At Appen, we provide high-quality training data for machine learning and artificial intelligence. Follow us to stay up to date on industry trends. In this edition of our roundup, news on: AI in Patient Care and Operations, AI as a Preventive Tool, and How Major Hospitals are Already Using AI. Check back for regular news roundups and subscribe to our YouTube channel for video updates.
AI in Patient Care and Operations
Artificial Intelligence is on track to wholly alter the future of healthcare. With the integration of AI into the work of both medical professionals and hospital systems, expect to see dramatic changes in both patient health outcomes and in the operational efficiency of hospitals.
Improved Health Outcomes
Doctors and nurses make dozens of critical decisions on patient care daily using patient consultations, lab test results, imaging scans, and more. In the future, anticipate AI being used more ubiquitously to scan this data, compare it amongst hundreds of thousands of other cases, and provide diagnostic and treatment plan recommendations.
The concept, experts say, is not that AI will replace doctors – but rather that doctors will supplement their decisions with AI to maximize accuracy and quick turnarounds. The result? More successful treatment plans and better health outcomes for patients.
Hospitals must anticipate patient needs, patient flow, and required resources on a daily basis, planning strategically using predictions based on past behavior. The complexity in hospital operations often results in an inability to predict these factors with full accuracy, leading to – for instance – no patient appointment slots in the near future, long wait times, or not enough staff on hand.
It’s no wonder, then, that hospitals are already looking to AI for help. AI will be used to simulate models based on historic volumes of patients, appointment types, average arrival times, and time duration for particular services. These models will be used to provide highly accurate, real-time recommendations on ideal patient flow decisions, enhancing patient care through sufficient time for consultations and an overall heightened patient experience. With better planning, hospitals could see more patients daily and reduce wait times by as much as 50%.
Learn more at MedCity News.
AI as a Preventive Tool
AI’s applications in patient care are already numerous, with experts foreseeing AI being used to assist doctors in diagnosing and treating various illnesses, injuries, and diseases. Another medical use of AI gaining traction is in preventive care. Already there are many exciting examples of AI being used by researchers as an early preventive intervention tool; for example, by catching type 1 diabetes, discovering indicators for Alzheimers, and predicting breast cancer.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are well on their way to using AI to tackle the most common cause of death globally: cardiovascular disease. Their machine, RiskCardio, was designed to determine the risk of cardiovascular death in already at-risk patients. RiskCardio monitors a patient’s ECG (electrocardiogram) signal, and can identify in just 15 minutes which risk category the patient belongs to. Doctors could use RiskCardio’s findings to develop more customized treatment plans catered to the individual’s risk level, leading to a higher likelihood of successful care.
By supplementing medical decisions with AI, doctors can hope to see better health outcomes for patients, both through earlier prevention and proper diagnoses. Moving AI from the research stage into practical application will be the next step as AI more readily integrates into the healthcare industry.
Learn more at Observer.
How Major Hospitals are Already Using AI
Research has hinted at the incredible possibilities of AI being used in healthcare. The question remaining now is “when?” In fact, major hospital systems are already starting to implement AI in patient care and operations.
Hospitals are moving away from decision-making based on descriptive analytics – that is, “what happened” – and toward predictive analytics, or “what should happen”, using AI. Predictive analytics can mean many things. For one, the processing of health data by AI to provide more accurate diagnoses and treatment plans. Second, the usage of health trackers to monitor patients’ conditions remotely and create a more customized experience (think a mobile-enabled EKG device that can detect cardiac arrhythmias in individuals who are miles away from any hospital).
Predictive analytics is also being used to monitor patient flow. John Hopkins hospital system in the U.S., for example, recently launched a command center that uses predictive analysis to help make all of those day-to-day decisions that go into running a hospital effectively. The result is a more efficient, timely flow of patients – reportedly a 60% improvement in patient admittance for complex conditions – and a better utilization of resources.
Hospitals are also testing out more extensive use of chatbots. These AI-based applications can manage more routine inquiries through automation. UCLA Medical Center researchers are at the forefront of this, having built a Virtual Interventional Radiologist (VIR) prototype that lets patients quickly receive answers to common questions about their radiology treatments and next steps.
With predictive analytics, health tracking, chatbots, and future solutions in their pocket, hospitals are poised to save millions annually by taking advantage of AI.
Learn more at Emerj.
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