The use of artificial intelligence (AI) and machine learning (ML) has been revolutionizing the healthcare industry in recent years. These advanced technologies are making way for a more resilient and sustainable patient-centric healthcare that benefits the patient and all the other stakeholders of the healthcare ecosystem, including the payers and the providers.An increasing number of healthcare organizations today are leveraging AI, ML, and big data analytics to deliver personalized patient care, predict and improve patient outcomes, and reduce healthcare costs - enhancing the overall patient experience.AI and ML are improving the accuracy of medical diagnoses phenomenally. Using these technologies, providers are analyzing large volumes of healthcare data and EHR/EMR records to detect patterns in patient behavior, predict the future health needs of patients and provide proactive treatment.AI/ML is also making it possible to forecast future trends in healthcare. And improve the overall quality of healthcare by helping to identify and address potential problems in the healthcare system. In addition to these, drug discovery, analysis of medical imagesFrom streamlining and automating administrative tasks like medical coding and billing, appointment management, etc. to enhancing patient engagement through personalized treatments for individual patients, advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) are transforming healthcare in more ways than one.Let's take a look at some of the use cases of AI and ML in Healthcare.

Patient 360

For a seamless patient experience and more importantly personalized care delivery, healthcare businesses need a full view of their patients. This 3600 view of the patient or patient 360 helps them to accurately diagnose, treat, and deliver the best medical care.AI and ML technologies enable this comprehensive view of a patient's entire medical history and deliver a full picture of their health. A robust data platform with AI capabilities integrates data from disparate sources and AI collectively analyzes all this patient data, including information on their medical records, lab reports, lifestyle, health history, patient family medical history, and any relevant information to derive meaningful insights. This analysis allows healthcare providers to make better and more informed decisions about diagnosis and treatment for their patients.With Patient 360, the providers can track patient activity to serve them better, improve patient engagement through patient intelligence, analyze feedback to take subsequent improvement measures, and improve the overall quality of care and efficiency of operations.

Patient Recommender Engine

A patient recommendation engine is a tool that uses data analytics, artificial intelligence, and machine learning algorithms to recommend personalized healthcare services to patients. The system analyzes patient data for things like the patient's demographics, lifestyle, medical history, etc., tracks patient progress, and provides feedback to physicians and other healthcare providers for informed decision-making and care.The objective of such a system is to improve patient satisfaction, reduce healthcare costs, and deliver positive patient outcomes - in line with the principles of a patient-centered healthcare model.The role of a recommendation engine in preventive care and chronic disease management is particularly noteworthy. AI/ML can be used to develop predictive models to identify potential health risks and clinicians can recommend lifestyle changes, such as diet and exercise programs as preventive measures, to help the patients stay healthy and avoid/delay the onset of chronic diseases. With the help of AI/ML-powered intelligent solutions, patients can manage their own healthcare more easily which goes a long way in enhancing the overall patient experience.

Patient No-show Prediction

Patient No-show Prediction systems are taking patient care to a whole new level and enabling providers to channel their resources more effectively. Patient no-shows make for one of the biggest contributors to lost revenues. Very often, clinics line up staff and equipment for patients only to learn too late that the patient would not be turning up. This translates to wasted resources, time, and money.With a Patient No-show Prediction system that is designed to predict whether or not a patient will show up for their scheduled appointment, the clinic can easily overcome this challenge of a no-show.The system works using machine learning algorithms that analyze patient data such as age, gender, location, past appointment history, etc. to generate a probability score for each patient. This probability score indicates the likelihood of patients attending their appointment.Empowered with this score, healthcare providers can better plan for their day and allocate resources more efficiently. The system can also come with other handy features such as sending out reminders to patients, in order to reduce the number of no-shows.

Healthcare Virtual Assistants - Conversational AI

The use of Conversational AI in healthcare is gaining rapid momentum, as the technology is being explored by an increasing number of providers to offer a more personalized experience to their patients. Simulating human conversations, voice cloning, Intelligent Virtual Assistants or Chatbots are enabling automated interactions between patients and healthcare professionals, making communication faster and more efficient and the overall experience more interactive. Chatbots can be used to answer questions, provide medical advice, remind patients of appointments, and even collect data from patients.With the 24x7 accessibility of chatbots, patients are able to stay connected with their providers and feel cared for.Besides, chatbots can help in reducing the administrative burden on healthcare providers by automating mundane tasks such as scheduling appointments and refilling prescriptions.In instances where a virtual conversation requires escalation, AI enables the patient request to be seamlessly directed to the most appropriate channel and form of care.Healthcare Virtual Assistants are a boon for the healthcare system, enabling healthcare businesses to scale during times like the COVID-19 pandemic when there's a high volume of patient inquiries.

Healthcare Workforce Optimization

Efficient and judicious resource management translates to reduced costs and improved patient care. Using an AI-based workflow management system, healthcare organizations can identify trends in staffing and resource utilization, optimize the scheduling of staff, forecast demand, automate administrative tasks and repetitive tasks, and improve overall operational efficiency. Besides, AI can also be used to measure employee performance.These are just some of the many use cases of AI. With the right strategy and guidance on where and how to apply AI, ML, and Data Analytics intelligently, healthcare businesses can build a robust healthcare business and deliver optimum patient experience.Contact us for more information.