Whether one realizes it or not, machine learning technology is already a huge part of healthcare in the United States, forming an approach to health informatics that has been widely integrated into the healthcare data analysis of modern healthcare systems.
Whether formulating intensive care unit (ICU) risk scores, identifying disease clusters in electronic health records, predicting treatments for patients, or streamlining payment services, machine learning algorithms represent a critical piece of digital health care that payers and providers need to know.
And Alaffia Health has implemented machine learning as the backbone of its Payment-Integrity-as-a-Service℠ solutions with a proven three-step methodology for bill reviews powered by artificial intelligence. Read on to learn more.
What is Machine Learning?
Machine learning (ML) is a type of artificial intelligence (AI) that employs algorithms that adapt and learn over time to continuously improve the processing and analyzing of large amounts of ever-changing data.
So, instead of a human person creating rules to run on a data set to identify a pattern or find an answer, ML models learn from data to create their own rules to understand the data collection or data sets—regardless of the presence or absence of structure or labels.
An everyday example of ML is how Google adjusts its search results based on the searcher's interests. Simply put, ML employs self-learning algorithms that consume data to predict outcomes.
The most common ML techniques are supervised learning, which identifies relationships from labeled-data inputs to classify data or predict outcomes—and unsupervised learning, which clusters and probes unlabeled input data to identify patterns and groupings without the need for human intervention.
How Can Healthcare Payers and Providers Benefit From ML?
Healthcare professionals may benefit from the supervised learning technique to control costs by predicting the healthcare expenditures of individuals using claims data, which often has labels, like rows and columns in a data set.
On the other hand, unsupervised ML can mine electronic health record data to discover disease clusters to assist in clinical decision-making by linking unlabeled data from different sources to identify patterns and make the appropriate groupings.
Equally important are ML’s applications in bill reviews. By digitizing unstructured itemized bills, scanning them for errors and waste, and then compiling the errors and maximizing savings, harnessing the power of AI results in considerably faster reviews, accuracy, and cost savings.
Deep Learning Takes ML a Step Further
Deep learning is a type of ML consisting of a layered hierarchy of algorithms that learn from the data and each other.
For example, one layer might be collecting data on charges. Simultaneously, another looks for pre-negotiated reimbursement rates, the layers then using such data to determine the actual amount to pay on a bill.
And on an ongoing basis, the technology returns to the ever-changing "training data" to continuously learn, adapt, and adjust through future iterations.
Deep learning models are currently the most advanced form of AI, using artificial neural networks that mimic the human brain—far surpassing human capabilities to ingest, transform, and link vast amounts of data.
Deep learning enables virtual assistants like Alexa, Cortana, and Siri to perform practical actions for consumers. And in medicine, convolutional neural networks—a type of deep learning—are used in cancer diagnosis, treatment, and research.
Read more about deep learning vs. machine learning here.
What are the benefits of ML Technology for Healthcare Payers and Providers?
The overwhelming benefits are efficiency, value, and cost-effectiveness—not only for payers and providers but throughout entire healthcare systems.
For example, using AI in healthcare and applying ML models benefits payers and providers by identifying and predicting patients at risk for acute and high-cost care, a practice called “predictive analytics."
In healthcare institutions, patient, clinical, and medical data are often unlabeled, unstructured, or unlinked, making it difficult to query, identify data patterns, or find answers quickly. So, implementing ML streamlines claims submission and processing, reducing inefficiencies and inaccuracies, and can assist with repetitive tasks such as bill reviews.
Predictive Analytics Reduces High-Cost Care
Employing an ML methodology on medical claims and related data results in cost savings for payers by helping them predict life-threatening conditions and high-cost care, such as kidney disease, cardiac arrest, and preventable hospital emergency department visits.
A McKinsey & Company case study concluded that ML applied to electronic health record data can reduce spending on emergency department services by up to 50 percent by “learning” from medical and demographic data and then “predicting” likely outcomes.
In this case, ML models “learned” from medical claims, electronic health records, and socioeconomic data collected about chronic obstructive pulmonary disease (COPD) patients to effectively identify preventable emergency health visits, thereby achieving significant savings.
Streamlining Payment Services
Fraud, waste, and abuse (FWA) in healthcare systems is a problem all payers and providers must face. However, a 2018 study found that implementing ML in the healthcare value chain can reduce FWA by $20–30Bn from the US healthcare value chain.
Alaffia’s bill reviews leverage proprietary AI-powered optical character recognition (OCR) to review itemized bills five times faster than traditional methods, saving clients 22 percent more on average.
For payer and provider-centered healthcare organizations, taking this proactive approach in adopting ML algorithms to implement a payer claims editing system improves claim accuracy and reduces administrative inefficiencies, making Alaffia’s solutions the ideal preventative measure in streamlining payment services.
Implement Cost Savings and Value by Putting ML Technology to Work for You
Algorithms are already a part of medical care. So why not put ML to work for you? After all, removing administrative inefficiencies from bill reviews, claims editing, and claims negotiations are process improvements that directly impact your bottom line.
Health plan administrators, third-party administrators, reinsurers, and government agencies benefit from increasing their efficiency of scale through AI solutions designed to counter medical claims' potential inaccuracies and complexities.
So schedule a call to learn how Alaffia Health can streamline and improve your organization's operations to add tremendous value and precision to your payment-integrity process.