Machine learning is making huge strides in healthcare
The potential of machine learning for healthcare
Machine learning is a growing technology with exciting implications for healthcare. It is already helping solve some of the toughest problems in the space, from understanding huge volumes of patient data to improving the quality and personalization of treatment and care. So what is machine learning and how could it improve healthcare in the years to come? We explore how this is already transforming the sector and its potential.
What is Machine Learning?
Machine learning is a type of technology within the group of technologies known as artificial intelligence. One definition of machine learning is a statistical technique for applying models to data and having AI learn by training those models with data. Additionally, machine learning also refers to systems, applications, or programs that can identify patterns in huge volumes of data to make predictions. Alternatively, another way to define machine learning is to conceptualize it as the development of algorithms and applications based on past experiences and current data – both historical and real-time data.
It’s not just the healthcare sector that benefits from technology. For example, the agriculture, manufacturing, hospitality, retail, and banking sectors also rely on data science tools, including machine learning. Moreover, even non-profit projects like humanitarian aid can use machine learning.
9 Machine Learning Trends in Healthcare
Here are some of the top machine learning trends in healthcare:
1. Precision medicine and personalization of healthcare – Machine learning is already widely used for precision medicine. It predicts successful treatment protocols using patient data and treatment context. Precision medicine enables highly specific and personalized treatment plans and can lead to better clinical outcomes.
2. Categorization requests – Categorization applications include processes such as determining whether or not a patient will develop a certain condition. This can be used to inform effective prevention policies and measures, and help providers plan for capacity.
3. Analyze imagery – Machine learning is already used to analyze radiology and pathology images. Additionally, it is used to classify large volumes of images quickly. In the coming years, the use of machine learning for these processes could become even more sophisticated and precise.
4. Administration of Claims and Payments – Incorrect claims can cost insurers, governments and providers a considerable amount of time, money and effort. Machine learning can streamline claims and payment administration, for example by facilitating more accurate claim data and ensuring that claims are correct.
5. Other administrative procedures – Machine learning can be used in a wide range of administrative processes, including claims processing, clinical documentation, revenue cycle management, and medical data management. It can even be used to develop tools for patients, such as chatbots for telehealth, mental health and wellness support, and other general interactions that don’t require physician intervention.
6. Forecasting and health policy – Machine learning offers immense potential for predictive modeling and health policy. For example, population health machine learning models can be used to predict which populations are at risk for certain accidents or conditions and even hospital readmissions. Similarly, mining data on the social determinants of health and using machine learning to identify trends can inform policy. Governments and organizations could better target patients at higher risk for preventable diseases like heart disease and diabetes.
7. Electronic Health Records – Machine learning can help make sense of the vast amounts of data now available through electronic health records (EHRs). Most of them are in the form of free-form text inputs, also known as unstructured data. Machine learning has the potential to quickly interpret this free-form data to glean valuable insights at scale, for millions of patients, to enable better decision-making throughout the patient care cycle.
8. Diagnosis and treatment – Machine learning is increasingly used for diagnosis and treatment recommendations. Clinical decision support tools (CDS), in particular, can leverage machine learning to improve healthcare provider decision processes to deliver the best care possible. CDS tools analyze huge volumes of data to inform treatment suggestions. They can also flag probable problems so providers can take preventative action.
9. Drug Development – Researchers are leveraging machine learning to build cohorts for expensive clinical trials, paving the way for better studies and faster, more efficient drug development. Thus, researchers can make data-driven decisions and more easily identify key patterns and trends, and therefore achieve greater efficiency in their studies.
Machine Learning and Healthcare in the Years to Come
Machine learning is already beginning to realize its potential for healthcare, from facilitating more efficient drug research and development to patient care and administrative processes. In the coming years, widespread adoption of machine learning and other AI technologies is likely. Rather than completely replacing clinicians, these technologies are likely to complement and enhance their roles. Long-term outcomes could include better quality of care and a more efficient and cost-effective healthcare system, which can only benefit patients, providers, insurers, regulators and policy makers.
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