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Data is the Foundation of AI Use in Healthcare

AI in Healthcare

Reports from the Global Artificial Intelligence Study by the PwC analyzed the potential impact of AI on each industry. The report revealed that healthcare (as well as the financial and retail services sectors) will enjoy the biggest benefits of AI in terms of increased consumption, better product quality and improved productivity.

Unprecedented Increase in Patients’ Data

Indicators show that the healthcare industry is on the threshold of witnessing a dramatic growth and acceptance of AI technology. Frost & Sullivan, a growth partnership company, forecasts a 40% ($6 billion) growth in the AI healthcare market between 2014 and 2021. Of healthcare providers worldwide, 63% have already invested in AI, with 74% of providers saying that they plan to follow suit. However, the unparalleled increase in the volume of patients’ data, precipitated by the Fourth Industrial Revolution with its plethora of smart and “internet of things” devices, caught the healthcare industry unaware.

High Volume of Unstructured Data

Unstructured data refers to the data residing outside organized databases, such as lab reports, clinical trials, academic research and electronic health records. Unstructured data also includes genomic information, sensor data, physician notes, recorded dialogues, medical images, genetic profiles, electrocardiograms, liquid biopsies and many more.

Such data is particularly difficult to organize using conventional computational algorithms and languishes on clouds and servers. Tapping into this data may open up a world of amazing possibilities and make patient care more cost-effective and efficient than ever before.

Unavailability of High-Quality Data

The major barrier to the successful deployment of AI technology in the healthcare industry is the unavailability of large quantities of high-quality data. The effectiveness and correctness of machine learning algorithms are largely dependent on the quality and preciseness of the input data. As such, the inconsistencies, biases and discrepancies present in unstructured data as well as its very nature hinder the deployment and use of AI in health care.

In most organizations, patient data isn’t in a usable format. It’s also saved in disparate servers and silos and usually contains biases that will lead to bad recommendations and decisions. Keep in mind that AI cannot learn or be effective when it is fed poor-quality data. Herein lies a major problem since AI machine learning algorithms must continually ingest large amounts of accurate data in order to be truly effective.

Importance of a Data Management Strategy

Once healthcare organizations have the ability to operationalize access to key data sets and put in place the right teams, process analysis and technologies, the impact of AI in the healthcare industry will be potentially limitless. Organizations must first assess their existing information systems and data flows in order to identify areas that are ready for automation and machine learning algorithms … all of which require a data management strategy. Data required for the effective deployment of AI in the healthcare industry must be:

  • High in volume
  • High quality
  • In a single place (not siloed)
  • In a unified format (interoperable)
  • Unbiased

Companies that want to maximize their success with AI and machine learning systems must come up with a data management strategy as well as a way to manage all of their data. They must understand the data flows that go into AI and its machine learning algorithms in real time. Companies should also understand the quality of the data and how it can be augmented, stored and used for future training of AI systems. The key elements needed for a comprehensive data management strategy include the following:

  • A strong leadership/administration with an in-depth understanding of both clinical and business practices
  • Engineers who will provide support to the clinical team. They should also be proficient in the manufacturing or clinical industry to translate processes
  • Business analysts for visualizations into data and the building of adaptive processes into clinical workflows
  • Support from subject matter experts

Conclusion

Data paves the way for AI, and there is no substitute for good data. The key to long-term success of AI in health care is clean data, and this means moving beyond data warehouses and electronic healthcare records. An effective large-scale data management practice that enables the successful deployment and implementation of AI technology in healthcare will enable the early detection of diseases, faster delivery of preventative care and improved diagnosis and decision-making. It will also enable enhanced treatment options, better end-of-life care and, above all, decreased medical costs for all concerned.

At MicroAge, we provide innovative information technology solutions to meet unique business challenges. To learn more about how we can help you, contact us today.

 

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