AI in healthcare

Artificial intelligence (AI) technologies are present in modern business, our daily life and also in healthcare. The use of AI in healthcare is not new. But it's growing in popularity as we continue to see real data and evidence play a bigger role in the covid-19 pandemic.


The impact of technology on healthcare has been widely felt during the pandemic. We've seen years of acceleration fly by in a matter of months. The pandemic has revealed gaps and opportunities in the use of real-world data and evidence that the world working together save lives and can fight a raging virus. We are also seeing the increasing use of AI to better understand health data, especially population health.


The potential for using AI in healthcare is very real. We are seeing the rise of cloud and computer systems that can ingest, aggregate and manipulate different types of data. At the same time, we have this new technology that handles large amounts of data (structured to unstructured, raw pixel or text) very well. The question is how we can use AI to improve the effectiveness and value of healthcare.

AI can help in several ways. As a supportive technology, AI can help automate all aspects of administrative processes and reduce administrative costs and burdens. There are several administrative applications for AI in healthcare. The use of AI in hospital settings is somewhat less game-changing in this area as compared to patient care. But artificial intelligence in hospital administrative areas can provide substantial efficiencies. AI in healthcare can be used for a variety of applications, including claims processing, clinical documentation, revenue cycle management and medical records management.


Another use of artificial intelligence in healthcare applicable to claims and payment administration is machine learning, which can be used for pairing data across different databases. Insurers and providers must verify whether the millions of claims submitted daily are correct. Identifying and correcting coding issues and incorrect claims saves all parties time, money and resources.


Sometimes the workflow requires human intervention, causing care providers to communicate or share information manually. It is possible to solve many problems with better standards and data exchange formats; as AI and natural language processing (NLP) can handle larger semantic problems, and automate some of them. Although the emergence of fully cognitive systems may take some time given advances in natural language processing, the prospect of AI agents being able to leverage and respond to policy manuals and billing policies within healthcare systems is promising.


The healthcare ecosystem is heterogeneous and has various silos. However, if we implement APIs and interoperability between these data sources, we can transform the data into streams that help us better understand certain aspects. For example, to support patient-centred healthcare, we can look at patient data to track what happens as people move through the healthcare system. IT can also track trends, or the progression of a disease or diagnosis, from the perspective of an individual healthcare provider. This analysis can be done in great detail by using unstructured data sources such as doctor's notes and lab results.


Given this way of accessing data and the various ways it can be broken down, there is a lot to discover. For example, it is possible to flag a behaviour and identify early interventions based on historical patterns. It can be possible to examine how costs are linked to transactions and how they affect patient outcomes among providers, and provide transparency to create true partnerships among stakeholders; find target groups for clinical trials; assess the effectiveness of health programs and personalise them; or identify groups of people who could be better served by tailored programs based on their current health history.


As the technology landscape changes, AI still has opportunities to optimise and automate care. It is possible to build systems that simplify the interaction between providers and payers, ultimately delivering better, more cost-effective care to patients. The healthcare field is evolving in a way that encourages interoperability, collaboration, and the right application of technology, including AI, for better patient care, better outcomes, and value.


The use of AI in healthcare has the potential to support healthcare providers in many aspects of the patient care and management process, helping them improve existing solutions and overcome challenges faster. The biggest challenge for AI in healthcare is not whether these technologies are powerful enough to be useful, but ensuring that they are used in everyday clinical practice. Over time, clinicians can perform tasks that require uniquely human skills, tasks that require the highest levels of cognitive function. Perhaps the only healthcare providers who will lose out on the full potential of AI in healthcare may be those who refuse to work alongside it.


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