Published on August 24, 2021
As part of Quality programs, payers often send in-home test kits to members. These screening tools assist with detection of certain cancers and with monitoring for chronic diseases, like diabetes. These kits can be important aspects of patient care and help remove barriers to health. In both cases, they can assist in improving outcomes and managing long-term healthcare costs.
However, for payers to see those returns on investment (ROI) from Quality programs in general (and in-home test kits specifically) they must identify the members most likely to benefit from engagement. In this Q&A, Nick Boerger, Senior Manager on Carrot Health’s Product Management Team, explains why consumer data insights and data science techniques that offer a 360-degree view of the member are critical for effectively engaging the right members.
How do in-home test kits assist with payers’ Quality efforts?
Some members may have barriers to receiving care through traditional healthcare venues, like office visits. Performing these types of tests in the convenience of the member’s home, either through having nurses make in-home visits or by issuing test kits via mail, can complement traditional healthcare interactions. Payers can mail kits right to the member to collect samples that can be used to screen for cancer, for instance, or to test blood glucose levels.
We’re really trying to improve overall access to testing for members by making in-home test kits a cost-effective and complementing modality.
What are the current challenges insurers face when deploying in-home test kits?
I’d say the majority of health plans without our solution rely purely on observational data. They’re going to look at their population to see who has outstanding screenings. That’s typically going to guide most of payers’ decision-making around who’s going to ultimately get a kit. They may also look to see who has received and completed a kit in the past. (If the member has historically completed a kit, they could be a “slam dunk” person to ship the kit to again.)
But for the most part, plans are just looking at members’ current compliance status, i.e. whether or not they are overdue for one of the screenings that the various kits support, and then shipping these kits out to potentially everybody on that list. Plans don’t have a way to discern between who is likely to complete the kit in the first place, who’s likely to be receptive to it (versus finding it abrasive) or who’s already likely to get the care through more traditional venues
So most health plans request member lists based on compliance status for specific screenings and then make sweeping decisions around who’s going to receive an in-home test kit. This can make for a large and expensive list of candidates.
How does supplementing insurer data with consumer data improve decision-making around in-home test kits?
Augmenting the health plan’s own data with complementary data sources like consumer data and the right data science techniques builds a much more comprehensive picture of each member. This allows payers to make more informed decisions that are better aligned with each member’s healthcare journey and engagement preferences.
Members are not just people with compliance flags for colorectal cancer screening, kidney disease monitoring, etc. They’re individuals who also have propensities to resolve those issues on their own. They have preferences around whether or not you call them, mail them, ship them a kit or do something else. With big data and the right data science techniques, you can do proactive planning see to it that all Quality initiatives are executed on the best-fit population… while ensuring that you’re being cost effective and non-abrasive with members. It’s thinking of members not just as their compliance status but as a whole person instead – and how you can best align your engagement strategies with that greater knowledge.
Does consumer data help health plans predict outcomes so they know where to invest in modalities like in-home test kits?
Yes. Some health plans are starting to explore data science and how they can use their own data to predict which members are likely to do something like complete an in-home test kit. For the most part, when they’re doing that, they’re going to be limited to the data that’s within the four walls of their organization: membership data, claims data, who’s complying with what care, etc. And those data points can certainly provide predictive power.
But what we found in our research – pretty much with every model that we develop – is that additional consumer data insights can provide a lot more predictive power. Consumer data allows us to unlock information like what is the member’s household composition? What’s their position in the household? What is their social determinant of health risk? What are their retail purchasing patterns? (Are they somebody who’s likely to purchase things online versus through a catalog, for instance?)
For some predictive models, these consumer data insights provide the majority of the predictive power. In fact, many models will have their predictive power more than double when you add consumer data. So when you start leveraging consumer data for modeling – and test kits are definitely within that section of models that really benefits from consumer data – you see a tremendous amount of lift. This ultimately augments the payer’s ability to be cost effective, efficient and non-abrasive to the member.
Can refining the audience and outreach method for in-home test kits improve engagement and avoid member abrasion?
In-home test kits can be cost intensive, depending on the size of your population and how many people need to receive care. There’s an art as well as a science to figure out the best ways to offer certain modalities to members to make these kits cost effective. For instance, with in-home test kits, you can think about who are individuals that you’ll ship kits directly to and who are individuals that you’ll send a postcard to letting them know they have the opportunity to request a kit. This gives everyone a chance to complete the kit while keeping cost and abrasion to a minimum.
It’s ultimately a game of resource allocation: How do we think about engaging people in the right way? For some people, it’s better to call them on the phone and talk to them and then send the in-home test kit. Some individuals may be more responsive to a mailer or postcard. It’s about aligning members with modalities that are palatable and non-abrasive to them. One member might not respond favorably to test kits showing up in a box on their doorstep; they might not like feeling accountable for that kit.
How long has Carrot Health been helping insurers with in-home test kit deployment, and what have you seen so far in terms of results?
We have been doing in-home test kit lists for a few years now. Part of what we’re able to provide to Quality teams is the information they need to go back to their CFOs to defend Quality investments. Quality teams and health plans want to know if their in-home test kit investments are achieving novel compliance, i.e. individuals who otherwise would not have become compliant but achieved compliance thanks to the use of this additional modality.
When we evaluate Carrot Health’s contributions to these programs, we validate that our models and recommendations deliver on ROI and generate higher engagement rates. For example,
- Predictive modeling allowed one of our health plan customers to double test kit completion rates.
- Unused test kits were decreased by 33%.
- This means overall kit efficiency was increased from a financial perspective, and member abrasion was decreased.
Even when something like COVID-19 shakes up the healthcare industry, including the Quality world, we’ve helped health plans reorient themselves with in-home test kits (among other outreach modalities). It’s been really great to work with health plans that needed to rethink their traditional approach and to offer them cost effective alternatives.