics can help solve problems
or contribute to improvement opportunities must be
developed.
Measure: This stage involves identifying internal and
external data sources from which data can be gathered.
Tools such as the goal-question-metric approach and
voice of the customer can be used to understand the
data sources needed.
Accessibility, usability, quality and quantity of the data
are assessed, the richness of the data is explored and the


http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - September 2017

Seen and Heard
Progress Report
Mr. Pareto Head
Career Coach
Expert Answers
Field Notes
Data Disruption
The Deal With Big Data
Better Intelligence
A Study in Measurement
Innovation Imperative
Statistics Spotlight
Standard Issues
ASQ's 2017 Quality Resource Guide
Marketplace
Footnotes
Back to Basics
Quality Progress - September 2017 - Intro
Quality Progress - September 2017 - cover1
Quality Progress - September 2017 - cover2
Quality Progress - September 2017 - 1
Quality Progress - September 2017 - 2
Quality Progress - September 2017 - 3
Quality Progress - September 2017 - 4
Quality Progress - September 2017 - 5
Quality Progress - September 2017 - Seen and Heard
Quality Progress - September 2017 - 7
Quality Progress - September 2017 - Progress Report
Quality Progress - September 2017 - 9
Quality Progress - September 2017 - Mr. Pareto Head
Quality Progress - September 2017 - 11
Quality Progress - September 2017 - Career Coach
Quality Progress - September 2017 - 13
Quality Progress - September 2017 - 14
Quality Progress - September 2017 - Expert Answers
Quality Progress - September 2017 - Field Notes
Quality Progress - September 2017 - 17
Quality Progress - September 2017 - 18
Quality Progress - September 2017 - 19
Quality Progress - September 2017 - Data Disruption
Quality Progress - September 2017 - 21
Quality Progress - September 2017 - 22
Quality Progress - September 2017 - 23
Quality Progress - September 2017 - 24
Quality Progress - September 2017 - 25
Quality Progress - September 2017 - The Deal With Big Data
Quality Progress - September 2017 - 27
Quality Progress - September 2017 - 28
Quality Progress - September 2017 - 29
Quality Progress - September 2017 - 30
Quality Progress - September 2017 - 31
Quality Progress - September 2017 - 32
Quality Progress - September 2017 - 33
Quality Progress - September 2017 - Better Intelligence
Quality Progress - September 2017 - 35
Quality Progress - September 2017 - 36
Quality Progress - September 2017 - 37
Quality Progress - September 2017 - 38
Quality Progress - September 2017 - 39
Quality Progress - September 2017 - 40
Quality Progress - September 2017 - 41
Quality Progress - September 2017 - A Study in Measurement
Quality Progress - September 2017 - 43
Quality Progress - September 2017 - 44
Quality Progress - September 2017 - 45
Quality Progress - September 2017 - 46
Quality Progress - September 2017 - 47
Quality Progress - September 2017 - Innovation Imperative
Quality Progress - September 2017 - 49
Quality Progress - September 2017 - 50
Quality Progress - September 2017 - Statistics Spotlight
Quality Progress - September 2017 - 52
Quality Progress - September 2017 - 53
Quality Progress - September 2017 - Standard Issues
Quality Progress - September 2017 - 55
Quality Progress - September 2017 - 56
Quality Progress - September 2017 - 57
Quality Progress - September 2017 - ASQ's 2017 Quality Resource Guide
Quality Progress - September 2017 - 59
Quality Progress - September 2017 - 60
Quality Progress - September 2017 - 61
Quality Progress - September 2017 - 62
Quality Progress - September 2017 - 63
Quality Progress - September 2017 - 64
Quality Progress - September 2017 - 65
Quality Progress - September 2017 - 66
Quality Progress - September 2017 - 67
Quality Progress - September 2017 - Marketplace
Quality Progress - September 2017 - 69
Quality Progress - September 2017 - Footnotes
Quality Progress - September 2017 - 71
Quality Progress - September 2017 - Back to Basics
Quality Progress - September 2017 - cover3
Quality Progress - September 2017 - cover4
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