Artificial Intelligence and Machine Learning have created a buzz in almost all industries. And there is no doubt that it has a high potential to upgrade the healthcare industry in various ways.

Healthcare has traveled a long way, from manual records to EHR (electronic health records) and now to interoperability. Interoperability in healthcare has emerged as a solution to the challenges EHR was facing such as security concerns, big data management, and high technological cost. 

Interoperability in healthcare information systems enables two or more systems to exchange information and data with each other in a structured way. Combining AI with data interoperability can transform the entire healthcare ecosystem and can bring together analytics, data science, and automation. 

Role of Interoperability in Health AI

An interconnected healthcare system of wearables, telehealth software, and digital therapeutic applications, all feeding data into AI to analyze and interpret massive data, will not be as effective without Interoperability in healthcare information systems. 

Here are a few strategic roles of interoperability in health AI:-

Improved Efficiency & Patient Care

The ability of Interoperability in healthcare to streamline the process of diagnosis and treatment can help doctors make accurate diagnoses, develop better treatment plans, and reduce medical errors leading to improved patient care. Moreover, it reduces manual data entry by enabling automation, ensuring the efficiency of the healthcare industry.

Enhanced Data Analytics

In today's healthcare environment, data analytics is a critical component to the success of any organization. It has been shown that the use of data analytics can lead to improved patient outcomes and reduced costs. Interoperability brings together data from different healthcare systems and harmonize it, enabling AI to offer enhanced data analytics.

Cost Savings

Interoperability can be a cost-saving solution as it eliminates the need for duplicate tests and manual data entry process that can be prone to human error and often requires more time and expenditure than automated processes do.

Improved Patient Engagement

With interoperability in healthcare information systems, patients can access their medical records in real time and will have greater control over them. 

Security and Privacy Considerations

As the amount of data collected through health AI increases, there is an increased risk of breaches or misuse. Therefore, developers need to ensure that their systems are adequately protected against cyberattacks and other threats by following best practices such as encryption), authentication, and authorization.

Conclusion

Since healthcare deals directly with human life and well-being, caution and careful evaluation are important. AI applicability along with Interoperability to analyze data, natural language processing, processing insurance claims, fast diagnosis, pattern recognition & accuracy are notable in the healthcare ecosystem.

According to a report by Markets and Markets, the market for health AI catered by interoperability is going to grow exponentially. The market is expected to reach $102.7 Billion by 2028 from $14.6 Billion in 2023, with an expected CAGR of 47.6%,

There are various companies that offer interoperability in healthcare AI, one of the well-known companies is MITYUNG which provides AI/ML-related services, such as data collection and annotation, model development, MLOps, security, IT support, analytics & monitoring, and much more.