All articles tagged with #electronic health records
Orders for the unnecessary 1,25-dihydroxy vitamin D test are increasing, driven by electronic ordering systems and lack of clinician awareness, despite guidelines recommending its use only in specific conditions, leading to higher costs and potential misinterpretation of vitamin D status.
AI scribes are being increasingly adopted in healthcare to alleviate the documentation burden on clinicians, enhancing patient interactions by using ambient listening and generative AI to create progress notes. However, they also introduce new challenges, such as the need for clinicians to review and edit AI-generated text, and may reinforce the culture of excessive documentation. While AI scribes can reduce after-hours work and burnout, the root causes of documentation practices need to be addressed to truly improve efficiency and care quality.
Researchers from the Regenstrief Institute, Indiana University, and Purdue University have developed a low-cost, machine learning-based method to predict dementia risk using electronic health records. This scalable approach, costing less than a dollar, extracts relevant data from medical notes to identify individuals at risk, enabling early intervention and resource access. The method, currently in clinical trials, aims to improve care management and potentially slow cognitive decline by addressing risk factors. The study highlights the clinical value of electronic health records in predicting dementia risk.
Researchers at the University of California San Diego School of Medicine used AI and genome-wide association studies to identify 461 new candidate genes associated with tobacco use disorder, primarily expressed in the brain, by analyzing electronic health records of 898,680 individuals. This study not only advances our understanding of tobacco addiction but also points to numerous potential drug candidates for treatment, highlighting the potential of electronic health records as a valuable resource for genetic research in addressing this pervasive public health challenge.
Hospitals are grappling with the validation of AI-generated clinical summaries, as the promise of large language models like OpenAI’s GPT-4 to summarize medical records raises concerns about their readiness for high-stakes clinical applications. While electronic health records have created larger haystacks for clinicians to navigate, the potential impact of a single missing word on a diagnosis has led to uncertainty about the reliability of AI-generated summaries in healthcare settings.
About 250,000 veterans are at risk of receiving incorrect medication due to issues with the Department of Veterans Affairs' new electronic health records system, which has led to incorrect medication records and potential drug interactions. The faulty medication records are the latest problem in the troubled rollout of the Oracle Cerner Millennium system, prompting lawmakers to express frustration and concern over patient safety issues. The VA has paused further implementation of the system while working to address the network's problems, and the VA's inspector general has raised concerns about patients not being adequately informed of their individual risk.
Hospitals are turning to artificial intelligence (AI) solutions to combat doctor burnout and improve healthcare efficiency. Baptist Health in Jacksonville, Florida, is using the DAX app, powered by Microsoft's Nuance division, which transcribes doctors' and patients' comments to create clinical physician summaries for electronic health records. By automating administrative tasks, such as documenting visits and processing bills, AI could help hospitals cut costs by 5% to 11% in the next five years. However, concerns remain about the potential elimination of human involvement and the responsible use of AI in patient care.
The Veterans Affairs Department's troubled rollout of a new electronic health records system has caused anger among lawmakers, leading to proposed legislation to shut down the project or increase oversight. The system, which is billions over budget and linked to veterans' deaths, has been put on pause indefinitely. While some lawmakers are giving the VA another chance to fix the issues, others are pushing to end the program. The problems with the system include a lack of leadership, poor preparation, and resistance from VA clinicians. The future of the project remains uncertain, but lawmakers are demanding reforms to prevent similar problems in the future.
A survey conducted by GE Healthcare reveals that 55% of medical professionals believe that AI is not ready for medical use, with only 42% globally and 26% in the US trusting AI. The survey included 7,500 clinicians, patients, and patient advocates in eight countries. GE Healthcare's chief technology officer, Dr. Taha Kass-Hout, acknowledges the skepticism and emphasizes the need to address the needs and pain points of clinicians, who often struggle with technology that is not intuitive or user-friendly. The survey also highlights concerns about data privacy and security, with 39% of patients feeling that their health data is not kept safe. However, there are successful applications of AI in areas such as medical imaging and health data analysis. The challenge lies in building trust and implementing effective training programs.
A physician shares their experience of burnout caused by the overwhelming number of patient messages received through a patient portal. They discuss the challenges of managing these messages and the impact it had on their well-being. The physician explores various strategies to reduce inbox burden but highlights the limitations of some suggestions. Ultimately, they decide to reach out to their patients, setting guidelines for portal use and requesting their understanding and support. The response from patients is overwhelmingly positive, leading to fewer portal messages and improved well-being for the physician. They emphasize the importance of maintaining a strong patient-physician relationship and using technology judiciously to avoid personal strain and disconnection.
The 21st Century Cures Act requires US healthcare providers to give patients rapid, complete access to the health information in their electronic medical records free of charge. For cancer patients, this has led to instantaneous access to scan results, which can be both a relief and a burden. While access to medical records can make patients feel more knowledgeable about their care, it can also be difficult to comprehend and synthesize without a clinician to help decode and demystify them. For one family, the solution was to open CT reports together, one hour before meeting with their oncologist, to navigate the roller coaster of cancer together.
Navina, a New York-based medical tech company, has created an AI tool that uses generative AI to transform how data informs the physician-patient interaction. The platform compiles data from multiple sources, including lab results, imaging scans, and notes from specialists, and presents it to the doctor in a clear, concise way. Navina has already helped doctors identify potentially life-threatening diseases, including diabetes with chronic complications, chronic kidney disease, and morbid obesity. The tool also helps doctors better leverage the data at their fingertips to get financial credit from value-based programs such as Medicare and Medicaid for the care they provide.
Adults with obstructive sleep apnea who have had COVID-19 are more likely to experience long-term symptoms suggestive of long COVID than those without the sleep disorder, according to a large study supported by the National Institutes of Health (NIH). The study, which analyzed electronic health records of more than 2.2 million Americans with COVID-19, suggests close monitoring after a COVID-19 infection may help adults with sleep apnea. The findings may also strengthen understanding of why some people are more likely to develop the post-viral syndrome after acute infection.
Machine learning models using genetic risk scores, non-genetic information, and electronic health record data from nearly half a million individuals were used to rank risk factors for Alzheimer's disease. Results showed that genetic risk may outweigh age as a predictor of whether a person will develop Alzheimer's disease after age 65. A low household income also emerged as an important risk factor. The models also identified important predictors from electronic health records, including urinary tract infection, syncope and collapse, chest pain, disorientation, and hypercholesterolemia.