Tag Archives: Marcos Dachary

Milliman chat will focus on healthcare waste

What: #HealthWaste

Where: Twitter

When: Thursday, August 2, at 10 a.m. PST/1 p.m. EST

Topic: Identifying wasteful spending in healthcare

Moderator: @MillimanHealth

About this chat
In recent years, few issues have been spoken about more than the need to curb unnecessary healthcare spending across the United States. As discussions continue at various parts of the government, Milliman invites you to join our #healthwaste Twitter chat. The chat features Milliman MedInsight consultants Rich Moyer and Marcos Dachary, who will discuss ways in which the healthcare industry can identify wasteful spending and what can be done to address the underlying issues. Rich and Marcos will be joined by two of the premier experts in this area, Dr. Mark Fendrick of the Center for Value-Based Insurance Design (V-BID), and Dr. Michael Chernew of Harvard Medical School.

Rules of engagement
• To participate in the chat, follow the hashtag #healthwaste.
• Answer Q1, Q2, Q3… with A1, A2, A3….
• Remember to include the hashtag #healthwaste in all your tweets.
• If you are new to Twitter chats, considering using TweetChat.com.

Critical Point podcast: “Healthcare waste and how to find it”

Milliman’s new podcast, Critical Point, presents unique perspectives from the firm’s professionals. The podcast’s debut episode, “Healthcare waste and how to find it,” features Jackie Sehr, Marcos Dachary, and Dr. David Mirkin from Milliman MedInsight®, a data warehousing and healthcare analytics platform. In this episode, they discuss healthcare waste and approaches to minimize waste and reduce unnecessary costs across the American healthcare system.

To listen to this episode of Critical Point, click here.

Considerations for creating health condition benchmark adjustments

Using benchmarks for comparative analysis is essential (e.g., weighing the risk of a new reimbursement structure, determining performance targets, or measuring outcomes). In fact, when creating performance-based benchmarks for a population, one must consider how to account for the disease burden. The question becomes, how does one adjust for circumstances outside of the control of a healthcare system (e.g., genetics and environment) or where the health system has limited influence (e.g., lifestyle choices)? The objective is to try and remove all uncontrollable factors, thereby identifying any variance from the benchmark as an opportunity that could be addressed by either medical management or contractual change. Variance from the benchmark would then illuminate potential calls to action.

In order to create this ideal condition benchmark adjustment, we have found the following characteristics to be essential in the adjustment factor development process:

1. Broad vs. narrow disease categories:
• Cohorts should present in credible amounts for a condition in order to reflect the overall disease prevalence
• A broad definition captures all stages of a disease’s progression
• Broad condition adjustments can be used for population predictive modeling

2. Condition definitions should not be weighted too strongly by bursts of healthcare that are due to short-term events.

3. The methodology assumptions should not restrict the data sets used in benchmark construction. Traditional large medical and prescription claim data sets can be used, yet we can also leverage credible electronic health records (EHR) or billing/charge master data sets as well if these are available

In MedInsight, we have created several condition-based benchmark adjustment factors, each targeted at providing a solution for a specific, often narrow, need, yet we feel the best overall method for condition adjustment (adj) is our Chronic Condition Hierarchical Groups (CCHG) product. The CCHGs assign all individuals to a single category (100% of the population, both the sick and the healthy), the cohorts are clinically similar, the number of cohorts are small or manageable, and, most importantly, the hierarchy algorithm stems from the clinical point of view; physician decision making is at the heart of the system.

Traditionally, we combine the CCHG condition factor with our Milliman Health Cost Guidelines™ (HCGs) benchmarks as a means to improve our trend management benchmarks. As seen below, we have a traditional factor table for some of our inpatient HCG-level adjustments:

In the HCG benchmarks above, the CCHG condition factors are meant to resolve or tie out to the HCG benchmarks to adjust for uncontrollable disease-related factors. Later in 2012, we hope to apply the CCHG methodology as an optional benchmark factor itself and not a part of the HCG benchmark. We believe this will improve our risk adjustment process and better explain results in both clinical performance projects and health plan marketing activities. Using the CCHG-based condition adjustment factors will allow plans to better quantify their value propositions while identifying new ways to improve their overall efficiencies.

This article first appeared at Milliman MedInsight.