Optimize Your Patient Mix with AI for Better Results

patient mix

What does the patient mix look like at your health system? Are you proactively reaching your highest risk patients first? Are you successfully driving them to the appropriate appointment type? Once they are through your doors, are you tracking their outcomes over time and optimizing for quality metrics? 

An aging population, increases in chronic illnesses, and delayed care due to COVID-19 have driven increases in demand for many healthcare services. But workforce challenges, physical space limitations, and other resource constraints are leaving health systems scrambling to make the best use of their resources. Health systems have to optimize their patient mix in order to drive revenue, boost quality scores, and provide a seamless experience for their patients.

Patient Mix – Defining Your Patient Population

A health system’s patient mix is the makeup of a system’s patient population based on various demographic, disease-related, and socioeconomic factors. Patient mix can be looked at from a number of different angles, including simple demographic factors like age and gender. But there are countless other data points that, taken together, can give a much more comprehensive picture of a health system’s patient population.

Patient Mix Variables
age variable

gender variable

race and ethnicity

Race and Ethnicity
diagnoses healthcare

lifestyle factors

Lifestyle Factors
family health history

Family Health
smoking status patient mix variable

Smoking Status
insurance variable for patient

Insurance Status
blood pressure variable

Blood Pressure /
Cholesterol Level
social determinants of health for patient


Why Traditional Patient Outreach Approaches Fail To Deliver


Traditional approaches to optimize patient mix through outreach efforts have focused on one or two data points – typically age and gender. These efforts may take recommendations from the U.S. Preventive Services Task Force to target certain subpopulations for screenings. For example, health systems may target all female patients between 40 and 74 years for a biennial breast cancer screening, or they may aim for a diabetes screening for all overweight adults 40 to 70. Health systems may also focus on payer recommendations for timing chronic disease management and wellness visits. 

While focusing outreach on subpopulations of patients is an important first step, it fails to deliver the level of targeting that health systems need. Some problems with this standard approach to outreach include: 

  • Limited capacity: The standard outreach approach casts a wide net. But the more patients you target with your outreach efforts, the fewer resources you have to serve each patient. In fact, casting a wide net leads to a greater chance that limited appointment slots will be filled with patients who are not high-risk, when those appointments may have made more of an impact on your high-risk population.
  • Extraneous relevance: When health systems cast a wide net in their outreach, they risk sending patients irrelevant information, resulting in frustration or confusion for the patient.
  • Inefficient and costly: The cost of outreach can be significant, especially when health systems are using critical call center resources. Casting a wide net via this standard outreach approach increases costs to your system.

Fortunately, casting a wide net in patient outreach is not the only option for optimizing health systems’ patient mix. Algorithms powered by artificial intelligence (AI) can unlock patient data from the EMR and identify hyper-targeted patient audiences. That means the highest risk patients get through your doors first, all of your patients receive the most relevant information for their unique health situation, and you can cut costs on patient outreach efforts.

patient mix audience

Four Reasons To Optimize Outreach For Patient Mix

  1. Make the best use of limited resources. When patient demand surpasses appointment supply, health systems have to make a concerted effort to optimize patient mix. Whatever the supply constraints may be – physical space, clinical workforce, and more – many health systems are facing shortages in one or more service lines, especially in primary and behavioral health care. Making the best use of precious resources in those areas is critical. That means making intelligent decisions about which patients are booking appointments. 

    If you can leverage the data from your EMR to target your highest risk patients – patients with multiple comorbidities, for example – that allows you to not only fill available appointment slots but also make an impact on quality of care. Prioritizing high risk patients means moving the needle on population health goals without being limited by resource constraints.

  1. Maximize your quality of care metrics. Health systems are under greater and greater pressure from multiple payers to boost quality metrics, from preventive care to health outcomes and hospital readmissions. Whether your health system is trying to boost your star rating, increase MIPS scores, or generate shared savings from the Medicare Shared Savings Program, there are countless opportunities – and requirements – to track and report on quality of care metrics. 

    All health systems serve patients for whom appropriate care management and medication management can be the difference between positive health outcomes and avoidable hospitalizations. But unless your health system has a fine-tuned strategy to target these patients, you may be losing out on revenue from shared savings or pay-for-performance quality bonuses. An AI-driven patient outreach strategy can help optimize your patient mix in order to maximize quality scores and drive additional revenue to your health system. 
    quality metrics mix
  1. Drive downstream revenue through increased patient lifetime value. Optimizing for patient mix can drive revenue beyond quality bonuses – it can help increase downstream revenue. Accurately identifying the highest risk patients means bringing in the patients most in need of services. This outreach will increase rates of appropriate interventions and drive additional fee-for-service revenue to your health system.

  1. Boost patient satisfaction. Finally, optimizing patient mix results in happier, healthier patients. Targeting patients most in need of services – as opposed to casting an overly wide net – increases the relevance of your outreach efforts. And driving patients to the services they most need boosts patient satisfaction in addition to health and wellbeing.

Health systems optimizing for patient mix have the opportunity to boost results ranging from revenue to patient satisfaction. But it takes more than a standard outreach approach to deliver these results. Leveraging patient data from the EMR and AI-powered algorithms can deliver the level of targeting necessary to reach the right patients, at the right time in their care journeys. The result is a patient mix that is optimized for patients and health systems alike.

patient segmentation