Future Healthcare Trends for Machine Learning

Categorization applications include processes like working out whether or how likely a patient will develop a certain condition. This can be used to inform policy and effective prevention measures, as well as help providers plan for capacity.

Machine learning is already used to analyze radiology and pathology images. In addition, it’s used to classify high volumes of images quickly. In the coming years, the use of machine learning for these processes could become even more sophisticated and accurate.

Incorrect claims can cost insurers, governments, and providers a considerable amount of time, money, and effort. Machine learning can streamline claims and payment administration by, for example, facilitating more accurate claims data and ensuring claims are correct.

Machine learning can be used in a vast array of administrative processes, including claims processing, clinical documentation, revenue cycle management, and medical data management. It can even be used to develop patient-facing tools, such as chatbots for telehealth, mental health and wellness support, and other general interactions not requiring doctors’ input.

Machine learning offers immense potential for predictive modelling and health policy. For example, population health machine learning models can be used to predict which populations are at risk of certain accidents or conditions and even hospital readmissions. Similarly, tapping into data on social determinants of health and using machine learning to identify trends can inform policy. Governments and organizations could better target patients at higher risk of preventable conditions like heart disease and diabetes.

Machine learning can help make sense of the vast quantities of data now available through electronic health records (EHR). Most of these are in the form of free-form text entries, which are also known as unstructured data. Machine learning has the potential to interpret this free-form data rapidly to glean valuable insights at scale, for millions of patients, to empower better decision-making throughout the whole patient-care cycle.

Machine learning is increasingly being used for diagnosis and treatment recommendations. Clinical decision support tools (CDS), in particular, can leverage machine learning to enhance the healthcare provider’s decision processes for the best possible care. CDS tools analyse huge volumes of data to inform treatment suggestions. They can also flag likely problems so providers can take preventative measures.

Researchers rely on machine learning to put together cohorts for expensive clinical trials, paving the way for better studies and faster, more effective drug development. As such, researchers can make data-driven decisions and more easily identify key patterns and trends, and, consequently, achieve greater efficiency in their studies.