You’ve hung up the hospital marketing cap for the day, are now browsing Netflix, and the targeting feels weirdly personal. You see tailored selections, and they’re promoting each show with artwork eerily curated to your style. You finally land on The Witcher and think, “Where have you been all my life?!” beginning an 8-hour binge before moving to the next series.
This is fun for movies, shopping, and gaming, but…
Hospital marketing has a higher calling than Amazon and Netflix
FANGs (Facebook, Amazon, Netflix, Google) employ artificial intelligence (AI) to learn what leads to clicks, widgets purchased, or time spent. Their AIs use this data to suggest similar content hoping to maximize views, time spent on site, or widgets purchased. Missing The Witcher is a much lower stakes game than missing life-saving preventive care due to data neglect in healthcare.
When employing AI for hospital marketing purposes, we’re responsible to ask better questions than, “How do we get people to consume more?”
Garbage In/Garbage Out means bad data leads to bad decisions, but what about the questions we’re asking of the data? When employing AI for hospital marketing purposes, we’re responsible to ask better questions than, “How do we get people to consume more?”
- How does hospital marketing encourage the right consumption – clinically necessary preventions or interventions?
- Are we unfairly biased based on socioeconomic factors?
- Do we target services to the wrong people (false positives)?
- Are the services we’re marketing actually available/do we have enough capacity for excellent care?
- Is our outreach aligned to maximize incentives such as care that are available?
US healthcare misses on these questions, and it shows. For example, patients are often unengaged in managing preventable illnesses until they need costly Emergency Department (ED) services. ED visits per capita continue to rise despite concerted efforts to redirect to other areas such as urgent care.
You might hear from wrongly targeted patients directly, or even worse, they stop taking your communications seriously.
It’s equally troubling when people receive healthcare engagement that they shouldn’t. Consider the reaction if people in your market are wrongly targeted (false positives) for cardiac care or mental health services. You might hear from wrongly targeted patients directly, or even worse, they stop taking your communications seriously.
Health systems are even missing earmarked financial incentives
What’s even more troubling is that health systems have financial incentives to drive valuable care, but they miss those targets. Medicare’s Annual Wellness Visits (AWV) program, for example, provides over $200 in monetary incentives for quick check-ins with seniors. Fewer than half of providers even attempt to reach AWV incentives, and those who do only reach 23.1% of the in-need population. This impacts poor and underserved communities even more, according to a study that Health Affairs published in 2018.
With healthcare’s margins so think, why do we fail to reach patients even when offered direct incentives? Technology and culture. Given the myriad of metrics, targets, and contracts surrounding each patient, health systems aren’t equipped to know and reach the right people.
Addressing these issues has helped health systems improve utilization and increase AWVs by more than 380%. Let’s look at addressing technological and cultural issues head on…
Technology should make complex hospital marketing demands simple
There’s much to learn from the FANGs here. In healthcare, when identifying populations, we commonly slice groups by age, occupation, and a few other criteria.
This is woefully inadequate. Population and patient data exist as thousands of different data points in a myriad of systems. There’s no way hospital marketing and pop health experts can sort through it all! AI shines at making sense of this data to identify the next best actions for each person.
Without the right AI in place healthcare systems are data-rich, but insight-poor.
Without the right AI in place healthcare systems are data-rich, but insight-poor. This means that the data is available to predict illness risks and engage patients, but the action is not. Through data engineering and data science, health systems can make powerful leaps in how they engage their populations.
Consider your culture & enrich existing workflows
So now, there’s AI in place learning behaviors and suggesting Next Best Actions for your patients. Accessing, suggesting, and tracking these actions should come with minimal impact to existing workflows. Next Best Actions should flow seamlessly into existing CRM, access, and population health platforms or have their own easy-to-use environment.
Appreciating and enhancing existing workflows is key to moving from mere analytics to science & action.
Health systems have the world’s most valuable data. It’s noisy, it’s disparate, and it’s dirty, but advances in data engineering and data science clean the data and find next best actions for patients. Acting on these insights to find and engage patients in need takes humanity leaps and bounds further than encouraging Saturday afternoon binge-watching sessions.
Key Takeaways to Help Make Good Data-Driven Decisions
- Identify the Metrics You’re Focused on Moving
- Understand that your data, models, and decisions are subject to bias and fallacies. Take stock of these
- When possible, infuse existing workflows with Next Best Actions rather than creating new workflows
But what about OUR data?
This is all thinking at a high level, but incorporating AI in your hospital marketing efforts requires detailed data strategy. We can assess your readiness, share what similar health systems have done, and demonstrate options at the following link: Request A Demo.