When communities have access to better data, they can make better decisions. However, progress has not been equal across the globe, and there is a great need to focus on societal issues such as reducing health inequity and improving access to care for underserved populations. While researchers work to unlock lifesaving discoveries and develop new approaches to urgent health issues, advancements in technology can help accelerate and scale new solutions. In May’s upcoming Healthcare Innovation Forum, our panel of industry experts will discuss how AI and machine learning is revolutionizing the way healthcare data is analyzed and delivered, and how organizations can implement positive change to solve problems that plague the health system.
Integrating AI and machine learning into the healthcare ecosystem allows for a variety of benefits, including automating administrative tasks, easing workflows, and analyzing large data sets to deliver better healthcare faster, and at a lower cost. Insider Intelligence reported that spending on AI in medicine is projected to grow at an annualized 48 percent between 2017 and 2023.1
According to Business Insider, 30 percent of healthcare costs are associated with administrative tasks. AI can automate some of these tasks, like pre-authorizing insurance, following up on unpaid bills, and maintaining records, to ease the workload of healthcare professionals and ultimately save them money.1
The US healthcare system generates approximately one trillion gigabytes of data annually.2 These massive quantities of data have been accompanied by an increase in large-scale computing power. Together, they raise the possibility that both machine learning, and AI, can generate insights both to improve the discovery of new therapeutics, create more personalized treatment delivery, and enable the ability to extract actionable data to help improve operational outcomes for healthcare systems.
The NHS Business Services Authority (NHS BSA) is a Special Health Authority and an arm’s length body of the Department of Health and Social Care (DHSC). Amongst its activities, NHS BSA operates the NHS prescription service, including processing more than 54 million prescriptions per month. Of these, 30 million are paper forms, and data from many of these forms would need to be manually keyed in by an operator. NHS BSA decided to explore the opportunities to streamline this process through greater automation using Microsoft AI, machine learning technologies, and a machine vision solution. After the first pilot with one handwritten form, they were able to apply machine learning and AI to digitize the content and not only get above 90 percent of confidence from that data but use the data to drive further intelligence-led improvements.
Healthcare business group Ribera Salud, and its technological division FutuRS, have implemented a predictive AI model, which analyzes and processes the variables of each patient to predict their evolution, based on objective data analyzed by Microsoft Azure and machine learning (ML) tools. By including both technologists and healthcare professionals within the same team, Ribera Salud began to predict certain adverse effects using ML techniques and including this type of prediction within the day-to-day operations and care practices. The AI and ML supported tool has allowed Ribera Salud to have greater control over patient risks without incurring a greater workload for health professionals. Their model facilitates what is known as “Right Care, Right Now,” that is, providing the right care at the right time to achieve optimal results for the patient. In the last year, this model—supported by the Microsoft cloud platform and Azure Machine Learning tools—has helped reduce the number of patients who developed a pressure ulcer (PU) in ICU, up to 19 percent (11 percent cumulative incidence).
Adaptive Biotechnologies is a commercial-stage biotechnology company focused on harnessing the inherent biology of the adaptive immune system to transform the diagnosis and treatment of disease. Adaptive was built on the premise that the adaptive immune system can detect and treat most diseases in the exact same way, but the inability to understand precisely how that system works has prevented the medical community from fully leveraging its capabilities.
Building on the foundation of its immunosequencing technology, Adaptive built a proprietary immune medicine platform over the last decade that is uniquely capable of decoding the genetic language of the adaptive immune system at scale to understand exactly how it works. Adaptive needed to synthesize this huge system of biology and tap into the full value of the massive clinical immunomics database generated, so the company turned to Microsoft Azure for compute, storage, and machine learning capabilities. Adaptive worked with Microsoft to explore the cloud and create a roadmap for the company’s technology needs. Adaptive adopted Microsoft Azure to apply machine learning to exponentially accelerate the company’s ability to apply its proprietary immune medicine platform to gain novel insights from its clinical immunomics database. With a scalable immune medicine platform, researchers could begin computationally mapping trillions of TCRs to millions of disease-specific antigens that they are specifically targeted to attach to—called the TCR-Antigen Map—potentially enabling new approaches to diagnosing disease more precisely and earlier than is currently possible for many diseases.
Within the fields of life sciences, healthcare providers, and med devices, understanding when and how to deploy AI and machine learning can revolutionize the way healthcare data is analyzed and delivered. On May 20, 2021, join us at the Healthcare Innovation Forum to learn where AI and machine learning can drive greater care efficiencies, and uncover deep insights and relationships in healthcare data to help reduce risk, and ultimately lead to improved health outcomes.
- “Use of AI in healthcare & medicine is booming – here’s how the medical field is benefiting from AI in 2021 and beyond”, Alicia Phaneuf, Business Insider, January 29, 2021.
- “The fragmentation of health data”, Travis May, Medium, July 31, 2018.