Category Archives: Healthcare Intelligence

Milliman acquires Torch Insight, expanding industry’s leading portfolio of SaaS health market intelligence solutions

Milliman today announced the acquisition of Torch Insight® from Leavitt Partners, LLC. This acquisition will combine Torch Insight’s powerful market-centered data and analytics with MedInsight, Milliman’s flagship healthcare analytics ecosystem. 

Torch Insight brings Milliman decades of healthcare policy expertise and healthcare analytic experience backed by Leavitt Partners—recognized experts in the health sector. The team has integrated and linked thousands of data elements from dozens of public and proprietary data sources. The platform is the culmination of thousands of hours spent cleaning and validating data and splicing together siloed data sets to enable powerful market-centered analysis and data visualization, and integration with business intelligence platforms.

MedInsight founder and Milliman Principal Kent Sacia believes the acquisition of Torch Insight by Milliman represents a significant advancement in healthcare analytics. “Torch Insight and MedInsight are incredibly complementary to each other. MedInsight enables clients to apply robust, value-added analytic techniques to their own data. Torch Insight expands the scope of these analytics by layering in comprehensive data about the surrounding environment and delivery system. Together, MedInsight and Torch Insight provide a 360-degree view of client results in the context of their local healthcare market dynamics, competition, and partner relationships.”

Leavitt Partners Founder and Chairman, and former Secretary of the U.S. Department of Health and Human Services, Mike Leavitt, remarked, “Combining Torch Insight’s comprehensive data on the unique attributes of ACOs, bundled payments, and healthcare stakeholder relationships with MedInsight’s analytic suite, creates the most thorough market intelligence asset available.”

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What role will data play in transforming UK private medical insurance analytics?

Health insurers are relying more on advanced analytic tools as they move from reimbursement services through to provider management, care management, and care delivery services. The sheer volume and complexity of healthcare data can create bias, hinder analysis, and impair decision making. Inadequate data is costly because it is time-consuming to work with and often expensive to address.

Fortunately, data quality tools can identify specific areas of improvement to help actuaries and insurers carry out advanced operational and clinical analytics. In this paper, Milliman’s Joanne Buckle and Natasha Singhal highlight such a tool to assure data quality is properly vetted prior to actuarial analysis. The data quality tool was applied to data collected from five different private medical insurance (PMI) insurers in the United Kingdom. The authors also discuss ways that high-quality PMI data can be used to achieve decision confidence.

Milliman Advanced Risk Adjusters considerations for the UK PMI market

Can a risk adjustment algorithm do an effective job in population risk stratification, given data and system challenges, such that there can be a better understanding of member risk profiles within the context of UK private medical insurance (PMI)? This study by Milliman’s Joanne Buckle, Tanya Hayward, and Kishan Desai discusses risk categorisation techniques in the context of UK PMI and how the Milliman Advanced Risk Adjusters™ tool has been applied successfully in this environment.




Looking at cancer trends through Milliman UK Health Cost Guidelines

Milliman’s UK Health Cost Guidelines™ (HCGs) is a tool for modelling health costs and utilisation from a payer perspective to provide a consistent way to price and analyse claims experience. The 2016 Milliman UK HCGs cover a significant proportion of the private medical insurance (PMI) market, comprising base tables for each sector of the market: corporate, small and medium enterprises (SMEs), and the individual market.

Further analytics include looking at cancer trends and cancer-specific costs by service lines. In this article, Milliman’s Joanne Buckle, Neha Taneja, and Natasha Singhal provide an introduction to the HCGs. They also provide perspective on their HCGs analysis, focusing primarily on cancer-related research and future projections in cancer trends.




Benchmarking analytics for provider reimbursements

Managing provider reimbursement levels is an important function for health plans. Provider reimbursement analytics can offer health plans the foundation they need to effectively manage reimbursements.

In their article “Provider reimbursement analytics,” Milliman consultants David Lewis and Charlie Mills highlight the advantages and disadvantages of the two primary analytical approaches for evaluating provider reimbursement levels. The authors also discuss the pros and cons of the three main baseline fee schedules used in provider contract benchmarking, one of which includes Milliman GlobalRVUsTM.




Milliman Advanced Risk Adjustment™ (MARA™) software tops the competition in risk scoring study

laurent-dianeMilliman’s popular Milliman Advanced Risk Adjusters™ (MARA™) software topped the competition in the latest study by the Society of Actuaries (SOA), “Accuracy of Claims-Based Risk Scoring Models.” Milliman achieved the highest overall performance among concurrent models, beating out 11 other vendors. MARA’s prospective models scored similarly well, ranking at or near the top in all four primary metrics measured.

MARA’s performance is proof of what we’ve known all along—the Milliman Advanced Risk Adjusters software is a leader in claims-based risk scoring models, and one of the most accurate tools on the market.

Since the passage of the Patient Protection and Affordable Care Act (ACA), risk scoring models play a central role in predicting or explaining healthcare expenditures. As in each of the previous SOA studies, coefficient of determination, or R2, was used as one measure of predictive accuracy and indicates how well data fit a statistical model. With an R2 of 55.4%, MARA’s concurrent diagnosis and pharmacy model (CxAdjuster) leads the competition with the highest observed R2 for uncensored costs. For costs censored at $250,000, the same model achieved the highest R2 to occur in the study, at 66.7%.

We were especially pleased that in an exploration of ensemble methods of modeling, more weight was put on the MARA model than any other. To me that indicates that giving MARA the most credence resulted in the best performing composite prediction.

MARA models are valued by clients because they produce separate risk scores for major categories of service, including inpatient, outpatient, emergency room, physician, pharmacy, and other medical care. Beyond risk scores, all MARA models provide a probability of inpatient admission and emergency room visits, plus the contribution of clinical risk drivers for more than 1,000 medical conditions.

The study, released Monday October 25, is published by the Society of Actuaries.