For many Americans, healthcare came to a screeching halt in 2020 as doctors’ offices and hospitals closed their doors to screenings and elective procedures. For those at higher risk for diseases, such as breast cancer, not having access to proactive healthcare not only delays diagnosis and treatment, but also timing, which is critical to managing the disease. The reality of these delays is now coming into view: doctors are reporting a concerning uptick in advanced cancer cases.
As health systems play catch up with backlogged – and potentially life-saving – screenings, it’s important they prioritize people at higher risk, to ensure they have access to the preventive care they need.
One way to do this is by working with a privacy team and designing and executing the right data strategy.
Leverages data and AI to gain better patient information
By leveraging AI modeling,one can factor in payer and third-party datasets in addition to a patient’s age and certain demographics. For example, overweight and obese women have a higher risk of being diagnosed with breast cancer compared to women who maintain a healthy weight, especially after menopause. Current or recent past users of HRT have a higher risk of being diagnosed with breast cancer.
Leveraging this kind of rich and personalized data is critical because while age is an important factor for many diseases, there are also other components/aspects that play into a person’s risk. Where they live (and what they’re exposed to), other conditions with which they’ve been diagnosed and treated, and pregnancy history, for example, can put someone at higher risk of developing breast cancer.
Once you have identified the individuals at higher risk, you can reach out to those patients with personalized and relevant communications to build trust, allay fears of engaging in healthcare during the pandemic, and gently remind and inform them about the importance of screening. Our experience has been that this can boost screenings and can generate diagnoses for patients who may not have otherwise scheduled their screening.
Leverage AI effectively and fairly
While many health systems have diversity, equity, and inclusion efforts in place, they rarely address the algorithms they use to communicate with and engage millions of patients. Therefore, it’s critical for teams leveraging that data to understand that bias exists, and to take steps to mitigate it.
First: ensure you have a diverse team. Second: assume all models are biased. Third: collect the right kind of data. Fourth: Test and retest continuously.
Look and think locally
Many organizations leverage national data sets or models when reaching out to their patients, even though many of the factors that put someone at risk for a condition are regional. It’s important when designing a data strategy, then, that health systems use data that is relevant to their patient community. For example, if environmental factors put someone at a higher risk for breast cancer, data from a state on the other side of the country could not only be irrelevant, but harmful.
Engage the entire organization
Any data strategy should incorporate stakeholders from around the organization; it should not live in a departmental silo. For example, a campaign that is initiated in marketing to get women in for mammogram screenings should consult clinical experts to ensure accurate information and appropriate language.
For many health systems, members of the marketing, operations, and clinical teams are regularly collaborating in order to drive positive health outcomes and improve the patient experience. These organizations have better overall results, as they are working to remove silos and work together towards a shared focus on the patient.
Be firm in your goals, flexible in your approach
With data and AI, flexibility is part of the package. When you set out to reach a group of patients, you might find that after you’ve started, the algorithm needs to be tweaked slightly – especially if you find certain patients are left out due to unintentional bias. Be firm with the goal of a strategy or campaign, such as driving screenings, but flexible in the path you take to get there. And, when you find you’ve made a mistake, recognize it and then keep moving forward with a slightly different approach.
Always put the patient first
In healthcare, it should always be about the patient. Every strategy designed and tactic taken should be with the goal of improved health outcomes and patient experience. With that as a North Star, every data strategy should be designed with the individual patient in mind – how they will react, what next action you want them to take and which message is likely to resonate with them.
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