In an era of all-encompassing digitization, hospitals, practices, and medical practitioners are experiencing a gradual, yet pervasive, shift toward a more modern type of medicine – based in artificial intelligence and machine learning instead of the classical healthcare model.

With the shift comes an increased need for physicians and healthcare providers to remain educated on clinically relevant changes. More frequently, medical professionals are being encouraged by hospitals and insurance companies to adopt new technologies to improve population health outcomes, for example, by replacing sparse, sporadically collected patient datasets with continuous data and vital parameters  gathered from wearable medical technology.

While radiologists, cardiologists, and pulmonologists have all become more reliant on automated high dimensional image processing algorithms and other artificial intelligence technologies, the significant impact of data science is growing and will affect physicians regardless of specialty. Having a basic understanding of data science is imperative for clinicians; it can empower them to make the most effective and efficient use of digital health technology, optimizing healthcare strategies and introducing a new standard of patient care. 

Why Should Doctors Learn Data Science?

In an article published in Forbes, CEO and co-founder of leading AI-powered medical device startup Think Biosolution, Shourjya Sanyal details the importance of staying educated on data science, artificial intelligence, and other digital health advancements as a physician or healthcare provider. Below are his top five reasons behind why doctors should learn about data science.

 Diagnose Using Large Volumes of Data Generated from Continuous Monitoring

 The introduction of novel products from wearable medical device companies allows clinicians a continuous daily feed of patient biometric data collected over months. Such dense data is difficult to parse by hand thus, both primary and advanced data science strategies can prove helpful in deriving medically relevant conclusions. Providing a range of statistical measurements, such as average resting heart rate, stress index, and LF/HF ratio, wearable technology can assist physicians in determining overall patient health, as well as predicting possible cardiometabolic risks with increased accuracy.

 Diagnose Using Multiparameter Data

 Multiparameter data often offers physicians the most insight on patient health in turn, helping them diagnose more efficiently and accurately. Medical technology companies, such as Propeller Health, are increasingly developing complementary devices to gather multiple data sets simultaneously in order to better predict health outcomes. Understanding data fusion – or how data is merged in these systems to provide a more comprehensive picture of patient health – can help clinicians improve diagnosis.

 This can prove especially useful in the case of geriatric emergency care. With the help of new digital health technologies, such as Livio AI hearing aids which utilize motion sensors and measure biometric parameters, physicians may be able to analyze the cause of a fall via an advanced AI algorithm. To ensure the optimal use of these novel devices, it is important for physicians to understand the underlying data science systems at work.

Diagnose Using Data Visualization

 Learning data science can also help clinicians use data visualization as a method for improving diagnostic outcomes. Currently, radiologists examine high dimensional imagery – CT and MRI scans – using machine learning-based software to aid cardiometabolic specialists in delivering critical care. Understanding the strengths and limitations of the software may allow radiologists to refine diagnoses and enhance care pathways.  

 Understand AI Workflow

 Future healthcare will likely include a variety of health predictors such as early warning scores and patient utilization patterns, designed via deep learning. As an example, DeepHeart, a software developed by Cardiogram, is a semi-supervised deep neural network that can accurately predict cardiovascular risk through an Apple Watch.

 With the growing prevalence of AI technology and its vast possibilities, medical professionals who understand the limitations and strengths of various machine learning algorithms will be able to determine when to rely on early warning scores and other predictive insights in their practice.

 Understand the Statistical Significance of Clinical Studies

 Continuing medical education requires physicians to stay clinically current in their field; this involves studying the latest trial results, which often are not statistically significant due to their scale or other limitations. Healthcare providers who learn data science are better equipped to evaluate the statistical significance of clinical studies and whether or not to include their implications into their medical practice.

The burgeoning role of big data in cardiometabolic health urges physicians to stay clinically current on digital health technologies and the many benefits they can provide in the practice setting. Learning data science can help clinicians remain educated, maintain competence, and ultimately, better equip them to integrate forthcoming digital health technologies and data-based techniques into their own practice, improving both diagnosis and treatment outcomes.

For a limited time, CMHC is offering a Conference USB , providing clinicians with the opportunity to delve into the latest, expertly-curated educational content on emerging digital health technologies presented at the 2019 Caridometabolic Technology Summit and 14th Annual Cardiometabolic Health Congress.