By Taha Kass-Hout, Director of Machine Learning at Amazon Web Services (AWS)
Times of crisis spark innovation and creativity, as evidenced in the way organisations have come together to innovate for the greater good during the COVID-19 pandemic. 3D printing companies made face shields and nasal swabs to meet massive demands and auto companies shifted gears to make ventilators.
Machine learning (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—has also played an important role, supporting practically every aspect of healthcare. Amazon Web Services has supported customers as they enable remote patient care, develop predictive surge planning to help manage inpatient/ICU bed capacity and tackle the unprecedented feat of developing an messenger ribonucleic acid (mRNA)-based COVID-19 vaccine in under a year.
We now have the opportunity to build on our lessons from the past year to apply ML to help address several underlying problems that plague the healthcare and life sciences communities.
Supporting healthy populations anywhere
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 center interactions to better triage and direct patients to the information and care they require and online self-service prescreenings are helping optimise patient experiences and streamline provider assessments and diagnostics.
For example, GovChat, South Africa’s largest citizen engagement platform, launched a COVID-19 chatbot in less than two weeks using an artificial intelligence (AI) service for building conversational interfaces into any application using voice and text. The chatbot provides health advice and recommendations on whether to get a test for COVID-19, information on the nearest COVID-19 testing facility, the ability to receive test results and the option for citizens to report COVID-19 symptoms for themselves, their family members or other household members.
In addition, early in the COVID-19 crisis, New York City-based MetroPlusHealth identified approximately 85,000 at-risk individuals (e.g., comorbid heart or lung disease, or immunocompromised) who would require additional support services while sheltering in place. In order to engage and address the needs of this high-risk population, MetroPlusHealth developed ML-enabled solutions including an SMS-based chatbot that guides people through self-screening and registration processes, SMS notification campaigns to provide alerts and updated pandemic information and a community-based organisations referral platform, called Now Pow, to connect each individual with the right resource to ensure their specific needs were met.
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.
The shift toward 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 care 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 of the best examples 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.
Enabling better educated, more engaged patients
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.
Breastcancer.org allows individuals with breast cancer to upload their pathology report to a private and secure personal account. The organisation uses ML-based natural language processing to analyse and understand the report and create personalised information for the patient based on their specific pathology.
Making COVID-19 lessons count
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.
Looking ahead, imagine a time when our pernicious medical conditions like cancer and diabetes can be treated with tailored medicines and care plans enabled by AI and ML. The pandemic was a turning point for how ML can be applied to tackle some of the toughest challenges in the healthcare industry, though we’ve only just scratched the surface of what it can accomplish.
