The ROI of AI-Powered Prospective Risk Adjustment

For health plans and systems looking to make significant improvements on quality outcomes and cost savings, prospective risk adjustment holds the answers. By leveraging large amounts of data to target high-risk patients with incredible accuracy, healthcare organizations can reduce medical complications, unnecessary hospitalizations, and other high-cost downstream effects.  

Using artificial intelligence (AI) for prospective risk adjustment makes the entire process seamless, helping drive better outcomes without requiring significant staff resources. AI solutions can tap into external data sources, integrate disparate data points, and generate highly relevant and accurate clinical insights into patient risk profiles. As a result, AI-powered prospective risk adjustment programs have the potential to produce a huge return on investment (ROI) for the organizations implementing them.

Below are five ways AI solutions can produce a significant ROI and help plans and providers achieve their value-based care goals.

1. More effective resource allocation

Using AI-powered prospective risk adjustment solutions empowers organizations with highly accurate clinical insights and measurements of member risk. For example, in one study, using AI neural network models predicted the accuracy of current procedural terminology (CPT) codes from the text of pathology reports with 97.5% coding accuracy.  

These highly accurate insights help payers secure the most appropriate reimbursement levels for their member population. Providers can deliver critical care to their highest risk patients, helping achieve success in value-based payment arrangements.  

2. Short-term and long-term cost savings

Using AI solutions, risk-bearing organizations can automate a range of data analysis and coding tasks that would otherwise take significant staff resources to accomplish such as:

• Extracting data from provider electronic health records (EHRs), health information exchanges (HIEs), claims databases, and other data sources.

• Integrating disparate data formats including different units of measurement into a cohesive dataset.

• Unlocking insights from unstructured data such as clinical notes using natural language processing (NLP).

• Running models on integrated data to develop highly accurate insights into member risk status.

Looking further downstream, AI-powered prospective risk adjustment can drive significant medical cost savings by helping providers close gaps in care. Generating highly accurate insights empowers providers to treat patients before conditions escalate into more costly, complex conditions. It’s even been predicted that AI use by health plans could generate up to $360 billion in savings per year.

3. Better member outcomes

Leveraging AI solutions to analyze large amounts of clinical data leads to better clinical decision-making and interventions. AI-powered prospective risk adjustment can offer providers predictions at the point-of-care regarding a patient’s likely care gaps or chance of adverse health event.

With these insights, providers can conduct more effective patient visits to uncover possible health risks and spend more time on treatment conversations. Organizations using AI to support clinical decision-making in this way have experienced a 25% relative decrease in readmission rates.

Providers are on board, too. A survey conducted by the American Medical Association found that 72% of physicians believe AI solutions can improve their diagnostic ability. Sixty-one percent of respondents, which included even self-proclaimed “tech-averse” providers, believed AI could improve clinical outcomes.

4. Increased efficiency and productivity

One of the biggest benefits of AI solutions is their ability to save countless hours of staff time, reducing tedious administrative tasks and increasing overall productivity. Automating some of the essential data integration and analysis that are critical to prospective risk adjustment allows staff to take greater advantage of available data without the extra effort. It also reduces the time spent integrating complex data formats.

An estimate by McKinsey & Company suggests that healthcare payers could experience up to a 25% reduction in administrative costs by incorporating AI solutions into their workflows. Healthcare organizations that have been increasingly challenged to find ways to cut costs will find the biggest gains through AI-powered technology.

5. Accelerated revenue capture

By delivering highly accurate clinical insights to providers, AI solutions have the potential to make significant improvements in revenue capture. For example, one Blue Cross Blue Shield plan was able to achieve a 40% increase in the value per chart by improving HCC coding.

Payers and providers using AI solutions for prospective risk adjustment see the benefits directly in the financial performance of their organization and the enhancement to their patients' health outcomes.  

Using AI-powered prospective risk adjustment solutions has the potential to transform clinical outcomes and drive significant cost savings for healthcare payers, contributing to their success in value-based care. By leveraging AI to identify undiagnosed conditions and deliver insights at the point of care, healthcare organizations gain better visibility into patient health, improving condition management. They can also proactively document, treat, and code conditions upfront, reducing the pressure on retrospective audits.  

The ROI is significant. Payers and providers that adopt sophisticated AI solutions will see dramatic increases in efficiency and productivity, leading to better financial and clinical outcomes. The technology necessary to drive this success exists today – organizations only need to tap into its potential to experience the results.