Category Archives: Quality of Care

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 Value-Based Insurance Design Health (VBID), 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.

Complex/high-risk patient targeting case study

Many health systems globally are introducing new care models that purport to replace expensive, and often clinically unnecessary, acute inpatient care with more primary and community-based services. This article by Milliman’s Lalit Baveja and Tanya Hayward explores a clinic-based community intervention designed to improve access and quality of care for high-utilising, high-risk patients over the course of three years.

Principles and evaluation of care management interventions

With greater emphasis on delivering quality health outcomes while reducing costs, organizations are making care management an indispensable part of their system. This paper by Milliman consultants Neha Taneja and Joanne Buckle illustrates the importance of evaluating interventions for policymakers, healthcare organizations, payers, and providers seeking to implement care management.

Reduce healthcare’s long-tail problem with telemedicine

Technology has enabled many industries to reduce or eliminate the long-tail problem. Similarly, telemedicine offers the healthcare industry a solution to its long-tail problem—access barriers to healthcare services. A new article entitled “Telemedicine and the long-tail problem in healthcare” by Milliman’s Jeremy Kush and Susan Philip explores the benefits of telemedicine as a mode for healthcare delivery. The authors also analyze current levels of telemedicine utilization and identify five factors limiting adoption.

All risk adjusters are not created equal

Incorporating risk adjustment into an alternative provider payment arrangement can help payers and providers share risk, align financial incentives, and reduce health plan costs. There are many risk adjustment models on the market for payers and providers to choose from. However, both parties need to know the predictive abilities of a risk adjuster and its implications on projected reimbursement levels. Milliman consultant Ksenia Whittal provides some perspective in her article “Provider payment: What does risk adjustment have to do with it?

Not all risk adjusters are created equal. There are multiple models available on the market and they vary in their predictive abilities, the populations they are calibrated to assess, and the time periods for which analyses can be conducted. It is no surprise that the results of any risk-adjustment analysis will be more reliable using a model with stronger predictive power. In large part, the predictive power will be driven by the algorithms underlying the model, but consideration should also be given to the model’s intended use. For provider payment specifically, it is critical to ensure that the variables used for risk score development are resistant to manipulation by providers and do not create perverse incentives. Examples of potentially problematic variables include incurred cost in a prior period, procedures, or diagnostic testing, because these items could lead to model exploitation and distort true morbidity levels.

Arrangements should also specify whether risk adjustment will be applied to actual experience at the end of a contract period (a concurrent analysis), and result in a retrospective adjustment to prior payments to account for the risk level actually encountered, or if the rate will be set prospectively, using current experience to project the appropriate rate for the next contract period. Concurrent risk adjustment is far more accurate than prospective because it seeks to explain what already happened rather than predict what will happen. However, concurrent models can introduce uncertainty during the payment year as to what the retrospective settlement may be. This uncertainty can create challenges for insurers and providers in their budgeting and financial reporting processes. Additionally, a prospective approach should exclude the use of prior cost levels to project future risk levels in order to avoid incentivizing activities that would artificially inflate costs and result in exaggerated prospective risk scores.

Beyond ensuring that the model used is a strong and accurate predictor, it is also important to choose a risk adjuster that will most closely model results for the population in question or the payment arrangement in place. Risk-adjustment models should ideally be calibrated for a population reasonably similar to the one being analyzed. For instance, a model calibrated to a commercial population will not generally be appropriate for risk-adjusting a population of Medicare enrollees, and vice versa. Broad population categories such as these have different morbidity profiles, and risk adjusters should target and be calibrated to capture these nuances, or at least a study should be done to check if a model is performing adequately if it is used on a population that differs significantly from the one used in calibration.

For more Milliman perspective on risk adjustment, click here.