Breaking Down Barriers: Communication and Interoperability in the EHR

Introduction

Artificial Intelligence (AI) is no longer a thing of the future. It’s here. It’s in our smartphones, home assistants and cars, and it will only grow more prevalent in our lives over the next decade. That presents an opportunity for healthcare providers to use AI to improve patient care while also addressing some of the biggest barriers to seamless digital health care: interoperability and data security.

Artificial Intelligence (AI) is no longer a thing of the future. It’s here.

You’ve heard the buzzwords: artificial intelligence, machine learning and deep learning. But what do they mean? And how does it all relate to healthcare?

To answer these questions we need to break down the concept of AI into its simplest form. AI is a computer that can learn on its own. This means that instead of having a human program an algorithm for every task before running it (which would require constant tweaking), an intelligent agent can adapt itself according to new information as it comes in from its environment or user input.

AI has been around since 1950s when Arthur Samuel developed his checkers playing program – which he called an ‘expert system’. Since then there have been many advancements in this field including Natural Language Processing (NLP) where machines can understand human language well enough so as not just respond appropriately but also predict what you might say next based on context clues such as tone of voice or facial expressions; Machine Learning which enables computers to learn without being explicitly programmed using algorithms based on patterns found within large data sets such as images or text documents; Deep Learning which allows computers not only recognize objects but also relationships between them by analyzing millions upon millions examples until they reach some sort level understanding about how things work together.”

Interoperability is one of the biggest barriers to seamless digital health care.

Interoperability is one of the biggest barriers to seamless digital health care. It’s the ability for all of your medical data to be shared seamlessly, no matter where it’s stored or who you’re seeing. That means that when you go to see your primary care doctor, she can access all of your past visits with specialists and hospitals–and vice versa.

This is important because it has been shown time and time again that patients are better off when they have access to their entire medical history rather than just bits and pieces from each provider they see. For example: If you have an illness that involves multiple specialists (say diabetes), each specialist needs access not just to his own notes about this patient but also those written by other physicians so he can get an accurate picture of what’s going on before making a diagnosis or prescribing treatment options.

EHRs are a natural place to bring together AI and interoperability.

The EHR is a natural place to bring together AI and interoperability.

• AI can help with data collection and analysis, by automating tasks like capturing vital signs and lab results, or extracting key patient information from documents such as discharge summaries.
• AI can help patients engage with their care team more effectively by providing them with personalized reminders about upcoming appointments or test results, or even allowing them to ask questions directly from their EHRs through chatbots or voice assistants like Alexa.
• Data sharing–the ability for one provider’s patient records to be accessible by another provider–is critical for improving outcomes across the healthcare system but has been historically challenging because of privacy concerns around sharing PHI (protected health information). With patient consent in place, there are many opportunities for providers who use different EHR systems to benefit from each other’s data: For example, if a patient sees a cardiologist who uses Epic Systems Inc.’s software at his local hospital but then needs surgery at another location where Cerner Corp.’s system is used instead, having access only through the Epic platform won’t provide enough context about what happened before surgery occurred; knowing both sets could help surgeons make decisions based on full histories rather than just partial ones.”

Artificial Intelligence, Interoperability and Healthcare

As we have seen, AI can improve the quality of healthcare by reducing medical errors, improving treatment plans and advancing personalized medicine. While there are many ways in which AI can help your organization to provide better care at lower costs, it’s important to recognize that implementing an AI system requires careful planning and execution.

In order to maximize the benefits of using an artificial intelligence platform in your hospital or clinic setting–and reap financial savings along with it–you need an interoperable EHR paired with a robust data analytics solution (or two).

The Future of Artificial Intelligence in Healthcare

AI will help us fight medical errors, improve treatment plans and advance personalized medicine.

AI will allow us to improve the quality of care we provide and make it more accessible.

AI will help us reduce costs and increase revenue

AI will help us fight medical errors, improve treatment plans and advance personalized medicine.

AI will help us fight medical errors, improve treatment plans and advance personalized medicine.

For example, AI can help identify the most effective drug combinations for a particular patient. It can also suggest changes to a treatment plan based on new information or circumstances (like when someone has an allergic reaction to something).

The technology will also give us more accurate predictions about how patients are likely to respond to different treatments; this will allow them to be treated more quickly than ever before–and without unnecessary costs or side effects.

Conclusion

There’s no doubt that artificial intelligence is going to play a major role in healthcare. The question is not whether it will happen, but how quickly it can be implemented and what type of impact it will have on the system as a whole. We’re already seeing some promising results from early adopters like Intermountain Healthcare, who use AI tools like Watson for Oncology at their hospitals across the country.