Missed appointments, surgical complications and hospital-acquired infections are among the many adverse healthcare outcomes that artificial intelligence (AI) can help mitigate by taking over mundane administrative tasks that consume up to 70% of practitioner time.
Physicians must trust AI will not lead them down an unwise path or expose them to unexpected risks; to do this successfully, transparency is of utmost importance.
Healthcare organizations face difficulties with readmission rates. One cause may be that patients frequently return even sicker than when they first left hospital, prompting another hospitalization and readmission rate increase. Predictive modeling aims to avoid this by providing clinicians with tools they need for more informed decision-making regarding patient risk of readmission.
Machine learning and AI systems can process information more rapidly than humans can, providing answers that can be taken quickly in the real world. This makes them ideal for analyzing large datasets to develop predictive models to assist medical professionals better comprehend possible outcomes of various actions they might take.
Artificial Intelligence solutions can detect changes to X-ray images and identify disease markers based on them, while automating administrative tasks that take up significant amounts of time in healthcare organizations – helping streamline processes and cut costs while saving time for other duties. They can even categorize clinical notes using natural language processing (NLP), freeing up time to focus on more important duties.
Interpretable AI processes can quickly reduce large volumes of data to key facets for review, freeing healthcare professionals up to spend more time with patients while relieving hospital, physician and all medical staff of significant workload pressures.
Artificial Intelligence (AI) can play an essential role in helping identify patient concerns and alert providers of potential risk factors that could compromise outcomes. For example, predictive models might identify an increased risk for certain cardiovascular disease conditions and notify providers that medication adjustments or lifestyle modifications might be needed.
CDS tools are increasingly being utilized to identify and correct diagnostic errors, reduce costly testing/re-testing procedures and enhance patient outcomes and satisfaction. Unfortunately, their aim of increasing learning among stakeholders within health systems will be defeated unless prediction models are integrated into clinical pathways which direct high-risk patients towards services proven effective at mitigating their risks.
Artificial Intelligence in Patient Engagement
Artificial Intelligence can help healthcare professionals improve patient outcomes. But it’s also crucial that patients are engaged throughout the process so they can ensure their data is being utilized responsibly – including making sure AI uses its powers appropriately while protecting patient privacy.
Pattern recognition technology enables AI to detect patients at risk of disease development or experiencing one, or who are showing symptoms worsening over time. Once identified, AI suggests various interventions such as medication adjustments or lifestyle modifications in order to help patients adhere to long-term treatment plans and plan compliance.
AI can also scour through clinical notes to find opportunities to streamline processes, reducing operating costs and improving patient outcomes. Yet some healthcare leaders remain uneasy with AI being used this way, with 28% believing it would exacerbate bias and unfair treatment, while others worry that its data training may reflect human biases.
AI in Patient Monitoring
AI in patient monitoring aids in decreasing wait times for patients and relieving healthcare professionals’ workload, while simultaneously helping improve outcomes and providing preventative care.
Healthcare providers using AI-powered devices can remotely monitor a patient’s vital signs in an efficient and seamless fashion, allowing physicians to focus more fully on their patient and avoid further health complications between appointments.
Physicians can quickly and accurately recognize when their patients are experiencing medication side effects or missing doses, helping them address patient concerns and get them back on their treatment plans. Furthermore, interpretable AI simplifies analyzing patient data by cutting manual tasks for healthcare employees thereby saving both time and money – further helping healthcare organizations towards reaching the Triple Aim of healthcare.