Artificial intelligence is starting to become an accepted part of daily lives. From navigation apps to streaming media services and online shopping, AI is creating more personalized and more efficient experiences. Across industries, it is being used to improve process and results and, while healthcare may not be as far along some, AI has promise to not only increase efficiency in healthcare, but to improve patient outcomes as well, as part of the ongoing transition from traditional to connected, digital healthcare.
Data and Algorithms
Algorithmic healthcare solutions are based on established patterns of conditions, treatments, and results. AI engines compare patient data to stored data sets to determine the best course of treatment. It’s not unlike a doctor considering symptoms and prescribing a course of treatment based on experience. AI, however, can review and connect much more data faster than doctors can reach conclusions.
Connected healthcare is an ideal mechanism for driving the use of AI in healthcare. Not only does it inherently increase the collection of data available for algorithmic analysis, but it also expands data sets and analytics beyond geographic boundaries. In other words, physicians benefit from aggregated data sets aggregated across providers and healthcare systems to deliver better results based on patient information and performance of treatment options.
Pattern recognition software has advanced to the point where AI has been shown to be at least as accurate as doctors in identifying abnormalities and, in some cases, significantly more accurate. For instance, Stanford researchers found AI to be more effective in identifying and assessing severity of lung cancer. A separate study has shown that AI can be 95% accurate in identifying malignant melanoma from images, compared to 86.6% for the 58 dermatologists in the study. And another study showed AI to be more accurate in classifying electrocardiograms than doctors. As the technology continues to improve, it may become possible for AI engines to accurately assess many conditions via patient-submitted images or even directly from video consultations.
Natural Language Processing (NLP) is a technology that allows computers to recognize and understand human speech. Its use in healthcare can increase physician efficiency by allowing doctors to dictate notes into recording devices or directly into AI engines, which can be transcribed and appended to EHRs either in real time or at the end of consultations. The result is increased efficiency, as doctors and other clinical staff are able to handle other patients or tasks, rather than having to manually transcribe notes and prescriptions into patient records. Eventually, it will be possible to automate post-evaluation actions, such as prescription orders, using AI engines and connected health solutions.
There has long been a fear of AI replacing humans in the workplace, including in healthcare, but the reality is it can be a valuable tool in allowing physicians to perform more effectively and efficiently – not as a replacement, but to aid them. While AI has been shown to deliver positive results in many instances, physicians are still required to confirm results, engage with patients, and actually deliver treatment programs. As advances continue to be made in computing power and accuracy, AI will be able to sort through massive volumes of data to increase the accuracy and consistency of healthcare services, saving time and delivering better outcomes – possibly saving lives through earlier identification of health issues. The efficiency gains allow doctors to see more patients without increasing their workloads, also helping address burnout and the existing physician shortage.
To learn more about how you can benefit from connected health technologies, visit us here.