Background: Medical scribes are a clinical innovation increasingly being used in primary care. The impact of scribes in primary care remain unclear. We aimed to examine the impact of medical scribes on productivity, time spent facing the patient during the visit, and patient comfort with scribes in primary care.
Methods: We conducted a prospective observational pre-post study of 5 family and internal medicine-pediatrics physicians and their patients at an urban safety net health clinic. Medical scribes accompanied providers in the examination room and documented the clinical encounter. After an initial phase-in period, we added an additional 20-minute patient slot per 200-minute session. We examined productivity by using electronic medical record data on the number of patients seen and work relative value units (work RVUs) per hour. We directly observed clinical encounters to measure the amount of time providers spent facing patients and other visit components. We queried patient comfort with scribes by using surveys administered after the visit.
Results: Work RVUs per hour increased by 10.5% from 2.59 prescribe to 2.86 post-scribe ( < .001). Patients seen per hour increased by 8.8% from 1.82 to 1.98 ( < .001). Work RVUs per patient did not change. After scribe implementation, time spent facing the patient increased by 57% ( < .001) and time spent facing the computer decreased by 27% ( = .003). The proportion of the visit time that was spent face-to-face increased by 39% ( < .001). Most (69%) patients reported feeling very comfortable with the scribe in the room, while the proportion feeling very comfortable with the number of people in the room decreased from 93% to 66% ( < .001).
Conclusions: Although the full implications of medical scribe implementation remain to be seen, this initial study highlights the promising opportunity of medical scribe implementation in primary care.
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http://dx.doi.org/10.3122/jabfm.2018.04.170325 | DOI Listing |
Nature
January 2025
Innovative Genomics Institute, University of California Berkeley, Berkeley, CA, USA.
Rubisco is the primary CO-fixing enzyme of the biosphere, yet it has slow kinetics. The roles of evolution and chemical mechanism in constraining its biochemical function remain debated. Engineering efforts aimed at adjusting the biochemical parameters of rubisco have largely failed, although recent results indicate that the functional potential of rubisco has a wider scope than previously known.
View Article and Find Full Text PDFPlast Reconstr Surg Glob Open
January 2025
Department of Computer Science, Johns Hopkins University, Baltimore, MD.
Artificial intelligence (AI) scribe applications in the healthcare community are in the early adoption phase and offer unprecedented efficiency for medical documentation. They typically use an application programming interface with a large language model (LLM), for example, generative pretrained transformer 4. They use automatic speech recognition on the physician-patient interaction, generating a full medical note for the encounter, together with a draft follow-up e-mail for the patient and, often, recommendations, all within seconds or minutes.
View Article and Find Full Text PDFACS Appl Bio Mater
January 2025
Department of Medical Devices, National Institute of Pharmaceutical Education and Research, Guwahati, Assam 781101, India.
Using a laser-scribed (direct printing) technique, we have fabricated an enzymeless, mediatorless, and paper-interfaced electrochemical device (P-LSG) for uric acid detection on a flexible polyimide sheet. Various paper substrates were investigated, and it was found that Whatman filter paper-1 is promising to obtain the best electrochemical signals at the small volume of electrolyte, i.e.
View Article and Find Full Text PDFNat Med
January 2025
Department of Medicine-Medical Oncology, University of Colorado Cancer Center, Denver, CO, USA.
Effective targeting of somatic cancer mutations to enhance the efficacy of cancer immunotherapy requires an individualized approach. Autogene cevumeran is a uridine messenger RNA lipoplex-based individualized neoantigen-specific immunotherapy designed from tumor-specific somatic mutation data obtained from tumor tissue of each individual patient to stimulate T cell responses against up to 20 neoantigens. This ongoing phase 1 study evaluated autogene cevumeran as monotherapy (n = 30) and in combination with atezolizumab (n = 183) in pretreated patients with advanced solid tumors.
View Article and Find Full Text PDFBMJ Qual Saf
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.
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