Predictive modeling is a subset of concurrent analytics, which uses two or more types of statistical analysis simultaneously. The goal of predictive modeling is to anticipate an event, behavior, or outcome using a multivariate set of predictors.
In the healthcare industry, predictive models take these anticipations a step further by analyzing consumer and patient demographics, psychographics, preferences, and lifestyles. Healthcare marketers can then create outreach messaging targeted at particular audiences and delivered through specific channels.
Benefits of Predictive Models in Healthcare
Predictive models derive insights from patterns and correlations found in vast amounts of consumer and patient data. Healthcare marketers use these insights to inform campaign creation and optimization.
Predictive modeling helps healthcare marketers improve ROI by focusing spend and resources on individuals most likely to engage with the organization. This is a much more effective acquisition and retention strategy than general outreach campaigns because they include hyper-personalized elements such as location-specific services.
The video below – a clip from a predictive analytics-focused webinar – explains predictive modeling further:
How to Implement Predictive Modeling in Healthcare
First and foremost, health systems need the right tools and technology. Chief among them is a healthcare CRM (HCRM), which weaves together data sources like demographics, psychographics, social, behavioral, clinical, financial, website, call center, and provider credentialing. The platform analyzes health data to provide complete, 360-degree views into patient habits and activities, making predictive modeling possible.
Predictive models are unique to each health organization, so health analytics processes need to be as well. Be sure to consider time-to-value when implementing a health analytics platform—how quickly does it need to be up and running to meet your goals? What types of data do you want to prioritize?
Additionally, think carefully about the platform provider so you can accurately plan around their implementation and support services. Make sure the provider can deliver what you need and matches your organization’s communication style to foster a true partnership.
A Sample Multivariate Predictive Model: Hip Replacement
Healthcare marketers could create a predictive model that helps identify consumers who are likely to need hip replacement surgery in the future.
This model would be based on health, demographic, and lifestyle variables that increase the likelihood that a prospect will be a candidate for this procedure. In this case, the number of radiology visits, BMI, Medicare supplements, older mosaics, and reading habits are all variables that affect a patient’s likelihood of needing a hip replacement.
Each of these variables has a predictive weight. The sum of a patient’s predictive weight indicates how likely they are to need a specific procedure in the future. If the sum of these variables is high, that patient is a likely candidate for hip replacement surgery.
Refining the list of relevant prospects for any given campaign can improve response rates while cutting marketing costs. For example, marketers could save a significant marketing budget by only sending direct mailers to a refined group of prospects.
Instead of sending out 100k mailers to untargeted prospects, marketers could instead send out 60-70k to targeted prospects and see the same response rate. They can then reallocate this budget into different campaigns, which helps improve ROMI in healthcare.