Introduction:
In today’s world, data is gold. Big data is helping each sector change the way it functions and streamline processes and make them more efficient. Hence, leaders are putting their money on technological advancements that will help them keep abreast with the rapid changes that organizations are witnessing. Artificial Intelligence and Machine Learning are promising fresher insights into problems, leading to quicker decision-making. The healthcare sector has immense potential to better the traditional processes it follows if it can embrace new technology.
Challenges:
A major challenge that the healthcare industry faces is readmissions. Statistics say that 20% of the discharged elderly patients end up returning to the hospital in less than 30 days. This in turn puts a burden on the hospitals, their staff, insurance companies and the patient himself. There may also be a possibility of the doctor not knowing the appropriate course of action that would be suitable to the patient, as there is a high chance that a medical treatment provided to many before may not prove suitable to the patient. This will risk the patient’s health and increase the cost and time overhead. In such a case, patient satisfaction becomes a major concern. Predictive Analytics bridges the gap between high satisfaction and low costs.
How AI and Data is impacting the sector
According to Stanford Medicine Health Trends White Paper 2017, 153 exabytes (1 exabyte= 1 billion gigabytes) were produced in 2013, and an estimated 2,314 exabytes of data will be produced by 2020, which is an increase of at least 48% annually. Electronic Medical Records, Electronic Patient Records, Prescriptions, Insurances, information about machine-generated vital signs and other administrative data are some of the examples of the types of data generated daily in a hospital. Apart from this obvious example, IoT-powered fitness bands are also generating important insights.
This is where Predictive Analytics plays a pivotal role. It analyses historical data to predict future trends. It uses several statistical methods that use the data generated to make better predictions over time. In some cases, other sources of information such as weather forecasts are also taken into consideration before showing trends. Doctors can take the assistance of the machine-generated recommendations to make better-informed decisions about the patient’s course of treatment. This not only improves the accuracy of the diagnosis but also paves the path for tailor-made treatment that suits the patient. The large amounts of data available for processing can help medical practitioners to identify trends and predict potential future health issues. Pharmaceutical companies also benefit from Predictive Analytics as they would be able to anticipate the medication requirements of public better.
How BigAI can help:
With BigAI, unstructured and structured data can be fed into HBI Risk Model Engine. Modeling processes include ensemble, decision trees, random forests and survival. Our solutions can help healthcare providers to identify cost inefficiencies, waste and risk and streamline steps in processes. With Big Data Analytics, hospitals can provide exceptional healthcare experience by monitoring, analyzing and improving their services. It uses pattern recognition to identify patients at risk of developing a condition or health scare. BigAI’s Machine Learning algorithm can analyze 3D scans up to 100X faster.
Conclusion
Predictive Analytics in Healthcare is no supplement to the existing treatment methods and doctors. It is aimed to reduce the disadvantages of current practices. If implemented correctly, it promises to revolutionize the Healthcare sector by improving medical practice and reduce costs.
