Category Archives: Electronic Health Records

Population health analytics for India’s health insurance industry

Javier SanabriaCost, Electronic Health Records, , , ,

In India, recent regulatory changes mandating guaranteed renewability, lifetime coverage and restricted premium revision opportunities imply that any substandard risk in the current portfolio could potentially be retained for life. This necessitates a different approach to managing insurers’ growing portfolios.

This article by Milliman’s Lalit Baveja explains how insurers can benefit from treating their covered members using analytics based on population health principles. Such an approach requires a better clinical understanding of member populations to identify the most effective and cost-efficient strategies for managing members’ health and preventing hospitalizations and claims in the long run.

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Milliman RBRVS for Hospitals

Javier SanabriaElectronic Health Records, , , , , , ,

The Milliman RBRVS for Hospitals™ Fee Schedule provides a simple solution for comparing hospital contractual allowed amounts, billed charge master levels, relative efficiency, and patient mix differences. The fee schedule is based on Relative Value Units (RVUs). There are several advantages of RBRVS for Hospitals. For example, RVUs have been developed for all hospital services, so they reflect the relative resources required to perform the care. Also, a single conversion factor can be used to benchmark a hospital contract. Milliman actuaries provide some perspective in this paper.

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Cascade Health Alliance chooses Milliman PRM Analytics for population risk management

jeremy.engdahl-johnsonElectronic Health Records, , , ,

Milliman PRM Analytics™ (PRM), a leader in data-driven value-based healthcare support systems, today announced that Cascade Health Alliance (CHA), a coordinated care organization (CCO) serving Klamath County, Oregon, has selected the PRM Platform and its suite of cloud-based analytic and population risk management solutions to support their growing clinical integration initiatives.

“By giving us greater ability to better manage our population health, the PRM tool allows for more efficient risk stratification and management,” said Peter Waziri, CHA’s Chief Financial Officer. “Working with Milliman and PRM Analytics will help CHA to better serve our members by allowing staff deeper insights to those members’ health information.”

“We are pleased to be selected by Cascade to help them manage their risk-based populations. Milliman continues to be the industry leader in helping providers manage population risk. PRM™ represents a disruptive approach to population stratification and management. The analysis focuses on the prospective opportunity for potentially avoidable costs so the patient care team can focus on them in advance. Care management can then be focused on the patients with the greatest potential to ‘bend the cost curve’ resulting in the optimal deployment of limited care management team resources,” said Art Wilmes, FSA, MAAA, a Principal and Consulting Actuary in Milliman’s Indianapolis office.

“I have access to new ways of seeing cross-sectional data and how it all works together,” said Angela Leach, CHA’s medical informatics analyst. “Case managers will use it to get at-a-glance profiles of patients they are caring for, and the quality management department can use it to find ‘hot spots’ that may benefit from additional programs.”

The case management team at CHA can use PRM Analytics in a variety of ways, according to Diane Barr, Director of Case Management. “We can identify high-cost members, plus we can filter for diagnosis and identify members for disease management,” Barr said. “We can also look at an individual member to determine their utilization, chronic conditions and other details. The best part is we can do risk stratification and identify members that are at the highest risk for re-hospitalization or emergency department utilization. The program is easy to use and provides us with volumes of useful information.”

Transition from RAPS to EDS data decreases Medicare Advantage risk scores

Javier SanabriaElectronic Health Records, Medicare, , , , , , , , , ,

Milliman consultants Deana Bell, David Koenig, and Charlie Mills performed a study of how the transition from Risk Adjustment Processing System (RAPS) data to Encounter Data System (EDS) data is affecting payment year (PY) 2016 risk scores and revenue for Medicare Advantage organizations (MAOs). Fifteen MAOs participated in the study, reflecting a cross section of small- and medium-sized organizations and representing over 900,000 members in 154 plans. The consultants offer perspective in their article “Impact of the transition from RAPS to EDS on Medicare Advantage risk scores.”

Overall, the study found that the median percentage difference between PY 2016 risk scores based on RAPS and the EDS-based risk scores is 4.0%. The percentage difference is larger for special needs plans (SNPs) and smaller for general enrollment plans as shown in Figure 1. The prior year’s diagnoses make up a larger component of SNP members’ risk scores, compared to general enrollment plans, so the risk score impact for SNP plans is larger.

[The authors] have not attempted to quantify what portion of the difference between RAPS and EDS is due to incompleteness of the EDS submissions, issues with CMS’s return files (revised MAO-004 files), changes to filtering logic, and the effect of claims coding errors.

As an illustration, the potential Part C PY 2016 revenue using the median difference of -4% between RAPS and EDS results in a reduction of approximately $40 per member per year, assuming approximately $800 in Part C risk-adjusted revenue and a 1.0 RAPS-only risk score. To the extent that this -4% gap persists in future years, the revenue impact will grow because the EDS-based risk score will make up an increasing portion of the final risk score (e.g., with the 25% EDS weight in PY 2017, the per member reduction would be about $100 per year).

This article is the second in a series of articles on the transition to EDS. For more information about the EDS and RAPS data used in MA risk scores, read “Medicare Advantage and the Encounter Data Processing System: Be prepared.”

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Benchmarking analytics for provider reimbursements

Javier SanabriaElectronic Health Records, Healthcare Intelligence, , , , ,

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.

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Developing population health management programs under risk-based contracts

Javier SanabriaElectronic Health Records, , , , , , , , ,

Risk-based contracts are driving the development of population health management programs (PHMPs) that are designed to achieve the Institute for Healthcare Improvement’s Triple Aim goals. Health systems may need to redesign how they deliver healthcare to meet these goals. Risk-based contracts often give providers both the financial flexibility and incentive to redesign care.

In the article “Population health management program development: The path to the Triple Aim,” Milliman’s Nick Creten and Blaine Miller discuss the following five steps healthcare organizations must address when developing a PHMP in a risk-based contracting environment.

Step 1: Assess population costs, utilization, and risk
Step 2: Identify opportunities
Step 3: Segmentation
Step 4: Intervention development
Step 5: Monitor, assess, and improve

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