Machine learning has permeated everyday life in a variety of ways. It is used for everything from spam filters to purchase recommendations to speech recognition, as well as in fields such as robotics and biology. Unfortunately, it has so far been underused in the healthcare industry: due to its cost and complexity, it is often inaccessible to healthcare organizations.
One company is looking to change that, however. Earlier this year, the data and analytics company Health Catalyst launched healthcare.ai, an open-source collection of machine learning resources that will bring this technology within reach of healthcare providers of all kinds. Health Catalyst is also embedding machine learning capabilities in all its products going forward.
With AI and machine learning becoming ubiquitous in healthcare, huge troves of unstructured data sets are now available for analysis. Speak to an analytics expert to know how we can help you extract maximum insights from these data sets.
There are many different ways that healthcare industry can benefit from machine learning. While the technology may not yet be widespread within the industry, many organizations are already making innovative use of it. From administrative applications to diagnostics and mHealth, machine learning is already beginning to change the face of the healthcare industry.
Researchers at Indiana University-Purdue University Indianapolis have created an algorithm that accurately predicts relapse and remission rates for acute myelogenous leukemia (AML). It draws data from bone marrow samples and patient medical history, and compares that with healthy individuals to make a prediction. This analysis is normally performed by doctors, but it is a difficult and complex task, and even highly skilled practitioners will not always agree. Machine learning removes subjectivity from the equation and is able to provide more accurate results.
This is just one specific example for one specific type of cancer. Many other studies are being done, and programs are being created that can help doctors make diagnoses faster and more accurately. As machine learning becomes more widespread and amasses more data, patient outcomes stand to improve substantially.
It can be challenging to determine the right treatment for a patient, or learn what medications are most effective under what circumstances. Machine learning can analyze large amounts of data more effectively than other methods, and make discoveries that otherwise would have been difficult or impossible to see. This can not only improve the patient’s experience and health but also save hospitals money, as it allows them to optimize their use of medication and other resources.
Healthcare analytics can also help determine which patients need urgent care and which ones can be safely released, minimizing the number of people who need to return to the hospital at a later date, as well as preventing the facility from spending more time with a patient who does not need it.
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Administration and Planning
Another area where machine learning is useful is improving the flow of patients through a health facility and allowing for more efficient use of resources. It can benefit scheduling in many ways, such as by predicting appointment cancellations or determining which patients are mostly likely to need readmission. A hospital in Brazil managed to reduce the length of patients’ stays using this technology, freeing up ICU beds and increasing the number of people it could treat in a month.
There are also several ways that mobile health applications can benefit from machine learning. In the healthcare industry, it is currently being used to monitor and treat patients remotely, as well as to provide support during long treatments or remind users to take their medication. There are multiple apps being developed that can assist both patients and their doctors, which is especially helpful in regions where there are few physicians and hospitals.
These are only a handful of the means by which machine learning can benefit the healthcare industry. As technology becomes increasingly accessible and widespread, medical data analytics applications and their effectiveness will rise, leading to better care and more savings across the healthcare industry.