It’s no secret that the future of healthcare is data-driven and Artificial intelligence (AI) is poised to revolutionise the way healthcare providers and patients interact with data. It’s also old news that big data will transform healthcare. However, it’s important to remember that data by itself is useless. To be useful, data must be analysed, interpreted, and acted on. Thus, it is algorithms – not data sets – that will prove transformative.
Machine learning is key to enabling AI-driven healthcare, finding patterns in large data sets to enable decision-making in clinical practice. The effective use of machine learning will help healthcare professionals and organisations extract the insights locked in large repositories of data from sources such as electronic medical records, clinical trials, and billing and claims.
It would be remiss of me to talk about machine learning without mentioning predictive analytics or data driven intelligence. After all, it is a branch of AI and machine learning. Predictive analytics is the analysis of data and the discovery of patterns that provide novel insights to inform workflow and decision making. It uses health data to help improve capacity management and patient flow through the health system, develop patient-centred evidence-based models of care, address the burden of chronic disease, health monitoring and management of home-based care.
Machine learning makes predictive analytics more accessible by reducing barriers to more accurate predictions. From enhancing radiology, pathology, and clinical decision support to streamlining administrative tasks across the care continuum, the applications for machine learning are virtually endless. At an administrative level, this technology has the capacity to help clinics and practitioners meet growing patient demands, improve operations and lower costs.
At a clinical level, machine learning applications may help practitioners detect and treat disease and health conditions more efficiently and with more precision and personalised care. It allows them to improve the accuracy of treatment protocols improving patient prognosis and health outcomes.
So, what does the future have is store for machine learning? We, at HealthBank believe that precision healthcare will be the most important application of AI. If we improve the way we match health interventions/treatment protocols to individual patients, we can improve outcomes and lower the total cost of care. It’s important we understand what works for whom, what the underlying mechanism is, how a biological system operates, and how a treatment intervention impacts that. Machine learning is a critical steppingstone for that process because it helps amalgamate the little bits of what we think we know and presents more timely insights than humans can typically uncover.
HealthBank’s outlook for machine learning applications is that with the right data, integration methods, and personnel in place, we believe that machine learning has the potential to advance clinical decision support and help health practitioners deliver optimal and timely care.