Taha Kass-Hout, director of Machine Learning (ML) at Amazon Web Services, explains why the COVID-19 pandemic has been a defining moment for ML.
Moments of crisis spark innovation and resourcefulness, as demonstrated by the way organisations have united to pioneer for the nation’s wellbeing during the COVID-19 pandemic.
Liquor distilleries produced hand sanitiser, 3D printing companies constructed face shields and nasal swabs to meet massive demands, and auto companies switched lanes to make ventilators.
ML computer systems that learn and adapt autonomously by using algorithms and statistical models to analyse and draw inferences from patterns in data to inform and automate processes. It has played a major role in supporting practically every aspect of healthcare. During the COVID-19 pandemic, healthcare organisations used ML to enable remote patient care, develop predictive surge planning to help manage inpatient/ICU bed capacity, and develop a messenger ribonucleic acid (mRNA)-based COVID-19 vaccine in under a year.
Healthcare organisations now have the opportunity to build on lessons from the past year to apply ML to address underlying challenges that have plagued the healthcare and life sciences communities worldwide.
Supporting healthy populations any place, any time
Telehealth was on the rise before COVID-19, but it revealed its true potential during the pandemic. Telehealth is often viewed simply as patients and providers interacting online via video platforms but has proven capable of doing much more. Applying ML to telehealth provides a unique opportunity to innovate, scale, and offer more personalised experiences for patients and ensure they have access to the resources and care they need, no matter where they’re located.
ML-based telehealth tools such as patient service chatbots, call centre interactions to better triage and direct patients to the information and care they require, and online self-service pre screenings are helping optimise patient experiences and streamline provider assessments and diagnostics.
For example, Babylon Health, a digital-first health service provider, sought to address challenges to healthcare access through its mobile application, which connects users to services while preserving valuable healthcare resources. The app reduced the need for users to travel from remote locations—or come out of self-isolation—to see a doctor. Through its technology supported by AWS AI and cloud capabilities, Babylon can monitor, observe, and take insights from the behavior of our members so that we can see their issues before they develop to help them stay healthier.
By providing an easy way for patients to access the care, recommendations, and support they need, ML has given providers the ability to innovate and scale their telehealth platforms to support diverse and continuously changing community needs. Agile, scalable, and accessible telehealth continues to be important as providers look for ways to reach and engage patients in hard-to-reach or rural areas and those with mobility issues. Organisations and policymakers globally need to make telehealth and easy access to care a priority now and going forward in order to close critical gaps in care.
Taking steps towards precision treatment and prevention
Beyond the unprecedented shifts in the approach to engaging, supporting, and treating patients, COVID-19 has dictated clear direction for the future of patient care: precision medicine.
Guidelines for patient care planning have shifted from statistically significant outcomes gathered from a general population to outcomes based on the individual. This gives clinicians the ability to understand what type of patient is most prone to have a disease, not just what sort of disease a specific patient has. Being able to predict the probability of contracting a disease far in advance of its onset is important to determining and initiating preventative, intervening, and corrective measures that can be tailored to each individual’s characteristics.
One example of how ML is enabling precision medicine is biotech company Moderna’s ability to accelerate every step of the process in developing an mRNA vaccine for COVID-19. Moderna began work on its vaccine the moment the novel coronavirus’s genetic sequence was published. Within days, the company had finalised the sequence for its mRNA vaccine in partnership with the National Institutes of Health. Moderna was able to begin manufacturing the first clinical-grade batch of the vaccine within two months of completing the sequencing—a process that historically has taken up to 10 years.
Another example is UK headquartered, global biopharmaceutical company AstraZeneca. AstraZeneca uses petabytes of genomic sequencing data to inform drug research and development. To rapidly process data at scale, they turned to AWS to build a fast, efficient solution for extracting impactful genomics insights. By using AWS, AstraZeneca was able to speed up and improve productivity, and because the company can run analysis at scale when needed, data is available for analysis sooner. AstraZeneca can now run over 51 billion statistical tests in under 24 hours, studying the effects of individual mutations or individual genes, each with a broad range of phenotypes.
Engaging patients on an individual level
Personalised health isn’t only about treating disease, it’s about providing access to resources and information specific to a patient’s needs. ML is playing a key role in curating content that can help to educate and support patients, caregivers, and their families.
Total health solutions company Cambia Health Solutions (Cambia) used AWS to develop Journi, a digital all-in-one health solution guided by data-driven intelligence and human expertise that helps health plan members and their families in the US make the most of their health benefits.
Cambia used multiple AWS services, including Amazon Comprehend Medical, a natural language processing service that uses ML to extract relevant medical information from unstructured text, and Amazon SageMaker, a fully managed service that provides developers and data scientists the ability to build, train, and deploy ML models quickly.
Built using best practices in human-centered design, Journi brings together health plan benefits, digital care tools, well-being and clinical expertise, and human support to combat healthcare complexity and create a better experience by making it easier for people to understand and make good use of their health benefits. Based on an individual’s health profile, claims history, and past actions, Journi can provide users with relevant insights and personalised recommendations.
Lessons learned from COVID-19
For the last decade, organisations have focused on digitising healthcare. Today, making sense of the data being captured will provide the biggest opportunity to transform care. Successful transformation will depend on enabling data to flow where it needs to be at the right time while ensuring that all data exchange is secure.
Interoperability is by far one of the most important topics in this discussion. Today, most healthcare data is stored in disparate formats (e.g., medical histories, physician notes, and medical imaging reports), which makes extracting information challenging. ML models trained to support healthcare and life sciences organisations help solve this problem by automatically normalising, indexing, structuring, and analysing data.
ML has the potential to bring data together in a way that creates a more complete view of a patient’s medical history, making it easier for providers to understand relationships in the data and compare specific data to the rest of the population. Better data management and analysis leads to better insights, which lead to smarter decisions. The net result is increased operational efficiency for improved care delivery and management, and most importantly, improved patient experiences and health outcomes.
The future of healthcare is bright
Picture a time in the future when pernicious medical conditions like cancer and diabetes can be treated with medicines and care plans, enabled by AI and ML, and tailored specifically to each patient’s needs. COVID-19 has been a clear turning point, demonstrating how ML can be applied to tackle some of the toughest challenges in the healthcare industry. What’s clear is that we’ve only just scratched the surface of what can be accomplished.
