Industry Voices—How cloud, AI and machine learning are transforming healthcare through COVID-19 and beyond – FierceHealthcare

Industry Voices—How cloud, AI and machine learning are transforming healthcare through COVID-19 and beyond – FierceHealthcare

As COVID-19 began spreading across the U.S., healthcare organizations were forced to quickly reassess their technology, and pull future plans for digital transformation forward.

In record time, many organizations overhauled legacy systems to better manage and care for the uptick in patient visits, while safely storing data to ensure efficiency as the pandemic evolved.

One of the most pressing priorities for healthcare organizations was expediting their adoption of cloud technologies to more efficiently manage the deluge of patient information, ensure streamlined workplace practices and enable information sharing with greater ease. As local leaders made decisions about how to keep their populations safe, cloud infrastructure provided the ability to collect, analyze, and share data securely across and among a global network of organizations.

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Through this period of rapid cloud adoption, there has also been a swift uptick in the use of artificial intelligence (AI) and machine learning technologies. From enabling information sharing and analysis without sacrificing data privacy, to ensuring patients with the most urgent needs are given the quickest response, these technologies have revolutionized the COVID-19 healthcare response and will remain critical well beyond the pandemic.

RELATED: Northwell Health research arm develops AI tool to help hospital patients avoid sleepless nights

Here are just a few of the ways in which COVID-19 has spurred lasting digital transformation within the healthcare industry:

De-identification of patient data

With machine learning capabilities, healthcare organizations are better equipped to ensure the privacy of patient data, making it easier to aggregate data across multiple sources and garner helpful insights about the COVID-19 virus. De-identification, the process of removing identifying information from patient data, is critical to the sharing of health information with non-privileged parties for research purposes, the creation of datasets from multiple sources for analysis, and anonymizing data so it can be used in advanced analytics and machine learning models.

As an example, the Google Cloud Healthcare API can detect sensitive data, such as protected health information (PHI), and mask, delete, or otherwise obscure it.

To enable researchers to study critical COVID-19 information for fighting the virus, patient identities from DICOM assets, such as lung x-rays, can be removed at scale using the same type of machine learning technology that scans YouTube for copyright infringement, making the data usable for analytics in high-definition. Further, testing data can be de-identified, accelerating discovery. When properly hashed, such data can then be safely re-identified allowing researchers to more effectively recruit for public health programs like clinical trials.

RELATED: Early AI adopters beginning to see success predicting readmissions, ED visits

Natural language processing for call center responses

All types of public health organizations today are inundated with more patient requests than ever before and many were not initially equipped to manage this increase.

With cloud-based AI and machine learning models, however, organizations can build the call center of the future. Using natural language processing and sentiment analysis, healthcare providers can automatically prioritize calls based on need.

This technology allows an organization to optimize its approach to answering/prioritizing inquiries based on everything from the distress of the voice to the age of the voice. And while they’re smart, many of these APIs are engineered with privacy in mind. They don’t store private data, helping ensure patient confidentiality.

RELATED: The unexpected ways AI is impacting the delivery of care, including for COVID-19

Supply chain decisions informed by predictive analytics

Cloud isn’t just supporting healthcare organizations through research and treatment decisions. It is also helping them get ahead of supply shortages at a time when equipment is more critical to survival than ever before.

As organizations look to provide critical healthcare equipment such as PPE and ventilators to those in need, cloud’s predictive analytics can help those managing the supply chain better understand where shortages exist, and where they will soon be, in order to allocate before there is an issue.    

Matching algorithms are easily implemented alongside predictive services to reduce waste in the supply chain, enabling real-time visibility to both suppliers and procurers.

Cloud-enabled Al and machine learning are providing healthcare stakeholders with the tools needed for a faster and smarter approach to combatting the COVID-19 virus. While the mission today is singular, this technology, along with the innovative ideas coming from our nation’s top minds, will change the face of healthcare as we know it, allowing for a greater patient experience than ever before.

Lisa Noon is managing director at Deloitte.

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