White Papers & Case Studies

Real world results in healthcare cost reduction

Welcome back,
The white papers and case studies below are available for download.

Case Study:
Colorado Healthplan 6.3% Reductions ($6 PMPM)
in ER Utilization in a Medicaid Population

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How Long Do Nudges Last?
Positive Behavior Persistence in a Targeted Medicaid Population

This follow-up study assesses the “persistence” of behavioral nudges. That is, without any additional interventions in the target population, could positive effects (such as a change in behavior) still be discerned four months later?

The program level results show that even though no new nudges (i.e. phone calls or direct mail) had gone to members since early September 2015, significant positive lift for ER visits (“lift” in this paper is defined as a reduction in the trial population visits vs. the control group) at the program level was measurable in data through December 2015 (four months later), and nearly significant lift was observed for ER cost. At the same time, the results had clearly degraded from those measured using data through August 2015. ER visit lift was about 35% of its August level while ER cost lift was about 55% of its August level. It is apparent that nudge effects can persist, but not indefinitely.

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Causal Outcomes: 25% Reductions in Emergency Room Visits
Implementing Behavioral Economics and Predictive Analytics in a Targeted Medicaid Population

Can overuse of emergency departments (EDs) be significantly reduced in Medicaid populations without intensive and costly programs, such as individual counseling of members by physicians or nurses, to change the behaviors of members having a history of avoidable uses? Can overuse of emergency departments (EDs) be significantly reduced in Medicaid populations without intensive and costly programs, such as individual counseling of members by physicians or nurses, to change the behaviors of members having a history of avoidable uses?

This paper describes the results of deploying a low-cost, highly scalable software platform, NextNudgeTM, which uses machine-learning techniques to target “nudges” – carefully crafted messages that apply principles of behavioral economics to elicit desired changes in behavior – to those members who are predicted to be most likely to respond to them. The platform was used to identify members to nudge, such as relatively healthy members with no recent wellness visits but with a history of recent ED use, and to track the results of nudging them. Nudges consisted of a combination of live outbound calls and follow-up direct mail offering information about a free Nurse Advisory Line (NAL) and the availability of nearby primary care physicians (PCPs) with convenient locations and hours; a refrigerator magnet with the NAL free telephone number was also included in the direct mail. The difference in subsequent ED visit rates (ED visits per member per quarter) between nudged members and similar non- nudged members were used to determine the causal impact of the nudges on ED visits.

Within six months of deploying the NextNudgeTM platform, the average ED visit rate and costs had dropped by roughly 26% and 39% respectively among nudged compared to historical averages among the target populations – a highly statistically and economically significant reduction. These preliminary results suggest that relatively inexpensive, information-based nudges can be very effective in changing member behaviors when they are targeted to members who are most likely to respond.

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