Many health systems around the world are introducing new care models which claim to replace expensive acute inpatient care with more primary and community-based services. This paper by Milliman consultants examines the primary care redesign of seven US practices over the course of three years, including their reported utilisation and savings achievements.
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.
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.”
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
Predictive analytics can improve medical outcomes by identifying patients needing medical interventions. Population segmentation is a common method used to identify such patients. Individuals are grouped into cohorts to help improve the quality of their care. In this paper, Milliman’s Jordan Paulus and Nick Creten explore four common methods for population segmentation: cost cohort segmentation, condition cohort segmentation, utilization cohort segmentation, and social cohort segmentation.
In previous blogs, we’ve discussed population management concepts and given specific examples of pediatrics, accountable care organizations (ACOs), and clinical populations. In this blog we turn to the Medicaid population. Medicaid has unique characteristics because of the nature of the financing and because of the social demographics of the population served. This population is going through big changes because of Medicaid expansion and the advent of programs that may resemble some of the characteristics of Medicaid, such as subsidized rates through the federal and state exchanges.
A key to analyzing this population is to create homogeneous sub-populations. There are several ways to define the subpopulations:
• Program: Children’s Health Insurance Program (CHIP), Temporary Assistance for Needy Families (TANF)/Aid to Families with Dependent Children (AFDC), Aged, Blind, and Disabled (ABD) Medicaid (duals), waiver programs.
• Type of delivery model: Managed care and fee-for-service.
• County: Rates are typically defined at the county level for managed care and care delivery is organized around counties.
• Special populations: Pregnant women, children, mentally ill.
There are specific issues that drive the metrics and analysis for Medicaid recipients, including:
• High levels of emergency room (ER) use: This population has much higher ER usage rates, which often reflect access and sociodemographic issues.
• Maternity: This population has higher rates of maternity and has wider variability in maternity outcomes.
• Behavioral health: Managing behavioral health is a more important component of care.
• Community resource access: Population wellness is much more dependent on additional community resources such as case workers, food banks, and social workers.
It’s important to focus on the key issues that drive the results for the population and to create metrics that reflect these key issues and characteristics. Below are some sample metrics that should be tracked and improvement goals developed.
This article first appeared at Milliman MedInsight.