How AI Can Unlock the Power of Healthcare Data

Author

Michael Ridland

Category

AI, Cloud, Microsoft

Date

09 / 04 / 2024

Healthcare data is growing at an unprecedented rate, far beyond that of other industries. The surge in data is caused by the move to electronic health records (EHRs), the growth in wearables and remote monitoring, and the rapid increase in telehealth and new data types are continually emerging. While this data has the potential to revolutionise healthcare, it also poses significant challenges. Healthcare organisations need to develop a robust data foundation and have the ability to harness both structured and unstructured data. Fortunately, recent advances in artificial intelligence (AI) and development by industry leaders are putting AI in the hands of healthcare organisations and helping them to generate new insights to drive informed decisions about patient care.

The Challenges of Harnessing Healthcare Data

Data is a fundamental tool in the healthcare industry. Clinicians use it to make diagnoses, prescribe treatment and allocate resources. And data is just as important in policy and research, forming the foundation of decision-making and providing evidence to define best practices. If that data is inaccurate or lacking, the consequences can be significant. Inaccurate data can lead to misdiagnosis, ineffective treatments, and a misallocation of resources, ultimately compromising patient care.

Healthcare organisations manage an astonishing volume of data sources. However, the data is often difficult to access, and only about 20% is structured. Unstructured data includes clinical notes, lab tests, medical images and sensor readings. As healthcare software systems aren’t able to query this data, it isn’t used to its full potential and could lead to delayed diagnosis, missed opportunities for preventative care and more. Another problem is that information exists in silos, with systems operating independently and a distinct lack of interoperability. In addition, the potential for information overload is considerable. Without the means to extract the most relevant and actionable insights, healthcare professionals are left sifting through mountains of medical records. Low-quality data can get in the way of significant trends and patterns and clinicians can fall victim to time-consuming processes instead of focusing on patient care.

However, if healthcare organisations are able to sift through the vast columns of data and deliver critical insights, personalised treatment plans and optimised workflows, they have the potential to revolutionise the patient experience and this is where AI is making a huge difference.

Healthcare AI Solutions with Azure AI

When it comes to innovative ways to analyse the vast amount of healthcare data and generate new insights, Microsoft is leading the way. The company announced its end-to-end analytics platform, Microsoft Fabric, and has now taken a step further with healthcare AI capabilities. Through Azure AI, these new capabilities are working to put technology in the hands of healthcare organisations, unify data across siloed formats and sources and generate invaluable insights. Some of the core Microsoft solutions include:

  • Azure AI Health Insights – a cognitive service providing pre-built models to analyse information and provide insights that can be used by clinicians and researchers. Three of the newest models include:
  • Patient Timeline – using generative AI to display a timeline of key events from unstructured data to help inform patients’ medical history
  • Clinical Report Simplification – using generative AI to remove medical jargon and translate complex documents into simple language so that it can be shared with patients.
  • Radiology Insights – analysing medical images and reports, identifying potential mismatch between reports and image data and highlighting key findings. The solution can help radiologists to catch inconsistencies, prioritise tasks and uncover hidden patterns.
  • Azure AI Health Bot – a customisable bot that organisations can embed into clinical workflows to gain intelligent, accurate information. It can also be used as a patient-facing solution to help patients answer basic health queries 24/7, thereby reducing the burden on busy healthcare systems.
  • Text Analytics for Health – analysing and coding medical information from unstructured data sources using Azure AI Language. The AI tool can analyse clinic notes, reports and imaging results and extract key details, thereby improving patient care and overall efficiency.
  • Dragon Ambient eXperience (DAX) Copilot system – using ambient listening and natural language processing to pull pertinent patient details and create draft notes. All the clinician needs to do is review and edit the note before it’s submitted, giving them more time to focus on their patients.

These services have the potential to revolutionise patient care, helping decipher insights from siloed data sources, delivering information on-demand and empowering physicians to share more information with their patients. Ultimately, AI offers the potential to solve significant problems in healthcare, collating and presenting information in many different ways for many users.

The Importance of Building a Strong Data Foundation

Of course, Microsoft’s AI solutions, as well as others available for healthcare, are only as good as the data they have access to. This is why it’s so important to build a strong data foundation, ensuring data quality, accuracy and completeness. Moreover, data must be handled in a safe and responsible way to ensure its safe and effective use in the healthcare industry. To be able to use AI to solve healthcare challenges, healthcare organisations must consider:

  • Data Accessibility – AI algorithms need access to vast amounts of data, with restrictions hindering the development and optimization of AI models. Data should be made readily available to authorised personnel and systems across a unified data platform, fostering collaboration and knowledge sharing and enabling user-friendly data access across intuitive systems.
  • Data Quality – AI models are only as good as the data they’re trained on with inaccuracies leading to biassed or unreliable outputs that could lead to significant risks to patient safety. Healthcare organisations must ensure data accuracy, completeness and consistency so models can learn from reliable information and produce trustworthy results.
  • Data Compliance – healthcare is a highly regulated industry with significant risks associated with non compliance. To ensure sensitive patient information is protected, healthcare organisations should implement data encryption and access control, anonymize data where relevant and put an enterprise governance framework in place.
  • Data Integration – data silos prevent AI from accessing a holistic view of any healthcare system. By breaking down these silos and enabling seamless data integration, AI models can deliver more comprehensive and informative insights.

The Future Potential of AI in Healthcare

AI holds incredible potential for positive impact within the healthcare industry. It can help improve access to care, increase healthcare literacy and deliver better healthcare outcomes. Of course, the success of these solutions will hinge on the ability of healthcare organisations to build the right data foundation and to ensure compliance, accessibility, quality, and security. Ultimately, there is still a lot of work to be done. However, with big players like Microsoft delivering tools to help pave the way, AI has the potential to empower frontline workers and clinicians to vastly improve patient outcomes.