February 01 2018 | 0 Comments | 172 reads Average Rating: 3

5 Data Dives Needed to Determine Intervenability Levels

by Lalithya Yerramilli in Population Health

Patients are a bit like snowflakes. At first glance, they look alike but upon further inspection, it becomes apparent that they are not. While two patients might have the same health conditions and care gaps, the potential associated with working with them varies from individual to individual.

One of the differences that is not readily apparent when initially assessing patients is “intervenability” – or the likelihood that patients will actually complete the interventions required to close care gaps, and adhere to treatment protocols and prescribed medications. Intervenability also measures the willingness of the subject to become engaged (such as patients actively following plans of care) as well as their ability to become engaged.

The problem, however, is that healthcare professionals can’t take an intervenability reading with a blood pressure monitor, scale or thermometer. And, they can’t assess intervenability based on clinical or claims data commonly found in electronic health records.

Instead, healthcare organizations must collect and analyze different types of data that will enable them to arrive at a more detailed, 360-degree view of behaviors and attitudes. For example, healthcare organizations looking to assess the intervenability of a cohort of cardiac patients who have high risk scores will need to collect and consider patient data related to the following five categories:

#1: Behavior. How have they behaved in the past? Have they generally followed plans of care, such as taking medications and making follow-up appointments when required? Did they change their eating habits, even if only for a limited period?

#2: Living situation. Where do they live? If the plan of care is to prescribe a change of diet and increase in exercise, do they have easy access to healthy foods and an affordable gym membership or other means to get active? If their health plan requires purchasing medications for chronic conditions through a mail order pharmacy, do they have a mailbox that can accept a 90-day supply of those medications? Do they have the income to support medications, physical therapy, or other treatments as-prescribed based on demographic and socioeconomic data?

#3: Buying habits. Where and how do they shop? Just because there is access to healthy foods doesn’t mean the patient goes to a grocery store that offers them. Even if they do, what do they purchase when they’re there? Is the cart loaded with fruits, vegetables, healthy proteins, etc. or does their purchase history lead more toward sweet and/or salty snacks, processed foods, or junk food? Do they shop online, and if so for what?

#4: Activity levels. Are they active or sedentary? Does the patient own a gym membership and go on a regular basis? Does she subscribe to exercise-related magazines? Does he purchase clothing and other items from sporting goods stores? Does her job require sitting for long periods of time?

#5: Attitudes. Asking questions about a patient’s willingness or motivation to comply with a care plan is a bit trickier. While it is important to include such information, some patients might not be willing to share their true feelings because they are concerned about privacy, and instead opt for the “safe” answer. As a result, great care should be taken in framing the questions, explaining the purpose, and ensuring anonymity in order to gain the insights into attitudes that will provide insight into intervenability.

This is just a brief overview of the data needs associated with intervenability. For a more comprehensive discussion on how intervenability can drive the behavior that results in better care outcomes, check out our recently e-book chapter on intervenability: Drive Better Outcomes by Enhancing Engagement

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Lalithya Yerramilli
VP, Healthcare Solutions

Lalithya Yerramilli has 15 years of experience in analytics in insurance, healthcare, and life sciences industries working with customer info-base, transactional, physician level, patient level, claims and longitudinal datasets.

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