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Discover Emerging Practices in Artificial Intelligence
Interoperability and Data Management
In this section, you will find articles and resources pertaining to the integrating of Artificial Intelligence and Interoperability. Interoperability is a critical topic for healthcare technology. By definition, it is the ability to enable swift and efficient data sharing between various information technology systems. Without this, data is often overlooked or misinterpreted resulting in errors and inefficiently which basically renders the data unusable. Combining this ability with artificial intelligence can speed up the exchange of usable data between the system at an exponential rate. It is also used to ensure compliance with legal, ethical and regulatory/professional standards of health information technology. The resources and links in this section demonstrates the value that artificial intelligence can have in this area. For example, you can find out more information on how “symbiotic” the relationship between interoperability and AI technology. Explore the videos at the bottom of the page for more information and in-depth discussion.
4 Ways Interoperability Can Improve COVID Vaccine Distribution & Data Collection
Discover four different ways that Interoperability can improve COVID-19 vaccination distribution and administration. These four ways utilize Artificial Intelligence in order to streamline certain processes. Due to the new and various ways that governments are distributing the COVID vaccine, interoperability has become increasingly difficult. Using AI combined with Interoperability, such as with EHRs, can help to alleviate these issues. Read more here to find ways in which that can occur.
Leveraging AI and Machine Learning to Advance Interoperability in Healthcare
Interoperability is a vital part of the health care system. A lack of interoperability can inhibit data sharing. Without fluid data sharing between providers and partners, errors can occur and data can “fall through the cracks”. This article discusses using AI and Machine Learning to optimize Interoperability. This can help shift the industry from a fee-for service to a value-based model that provides the most value to the patient.
How Improved Interoperability Can Help Healthcare AI Flourish
There are often numerous obstacles that can arise through improper or lack of interoperability in patients’ medical records. These issues can include HIPAA release forms, obsolete transfer methods, long wait times, and even lost requests will bog down any health care organization. This post discusses the importance of incorporating AI in this process and can help to greatly reduce these issues. Read more here at Apixio.
Healthcare Data Acquisition: The Journey from Flintstones to Jetsons
This webinar lecture by Apixio and hosted by senior-level analysts, discusses the development of data acquisition since the beginning. The lecture also touches on the regulatory and market factors “accelerating the adoption of new data acquisition methods”. It also discusses the options that providers and organizations have to benefit from interoperability and succeed with digital data acquisition. Watch the lecture to learn more.
This article highlights three different ways that incorporate Artificial Intelligence into healthcare, specifically regarding interoperability. According to the article, it has been predicted that AI-driven technologies in healthcare will exceed $6 billion in three years. This is mainly due to the evolving demand of the consumer to have access to more convenient, affordable, accessible health care that is AI-driven, safe and virtual based. Read more below.
3 ways artificial intelligence can disrupt healthcare
This article highlights the efforts being done at the University of Pittsburgh Medical Center to integrate natural language processing to make total use of the large swaths of unstructured data. Currently about 80% of all healthcare data is unstructured which means that the data is either not organized in any predefined fashion or it is of a predefined model. These data sets are often extremely “text-heavy” meaning they can contain characters, dates, facts, numbers, etc. Read more below.
UPMC turns to NLP to make sense of unstructured data
This Q&A blog post, provides you with several commonly asked questions that not only most professionals would ask but they are also questions most general people would have regarding AI and healthcare. Currently only around 25% of providers are integrating AI technology into their data processing systems. One of the drawbacks highlighted in this Q&A is that organizations often find AI integration to be too costly and difficult to understand. This is unfortunate as many predict the entire market to be using AI technologies in 2-3 years. Find out more below.
Q&A: Why AI will revolutionize healthcare data
Interoperability
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