The following article is taken from the recent Gartner report – Hype Cycle for U.S. Healthcare Payers, 2016, published: 1 July 2016.
Smart Health Plan Selector Tools – Analysis By Constance Sjoquist
Definition: Smart health plan selector tools apply advanced analytics, such as machine learning
and cognitive computing, to large and diverse datasets to assist consumers in shopping for a
suitable health plan based on factors such as their health risk, financial risk tolerance, current
prescriptions and physicians, and experience preferences. These tools are in contrast to simple plan
selection tools that use rule-based scoring to sort and filter health plans.
Position and Adoption Speed Justification: Smart health plan selector tools are advancing to aid individuals in picking health plans that most closely align with their personal, financial, and health risk tolerance and goals, and are being adopted in response to the increase in:
■ Use of enrollment portals where individuals go through the plan selection process alone — with minimal knowledge of plan designs and little human support
■ Number of health plan designs that individuals choose from on enrollment portals, making the decision process for individuals overwhelming
■ Number of employers switching to defined contribution benefit designs to cap their benefit
costs and shift costs onto individual employees, with the assumption that more choices will
make up for higher individual out-of-pocket costs
■ The government’s, employer’s and payer’s desire to align an individual’s health plan selection
with their health history to determine the health plan that will result in the best health outcomes
Most health plan selection tools are embryonic and not truly “smart,” but rely on decision workflows and Excel worksheets to facilitate individual plan selection. Smart health plan selector tools are often coupled with online or mobile enrollment solutions. Expect their velocity along the Hype Cycle to closely align to the velocity of private exchange technologies and other online and mobile enrollment solutions.
Health plan selector tools are often integrated into payer, broker or group enrollment, or private
exchange portals. The limitations of Generation 1 (rule-based) plan selector tools are triggering
Generation 2 plan selector tool development and adoption. With the addition of smart technologies, such as virtual consumer assistants and recommendation algorithms, we expect the rate of adoption of smart health plan selector tools to increase.
User Advice: Healthcare payer CIOs and chief marketing officers (CMOs) should understand that
there are few vendors who actually use smart technologies — the use of data analytics and
algorithms — in their health plan selector tools. Most representative vendors we listed are largely
aspirational. Look for vendors whose solutions can perform perceptual tasks — classifying large
and variant data input streams in nonobvious ways — to inform decision making at a greater depth than using only member-, payer-, provider-, or employer-specific data. By combining health and financial data with behavioral, social, demographic, and other nonhealth data, payers can expect to more closely align individual plan preferences with health outcomes.
Payer CIOs and CMOs should:
■ Drive awareness and quantitative analysis of the growing impact of plan selector tools on payer revenue.
■ Track emerging vendors in this space, paying special attention to evidence of their incremental
growth in efficacy compared with Generation 1 tools.
■ Assess the opportunities for internally built cognitive solutions in this market. This begins with
an inventory of the structured and unstructured data available to train against, including claims,
products, provider network, member profitability, member satisfaction, and renewal.
Use the data from smart health plan selection tools as early in the enrollment phase as possible —beginning with post enrollment data from current plan years. Review how well individuals were
matched to plan selection and what modifications can be made for the next enrollment cycle.
Following the member’s health journey, look for over- or underutilization of benefits, customer
complaints or confusion of how to use their benefits.
Business Impact: All areas of the payer are impacted by the use of smart health plan selector tools.While the decision to implement these tools may reside with sales and marketing leaders, benefits derived will be evident beyond the enrollment stage of the member life cycle, as the payer’s success depends on members who understand how to choose and use their health benefits. Smart health plan selector tools help payers provide members with the optimal health plan that addresses members’ particular health and financial interests in a way that paper- or rule-based health plan selection tools cannot.
By incorporating data analytics and algorithms into the plan selection process, payers can go
beyond simple workflows and lead members along an enrollment path that is more personal and
results in a more suitable health plan match. Substantial consumer value can be unlocked using
cognitive approaches to match consumers to plans that improve their health outcomes and
experience, and lower their health costs. While payers may take a near-term hit on profitability, as enlightened members choose “skinnier” plans, their value will accrue value through improved
competitiveness, sales effectiveness, and member loyalty.
As this space matures and tools incorporate more cognitive capabilities, payers should expect to
improve product placement for desirable members, similar to search engine optimization. Cognitive plan selector tool algorithms can be used in the aggregate to determine whether a payer or an exchange vendor’s product portfolio has an appropriate mix of appealing and beneficial products for their prospective members.
Benefit Rating: High
Market Penetration: Less than 1% of target audience
Sample Vendors: Array Health; ConnectedHealth; Obeo Health; Picwell
“Predicts 2016: U.S. Healthcare Payers Are Challenged to Become Digital Health Payers”
“Industry Vision: Mass Personalization of Consumer Healthcare Engagement”