Analytics are transforming the customer-care continuum

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Data analysis equips payers to stage preventive care interventions to improve members’ quality of life

The dramatic spike in healthcare costs over the recent past is driving up health insurance expenses for both members and payers. Not surprisingly, therefore, insurers are looking to proactively mitigate risk by adopting care models based on prevention and timely intervention.

Insurers are turning to analytics to expedite predictive diagnoses and personalized care that enhances members’ experience and keeps costs in check, according to The Top 10 Technology Trends in Health Insurance 2019.

Analytics at each stage of the customer care continuum

Analytics can help to boost service at each phase of the customer-care continuum, from initial diagnosis to personalized care delivery. Predictive analytics offer insight into members’ potential health trajectories and the probability of becoming ill. Data analysis equips payers to stage preventive care interventions in coordination with providers that may improve members’ quality of life.

InsurTech Lumiata offers insurers actionable insights by using artificial intelligence to assess patient medical data to predict future health. The California startup’s Risk Matrix is a predictive tool that delivers personalized and time-based predictions with associated chains of medical reasoning on how each person’s health is likely to change and how fast that change will occur. Since its 2013 launch, Lumiata has developed models to predict disease risk and onset of certain chronic conditions for more than 20-million patient lives.[1]

Analytics can also significantly aid initial diagnosis, by effectively and quickly determining the source of an ailment. With analytics, initial diagnosis may also be made remotely – a convenience and cost-benefit for all involved. China’s popular online medical platform Good Doctor – from insurer Ping An – analyzes photos and videos uploaded by members via mobile app to deliver an initial diagnosis for certain ailments. By the end of June 2018, Good Doctor had 228 million registered users.[2]

Accuro Health, a New Zealand-owned not-for-profit health insurance cooperative, partnered with Amsterdam-based startup SkinVision in 2017 to leverage the Insurtech’s app that lets people check for skin cancer via their smartphone. The app allows users to upload photos of their moles and then applies machine learning (ML) and analytics to determine whether the moles are cancerous. Accuro made more than 60 skin cancer diagnoses within the first six months of the SkinVision partnership.[3]

After diagnosis, analytics may be leveraged to identify pertinent therapies and medication to craft the best course of treatment for the patient – personalizing care to the next level.

Managed-care consortium Kaiser Permanente took steps in this direction when it partnered with Israel-based Medial EarlySign, a startup that develops ML-based decision-support tools designed to expose the hidden layer of information in standard medical data. Kaiser Permanente utilizes Medial EarlySign’s platform to optimize care for individuals and prevent or delay serious health conditions by leveraging routine blood test results and common labs and electronic health record (EHR) data to make optimized clinical decisions as well as to improve patient outcomes, focus financial resources, and reduce overall costs. The platform also enables physicians to understand risks associated with a change in treatment more clearly.[4]

Finally, analytics can support adherence to personalized care plans and improve treatment. Insurers that leverage behavioral analytics to study customer preferences – even simple inclinations such as the preference for medicine over injection, or an outpatient facility over a hospital – can apply the insights to maintain better health outcomes and personalize the experience.  An engaged member is a more adherent member, including adherence to brand loyalty.

One of the largest health benefits companies in the United States, Anthem, leverages behavioral analytics to ensure meaningful customer engagement and deliver a personalized experience. Anthem uses integrated information such as claims data, clinical data, EHR, lab results, and other key data sets, to develop consumer profiles. This data enables Anthem to segment customers for messaging, accurate coaching, and additional services – resulting in a frictionless and personalized experience based on each customer’s engagement preferences.[5]

An Rx for competitive differentiation

Analytics can give insurers a stronghold in tomorrow’s competitive landscape. Predictive diagnoses help payers engage in proactive and timely care interventions, leading to improved quality of care and reduced claim costs. Behavioral analytics can enable care personalization, which boosts customer satisfaction and loyalty.

Insurers will need capabilities in data handling, artificial intelligence, ML, etc. – and a plan in place to act on the insights derived – to tap analytics’ full potential.

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[1] The PE Hub Network, “Lumiata raises $11 mln from Khosla, BlueCross BlueShield, Sandbox, Intel Capital, others,” Mark Boslet, July 10, 2018,

[2] Financial Express, “Chinese Insurance major embraces Artificial Intelligence,” November 24, 2017,, accessed August 2018

[3] Squirro website, “InsurTech Top 5  – DIA Munich and AI Data Analysis,” Maria Schuett, November 22, 2017,

[4] Health Data Management, “Kaiser to advance use of AI to personalize care interventions,” Joseph Goedert, May 31, 2018,

[5] Health IT Analytics, “Borrowed from Retail, Anthem’s Big Data Analytics Boost Member Engagement,” Jennifer Bresnick, August 3, 2017,

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