Importance & Use cases of Artificial Intelligence in Healthcare Supply Chain Management
The COVID-19 pandemic has shown that there are loopholes in our healthcare system. It resulted in the loss of thousands of lives and a significant incremental cost to the healthcare system. According to Human Development Report 2020, India has 8.6 doctors and 5 hospital beds per 10,000 people. Implementing AI solutions in the healthcare system can help countries to react faster to the increasing demand for healthcare facilities across the globe.
Here are some use cases of how AI can help to improve the Healthcare Supply Chain Management (SCM)-
· Disease Prediction and Detection — AI algorithms have been implemented to detect disease at its early stage so there are higher chances of the patient getting cured. Diseases such as Breast Cancer, Lung Cancer, Diabetes, Parkinson’s Disease etc. can be predicted or detected using AI and ML.
· Increased Accuracy of Forecasting — AI helps in analyzing all the records of past patients and suggesting methods of treatment that will work best for an individual. This increase in the accuracy of forecasting enables real-time decision-making to react to last-minute changes in demands.
· Mitigate Supply Shortages — AI can help in allocating resources efficiently; it could be deployed to help providers anticipate backorders and stockouts and help manufacturers gather data across their highly complex supply chains to better predict disruptions. AI in healthcare can revolutionize material manager’s ability to deliver critical supplies at right time, right place and at the right cost.
· Diabetic Retinopathy Detection — Deep learning algorithms have enabled computers to learn from large datasets in a way that exceeds human capabilities. Diabetic retinopathy detection with high-end retina scanner together with AI modules to detect the level of eye damage due to diabetes is one of the important breakthroughs for AI use in eyecare. One such application can be tried here
· Logistics Optimization — AI can help to determine the best transportation methods, frequency and routes to move products and health workers to several locations where they will be needed. It can chart the fastest ambulance routes to transport patients to the hospital or other care delivery sites.
· Inventory Level Optimization — The biggest challenge for health care supply chains is to manage inventory efficiently and keep up the satisfactory service level at the same time. In order to meet the increased demand for healthcare products. AI can help keep a well-maintained record of the inventories including their expiration date, side effects, demand etc. and hence can notify if there is a need to restock.
· Diagnosis and Medical Imaging — Machine learning can be used for recognizing patterns that can be applied to medical images that can further help in interpreting medical diagnoses. Image-guided surgeries as compared with conventional surgical approaches are less aggressive, have more precise targeting, and have better results. Imaging is used to plan, monitor progress, and assess results.
· Drug Discovery — AI technology can address many challenges and constraints in traditional R&D. AI is being used to analyze Drug Information Bank for predicting therapeutic potential, finding potential targets and enable target prioritization which is aimed at finding the efficacy target of a drug.
· Scanning Of EHRs and Biomedical Data — The number of radiological imaging exams is growing and to meet this increased workload demand, puts a lot of pressure on a radiologist which may lead to fatigue, burnout, and increased error rates. Automated detection and classification systems that successfully utilize both medical imaging data and clinical data from the EHR lead to better performance and more clinically relevant models.
· Detailed Analysis of Patient History — NLP and ML can read the entire medical history of a patient and detect symptoms, chronic infections or an illness that’s existing currently and needs treatment.
· Chatbots — QnA module based chatbot can help to reduce time of response by medical representatives to medical practitioners thus saving trillions of dollars of business for pharmaceutical companies. These bots can also play a critical role in making relevant healthcare information accessible to the right people at the right time.
AI has the potential to transform healthcare in enormous ways. With the ever-evolving challenges healthcare organizations are facing today, it’s not surprising that many have turned to AI to solve above and many other inefficiencies.
Contact us if you would like to explore solving any of your healthcare supply chain issues with Artificial Intelligence.
