Kamya Elawadhi is the Chief Customer Officer at PhD student.
Leveraging artificial intelligence is a transformative solution to long-standing issues related to health data management and patient outcomes, one that has the potential to ease the burden and create balance for patients and providers health care.
Adopting this technology in healthcare is more important than ever. Using AI to evaluate data sets does several things. It takes data out of silos, eliminates blind spots and connects valuable information to each other.
As a healthcare communications professional, I have direct experience using AI to make data-driven decisions, increase productivity, and streamline marketing processes, which can also make your life easier.
The prognosis: understanding what ails you
The number of datasets available in healthcare can be overwhelming. From demographic and behavioral data to prescriptions and claims, all these data sets remain siled without a consistent method to consolidate the information and give it value.
Siled data is a direct result of today’s insurance-dependent healthcare system. In healthcare, siled data not only hinders patient care, but also inhibits effective marketing, which is something I want to discuss.
Imagine you are a healthcare marketer and you activate an annual campaign. After three years, a massive amount of data is available on campaign exposure, including who saw the ad and what they did as a result.
If a doctor saw the ad 20,000 times in a single year and then wrote 1,000 scripts for the brand, that data is collected but not connected. Using AI to evaluate data sets allows the healthcare marketer to use the collected data for planning purposes.
As for the problem? The lack of tools (designed to collect and use available data wisely) means many benefits are missed.
The Cure: AI Tools and Technologies for Healthcare
Using the right tools can do the heavy lifting associated with breaking down complex medical data for analysis. I like to think of the different functions of AI as the left and right sides of the human brain.
Logical left-brain tasks, such as data analysis, can be performed by identity resolution engines. These software tools, which use algorithms and machine learning to link data from multiple sources, create streamlined profiles for patients. This, in turn, allows for a better understanding of individuals, which improves both marketing and customer service.
Creative and right-brain tasks, such as marketing and campaign planning, fall under generative AI. By using machine learning models to understand new patterns from data, Gen AI is able to literally generate new content (from images and text to music and videos) by making predictions based on the patterns newly acquired data.
The challenge remains: Healthcare marketers are largely unfamiliar with the tools available, as many have not been properly tested in the market. Yet options abound.
Personal content is something everyone is looking for in healthcare. Gen AI can be leveraged to personalize dynamic messaging for different types of patients. This saves time by eliminating the need for a human to create multiple different messages. Understanding what works and what doesn’t ultimately saves the monetary value spent by marketers. In the testing environment this also helps with efficiency. The benefits of personalization extend to patient engagement and healthcare outcomes, making the use of AI also valuable from a patient care perspective.
The results: challenges and benefits
The three stages of a marketing campaign can be connected to different parts of artificial intelligence.
When planning, AI helps refine information about the target audience. Analytical AI is essential to program execution. By consolidating data from a variety of sources, including patient demographics and healthcare provider feedback on prescriptions and claims, campaign planning and patient education improves.
During runtime, generative AI can be used to adjust messaging based on real-time feedback, leading to continuous optimization. In addition to evaluating the type of language, triggers and prompts that work for your target audience, it allows for a better understanding of the target audience.
Once a healthcare marketer can visualize their target audience (using the demographic and psychographic data they have), they are able to begin crafting their message and that is when the Activation of the campaign can take place. What affinity do audience members have? Which platforms do they prefer to learn on? Generative AI helps select specific options, like email or peer-to-peer learning.
When data starts coming back to them (about everything from print and location to script writing, for example), that information is theoretically available to plan and execute the next campaign. In reality, when faced with huge data sets, it becomes tedious to sift through the data and come up with a plan.
AI tools, on the other hand, can easily make sense of this data and optimize its use. If, among the original target audience, only 50% of healthcare professionals were high-value prescribers, a healthcare marketer might conclude that 75% of their total budget should be allocated to this group, leaving a small functional budget for the remaining target. audience.
As the fourth quarter ends and the new year approaches, the debate around AI in healthcare continues to evolve. If the goal of 2024 was to take the step towards its implementation, then I predict that 2025 will depend on the excellence and absorption of AI for better campaign and budget planning. Why wait for the entire year to pass (and possibly fail) before implementing changes that could benefit your bottom line?
Compared to traditional data analysis methods, using AI to evaluate healthcare datasets can speed up processes, reduce costs, and improve decision-making accuracy. This year, starting January 1, I suggest thinking about – and planning – about how monetary values will be distributed. By leveraging AI and using smaller pockets of planning, if something needs to change, it can be done in real time.
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