How Marketers Can Combat Disparities Of Healthcare Algorithms

If black patients cost less to treat than white patients, maybe their illnesses aren’t as bad. 

That's the underlying message signaled to clinicians by a widely used AI algorithm that has steered black patients away from higher-quality care, generating more complex treatment for white patients than for sicker black patients.

But in reality, black patients are sicker when they enter care — because they haven’t purchased as many healthcare services as white patients have — which is mostly because they have less access to equal-quality care.

A recent study published by the American Association for the Advancement of Science examined data on 6,079 black patients and 43,539 white patients.

Impact Pro, the AI algorithm in question, determined that the pool of sickest patients was 81.8% white and about 18% black. In fact, the researchers say if the algorithm accurately analyzed the sick patients, 46% of black patients should have been identified.

Why is this a problem for the industry? 

The American healthcare system relies on commercially developed algorithms for predicting heath needs and costs, managing risk, and planning care delivery and health needs — particularly for patients with complex medical needs. The scores they produce are input to decisions about future enrollment in a care coordination program that better manages the sickest patients.

In the U.S., we spend significantly less on care for diverse racial, ethnic and gender segments than on white male patients. When the cost of care is used as a proxy for a patient’s healthcare needs, services are inefficiently allocated, driving higher risk and more disparities in the quality of care.

Why are algorithms like Impact Pro biased?

  • Diversity is lacking among the teams building these models. The humans that train algorithms are largely male, and white, often untrained in gender, racial and cultural competence and cognitive bias.
  • Ignorance of disparities persist in access to care among all groups of patients. This is particularly problematic, as care delivery organizations are integrating access -- and other social determinants of health -- into workflows, EHR and managed-care decision making.
  • These algorithms may be built on the assumption that all patients analyzed have similar disease burden. They do not. Black patients are provided less information, enter care when they are much sicker, and are treated differently at nearly every point of care.

What can marketers do to address this problem? Here are three ideas.

  1. Ensure that hospital marketing leaders are always part of high-level planning for care strategies. At the planning table, we can bring our competency and experience in engaging diverse patients and communities, and our data on the disparities that exist within them, thus educating the builders and users of AI algorithms.
  1. Bring accurate data and insight. Hospital marketers seek to positively impact outcomes for all, but they can be stymied by race-based misconceptions at the clinical level. For example, one study of white medical students revealed that half believed that black people feel pain lessbecause their skin is thicker, their nerve endings are less sensitive, or their blood coagulates faster. Untreated pain is one reason black patients resist seeking care as long as possible, and present as sicker than patients of other races.
  1. Demonstrate innovations used in our marketing work, such as cross-cultural strategies fed by well-designed and trained AI algorithms. Show how we create a unique, tailored, culturally competent experience for each patient at all stages of care: inpatient, post-discharge and in care management.

Ultimately, we are the communicators and storytellers who are able to spot and address flawed racial and gender narratives affecting how the organization thinks and talks about the patients we serve. We can leverage those skills to change how the larger promise of AI can bring safe and equitable care to all patients.

1 comment about "How Marketers Can Combat Disparities Of Healthcare Algorithms".
Check to receive email when comments are posted.
  1. Xiao Faria daCunha from Westerlund Marketing LLC, December 5, 2019 at 2:10 p.m.

    This is great insight, and very interesting and useful information. Yet purely telling the story from the angle that "people training these algorithms are mostly white male" is somewhat insufficient and almost naive. Because at a quick glance we will also see how Impact algorithm harms the white patients as well as it will tend to put together more comprehensive treatment plans.

    If we are going down the racial rabbit hole here, we would inevitably be neglecting the overall negative impact of an algorithm that would favor opiod painkiller, for example, in some situations and not do so in the other. This is more than hindrance of giving treatment but the inability to accurately and correctly evaluate the need of healthcare treatment, because I think we can all agree on how absurd healthcare cost is, in general.

    It seems the failing system calls for an upgrade on infrastructure, administration and research more than on "marketers" at this point, because in order to feed and train an AI properly we are assuming that we are actually able to collect somewhat accurate data prior to a patient, black or white, reach out and seek help, whereas the fact is the complete opposite: How could the data be collected if the patient did not seek care, whether it's shopping from a pharmacy over the counter, or going to a doctor? What can communities do to help collect these data? How should these data be reported, sorted and integrated with other data in general? 

Next story loading loading..

Discover Our Publications