One way generative AI helps is by improving how patients and doctors talk to each other. Before, patients often had a hard time understanding medical words, lab results, and treatment plans. This caused confusion and sometimes delayed care. Now, generative AI programs explain these complex medical details in simple language that patients can understand.
An example is Mercy Health working with Microsoft. Mercy uses Microsoft Azure OpenAI Service to help explain lab results in easy terms. This helps patients feel more confident when talking about their health with doctors. The AI also lowers the number of follow-up calls and helps patients get answers faster, which makes them more satisfied.
AI tools can also handle patient phone calls. They help schedule appointments and give follow-up advice during the first call. This reduces the number of calls and lowers costs. Mercy also uses AI chatbots to answer staff questions about policies and human resources. This lets doctors and staff spend more time caring for patients.
Generative AI is also useful in making medical predictions, diagnoses, and treatment plans. A review of 74 studies shows AI can improve clinical results by making diagnoses more accurate and creating treatment plans tailored to patients.
AI helps in eight areas:
Fields like oncology and radiology benefit from AI tools that analyze large amounts of patient data. These tools find subtle patterns that doctors might miss, allowing earlier and more personalized treatments.
For healthcare managers, AI could mean better patient results, fewer hospital readmissions, and better use of resources. Predictive analytics also help hospitals assign staff and equipment more efficiently.
Clinician burnout is a big problem in U.S. healthcare. Doctors spend nearly half their workday dealing with electronic health records (EHRs). About one-third of this time is used just reviewing patient charts, which adds up to around 1.5 hours a day.
This heavy workload causes frustration, especially among primary care doctors, where half experience burnout.
Large language models like ChatGPT-4 can help with clinical notes and summaries. These AI tools can create summaries of clinical notes and radiology reports more accurately than doctors alone. AI also lowers mistakes from false or misleading information. This reduces the paperwork for doctors, so they can spend more time with patients.
Companies like Wolters Kluwer use AI in clinical decision support to automate routine tasks and reduce mental overload. Their AI platforms give evidence-based advice during patient care without adding more paperwork. This helps doctors make decisions faster and with less hassle.
AI is changing how healthcare offices work day-to-day. It is especially useful in front-office tasks like answering phone calls and scheduling.
Simbo AI is a company that uses AI to automate front-office phone work. Their system handles answering calls, scheduling, appointment reminders, patient questions, and follow-ups. This helps clinics manage more calls while cutting costs.
AI phone automation makes it easier for patients to get care. Calls are answered quickly with correct information, and scheduling is smooth and accurate. Healthcare staff can then focus on more important work.
AI also helps inside the clinic by automating note-taking, summarizing charts, and preparing patient care plans. For example, Oracle Health’s EHR platform uses AI to take notes during visits, suggest personalized care, and organize patient flow in hospitals. This reduces burnout and improves efficiency.
AI dashboards give healthcare leaders real-time information based on data from many sources. These dashboards show care gaps, resource use patterns, and regional differences. This allows better decisions to improve patient care and manage resources.
Healthcare providers are careful when adopting AI. They know it must be safe, transparent, and accurate. Experts say AI should be trained only on trusted medical information so clinicians and patients can rely on it during care.
Generative AI differs from traditional AI because it creates new content from data. It needs close supervision to keep it safe for medicine. Greg Samios, CEO of Wolters Kluwer’s Clinical Effectiveness Division, says the technology must be built with medical experts and strict rules to avoid risks like wrong information or bad advice.
Ethical issues include protecting patient privacy under laws like HIPAA, reducing bias in AI, making sure data quality is good, and monitoring AI performance over time. These points are important for fitting AI into clinical care responsibly.
AI-based clinical decision support systems (CDSS) are part of everyday healthcare work, especially in EHR systems. They let doctors quickly access the latest medical information. This cuts down time spent searching through papers or data.
AI search tools let doctors ask questions in natural language and get quick, evidence-backed answers. This leads to faster and better decisions about treatment and diagnosis.
AI also turns unstructured health data into useful insights with easy-to-read dashboards. Healthcare leaders use these dashboards to monitor quality, find care gaps, and plan how to use resources wisely. This helps with value-based care, where payment depends on patient health results, not just the number of services given.
Tools like UpToDate from Wolters Kluwer support over 3 million clinicians worldwide. They provide AI-enhanced medical knowledge right when it is needed. These systems improve patient safety and help standardize care across clinics and hospitals.
AI can help solve workforce problems in healthcare, like not having enough staff and high burnout rates. By reducing routine tasks and improving workflows, AI makes work better and more satisfying for clinicians.
For managers, AI offers ways to run clinics better while keeping or improving care. For example, AI phone systems handle patient questions and appointment booking. Larger staff teams are not required for these jobs. Also, AI-created clinical summaries help doctors review patient information faster and feel more sure about care decisions.
With responsible AI use, healthcare organizations can better support clinicians. This lets staff spend more time caring for patients directly and provide medical attention tailored to each person.
By mid-2024, Mercy Health plans to use several AI applications made with Microsoft. These will help improve clinical decisions, predict care needs, and enhance patient communication. The AI tools include smart dashboards and real-time data insights that aim to shorten hospital stays and improve clinical operations.
Events like HIMSS 2025 will show AI advancements in clinical support and patient engagement. These events will also include demonstrations of big generative AI systems that make healthcare work better.
Healthcare groups in the U.S. are advised to work across disciplines to use AI carefully. They should invest in training clinicians on AI tools and include patients in talks about AI in their care. Checking and improving AI tools regularly will help make sure the technology supports healthcare workers safely and well.
The use of generative AI in healthcare brings many benefits to patient care and clinician work in the U.S. It helps with patient communication, clinical predictions, lowering administrative tasks, and automating workflows. AI is shaping a more efficient and responsive healthcare system. Medical practice managers, clinic owners, and IT leaders can plan to use AI to better meet the needs of modern healthcare.
Microsoft and Mercy are collaborating to use generative AI and digital technologies to improve patient care and clinician efficiency, aiming to transform healthcare delivery.
Generative AI will assist patients in comprehending their lab results and facilitate informed discussions with providers by providing information in simple, conversational language.
AI will assist in handling patient calls for scheduling appointments and provide follow-up recommendations, minimizing the need for additional calls later.
A chatbot will help Mercy employees quickly find important information about policies and procedures, enabling them to focus more on patient care.
Mercy plans to explore over four dozen AI use cases and implement multiple new AI solutions by mid-next year to enhance patient care.
The Microsoft Azure Cloud helps centralize and securely organizes data, allowing Mercy to deliver insights that improve clinical decision-making and patient care.
AI will provide smart dashboards and better visibility into patient needs, helping reduce unnecessary hospital days and enhance operational efficiency.
The hackathon brought together teams from both organizations to co-develop and innovate generative AI use cases aimed at enhancing clinical experiences.
Mercy is recognized as one of the largest U.S. health systems, known for its excellent patient experience and integrated care across multiple states.
Microsoft aims to empower every organization by enabling digital transformation through intelligent cloud and edge technologies, including applications in healthcare.
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