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Gen AI to Predict and Automate Discharge

Gen AI to Predict and Automate Discharge

While electronic health records (EHRs) have improved data exchange for care coordination, they have also increased the clinical documentation burden on healthcare providers. Research from 2023 suggests that clinicians may now spend more time on EHRs than on direct patient care. On average, providers spend over 36 minutes on EHR tasks for every 30-minute patient visit. Generative AI, however, holds the potential to transform this. As defined by the Government Accountability Office, generative AI (GenAI) is a technology that can create content—such as text, images, audio, or video—based on user prompts. With the rise of chatbot interfaces like Chat-GPT, health IT vendors and healthcare systems are piloting GenAI tools to streamline clinical documentation. While the technology shows promise in reducing the documentation burden and mitigating clinician burnout, several challenges still hinder widespread adoption. Ambient Clinical Intelligence Ambient clinical intelligence leverages smartphone microphones and GenAI to transcribe patient encounters in real time, producing draft clinical documentation for providers to review within seconds. A 2024 study examined the use of ambient AI scribes by 10,000 physicians and staff at The Permanente Medical Group. The results were promising—providers reported better patient conversations and less after-hours EHR documentation. However, accuracy is critical for patient safety. A 2023 study found that ambient AI tools struggle with non-lexical conversational sounds (NLCSes)—like “mm-hm” and “uh-uh”—which patients and providers use to convey information. For instance, a patient might say “Mm-hm” to confirm they have no allergies to antibiotics. The study found that while the AI tools had a word error rate of 12% for all words, the error rate for NLCSes conveying clinically relevant information was as high as 98.7%. These inaccuracies could lead to patient safety risks, highlighting the importance of provider review. Patient Communication Patient portal messaging has surged since the COVID-19 pandemic, with a 2023 report showing a 157% increase in messages compared to pre-pandemic levels. To manage inbox overload, healthcare systems are exploring generative AI for drafting responses to patient messages. Clinicians review and edit these drafts before sending them to patients. A 2024 study found that primary care physicians rated AI-generated responses higher in communication style and empathy than those written by providers. However, the AI-generated responses were often longer and more complex, posing challenges for patients with lower health or English literacy. There are also potential risks to clinical decision-making. A 2024 simulation study revealed that the content of replies to patient messages changed when physicians used AI assistance, introducing an automation bias that could impact patient outcomes. Although most AI-generated drafts posed minimal safety risks, a small portion, if left unedited, could result in severe harm or death. Although GenAI may reduce the cognitive load of writing replies, it might not significantly decrease the overall time spent on patient communications. A recent study showed that while providers felt less emotional exhaustion when using AI to draft messages, the time spent on replying, reading, and writing messages remained unchanged from pre-pilot levels. Discharge Summaries Generative AI has also been shown to improve the readability of patient discharge summaries. A study published in JAMA Network Open demonstrated that GenAI could lower the reading level of discharge notes from an eleventh-grade to a sixth-grade level, which is more appropriate for diverse health literacy levels. However, accuracy is still a concern. Physician reviews of these AI-generated summaries found that while some were complete, others contained omissions and inaccuracies that raised safety concerns. Balancing AI’s Benefits with Oversight While generative AI shows promise in alleviating the documentation burden and improving patient communication, challenges remain. Issues such as accurately capturing non-verbal cues and ensuring document accuracy underscore the need for careful provider oversight. As AI technologies continue to evolve, ensuring that the benefits are balanced with provider review will be crucial for safe and effective healthcare implementation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Ambient AI and Doctors

Ambient AI and Doctors

A study published in JAMA Network Open found that nearly half of clinicians using an ambient AI clinical documentation tool reported positive outcomes. The tool, Dragon Ambient eXperience (DAX) Copilot from health IT vendor Nuance, leverages automatic speech recognition and natural language processing to draft electronic health record (EHR) documentation based on patient-provider conversations. The nonrandomized clinical trial included family medicine, internal medicine, and general pediatrics clinicians from outpatient clinics in North Carolina and Georgia within Atrium Health. Those who participated received an hour of training on the AI tool. Researchers compared the intervention group with a control group, which included clinicians encouraged to participate as controls by service line leaders and those who initially expressed interest in the AI tool but chose not to proceed after informational sessions. A seven-question survey was sent to 230 participants before and five weeks after implementing the AI tool to evaluate its impact on their EHR experience. The study showed that 47.1% of clinicians using the AI tool reported spending less time on EHR documentation at home, compared to 14.5% in the control group. Additionally, 43.5% of the AI tool users spent less time on clinical documentation post-visit, compared to 18.2% of the control group. Moreover, 44.7% of the intervention group reported reduced frustration with the EHR, compared to 14.5% of controls. However, around 44.7% of the intervention group and 68.7% of the control group indicated their EHR experiences remained similar before and after the AI tool implementation. The researchers acknowledged potential selection and recall biases as study limitations and called for further research to identify areas for improvement and explore the impact across different clinician groups and health systems. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more

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AI Assisting Nursing

AI Assisting Nursing

Leveraging AI to Alleviate the Documentation Burden in Nursing As the nursing profession grapples with increasing burnout, researchers are investigating the potential of large language models to streamline clinical documentation and care planning. Nurses play an essential role in delivering high-quality care and improving patient outcomes, but the profession is under significant strain due to shortages and burnout. AI Assisting Nursing could lessoning burnout while improving communication. What role could Salesforce play? The American Nurses Association (ANA) emphasizes that to maximize nurses’ potential, healthcare organizations must prioritize maintaining an adequate workforce, fostering healthy work environments, and supporting policies that back nurses. The COVID-19 pandemic has exacerbated existing challenges, including increased healthcare demand, insufficient workforce support, and a wave of retirements outpacing the influx of new nurses. Tectonic has nearly two decades of experience providing IT solutions for the health care industry. Salesforce, as a leader in the field of artificial intelligence, is a top tool for health care IT. AI Assisting Nursing In response to these growing demands, some experts argue that AI technologies could help alleviate some of the burden, particularly in areaTes like clinical documentation and administrative tasks. In a recent study published in the Journal of the American Medical Informatics Association, Dr. Fabiana Dos Santos, a post-doctoral research scientist at Columbia University School of Nursing, led a team to explore how a ChatGPT-based framework could assist in generating care plan suggestions for a lung cancer patient. In an interview with Healthtech Analytics, Dr. Santos discussed the potential and challenges of using AI chatbots in nursing. Challenges in Nursing Care Plan Documentation Creating care plans is vital for ensuring patients receive timely, adequate care tailored to their needs. Nurses are central to this process, yet they face significant obstacles when documenting care plans. AI Assisting Nursing and Salesforce as a customer relationship solution addresses those challenges. “Nurses are on the front line of care and spend a considerable amount of time interacting closely with patients, contributing valuable clinical assessments to electronic health records (EHRs),” Dr. Santos explained. “However, many documentation systems are cumbersome, leading to a documentation burden where nurses spend much of their workday interacting with EHRs. This can result in cognitive burden, stress, frustration, and disruptions to direct patient care.” The American Association of Critical-Care Nurses (AACN) highlights that electronic documentation is a significant burden, consuming an average of 40% of a nurse’s shift. Time spent on documentation inversely correlates with time spent on patient care, leading to increased burnout, cognitive load, and decreased job satisfaction. These factors, in turn, contribute to patient-related issues such as a higher risk of medical errors and hospital-acquired infections, which lower patient satisfaction. When combined with the heavy workloads nurses already manage, inefficient documentation tools can make care planning even more challenging. AI Assisting Nursing and Care Plans “The demands of direct patient care and managing multiple administrative tasks simultaneously limit nurses’ time to develop individualized care plans,” Dr. Santos continued. “The non-user-friendly interfaces of many EHR systems exacerbate this challenge, making it difficult to capture all aspects of a patient’s condition, including physical, psychological, social, cultural, and spiritual dimensions.” To address these challenges, Dr. Santos and her team explored the potential of ChatGPT to improve clinical documentation. “These negative impacts on a nurse’s workday underscore the urgency of improving EHR documentation systems to reduce these issues,” she noted. “AI tools, if well designed, can improve the process of developing individualized care plans and reduce the burden of EHR-related documentation.” The Promises and Pitfalls of AI Developing care plans requires nurses to draw from their expertise to address issues like symptom management and comfort care, especially for patients with complex needs. Dr. Santos emphasized that advanced technologies, such as generative AI (GenAI), could streamline this process by enhancing documentation workflows and assisting with administrative tasks. AI tools can rapidly process large amounts of data and generate care plans more quickly than traditional methods, potentially allowing nurses to spend more time on direct and holistic patient care. However, Dr. Santos stressed the importance of carefully validating AI models, ensuring that nurses’ clinical judgment and expertise play a central role in evaluating AI-generated care plans. “New technologies can help nurses improve documentation, leading to better descriptions of patient conditions, more accurate capture of care processes, and ultimately, improved patient outcomes,” she said. “This presents an important opportunity to use novel generative AI solutions to reduce nurses’ workload and act as a supportive documentation tool.” Despite the promise of AI as a support tool, Dr. Santos cautioned that chatbots require further development to be effectively implemented in nursing care plans. AI-generated outputs can contain inaccuracies or irrelevant information, necessitating careful review and validation by nurses. Additionally, AI tools may lack the nuanced understanding of a patient’s unique needs, which only a nurse can provide through personal, empathetic interactions, such as interpreting specific cultural or spiritual needs. Despite these challenges, large language models (LLMs) and other GenAI tools are generating significant interest in the healthcare industry. They are expected to be deployed in various applications, including EHR workflows and nursing efficiency. Dr. Santos’ research contributes to this growing field. To conduct the study, the researchers developed and validated a method for structuring ChatGPT prompts—guidelines that the LLM uses to generate responses—that could produce high-quality nursing care plans. The approach involved providing detailed patient information and specific questions to consider when creating an appropriate care plan. The research team refined the Patient’s Needs Framework over ten rounds using 22 diverse hypothetical patient cases, ensuring that the ChatGPT-generated plans were consistent and aligned with typical nursing care plans. “Our findings revealed that ChatGPT could prioritize critical aspects of care, such as oxygenation, infection prevention, fall risk, and emotional support, while also providing thorough explanations for each suggested intervention, making it a valuable tool for nurses,” Dr. Santos indicated. The Future of AI in Nursing While the study focused on care plans for lung cancer, Dr. Santos emphasized that this research is just the beginning of

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Gen AI Role in Healthcare

Gen AI Role in Healthcare

Generative AI’s Growing Role in Healthcare: Potential and Challenges The rapid advancements in large language models (LLMs) have introduced generative AI tools into nearly every business sector, including healthcare. As defined by the Government Accountability Office, generative AI is “a technology that can create content, including text, images, audio, or video, when prompted by a user.” These systems learn patterns and relationships from vast datasets, enabling them to generate new content that resembles but is not identical to the original training data. This capability is powered by machine learning algorithms and statistical models. In healthcare, generative AI is being utilized for various applications, including clinical documentation, patient communication, and clinical text summarization. Streamlining Clinical Documentation Excessive documentation is a leading cause of clinician burnout, as highlighted by a 2022 athenahealth survey conducted by the Harris Poll. Generative AI shows promise in easing these documentation burdens, potentially improving clinician satisfaction and reducing burnout. A 2024 study published in NEJM Catalyst explored the use of ambient AI scribes within The Permanente Medical Group (TPMG). This technology employs smartphone microphones and generative AI to transcribe patient encounters in real-time, providing clinicians with draft documentation for review. In October 2023, TPMG deployed this ambient AI technology across various settings, benefiting 10,000 physicians and staff. Physicians who used the ambient AI scribe reported positive outcomes, including more personal and meaningful patient interactions and reduced after-hours electronic health record (EHR) documentation. Early patient feedback was also favorable, with improved provider interactions noted. Additionally, ambient AI produced high-quality clinical documentation for clinician review. However, a 2023 study in the Journal of the American Medical Informatics Association (JAMIA) cautioned that ambient AI might struggle with non-lexical conversational sounds (NLCSes), such as “mm-hm” or “uh-uh,” which can convey clinically relevant information. The study found that while the ambient AI tools had a word error rate of about 12% for all words, the error rate for NLCSes was significantly higher, reaching up to 98.7% for those conveying critical information. Misinterpretation of these sounds could lead to inaccuracies in clinical documentation and potential patient safety issues. Enhancing Patient Communication With the digital transformation in healthcare, patient portal messages have surged. A 2021 study in JAMIA reported a 157% increase in patient portal inbox messages since 2020. In response, some healthcare organizations are exploring the use of generative AI to draft replies to these messages. A 2024 study published in JAMA Network Open evaluated the adoption of AI-generated draft replies to patient messages at an academic medical center. After five weeks, clinicians used the AI-generated drafts 20% of the time, a notable rate considering the LLMs were not fine-tuned for patient communication. Clinicians reported reduced task load and emotional exhaustion, suggesting that AI-generated replies could help alleviate burnout. However, the study found no significant changes in reply time, read time, or write time between the pre-pilot and pilot periods. Despite this, clinicians expressed optimism about time savings, indicating that the cognitive ease of editing drafts rather than writing from scratch might not be fully captured by time metrics. Summarizing Clinical Data Summarizing information within patient records is a time-consuming task for clinicians, and errors in this process can negatively impact clinical decision support. Generative AI has shown potential in this area, with a 2023 study finding that LLM-generated summaries could outperform human expert summaries in terms of conciseness, completeness, and correctness. However, using generative AI for clinical data summarization presents risks. A viewpoint in JAMA argued that LLMs performing summarization tasks might not fall under FDA medical device oversight, as they provide language-based outputs rather than disease predictions or numerical estimates. Without statutory changes, the FDA’s authority to regulate these LLMs remains unclear. The authors also noted that differences in summary length, organization, and tone could influence clinician interpretations and subsequent decision-making. Furthermore, LLMs might exhibit biases, such as sycophancy, where responses are tailored to user expectations. To address these concerns, the authors called for comprehensive standards for LLM-generated summaries, including testing for biases and errors, as well as clinical trials to quantify potential harms and benefits. The Path Forward Generative AI holds significant promise for transforming healthcare and reducing clinician burnout, but realizing this potential requires comprehensive standards and regulatory clarity. A 2024 study published in npj Digital Medicine emphasized the need for defined leadership, adoption incentives, and ongoing regulation to deliver on the promise of generative AI in healthcare. Leadership should focus on establishing guidelines for LLM performance and identifying optimal clinical settings for AI tool trials. The study suggested that a subcommittee within the FDA, comprising physicians, healthcare administrators, developers, and investors, could effectively lead this effort. Additionally, widespread deployment of generative AI will likely require payer incentives, as most providers view these tools as capital expenses. With the right leadership, incentives, and regulatory framework, generative AI can be effectively implemented across the healthcare continuum to streamline clinical workflows and improve patient care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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