How AI can transform hospital revenue cycle management — 5 thoughts

June 8, 2020

Artificial intelligence is making big waves across many industries, and healthcare organizations are exploring ways to harness AI’s transformative power in RCM and beyond.

Many hospitals and health systems are already leveraging AI to improve care, support clinical decisions and boost clinician satisfaction. However, the role of AI in healthcare isn’t limited to the clinical experience. The same technology can transform nonclinical processes.

From insurance denials to costs rising faster than reimbursements, revenue cycle management in today’s healthcare market faces numerous challenges. Hospitals and health systems see a potential solution to some of these issues: using AI in the revenue cycle process.

Below is an overview of key points that emerged from recent studies and surveys that provide insight on how AI can modernize revenue cycle management, and why hospitals and health systems see immense promise in this type of technology.

1. Claim denials are costly. One major area of opportunity in the revenue cycle for AI is in predicting denials. Constantly changing payer guidelines and human error are among the reasons hospitals and other provider organizations struggle with high claim denial rates. Reworking claims is costly, and every claim that is rejected or denied introduces the risk of a hospital not getting paid. It’s estimated that hospitals lose more than $260 billion annually from insurance denials.

2. Predict and minimize claim denials. Using AI, hospitals and health systems can pinpoint the reasons payers denied claims in the past and uncover denial trends. This enables healthcare organization to predict denials and resolve problems before claims are submitted, leading to lower denial rates and higher revenue.

3. Cut cost to collect. AI and automation also present an opportunity for hospitals and health systems to cut costs by streamlining and optimizing manual processes. Based on the number of revenue cycle positions that could potentially be performed by AI and automation, Crowe Horwath predicts the cost to collect at healthcare organizations will decrease between 25 percent and 50 percent over the next five to 10 years.

4. Improve coding and clinical documentation. Another aspect of artificial intelligence in healthcare that shows promise for transforming RCM is natural language processing. NLP enables computer programs to process and analyze unstructured data, such as free-text physician notes written in an EHR. Within the revenue cycle, application of NLP can improve coding and clinical documentation.

5. Investments in nonclinical AI. Hospital executives are interested in using AI and robotic process automation to increase efficiency in areas such as revenue cycle. A recent survey of 115 hospital and health system executives showed at least 50 percent plan on investing in nonclinical AI within the next two years. 

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