How is predictive analytics used in healthcare?

By : |September 10, 2019 0

Artificial Intelligence is being implemented in various industries including manufacturing, healthcare etc. Presently India has 1 doctor per 1000 people and by 2023 we are looking at a shortage of up to 600,000 doctors. AI seems to be the only practical alternative. Predictive analytics has become an important aspect of the healthcare industry today due to the vast range of opportunities it presents.

AI in healthcare is helping many ways to enhance the efficiency, reduce errors, make better decisions as well as it can predict the illness even before it occurs. But how? Predictive analytics is used in healthcare for the early detection of diseases.

Labelled medical images

Zoya Brar, Founder and Managing Director, CORE Diagnostics

Zoya Brar, Founder and Managing Director, CORE Diagnostics

Today, there exist computer vision models on large numbers of labelled medical images (e.g. X-ray, ultrasound) with matched and clinically validated patient diagnoses. Using such systems would help doctors process more patient cases and make fewer diagnostic mistakes.

There are models out there that are able to diagnose simple diagnostic problems such as a fracture on an x-ray or skin cancer better than humans can. As an example, the Google InceptionV3 network trained using 129,450 clinical images of 2,032 different skin diseases has learnt how to classify images based on pixel inputs and disease labels only. This system achieves superior performance compared to dermatologists.

However, if the data you have available to teach the AI from is not enough, AI cannot perform at the level of expertise that a human would.

Behaviour analytics

Deepak Pargaonkar, Vice President, Solution Engineering, Salesforce

Deepak Pargaonkar, Vice President, Solution Engineering, Salesforce

A common entry point is to use predictive analytics tools in conjunction with a business’s customer relationship management (CRM) system. Using their CRM allows companies to make predictions about customer behaviour across sales, marketing, and service channels.

This might include analyzing customers’ past behaviours, including product usage and spending, to identify opportunities for cross-selling. Or to find ways to optimize the products, offers, or content shown to each customer.

In healthcare with the help of advanced analytics and augmented intelligence, doctors are better equipped to find patterns specific to small groups of people so that they can have a better chance of encouraging action and change.

They can monitor patient activity, and detect and analyze anomalies using medical device-derived biometric data, real-time streaming analytics, and complex anomaly algorithms simply.

Also, prescriptive analytics is a more abstract form of data analytics. It allows users to create “what if” scenarios, and extrapolate outcomes based on variables. This type of advanced analytics is often used in healthcare, where a doctor’s interpretation of facts is as important as hard evidence.

Analyzing the vast amount of data

Sharan Grandigae, CEO & Founder of Redd Experience Design

Sharan Grandigae, CEO & Founder of Redd Experience Design

One of the abilities of AI-based systems is their ability to consider vast amounts of related and seemingly unrelated data and use it to predict a specific result. Therefore, with enough sample data fed into such a system, very accurate estimates can be made of the possibility of a specific outcome.

So, for example, AI systems fed with the demographic details, exercise habits, hereditary conditions, medical conditions and lifestyle factors could potentially make a prediction of the likelihood of a specific kind of cancer or other diseases.

There are also no limits to how many data points can be supplied into the system, AI systems will find the relationship between these data points and specific outcomes if there is one.

 

Machine Learning

Rohan Shravan, Founder, Inkers

Rohan Shravan, Founder, Inkers

Machine learning is the best tool we have at our disposal to learn patterns in structured and unstructured data. A lot of decisions in healthcare are actually taken process-driven.

A symptom or set of symptoms are seen, and then a predefined step is taken. Anomalies are of course dealt with caution. Predictive analytics is learning these repetitive decisions, and it is as good as humans currently.

Predictive analytics is currently being used for prediction of life-threatening diseases, diagnosis, early-symptoms, anomalies and big-data based cross correlation between symptoms and diagnosis earlier not linked together. Predictive Analytics is done through Machine Learning which is a domain in AI.

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Dr. Ashutosh Tiwari, Chairman & Managing Director, VBRI (Vinoba Bhave Research Institute)

Dr. Ashutosh Tiwari, Chairman & Managing Director, VBRI (Vinoba Bhave Research Institute)

Predictive analytics leverages technology and statistical methods to find out bulk information about patients to predict outcomes for each one of them. The information is used to compare the data from past treatment with the latest medical facts.

It is used in several other ways including to enhance the precision of diagnoses, to help in preventive medicines and to promote public health, assist physicians to get customized answers for every patient.

Hospitals and medical professionals rely a great deal on patients’ historical data, family medical history and various diagnostic test results to chart an effective course of treatment. The various predictive analytics use cases can be getting risk scores based on lab tests, vital health data collection and biometrics.

Neha Rastogi, Co-Founder and COO-Agatsa

Neha Rastogi, Co-Founder and COO-Agatsa

It can also be used to avoid hospital readmissions, predicting possibility of having a disease like a cardiovascular disease, predicting patient utilization patterns etc. The involvement of health data becomes even more important in cases of chronic ailments such as cardiac problems. Devices collect heart health data on a regular basis by doing easy ECG and alerts the user about any potential anomaly or threatening situation.

From the perspective of hospitals, these analytics-based tools are very useful in continuous seamless operations. They play a vital role in predicting demand and supply flows, resource consumption and several other such important needs. It is this widespread utility that has seen AI becoming increasingly integrated into improving patient care, managing chronic diseases, hospital administration and enhancement of supply chains.

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