Machine learning models are on the rise. This is due to their potential for advanced predictive analytics, which is creating many new opportunities for healthcare. From models that can predict chronic ailments like heart diseases or acute diseases like infections and intestinal disorders, the capability of machine learning to predict infectious as well as non-communicable diseases is on its way to delivering considerable benefits to doctors and hospitals.
This is transformative as, over the years, a large number of people have been getting diagnosed with infectious as well as non-communicable diseases which require complicated and costly treatments. These can be temporary or long term treatment, which may require hospitalization and at times re-hospitalization. Individually and collectively, these are adding to the rapidly rising healthcare costs. It seems emerging machine learning models could offer a much earlier prediction of which patients will develop which specific disease, and thereby create effective intervention methods to help prevent the onset of illness, hospitalization, or re-hospitalization. This begins the much-needed healthcare transformation. That raises an important question: Will these models be used for denying people insurance based on what models predict?
Acknowledging this emerging reality, Risk Group initiated a much-needed discussion on Machine Learning on Insurance Data to Predict Hospitalization with Dr. Don Vaughn from the United States on Risk Roundup.
Disclosure: I am the CEO of Risk Group LLC.
Risk Group discusses “Machine Learning on Insurance Data to Predict Hospitalization” with Dr. Don Vaughn, Neuroscientist, Data Scientist and Author of Elsevier publication from the United States.
Technology Triggered Transformation of Healthcare
Along with the machine learning intervention models, mobile apps, wearable sensors, electronic health records, DNA analysis, and predictive analytics that are now more accepted are contributing to the ongoing healthcare transformation. Besides, many forms of remote care (telemedicine) and self-care are also booming, sometimes to the displeasure of medical professionals. Moreover, due to advances in healthcare consumer technology, consumers are now purchasing high-tech wearables and connected devices by the millions. This is leading to much greater awareness of human disease indicators, and consumers are getting more involved in self-care and wellness. As consumers begin to demand digital access to health records, it will likely drive cloud adoption of electronic health records and perhaps a centralization of health records. The growing healthcare data will drive further growth in machine learning models to fundamentally transform healthcare practices.
It is therefore essential to understand and evaluate how machine learning is driving the transformation of the healthcare ecosystem and re-shaping administrative, diagnostic, therapeutic, and clinical healthcare functions to help deliver affordable, accessible, reliable, transparent, and effective medicine and healthcare all over the world.
Machine Learning Model
Many ongoing efforts use machine learning to predict hospitalization. Some of the emerging machine learning models consider the history of a patient’s insurance records and predict whether an individual patient will be hospitalized in the following year, thereby alerting the particular health care system. This may allow timely preventive actions.
The growing healthcare data (electronic healthcare records, insurance data, genetic testing data, wearables data, and more) will drive further advances in machine learning models. We must ask, however, which data sources are credible? And, do the records get updated in real time? Are the machine learning models able to access this data in real time?
As machine learning models advance and as diverse data sets are applied to get more accurate and credible forecasting, healthcare data security will perhaps play a much more significant role in how healthcare accepts further advances in machine learning models and applications. That brings us to an important question: Is healthcare data secured? And, how do we protect individuals from bearing the cost of higher premiums due to the prediction of a pre-existing condition? In other words, it will be interesting to see how this application of machine learning works with existing and future healthcare policies.
Data privacy and security are distinct challenges in this field. Like any other technology in healthcare, changes cannot be brought in without overcoming diverse regulatory barriers from across nations, interoperability challenges with legacy hospital IT systems, and limitations on real-time access to crucial patient data essential for building advanced machine learning models. The time is now to evaluate the promise and perils of machine learning models for the healthcare industry.
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