In the patient and people-centric world of healthcare, our understanding of how machine learning and business intelligence can improve patient care and save valuable time and resources is only just starting to be uncovered. The idea that machines can learn about their patients and help them is just now becoming more widely accepted across parts of the medical field. To many, it seems like foreign, and even dangerous, concept. Similarly, it may seem strange to talk about “business intelligence” in a sector devoted to helping people get better and stay well. That is until we realize that BI concepts like descriptive, diagnostic, predictive, and prescriptive analytics, all which sound like the medical terms, can actually be applied to healthcare BI in life-saving ways.

Machine Learning vs Business Intelligence?

Machine learning and business intelligence can be used for practically all aspects of healthcare. From patient reception to treatment, monitoring, and recovery. Whether for curing ills or maintaining wellness, these two disciplines are now helping medical care move to a new level of effectiveness. Want to learn how to improve efficiencies in healthcare with BI? Listen to this exclusive audio recording with ReMy Health and Advocate Radiology. However, both machine learning and business intelligence offer different approaches. If we look at their functions, the differences become clear. Machine learning is a general tool to be used on many different problems, whereas business intelligence is directed at understanding and improving a business. Machine learning is a part of artificial intelligence (AI), which has recently come to the forefront thanks to advances in computing and the possibilities to handle large volumes of data. Business intelligence has existed in one form or another since businesses started, using whichever tools are the most helpful and the most accessible to improve data analysis. However, despite their differences, it’s when business intelligence makes use of machine learning that we see some exciting developments.

Medical Diagnosis Using Machine Learning

But let’s get to the heart of the matter with some real-life examples. Take the images generated by magnetic resonance imaging (MRI) scanners to detect problems such as brain tumors. Radiographers and other medical staff will scrutinize each MRI image to assess the state of the patient. MRI images can also be entered into a machine learning system as two distinct sets. One set shows brain tumors. The other set shows no brain tumors. The ML program analyzes the images to detect the patterns that typically distinguish one case from the other. When new images are then entered without being labeled, either way, the ML program applies what it has learned previously to decide if the new image represents a brain tumor or not. The more images the ML program treats, the more it learns and the better its diagnoses become, saving medical staff time while offering trustworthy assessments.

Big Data, a Medical Fact of Life

The amount of available medical knowledge available today has long since overtaken the capabilities of any physician to remember or apply it. Take an MRI machine, for example. Typically several megabytes in size, it doesn’t take much for MRI image training sets – or a set of images that fit a set parameter for the algorithm to learn from – to reach sizes that need big data machine learning. In other areas of medicine, big data is also prevalent with large volumes of structured or unstructured data, coming at high speed from multiple sources. By combining these huge quantities of siloed sources of data, yet even more potentially life-saving medical knowledge could be created.

Big Data Business Intelligence for Healthcare Insights

Consider data gathered about the use and effects of a specific drug. The data may be rich in elements like patient demographics, timelines, combinations of the drug with other medicaments or treatments, methods of administration (injection, oral, topical, etc.) and so on. The challenge is to find out where the drug works best, and if there are circumstances where the use of the drug should be avoided. With patient populations often in the millions, no human analyst can pick out the vital nuggets of information from such a mass of big data. On the other hand, business intelligence can. BI techniques allow you to “slice and dice” data to examine relationships between different data dimensions. For example, you might want to see if the effects of the drug varied at certain periods over the recent years. If such a period turns up, you might then dice your data further to see if any specific administration methods were favored, or if the period coincided with the release and prescription of another drug. A capable business intelligence system will let you try different approaches and ask ad hoc questions, following up on hunches, to get at the truth.

BI Can Do It All: Describe, Diagnose, Predict, and Prescribe

In the example above, BI can bring you descriptive analytics to show you what happened with the drug use, as well as when and where. It can also bring you diagnostic analytics to show you why it happened (types of drug format, combinations with other drugs, for example). It can help you with predictive analytics to predict the effects of the drug on other patient communities, according to their demographics or trending medical conditions. Finally, at the most advanced level of BI, prescriptive analytics can offer recommendations on the best course of action for the drug in order to lower risk.

ML and BI Will Continue to Improve Healthcare

We’ve only scratched the surface of what machine learning and business intelligence can do for healthcare. For instance, technologies like natural language processing (NLP), a form of machine learning, now also make it possible to ask a business intelligence system questions in everyday English. Physicians can type their questions to a chatbot as if they were conversing with a colleague, and the BI chatbot then converts the English input into instructions the BI system understands, gets the result, and gives it back to the physician once again in everyday English or displayed as a screen graphic that can be understood at a glance. As technologies like NLP, AI, and Machine learning get smarter and expand in their uses, it only makes sense that combining them with the power of a comprehensive BI solution will continue to change the way we think about healthcare. But don’t take it from us. If you want to learn how to improve efficiencies in healthcare with BI, listen to this exclusive audio recording with ReMy Health and Advocate Radiology.
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