Objective: The global coronavirus pandemic has placed an unprecedented and enormous burden on health systems worldwide. In addition to a shortage of resources, nurses were also confronted with high levels of sick leave and an increasing exodus from the profession. Automating documentation obligations is an effective way of reducing the burden on the workplace.
Patients And Methods: The study was conducted at a tertiary university hospital. The time required for the manual documentation of administered medication and dose changes of syringe and infusion pumps was recorded using the patient data management system (PDMS) representing all intensive and intermediate care wards (n = 6). Subsequently, all medication administration - grouped into five classes - was evaluated from January 1st, 2019, until December 31st, 2022.
Results: A total of 1,373,340 drug applications were studied, treating 32,499 patients. Data were obtained from ICUs (68%) and IMC wards (32%). This corresponds to an overall time of 2,901 ± 233 hours per year. Based on publicly known national rates for intensive care nurses, an annual financial expenditure of approximately 83,300 € (~ USD 89,300) per year was estimated.
Conclusions: A non-negligible part of the daily working time in the medical sector is spent on documentation duties. This aggravates the high workload, which has increased in recent years. Automated documentation systems can lead to considerable relief and the possibility of focusing primarily on the patient and on other core competencies and activities. This is even more important, as available staff will be a key resource in patient care for the foreseeable future.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.26355/eurrev_202404_35908 | DOI Listing |
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFJ Drugs Dermatol
January 2025
Background: The prevalence of burnout among United States (US) dermatologists has surged, reaching 49% in 2023, with a growing volume of bureaucratic tasks (eg, charting, paperwork) the leading factor behind professional fatigue. We seek to explore the competitive landscape and efficacy of AI-powered patient documentation to alleviate burnout among dermatologists by optimizing documentation practices while maintaining accuracy.
Methods: We conducted a review of eighteen AI-powered automated documentation products available in the current healthcare landscape, focusing on their integration with electronic health record (EHR) systems, HIPAA compliance, language support, mobile accessibility, and consumer type.
Sci Rep
January 2025
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based framework employs two publicly available lung CT scan datasets which are considered as labeled and an unlabeled dataset. The unlabeled dataset is split into three subsets which are assumed to be collected from three hospitals.
View Article and Find Full Text PDFCurr Urol Rep
December 2024
Department of Urology, Lahey Hospital and Medical Center, MA, Burlington, USA.
Purpose Of Review: Artificial Intelligence (AI) has produced a significant impact across various industries, including healthcare. In the outpatient clinic setting, AI offers promising improvements in efficiency through Chatbots, streamlined medical documentation, and personalized patient education materials. On the billing side, AI technologies hold potential for optimizing the selection of appropriate billing codes, automating prior authorizations, and enhancing healthcare fraud detection.
View Article and Find Full Text PDFLancet Digit Health
January 2025
Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA. Electronic address:
Background: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!