Engineer’s Perspective: 2024 Health Tech Predictions in AI/ML

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There are quite a few applications of AI in the healthcare business. While we don’t have a crystal ball to predict what aspects of healthcare AI companies will focus on, we can certainly mention a few areas where the use of AI may be beneficial.

Doctors and nurses spent a good deal of time working on paperwork and administration.

Such tasks are multiple. For instance, doctors need to create and maintain patient records. These records contain personal information, medical history, test results and treatment plans.

These records have been replace by Electronic Health Records (EHR), and the specialized staff to organize and maintain these records on computer and database systems.

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Furthermore, health insurance is a fundamental component of the US healthcare system. Hence, nurses and administrators must handle insurance claims, billing and coding. Coding is the use of systems such as the ICD-10 andCPT to correctly enter the code of a diagnosis from an insurer’s point of view.Administrators and receptionists also manage calls and appointments.

Doctors, nurses, and staff must follow all the regulations pertaining to privacy standards (HIPAA compliance) and ensuring the accuracy of the documentation. Administrators also track and manage medical supplies and equipment, making sure of their correct functioning and availability. Finally, doctors need to communicate and educate patients on the treatment plans and follow-up care.

Another important aspect of healthcare is to assist and advice doctors and nurses on prevention, diagnosis and treatment on the basis of clinical history of patients. The latest transformer architectures can be used to predict health outcomes utilizing the various clinical databases available. 

AI  systems can optimize scheduling by considering various factors such as physician availability, patient preferences, and even predictive analysis to minimize no-shows or optimize resource utilization.

Deep learning and Large Language models can help in streamlining billing and coding processes, improving accuracy and reducing errors. It can analyze billing patterns and suggest improvements for better revenue cycle management.

AI algorithms can analyze large sets of data to predict patient admission rates, optimize resource allocation, and identify potential operational bottlenecks before they become significant issues.

AI tools can assess population health trends, identify high-risk patients, and provide insights for targeted interventions, aiding in proactive healthcare management.

A blend of expert and machine learning systems can assist in ensuring compliance with healthcare regulations by analyzing data and identifying areas that may need attention to meet standards.

 To conclude, there is a plethora of interesting AI applications that can drive down costs, increase process efficiency and assist healthcare professionals in delivering the best quality care. 

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