How AI Increases the Impact of Your Grateful Patient Program
You have probably benefited from artificial intelligence by finding your next binge-worthy show using a Netflix recommendation or by telling Alexa to shuffle songs by Lady Gaga, but using AI in healthcare fundraising has, until now, been the stuff of fantasy, or at the very least of extremely early adopters. As AI and machine learning become more common, though, its use in fundraising will expand. And the good news is that it has huge potential benefits.
What Are Artificial Intelligence and Machine Learning?
AI is the ability for machines to “learn, reason, and act for themselves.” (Not sure if the machine you are thinking about uses AI? Here’s a cool flowchart to help you decide.) Most AI you encounter is facilitated by machine learning. Machine learning refers to using algorithms to find patterns in large amounts of data and applying those patterns to predict future behaviors.
How Healthcare Fundraisers Can Benefit from AI
Most fundraising professionals recognize that individuals give because of how they feel about an organization, not just based on the fact that they are wealthy and have extra money in their pocket.
“Our data has shown that those who have the greatest capacity are not the greatest supporters,” said Scott Rosencrantz, director of data analytics at GOBEL, formerly Futurus Group, a consulting firm focused on artificial intelligence in fundraising. “The experience an individual has with your organization is much more indicative of how they will give.”
Despite this conclusion, grateful patient screening processes have primarily used capacity to identify prospects, because wealth is relatively straightforward to quantify. Non-AI predictive models that do exist use only a handful of measurements and are static, meaning they are a snapshot of a particular moment in time and quickly become outdated. With machine learning, it is possible to analyze more data points on a more regular basis to more accurately predict which patients are likely to give.
Tips from an Early Machine Learning Adopter
Angie Lancaster, operations director at Carle Center for Philanthropy in Urbana, IL, recently worked with GOBEL to add a machine learning component to her organization’s grateful patient program.
The biggest hurdle to getting the project off the ground was educating everyone involved—fundraising staff, clinical staff, IT, and compliance—not just that the program was happening but why it was happening. Once everyone understood the purpose and the potential benefits, they were more likely to dedicate the time needed to get the program set up and to offer feedback on how to deploy it successfully.
Because the model relies on large amounts of patient data, working with IT and compliance to understand your data sources is important. Angie worked with IT to build out the electronic medical record files that feed the model and continues to rely on IT to refine the data queries over time.
Working with sensitive patient data required the team at Carle to be creative with data collection. When compliance staff didn't feel comfortable with sharing a particular data point used in the model, the department worked with Angie to identify alternative data points that might serve the same purpose.
Because the predictive model is constantly evolving, Angie has found that getting ongoing feedback is key to its continued success. Recording physicians’ reaction to the patients the model suggests critical—even if the feedback is negative.
“If a gift officer meets with one of our physician champions and shares five names from the list, and that physician says, ‘No, I don't I don't think you should follow up with any of these particular patients,’ that's not necessarily a bad thing. Your initial reaction might be that [the prediction] isn't working. But that's not the case, because that information goes back into the model, and the model learns from that information.”
Carle medical staff have been supportive of the new approach.
“Physicians seem to be more receptive to a fundraising program that focuses on grateful patients rather than wealthy patients,” Angie said. “It makes the conversation so much easier between the gift officers and the physicians and makes it easier to recruit physicians into our grateful patient.”
Machine learning is an ongoing process, and Angie encourages other organizations considering an AI approach to be patient.
“It's not a magic bullet,” she said. “[Machine learning models] require a long-term investment and a commitment from the philanthropy team, your physicians, and your leadership. The more information you can provide, the better your results are going to be.”