Many healthcare organizations are turning to artificial intelligence to uplift patient adherence. And these organizations are all asking the same question: buy it, or build it?
It’s very natural for organization leadership to say, “Hey, I already have an analytics team. Why do I need to go to an external company to improve adherence?”
The in-house AI build
In-house AI systems cost less to build, because you are getting value from resources you already have: your internal team. And in-house AI may be good enough in the short term.
But to be a leader in adherence performance, good enough is probably not sufficient to sustain business objectives. For example, if you are a payer, quality ratings get harder every year. You may be a 4-star plan on adherence today. But if you’re not investing for long term performance, in two years you may fall back.
So your investment in patient engagement needs to deliver ROI long term, not just this year.
In-house AI blind spots
Typically, in-house AI makes predictions based only on the data that your organization collects. So in-house predictions can be less accurate than outsourced AI because there are gaps in their data set. Your developers might not have access to views of patient behavior in categories outside of the target drug classes; for example, in-house AI may not include data sources such as geographic variables, purchasing behavior, or combined payer, pharmacy and medical claims data.
Why details matter in healthcare AI
Many technology companies from outside of healthcare claim that using their platforms can make in-house AI builds easier. But the difference is not just having a tool set that can simplify the creation of a model. It’s also knowing what variables to put into that model—cleaning the data to make it usable, incorporating interventions, and figuring out how to optimize it. When the AI model is simplified, healthcare organizations lose a lot of value.
Outsourced builds: optimizing interventions
Every patient adherence or support program wants to get better at patient outreaches and make them more effective. So they want to try new intervention tactics. For in-house AI, this is a time-consuming process because the system needs time to learn from data on the new tactics.
For example, most pharmacies are converting patients to a 90-day prescription supply, and will try to do it with a phone call. But for pharmacies that have never tried a 90 day conversion, they will have questions: “Who do I actually outreach to? I’ve got hundreds of thousands or millions of prescriptions and patients that could use outreach. Who do I actually reach out to first?”
More accurate predictions on interventions
AllazoHealth’s AI engine can make accurate predictions on intervention tactics based on learnings we already have, across millions of outreaches. Even with no data about interventions in your population, our AI engine can make accurate predictions on individual patient behavior.
When a pharmacy or payer works with AllazoHealth, we can predict who’s going to respond based on time of year, time left in their prescription, when their prescription is available, what kind of prescription they have, co-pay levels—all sorts of insights we already have that can optimize outreach.
Keep in mind that there’s a difference between just hiring any outsourced AI company to create a bunch of predictive models, or bringing in a partner that has deep healthcare experience and comprehensive data sets.
AllazoHealth uses artificial intelligence to make a positive impact on individual patient adherence. We optimize medication outcomes for pharmaceutical companies, payers, and pharmacies. Our AI engine targets individual patients with the right intervention, the right message, at the right time.