Artificial intelligence and machine learning in healthcare claims data

The use of artificial intelligence (AI) and machine learning (ML) in healthcare claims data has the potential to revolutionize the way that healthcare organizations manage and process claims. By leveraging advanced analytics techniques, healthcare organizations can improve the accuracy and efficiency of their claims process, leading to cost savings and improved patient outcomes.

One example of the successful use of AI in healthcare claims data is the application of natural language processing (NLP) to extract and classify relevant information from unstructured claims data. NLP algorithms can identify key information such as diagnosis codes, treatment codes, and billing amounts, allowing healthcare organizations to more accurately process and reimburse claims. A recent study by Kaiser Permanente demonstrated the value of natural language processing (NLP) technology with clinicians identifying more than 50,000 patients with aortic stenosis, a common heart disease. Researchers trained an AI system that used natural language processing (NLP) to extract and classify certain abbreviations, words, and phrases associated with aortic stenosis and sort through over a million electronic medical records (EMR) and echocardiogram reports to identify patients with the disease. The software was able to accurately extract and classify information from claims and within minutes recognized nearly 54,000 patients with the conditions, a process that would have likely taken years for physicians to perform manually [1].

Another use case for AI in healthcare claims data is the identification of fraudulent claims. By analyzing patterns in claims data, AI algorithms can detect anomalies and flag potentially fraudulent activity for further investigation. This can help to reduce the burden on healthcare organizations and ensure that they are only paying out on valid claims. UnitedHealthcare implemented an AI system that analyzed claims data to identify anomalies and flag potentially fraudulent activity for further investigation. The system was able to identify fraudulent claims with a very high accuracy rate, leading to significant cost savings for the company [2].

In addition to detecting fraudulent claims, AI can also be used to identify potential errors in claims data. By analyzing patterns in the data, AI algorithms can identify discrepancies and alert healthcare organizations to potential mistakes, allowing them to take corrective action before paying out on incorrect claims. One example of this is the use of AI to identify coding errors in claims data. Coding errors can occur when healthcare providers use the wrong codes to describe a particular treatment or diagnosis, leading to incorrect reimbursement amounts. By analyzing claims data, AI algorithms can identify coding errors and alert healthcare organizations to the need for correction, improving the accuracy of the claims process.

Another important use case for AI in healthcare claims data is the analysis of trends and patterns in the data. By analyzing large amounts of claims data, AI algorithms can identify trends and patterns that may not be immediately apparent to humans. This can be particularly useful for identifying patterns in healthcare utilization and identifying areas where there may be opportunities for cost savings. For example, a healthcare organization may use AI to analyze claims data to identify patterns in the use of certain medical procedures or treatments. By identifying trends in utilization, the organization may be able to identify opportunities to reduce costs by implementing more cost-effective treatment options or by identifying and addressing overutilization of certain procedures.

AI can also be used to identify patterns in healthcare utilization that may indicate potential problems with the quality of care being provided. By analyzing claims data, AI algorithms can identify patterns in utilization that may indicate issues with patient safety or the effectiveness of certain treatments. This can help healthcare organizations to identify and address potential problems before they become more serious, improving patient outcomes and reducing costs.

In addition to its use in identifying trends and patterns, AI can also be used to predict future healthcare utilization and costs. By analyzing historical claims data, AI algorithms can predict future utilization and costs, allowing healthcare organizations to better plan and budget for future healthcare needs. This can help organizations to more effectively allocate resources and improve the efficiency of their operations. A study conducted by researchers at the University of Tokyo, Japan, found that machine learning based prediction models trained on clinical and claims data were able to accurately predict future high-need, high-cost patients [3].

As with any new technology, there are also challenges and limitations to the use of AI and ML in healthcare claims data. One challenge is the need for high-quality data to train AI algorithms. In order for AI systems to accurately analyze and process claims data, they need to be trained on a large and diverse dataset. This can be a challenge for healthcare organizations, as obtaining and cleaning large amounts of data can be time-consuming and resource intensive.

Another challenge is the potential for bias in AI systems. AI algorithms are only as good as the data they are trained on, and if the training data is biased, the AI system may also be biased. This can lead to inaccurate or unfair results, and it is important for healthcare organizations to be aware of this potential issue and take steps to mitigate it.

In addition to these challenges, there are also concerns about the security and privacy of healthcare claims data. As AI and ML systems become more widespread, there is a risk that sensitive healthcare data could be accessed or compromised. It is important for healthcare organizations to implement robust security measures to protect the privacy of their patients and ensure the confidentiality of their data.

Despite the challenges and limitations discussed above, the use of AI and ML in healthcare claims data is likely to continue to grow in the coming years. This growth will be driven by a variety of factors, including the increasing availability of data, the increasing capabilities of AI and ML algorithms, and the growing demand for more efficient and cost-effective healthcare.

One trend that is likely to drive the growth of AI and ML in healthcare claims data is the increasing adoption of electronic health records (EHRs). EHRs provide a digital record of a patient's health information, including diagnoses, treatments, and medications. By storing this information electronically, healthcare organizations can more easily access and analyze large amounts of data, making it easier to use AI and ML to improve the accuracy and efficiency of the claims process.

Another trend that is likely to drive the growth of AI and ML in healthcare claims data is the increasing focus on value-based care. Value-based care is a model of healthcare delivery in which providers are paid based on the quality and outcomes of care rather than the volume of services provided. By using AI and ML to analyze claims data, healthcare organizations can identify opportunities to improve the value of care and reduce costs, making it more attractive for providers to adopt this model.

In conclusion, the use of AI and ML in healthcare claims data has the potential to bring significant benefits to healthcare organizations. By improving the accuracy and efficiency of the claims process, healthcare organizations can save time and money, while also improving patient outcomes. By analyzing trends and patterns in the data, AI can help organizations to identify opportunities for cost savings and identify potential problems with the quality of care being provided. Factors such as the increasing adoption of EHRs, the focus on value-based care, and the demand for more efficient and cost-effective healthcare will drive even more innovative and impactful applications of AI and ML technologies in the future.

References:

1.??????McNemar, E. (2021, November). Identifying disease with Natural Language Processing Technology. Retrieved January 3, 2023, from https://healthitanalytics.com/news/identifying-disease-with-natural-language-processing-technology

2.?????Optum. (2022). Automate fraud, waste and abuse. Retrieved January 3, 2023, from https://www.optum.com/business/insights/page.hub.automate-fraud-waste-abuse.html

3.??????Osawa, I., Goto, T., Yamamoto, Y., & Tsugawa, Y. (2020). Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data.?NPJ digital medicine,?3(1), 148. https://doi.org/10.1038/s41746-020-00354-8