In the last decade, Machine Learning has come roaring out of the textbook to become an integral part of the healthcare industry. In addition to contributing to the increased efficiency of routine administrative procedures and reducing operational costs, it is also beneficial for managing the supply chain and logistics.
The Indian healthcare sector is expected to reach ₹19,56,920 crore ($280 billion) by 2020. Rising income level, greater health awareness, increased precedence of lifestyle diseases and improved access to insurance are set to be the key contributors to its growth. The report also states that spending on AI in healthcare is projected to grow at an annualised 48% between the year 2017 and 2023.
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Buying supplies, medicines, and equipment to support better health outcomes has been a large component of spending, and the healthcare providers spend a huge amount of their operating costs on such supplies and inventory management. Researchers noticed that a sophisticated and structured supply chain management can help healthcare providers to reduce their expenses by an average of 17.7% or $11 million annually. Therefore, the need to reduce costs and optimise supplies to provide a better outcome has never been more urgent. That is why many Indian healthcare providers are now turning to AI and ML-powered techniques for processing their patients’ data and creating a smoother supply chain operations.
Programs such as IBM’s Watson for Oncology, Microsoft’s AI focussed network for cardio related diseases and Edison AI and Analytics by GE Healthcare provides extensive analysis using the medical data helping doctors to prescribe the best course of treatment for their patients. Along, startups such as Onward Health, Advancells, Artelus, Liv Health to name a few also aim to bridge the gap in health tech.
The technology can help hospitals to gather unified data that can identify patients’ behavioural patterns to predict risk scoring for chronic diseases and design speedy admission and discharge protocols. Healthcare providers can use the provided data to send additional reminders to patients who are at risk of a no-show and provide them alternatives suiting their needs.
Predictive models can also assist in the maintenance and repair of medical devices. Philips, one of the premier players in medical device equipment, is one of the prime examples of leveraging these trends in order to create an advanced supply chain solution including remote upgrade and predictive maintenance. The aim was to reinvent the medical device supply chain by focusing more on diagnostic-as-a-service. The company can now remotely identify imaging system errors of the hospitals, diagnose the cause, and troubleshoot it in realtime to implement repairs, which in turn reduced the downtime to 0.1% for hospitals.
Kishore Bala, Chief Technology Officer, Syft, a national provider of healthcare inventory control, stated in a blog post, “This is a serious motivator for healthcare leaders, who are turning to sophisticated technologies like AI to help standardise processes and reduce these expenses. ML is proving to be especially valuable in supply chain applications after being used extensively in the areas of imaging and population health.”
Prediction Is The Key
In this competitive era, tracing the supplies and goods with GPS and codes is not enough. The healthcare providers are required to use ML algorithms using predictive analytics to analyse the data to detect patterns in their supply chain which can suggest the factors responsible to enhance the supply network. With the provided data, the machine can monitor the production, plan the inventory of drugs and equipment, and can create a smooth network for the supplies.
The combination of predictive analytics and real-time monitoring is not only helping scientists and researchers to find cures but can also help practitioners to determine optimal treatment methods for returning patients. Hospitals can now obtain a more accurate picture of their patient’s health trends and create better treatment procedures according to their requirements. Connecting predictive analysis to supply chain management not only provides transparency of products moving through the network but also streamlines the process of administration.
Rajendra P Patankar, Chief Operating Officer at Nanavati Super Speciality Hospital believes that AI is poised to play a major role in the healthcare industry. He stated, “With modern medicine facing a significant challenge of acquiring, analysing and applying structured and unstructured data to treat or manage diseases, AI systems with their data-mining and pattern recognition capabilities come in handy.”
Due to increasing competition among healthcare providers, strict government regulations, rising medical costs, and the demand for optimal quality of service have put enormous pressure on the whole industry. An enhanced and more structured supply chain analytics solution can help with all of these problems. It will become easier for healthcare providers to identify smoother networks, manage inventories, find better suppliers, to determine optimal order quantity, and increase ROIs.
The scope for AI and ML to enhance the healthcare industry does not end here. Researchers believe in the future ML clubbed with analytics will newer trends leading to path-breaking innovations in the healthcare supply chain management.