One big challenge when using AI in healthcare is making sure the AI gives correct and reliable results. In healthcare, patient safety and precise treatment are very important. If AI gives wrong answers, it could harm patients. This is especially true for AI tools that help diagnose diseases or support clinical decisions.
For example, Google’s DeepMind Health showed that AI can diagnose eye diseases from retinal scans with accuracy similar to human experts. AI can also find problems in X-rays and MRIs earlier and more precisely than many doctors. Even with these advances, healthcare leaders are cautious about fully using AI because errors and missed diagnoses still happen.
Doctors also worry because AI models need to keep improving to stay accurate as new data comes in. Dr. Jake O’Shea from HCA Healthcare says that in the next 10-15 years, the main issue will be how to reduce the computing power AI needs. AI systems that use too much power may give good results but require expensive computers that small clinics may not have.
Doctors need to trust AI, which requires understanding how AI makes decisions. Many AI systems are “black boxes,” meaning their processes are hidden. Without clear explanations, doctors hesitate to trust AI, especially for important patient decisions. To fix this, AI should be designed so clinicians can see and check the AI’s recommendations and give feedback.
AI technologies like machine learning need a lot of computing power. Hospitals and clinics need strong computer systems and special software to use AI well. These systems must handle big amounts of data, run complex calculations fast, and work without stopping.
Big hospitals and academic centers in the U.S. spend a lot on building AI systems. They often have special departments for digital projects, like HCA Healthcare’s Digital Transformation and Innovation team. For example, HCA’s use of the MEDITECH Expanse system has helped them organize data and build a base for AI to improve patient care coordination.
Smaller hospitals and clinics often have problems because they have little money, old computer systems, or no expert IT staff. As Mark Sendak, MD, MPP, explains, many community health centers face a “digital divide” where they cannot get good AI technology. Without upgrades, these places may find it hard to use AI tools that need real-time data or predictions.
Storing and protecting the large amount of patient data needed for AI is also challenging. Providers must follow privacy laws like HIPAA and manage security risks. Keeping patient data safe while using AI on a large scale is a careful task.
To use AI successfully, it must work well with existing healthcare IT systems, especially Electronic Health Records. EHRs hold patient information like notes, test results, medications, and care plans. AI depends on this data being available and accurate to analyze it correctly.
Old EHR systems often have problems like incomplete or inconsistent data. Different ways of recording information or missing details make it hard for AI tools that use language processing and prediction. Upgrading to better, standardized EHR systems is important.
HCA Healthcare’s MEDITECH Expanse platform helps by standardizing data across different sites, making AI data more reliable. Such platforms help maintain consistent care and support advanced data analysis needed for accurate AI results. They also reduce the time doctors spend on paperwork, helping with burnout and improving patient care.
AI tools should fit into daily work routines without causing interruptions. They need to support clinicians so that healthcare workers can trust their advice without workflow problems. This integration is important to improve care quality and work efficiency.
Besides the technical problems, ethical and practical issues also matter. AI decisions must be clear to avoid unfair bias. Healthcare leaders should check AI tools carefully to prevent mistakes that could affect some patient groups more than others.
Human oversight is still important. AI is meant to help, not replace, doctors. It acts like a helper that gives extra information to improve decisions. Dr. Eric Topol says AI use in healthcare is still new and requires careful study to prove it is safe for wide use.
Building trust with doctors and patients means ongoing teaching about what AI can and cannot do. Healthcare groups should create an environment where doctors feel free to question AI results and help adjust AI tools to fit their needs.
Healthcare rules must keep up with new technology. Organizations have to follow federal laws, privacy rules, and safety standards. Legal and compliance teams should be involved early to avoid problems when using AI.
AI also helps automate simple and repetitive tasks in healthcare. Front-office work like scheduling, sending reminders, handling insurance claims, and answering phones can be done by AI to save time.
For example, Simbo AI uses AI to handle phone calls in medical offices. Their system answers patient calls and appointment requests, which cuts down on the number of staff needed and shortens wait times.
AI chatbots and virtual assistants can work all the time, answer common questions, and guide patients about treatments or medicine. These tools help patients without adding more staff, which is good for busy clinics.
AI also speeds up data entry and claims processing, which reduces mistakes and makes billing faster. This lets clinical staff spend more time caring for patients instead of doing paperwork.
This automation improves patient experience by making communication clearer and faster. It also helps manage data better, which is needed for good AI analysis.
Healthcare groups must think about people when they bring in AI. Teaching doctors and staff how to use AI is very important. Some may resist because they don’t know AI or worry about losing jobs.
Dr. Jake O’Shea suggests building a culture of learning about AI and technology. Managers should encourage staff to learn about AI safety and benefits. Workshops, seminars, and online resources can help people feel more confident.
Getting doctors involved in creating AI tools helps make sure the tools fit real work needs. This increases the chance that staff will accept and use the new technology.
The AI market in U.S. healthcare is expected to grow a lot—from $11 billion in 2021 to $187 billion by 2030. AI is becoming a bigger part of how care is given and how health systems operate.
In the future, AI will do more tasks like remote patient monitoring, warning about disease progress, and acting as smart virtual health coaches. But issues like accuracy, computing needs, system upgrades, and ethics must be solved early. This will help make sure AI is useful, safe, and fair.
Hospitals that invest in better technology, clean data, good workflows, and staff education will be ready to use AI well. This also means helping smaller healthcare places get access to AI.
Healthcare administrators, owners, and IT managers in the U.S. face many challenges but also chances with AI. Problems with computing power and AI accuracy, along with workflow and staff training, need careful planning and ongoing work.
AI tools can improve patient care, engagement, and office work. But success depends on choosing the right tools for the organization’s size and strength, keeping good data, offering transparency, and keeping human checks in place.
Focusing on these points will help healthcare groups use AI responsibly, manage risks, and improve results for patients and staff.
Dr. Jake O’Shea was motivated by witnessing emergency care as a college student and a personal experience with his grandmother’s critical illness, which sparked his desire to understand and enhance healthcare.
Dr. O’Shea’s career evolved from a Regional Chief Medical Information Officer to the Chief Health Information Officer, focusing on integrating technology with patient care and improving electronic health records.
AI is integrated into healthcare as a tool to aid human caregivers, with the aim of improving patient care and operational efficiencies while addressing challenges in accuracy and computing power.
The DT&I department aims to enhance patient care and streamline operations through advanced technology and data systems, focusing on creating a more efficient healthcare delivery model.
Expanse is a modernized EHR system designed to improve data standardization, clinical workflows, and coordination of care, enabling healthcare providers to spend more time with patients.
AI integration faces the challenge of evolving models for accuracy and managing significant computing power requirements while adhering to responsible AI usage policies.
Dr. O’Shea emphasizes that the clinician’s voice and clinical input should drive the integration of technology into care, ensuring patient-centric practices.
Dr. O’Shea views technology as a means to augment the capabilities of healthcare providers, improving efficiency and patient care rather than replacing human interactions.
The standard build of Expanse supports enterprise-wide consistency, enabling effective data analysis and decision-making across multiple healthcare facilities.
Dr. O’Shea advises individuals to pursue their interests actively and emphasizes the importance of a genuine passion for helping others as a fundamental driver in healthcare.
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