One of the long-standing challenges in the healthcare informatics has been the ability to deal with the sheer variety and volume of the disparate data that is available and the increasing need to derive veracity and value out of it.
With the proliferation of smart, wearable health devices coupled with remote monitoring systems afford providers with better opportunities to assess their patient’s health outside the traditional settings. Unhealthy behaviour can be identified and precautions can be taken before the situation worsens. With the rise of sophisticated algorithms and machine learning, providers can now not only identify, but also predict adverse health events and in turn, facilitate customized care as per patient requirements. Well, earlier interventions lead to fewer complications!
Machine learning and predictive analytics have been prevalent in many industries for decades. The healthcare sector to has begun adopting these technologies and applying it in various ways including chronic disease management, staffing predictions, and population health risk assessment.
- Analytics provide valuable insights into the health of an individual based on collected data and contextual information.
- This is critical in predicting the likelihood of adverse events so that proactive measures can be taken to improve the rate of positive outcomes.
- Utilization of machine learning can help gain insights into the effectiveness of the existing programs and identify the treatment that yields the best results for the patients and their conditions.
- This customized approach enables the adoption of more precision in the process.
In this digital era, with the massive amounts of computational power, the machines are becoming more and smarter and can now analyse large sets of data points and apply relationship modeling in a more predictive way as well as in real time. The Big Data technology has the ability to leverage machine learning which enables accurate and real-time decision making, hence improving the overall operating efficiency. This, in turn, results in cost savings too.
Machine learning can be a huge boon to the healthcare industry. It increases efficiency and most importantly it helps in saving lives. The CT data can be analyzed and applied across patient records to see who or how many patients have a particular disease and how many are at a risk. Hospitals can also predict the post-discharge outcomes to reduce the re-admissions and optimize the patient flow. Above all, it can make medical diagnostics faster, accurate and more accessible!