Charting the future: Strategies for healthcare CFOs

By Milliman MedInsight

10 May 2024

Healthcare Chief Financial Officers (CFOs) are increasingly finding themselves at the helm of strategic decision-making that goes beyond financial management. Recent issues, such workforce retention and future uncertainty, are compelling CFOs to adopt new strategies and undergo a profound transformation.

Although healthcare systems have made strides towards achieving financial stability, it is evident that profit margins remain lower than pre-pandemic levels.1 To navigate this challenging landscape, CFOs must strike a delicate balance between maintaining healthy balance sheets and implementing strategic initiatives for long-term revenue growth.

As healthcare CFOs progress in 2024, two key areas have emerged as top priorities:

1) having a high-quality enterprise data and analytic strategy and,

2) creating a framework to support an artificial intelligence (AI) strategy

Leading with an enterprise data & analytics strategy

From a provider organization standpoint, the demand for scalable analytics and reliable data has reached a critical juncture. Historically, providers have been hesitant to venture into this realm due to the high costs and resource-intensive nature of implementing such technologies.

Nevertheless, as the healthcare industry transitions towards value-based care (VBC), the need to embrace advanced analytics solutions has become increasingly evident. Provider organizations are realizing that they must embrace technology that enables them to achieve more with fewer resources, allowing them to effectively navigate the complexities of VBC and deliver high-quality, cost-effective care.

To ensure that your data analytics center delivers the desired answers and insights, it is crucial to have access to dependable tools and datasets. Establishing a successful process for transforming analytics into valuable insights requires careful planning. It is not something that happens by chance. To ensure the reliability of your data, it is crucial to have a structured environment where people can easily access, define, and have confidence in the data.

A leading analytics strategy involves several key components:

  1. Data governance: Establishing a robust data governance framework ensures the accuracy, consistency, and security of healthcare data. This includes defining data ownership, access rights, and standards for data collection, storage, and usage. Setting up a strong infrastructure is crucial, especially with the increasing use of emerging technologies like generative AI (gen AI).
  2. Data analytics: By implementing analytics, you can establish a data infrastructure that ensures seamless integration of information from various sources. This includes maintaining a comprehensive inventory of source data and adhering to consistent naming conventions, data formats, definitions, logic, and rules to create a common data language.
  3. Data science: Unlocking valuable insights from vast amounts of data requires advanced capabilities such as artificial intelligence (AI) and machine learning (ML). With these advanced tools, data scientists can spend less time prepping data and more time identifying new opportunities and driving innovation.

Whether you opt to “buy” or “build” the analytics capabilities you require, it is essential to start by familiarizing yourself with the available tools and models for effectively managing, processing, and utilizing your data.

Working with AI

AI, particularly Gen AI, continues to be at the forefront of discussions within the healthcare industry, as its potential to revolutionize patient care and operational efficiency remains a compelling prospect. However, despite the excitement surrounding AI, researchers still need to do much work to fully harness its capabilities.

While AI has the potential to reduce administrative work, streamline workflows, and improve patient outcomes, CFOs must exercise caution when considering its implementation due to the significant upfront costs and potential disruption to existing systems.

Below are tips to consider when using Gen AI:

  • Be cautious of AI claims: Vendors may exaggerate AI capabilities. Labeling something as “AI” doesn’t guarantee success. Evaluate claims critically and seek evidence of real-world effectiveness.
  • Involve stakeholders in decision-making: When implementing AI, include all relevant stakeholders and consider their input. Rushing decisions solely for AI implementation can lead to suboptimal outcomes.
  • Prioritize efficiency and effectiveness: Optimize fundamental processes before investing in untested technologies. Streamline existing systems and processes before considering advanced solutions.
  • Stay focused on the problem: Clearly define the problem you’re addressing and remain focused on solving that specific issue.
  • Be flexible and adaptable: Circumstances change, so be prepared to adjust your AI strategy. Stay open to new information and challenges, and be ready to pivot if needed.

An effective AI implementation framework should encompass key elements such as identifying suitable use cases for AI, establishing data management and analytics infrastructure, developing AI algorithms and models, and integrating AI into existing systems and workflows. In addition, ongoing training and development opportunities is crucial to equip the workforce with the necessary knowledge and skills to harness AI effectively.

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1. Lower reimbursement a leading cause for poor margins, CFOs say (

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