Integrating Machine Learning into Your Healthcare Solutions

Posted by Susan Berndt on February 2nd, 2021
Author
Susan Berndt
Susan is a creative marketing professional with a demonstrated history of working in the hospital & health care industry. Susan joined Smart Data Solutions in 2016 focusing on marketing strategy, campaign execution and creating an inbound marketing funnel. She has over 10 years of marketing/advertising experience and over 12 years of customer service experience.

Machine learning is widely used in the healthcare industry and plays a critical role in many healthcare solutions, such as billing, patient care, and processing and managing medical records. Integrating machine learning into healthcare solutions is rapidly changing the industry and is becoming a necessity for many businesses. 

 

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) in which software or programs can take data, learn from it, and adapt its changes without human reprogramming or intervention. 

Machine learning makes businesses stronger, data more accurate, and workflows more efficient. Some major benefits to utilizing machine learning include higher accuracy rates, improved efficiency and scalability, and more flexibility with how data is processed.

 

Where is Machine Learning Being Used?

Certain areas of healthcare take advantage of machine learning in their systems more than others, particularly those processing patient data, health records, and other types of sensitive data. Here are a few solutions that utilize machine learning applications.

Mail Processing

Healthcare mailrooms that process medical bills, claims and other critical pieces of collateral rely heavily on machine learning to save time on manual mail sorting and scanning and improve accuracy overall. This is something we use heavily at Smart Data Solutions, and it has saved us from time-consuming work and limited errors on claims.

Medical Billing

Medical bills can often contain errors that cause the insurer to either be underbilled or overbilled. In both these cases, the wrong medical billing code is entered and the erroneous bill is sent for payment. Integrating automation and machine learning can aid in reducing common mistakes made by manual entry. Machine learning, or deep learning, can take large loads of data and learn from it. In this case, it could learn which codes go with which procedures and over time will become more and more accurate, reducing human errors and inaccurate claims being billed.

Medical Records

Medical records contain sensitive patient information and are essential pieces of healthcare data. Machine learning can help streamline medical record processing and filing by first scanning and transferring all paper records into digital documents, which are much easier to maintain and store. Medical record storage can be costly and take up large amounts of physical space.

Second, machine learning can quickly and easily read handwritten words on documents and match those to pre-programmed templates and forms fields in a digital format. This removes a large amount of manual transcription and ensures accurate, timely results.

Imaging and Diagnostics

Machine learning has helped many hospitals and specialty clinics make giant strides in their medical imaging and diagnostics. The machine learning algorithm can take images and compare them to other related images or data elements to help determine proper diagnosis and treatment. Major advancements include faster and more accurate breast cancer diagnosis with computer-assisted MRI and Mammogram machines.

Data Capture

Without machine learning, data fields captured by scanners need to be manually entered or reviewed by people to ensure accuracy. At Smart Data Solutions, we utilize machine learning to decrease the number of fields requiring manual entry or review. This advancement provides more efficiency, higher data capture accuracy, and faster throughput and is currently one of the best ways to utilize machine learning in healthcare.

How to Integrate Machine Learning into Your Healthcare Solutions.

When the time comes to add machine learning to existing AI systems and technologies, there are a few key things to do. It’s important to identify the pain points within your current workflow that could be improved as well as research companies that can fully integrate with your systems. Oftentimes outsourcing these services can be a smart solution to ensure full integration with little to no errors.

Companies like Smart Data Solutions have solidified how and where to integrate these solutions into your nuanced workflows. We know the major advantages of integrating machine learning into your processes, including fewer manual processes, increased scalability, and consistent performance and accuracy. We can provide a seamless transition between your existing systems and a new solution with our comprehensive AI and machine learning capabilities. To get started, contact us here.

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