Last week we explored how artificial intelligence is helping expand access to healthcare via telemedicine.
As a refresher, nearly 75% of healthcare organizations are increasing their investments in AI and telemedicine is one of those areas. Another area that is growing in AI use? Diagnostics and patient care.
First, what is Artificial Intelligence in Medicine?
Patterned after the brain and its immense span of neural networks, AI can use multiple layers of information to learn and decipher data.
With AI resources, healthcare professionals from doctors to administrators can address complex problems and create more efficient processes. AI technologies like IBM Watson are being implemented by healthcare professionals to bring insights from the data surrounding medical reports, patient records and more.
Enhancing Diagnostics & Patient Care
One of the largest impacts of AI in healthcare has come in the form of diagnostics and patient care. AI isn’t replacing our healthcare professionals, it’s enhancing them. As organizations implement electronic healthcare records (EHR) and cloud storage repositories, the opportunity to benefit from AI solutions only increases.
Finding Illness Early
AI utilizes web-based databases to allow healthcare professionals access to thousands of diagnostic resources. When paired with these extended resources and AI’s ability to “learn” each patient through machine learning techniques (ML) and natural language processing (NLP), these vast databases give clinicians an opportunity to diagnose early, accurately, and efficiently.
Often patients exhibit symptoms that can correlate with multiple conditions. As the patient is tested for each correlation, diagnosis is delayed. With AI’s machine learning approach, an algorithm can pull out patient traits — such as physical exam results, medications, symptoms, basic metrics, disease specific data, diagnostic imaging, gene expressions, and different laboratory testing — to create a structured data comparison across resources, find patterns, and help return a more targeted and accurate diagnosis.
These predictive analytics are supporting clinical decisions and informing providers of potential patient problems in advance. Leveraging AI for clinical decision support, risk scoring, and early alerting is the future of data analysis inside healthcare institutions.
Enhancing Evidence-Based Medicine (EBM)
Evidence-Based Medicine (EBM) is often referred to as the guiding principle of clinical practice. EBM is intended to optimize the healthcare decision making process by basing care option on scientific evidence. Research shows us that EBM improves the quality of patient care, but implementing it often fails. Why? The large (and continually growing) amount of scientific evidence available makes it nearly impossible for providers to keep up with the latest trials studies. The National Institiute for Health reports that only 20% of clinical care is based on research evidence.
How can AI help? It offers an efficient technological approach to EBM. Through pattern recognition of health trends and clinical research, artificial intelligence can help uncover what is known and what is unknown. Armed with the latest and most complete analysis, providers can offer patients treatment options likely to result in the most favorable outcomes.
AI-Powered Analytics within Remote Patient Monitoring (RPM)
While we like to think of hospitals as safe havens, the unfortunate reality is far bleaker. Even prior to Covid-19, the likelihood of spreading viruses and hospital acquired infections was found to increase the longer patients remained in hospitals, with hospital stays incurring a 17.6% risk of patient infection and increasing 1.6% each additional day. The vast majority of healthcare facilities rely on antiquated, manual spot-checking methods to monitor patient status. These spot-checks, which require medical professionals to physically check on patient vitals, occur every four to eight hours and by default can increase the risk of infection.
To help reduce the spread of infection and provide medical staff with accurate and reliable patient vital readings hospitals and healthcare facilities have turned to advanced remote patient monitoring tools to track patient vitals from a safe distance. These tools continuously monitor a wide range of vitals, from blood pressure, respiratory rate and heart rate to motion, temperature and sweat levels, providing medical staff with accurate, up-to-date views of patient health.
While remote patient monitoring is crucial for helping medical practitioners make key decisions, teams still lack patient management insight that allows them to properly triage patients and prioritize care. The inclusion of AI-powered patient analytics addresses this need by distinguishing continuous patient data so that it is actionable. By fast-tracking the implementation of AI-powered remote patient management platforms, providers are more capable of improving patient outcomes in the hospital and the home.
Does AI-growth have you looking for career growth?
Without boring you with the details of our step by step process, we are committed to shifting the burden of the paperwork associated with telemedicine staffing and locum tenens from you, so that you can focus on providing excellent care. Let us help you find your perfect career opportunity.