Healthcare AI priorities Partner voices Newsmakers: Larry Ellison, gastroenterologists, Natl Acad of Med, more AI in Healthcare

How Generative AI Can Reduce Administrative Burden in Healthcare

Finally, accuracy is the most important consideration in healthcare—without it, it’s impossible to properly treat patients. According to a 2018 Johns Hopkins study, over 250,000 people each year die in the U.S. as a result of human error. Generative AI struggles with medical administrative tasks, such as summarizing patient health records, leading to suboptimal performance in healthcare workflows. While we have explored the major advantages and applications of Generative AI in the healthcare sector, it’s crucial to also acknowledge that this transformative technology is not free of its challenges.

While excitement around AI technologies — including GenAI — has grown significantly in recent years, health systems have begun to face “pilot fatigue” in the last year, according to Vince Vickers, a KPMG U.S. healthcare technology leader. Healthcare organizations are known for their ability to drive innovation, and they are rising to the forefront of this transformation. Another example of how AI tools can help healthcare organizations is by reformatting data or research to be compliant with specific standards. Heisey-Grove points out that the organization may store data one way but need to share research with another entity in a different way for grant approval. She said that AI paired with human validation can accelerate the process of reformatting that data. The industry-wide move toward interoperability carries many challenges but also immense potential — improving access to data, creating more complete longitudinal patient datasets, and ultimately improving medical decision-making.

• The committee also emphasized that the agency must keep in mind the impact of these devices on health equity.
• Healthcare organizations are known for their ability to drive innovation, and they are rising to the forefront of this transformation.
• And so, we partnered with Microsoft on that because I wanted to teach people how to do prompt engineering, how to write a good prompt, which is at the core of all of this, right?
• Examples include drug discovery, personalized medicine, medical imaging analysis, or generating synthetic patient data for research.

Responsible artificial intelligence is basically the only way to go if one is implementing AI technology at their hospital or health system. But some in the industry have lamented that the regulatory environment is struggling to keep pace with innovation in the AI space, leading to concerns about the technology’s deployment. MacTaggart recommended creating a framework for discussions about AI to help the industry navigate conversations around implementation, whether the use is intended for patient engagement, clinical workflows or administrative efficiencies. “It’s a very successful endeavor, generating a lot of interest from physicians, nurses and other users,” said Helen Waters, executive vice president and COO of Meditech.

Data analysis

Further reflection required students to use the gathered information to determine whether the ChatGPT-generated interventions should be implemented or not, and to explain their reasoning. The assignment was designed to reinforce the learning objectives and provide practical application opportunities for participants. I have many physician friends who are eager for solutions that allow them to focus more on patient care rather than on administrative tasks like data entry. Ambient technology has the real potential to transform the day-to-day of healthcare professionals.

Always keeping that human in the loop and helping people understand it’s sort of a ‘trust, but verify’ strategy — that these tools can help accelerate progress on whatever you’re working on. You really are accountable for validating the results you receive and making sure they’re appropriate for the context. Healthcare isn’t immune to the GenAI hype, with studies and pilot projects demonstrating the technology’s potential value in the realms of clinical documentation, revenue cycle management and EHR workflow improvement. But with the rise of generative AI, enterprises are forced to navigate yet another technology hype cycle, in which selecting practical use cases for these tools and deploying them effectively is a major priority. Let’s compound the time, money and user frustration saved and shift our focus to the speed at which this low-value work can be performed.

Concerns were raised about the relevance and safety of ChatGPT-generated suggestions, with 36% of students skeptical of its evidence basis and 42% cautious of its lack of personalized client insights. This theme encapsulates the need for interventions to be both scientifically sound and tailored to individual client needs. Intelligent Medical Objects is a healthcare data enablement company that ensures clinical data integrity and quality — making patient information fit-for-purpose across the healthcare ecosystem, from hospitals to health information exchanges to payers, and beyond. Furthermore, leaders should develop a clear strategy for employing AI to increase operational efficiency. Improvements in efficiency support cost savings and additional resources to be reinvested in areas of growth and innovation.

Continuously improve AI models through rigorous testing and validation processes, focusing on specific healthcare domains and populations. Businesses can invest in extensive training datasets and collaborate with healthcare professionals to identify and address potential biases or limitations in AI algorithms. Implement ensemble or hybrid approaches combining AI with expert knowledge to enhance diagnostic accuracy. For example, medication errors are a major category of medical mistakes, resulting in numerous patient fatalities each year32,33.

Generative AI in Healthcare: 10 Applications and Use Cases Reshaping the Industry

By leveraging these benefits, generative AI has the potential to revolutionize the healthcare industry, making it more efficient, effective, and patient-centric. ANNs utilize a layered algorithmic architecture, allowing insights to be derived from how data are filtered through each layer and how those layers interact. This enables deep learning tools to extract more complex patterns from data than their simpler AI- and ML-based counterparts. Machine learning (ML) is a subset of AI in which algorithms learn from patterns in data without being explicitly trained. However, these initiatives require analyzing vast amounts of data, which is often time- and resource-intensive.

Her institution has already created and filled that role as part of its AI governance process, supported by both a steering committee and a health AI oversight committee. Vickers underscored concerns about growing cybersecurity threats in the industry, noting that increased uptake of emerging technologies is likely to create potential vulnerabilities for cybercriminals to exploit. To combat this, he suggested that healthcare organizations pay close attention to any new threats that might surface during technology deployments and anticipate how to prevent data breaches. Many organizations have made meaningful progress in AI adoption, particularly for administrative use cases like revenue cycle management, billing and EHR workflow optimization.

Those that pull ahead will focus on use cases that strengthen the patient-clinician relationship and not replace the human elements that are vital to delivering effective and compassionate care. These leading providers will include clinicians in strategic decision making from the outset. Clinician involvement will go a long way in addressing both patients’ and clinicians’ concerns and scaling winning applications. Generative AI is making a significant impact in the healthcare sector by providing tools that assist in managing and interpreting large datasets.

The findings of this novel study suggest a positive disposition toward integrating ChatGPT into occupational therapy education, driven by its potential to enhance creative ideation, time efficiency, and personalized care. The study underscores the necessity of a careful and informed approach to the integration of AI in clinical education, highlighting the potential for ChatGPT and similar technologies to augment, rather than replace, the critical reasoning and expertise of practitioners. A recent study by Google Cloud and The Harris Poll reveals the significant burden of administrative tasks on healthcare professionals, with doctors and nurses in the United States spending nearly 28 hours a week on paperwork. This includes maintaining patient records, completing insurance forms, and documenting procedures. Insurance staff face an even greater workload, dedicating 36 hours a week to administrative duties.

Several clients of EHR vendor Meditech have adopted an AI tool to explore and summarize medical records. Overall, gold carding combined with prior authorization automation has reduced provider administrative costs by 85%. “We collected extensive data from our nurses, who constantly provided feedback on the model’s performance,” he said. Having a long-term strategy is great, but each unit within an organization has to start somewhere on its journey withAI.

So, they put together a generative AI work group, and then I was asked to lead the education work stream underneath that work group. You introduced me as the vice president of planning– that is my role, [and] this is one of those other duties that we get in an organization, and we were given six weeks to develop education for 24,000 employees, our mission partners. We are facing the same challenges that almost every healthcare system across the country is, especially nonprofit healthcare. The pace of change in healthcare has greatly accelerated, and so, keeping up with the pace of change is always a challenge.

During the stage of converting prescription instructions into a standard format, pharmacy technicians may incorrectly record dosage, frequency, or route of administration32. Additionally, when patients transfer medications from their original packaging to other containers, it becomes difficult for pharmacists to recognize the medications, which could lead to omission errors33. Given that electronic health record recommendations and alerts are often imprecise, and traditional natural language processing methods require extensive human annotation, generative AI offers an attractive solution.

The Future Promise and Risk of Generative AI in Clinical Settings – Leonard Davis Institute

The Future Promise and Risk of Generative AI in Clinical Settings.

Posted: Tue, 22 Oct 2024 07:00:00 GMT [source]

We run on thin operating margins, especially in nonprofit healthcare, and so, we have to be as productive and efficient as possible. OSF HealthCare recently rose to this challenge by developing mandatory ongoing education for its employees in order to help them learn more about the benefits of using generative AI. To tell us more, we have Melissa Knuth, vice president of planning at OSF HealthCare, on the show today. “Government officials worry hospitals lack the resources to put these technologies through their paces. ‘I have looked far and wide,’ FDA Commissioner Robert Califf said at a recent agency panel on AI. ‘I do not believe there’s a single health system, in the United States, that’s capable of validating an AI algorithm that’s put into place in a clinical care system,’” Tahir writes.

Roughly four out of five respondents stated that these tools can improve care team interactions with patients, while 46 percent said that the technology can help facilitate timely care by coordinating scheduling across care teams. A recent survey commissioned by Wolters Kluwer Health found that physicians are increasingly optimistic about the use of generative artificial intelligence (AI) in healthcare, but maintain key concerns around the deployment of these technologies. We help leaders and future leaders in the healthcare industry work smarter and faster by providing provocative insights, actionable strategies, and practical tools to support execution. The convergence of AI, accelerated computing and biological data is turning healthcare into the largest technology industry.

Organizations are already testing the waters by combining generative AI and interoperable frameworks to enhance care coordination. This trend underscores the importance of creating AI-generated insights that are more than just isolated outputs—they are part of a continuous, collaborative healthcare ecosystem. These insights, when encoded with CQL, become actionable across multiple platforms, breaking down barriers that have hindered effective data sharing.

However, when it comes to generative AI, things are still pretty fresh, given the technology came to the forefront just a couple of years ago with the launch of ChatGPT. Gen AI models use neural networks to identify patterns and structures in existing data and generate new content such as text and images. They are applicable across sectors, including healthcare – where organizations cumulatively generate about 300 petabytes of data every single day. Through this event, domestic biomedical professionals will gain a deeper understanding of AI’s potential applications in smart healthcare and precision medicine.

They’ll also be able to save a lot of time by avoiding jumping back and forth between multiple platforms. Generative AI in healthcare offers medical professionals access to vast amounts of clinical data, which can be used to draw accurate conclusions for better diagnoses. This technology minimizes the risk of mistakes that can happen due to distractions or physical and mental exhaustion. Generative AI has limitations such as biased reproduction, lack of transparency, inaccurate information, and static knowledge, which hinder its further application in health care.

If you require legal or professional advice, kindly contact an attorney or other suitable professional advisor. AB 3030 attempts to enhance transparency and patient protections by ensuring patients are informed when AI-generated responses are used in their care. After 2 days of in-depth discussion about these issues, the committee members noted that developing this regulatory infrastructure would be an ongoing process. Bhatt acknowledged that this process would be incremental, but that establishing clear guidelines for the implementation of generative AI in the healthcare setting could help improve healthcare delivery across the country in the near future.

A legacy of progress, a future of promise

Additionally, the utilization of generative AI could enhance the quality of health services for patients while alleviating the workload for clinicians8,9,10. Healthcare firms are leveraging GenAI for targeted applications that deliver immediate benefits in innovation and customer service. Consider 60% of healthcare executives report deploying GenAI in product and service innovation, enhancing research and development capabilities and supporting the development of new healthcare solutions. Meanwhile, GenAI-powered customer service tools, such as automated response systems and enhanced chatbot capabilities, are improving patient engagement and accessibility. Digital health consultancy AVIA reports that the use of artificial intelligence technologies is beginning to show demonstrable results in terms of patient care, operations efficiency and outcomes. The blog also cites results from a large regional health system that used AI to streamline the process of analyzing patient records, synthesizing patient information and insuring accurate coding.

As reported by prestigious media organizations such as The Hill, OpenAI’s ChatGPT incorrectly diagnosed more than 8 in 10 pediatric case studies. Gen AI in healthcare has immense potential to identify anomalies in patient data, such as unusual patterns or outliers, alerting healthcare providers to providers to potential health issues or irregularities requiring attention. Generative AI reconstructs medical images to enhance resolution and clarity, aiding in accurate diagnosis and treatment planning.

They all have launched bots for automating tasks like patient registration, routing, scheduling, FAQs, IT helpdesk ticketing, and prescription refills. Further, many have even started deploying gen AI copilots that listen to the conversation between the patient and physician and generate summarized clinical notes, saving doctors the trouble of documenting and filing the information manually in an EHR. Nabla, one of the providers of such copilots, even uses these notes to generate a set of patient instructions, on behalf of the physician. Artificial intelligence (AI) will become a core driver of the global biomedical industry, pushing the development of smart healthcare and precision medicine to the forefront. The seminar will also include cross-domain technology and product demonstrations near the exhibition area. Through these expert insights, the event will aim to guide biomedical professionals in leveraging AI to advance smart healthcare and precision medicine, capturing opportunities in the booming healthcare industry.

California providers using generative AI should prepare to be compliant by January 1, 2025, and providers planning to use generative AI should consider these requirements before doing so. A. With the widespread use of generative AI, the public are increasingly using generative AI to obtain medical advice. Given that it is difficult to ascertain when the generative AI is correct or when it is wrong, there could be disastrous consequences for patients or caregivers who do not check with their clinicians. In addition, AI healthcare systems must be compliant with existing privacy laws, thoroughly tested, evaluated, verified and validated using the latest techniques before being deployed at a large scale.

This not only solves problems more efficiently but also helps manage costs and improve performance. Generative AI can process this information rapidly, identifying and prioritizing the most vital parts of the patient’s history. This could lead to more streamlined patient care and an increase in operational efficiency within these high-pressure environments. Arpan Saxena is the Head of Product at basys.ai (based out of Harvard University), a leading healthcare AI solutions company. Illumina and NVIDIA will work to make these tools accessible on the Illumina Connected Analytics platform. Just last year, Insilico Medicine’s gen AI-generated INS018_055 drug for idiopathic pulmonary fibrosis, which affects about 100,000 people in the U.S., went into clinical human trials and is now closing in on wider release.

When patients enter a healthcare facility, AI could offer a triage service to direct them for diagnosis or treatment. Another area where AI has potential is monitoring side effects and adherence to treatment programmes. The findings underscore ChatGPT’s value in enhancing time efficiency and fostering creativity in intervention planning by accelerating the process and reducing cognitive burden. ChatGPT’s ability to inspire innovative, tailored intervention strategies highlights its role as a catalyst for creative thinking in clinical planning. These results emphasize the importance of incorporating technology like ChatGPT in education to foster effective, innovative clinical interventions.

In contrast, the RAG system could integrate health data and lifestyle habits of individuals to build a comprehensive personal profile, which might enable more customized health guidance. The content generated by generative AI models could perpetuate biases inherent in the pre-training data, which are reflected in aspects including demographic characteristics, political ideologies, and sexual orientations12,13,20. Such biases can not only lead to unfair diagnoses and treatments but also exacerbate health inequalities for particular populations. To achieve this, these tools use self-learning frameworks, ML, DL, natural language processing, speech and object recognition, sentiment analysis, and robotics to provide real-time analyses for users.

In 2025, generative AI will lead the way for breakthroughs in conditions like Duchenne muscular dystrophy and tropical parasitic infections. Zameer Rizvi is CEO and Founder of Odesso, improving patient outcomes through artificial intelligence and machine learning. With the assistance of Gen AI in healthcare, businesses can develop patient-specific treatment plans by analyzing genetic, clinical, and lifestyle data and optimizing therapy options as per individual needs. Through personalized health information and educational materials, Generative AI for healthcare enhances patient engagement, understanding of medical conditions and treatment plans. In the dynamic healthcare landscape, generative AI holds immense potential to revolutionize patient care. Moreover, its capacity to analyze vast amounts of medical data expedites diagnosis, facilitates drug discovery, and enables the development of predictive models for disease prevention.