Creating data-driven insights about sleep medicine will become an indispensable tool for physicians and other healthcare professionals seeking to learn more about their patients. This type of technology approaches problems by learning rules from data, starting with patient-level observations, algorithms sift through vast numbers of variables, looking for combinations that reliably predict outcomes. The creation of algorithms requires millions of observations to reach conclusions and to handle large amount of data that are combined in highly interactive ways.
As patients’ conditions and medical technologies become more complex, the role of machine learning will grow, and clinical medicine will be challenged to grow with it. Expanding the use of electronic health records (EHR) and generating well-defined sleep data from the growth and development of national research repositories, along with establishing patient networks and registries will aid in developing these observations. Data collected by integrating patient data from wearable sleep testing and monitoring devices will also be used by this technology.
Machine learning applications will provide physicians with access to new kinds of sleep data, whose sheer volume or complexity would previously have made analyzing them unimaginable. Analysis of large-volume data holds the promise for improving the application of medicine to sleep, including improving the identification of patient who may specifically benefit from different therapies. Using data-driven insights will provide these key benefits:
- Boost the ability of physicians to establish a prognosis. Current prognostic models are restricted by a few variables, because humans must enter and tally the scores. However, data could instead be drawn directly from EHRs or claims databases, allowing models to use thousands of rich predictor variables. Using machine learning with massive data sets will drive rapid improvements in performance, and machine accuracy, as well as monitor and interpret streaming physiological data.
- Increase diagnostic accuracy by preventing human errors and the lack of interventions to reduce them. Algorithms will soon generate differential diagnoses, suggest high-value tests, and reduce the overuse of testing. The standard for diagnosis is still unclear in many conditions making it harder to train algorithms. High-value EHR data are often stored in unstructured formats that are inaccessible to algorithms without layers of preprocessing. Models need to be built and validated individually for each diagnosis.
- Drive utilization of enormous amounts of data from physiology and behavior to laboratory and imaging studies. Machine learning will become an indispensable tool for physicians seeking to truly understand their patients. As patients’ conditions and medical technologies become more complex, the role of machine learning will grow, and sleep medicine will be challenged to grow with it. Patients, whose lives and medical histories shape the algorithms, will greatly benefit from this technology.
By using data-driven insights, physicians will be able to quickly facilitate patient screening, diagnosis, and management of sleep disorders. They will be able to improve the recognition of differences in the susceptibility patients have to sleep apnea and other sleep disorders – leading to improved care management and outcomes. To increase the efficiency of sleep medicine diagnosis, we need to change our current research methods.
To learn how Somnoware helps sleep centers automate their testing processes, obtain predictive insights, and drive greater patient engagement, select this link.