scholarly journals The Electronic Health Record Objective Structured Clinical Examination Station: Assessing Student Competency in Patient Notes and Patient Interaction

MedEdPORTAL ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 10998
Author(s):  
E. Shen ◽  
Joseph Anthony Cristiano ◽  
Leslie Renee Ellis
2017 ◽  
Vol 92 (1) ◽  
pp. 87-91 ◽  
Author(s):  
Frances E. Biagioli ◽  
Diane L. Elliot ◽  
Ryan T. Palmer ◽  
Carla C. Graichen ◽  
Rebecca E. Rdesinski ◽  
...  

2018 ◽  
Vol 102 (3) ◽  
pp. 475-483 ◽  
Author(s):  
Helene F. Hedian ◽  
Jeremy A. Greene ◽  
Timothy M. Niessen

2021 ◽  
Vol 12 (05) ◽  
pp. 1082-1090
Author(s):  
Tom de Hoop ◽  
Thomas Neumuth

Abstract Objectives This study set out to obtain a general profile of physician time expenditure and electronic health record (EHR) limitations in a large university medical center in Germany. We also aim to illustrate the merit of a tool allowing for easier capture and prioritization of specific clinical needs at the point of care for which the current study will inform development in subsequent work. Methods Nineteen physicians across six different departments participated in this study. Direct clinical observations were conducted with 13 out of 19 physicians for a total of 2,205 minutes, and semistructured interviews were conducted with all participants. During observations, time was measured for larger activity categories (searching information, reading information, documenting information, patient interaction, calling, and others). Semistructured interviews focused on perceived limitations, frustrations, and desired improvements regarding the EHR environment. Results Of the observed time, 37.1% was spent interacting with the health records (9.0% searching, 7.7% reading, and 20.5% writing), 28.0% was spent interacting with patients corrected for EHR use (26.9% of time in a patient's presence), 6.8% was spent calling, and 28.1% was spent on other activities. Major themes of discontent were a spread of patient information, high and often repeated documentation burden, poor integration of (new) information into workflow, limits in information exchange, and the impact of such problems on patient interaction. Physicians stated limited means to address such issues at the point of care. Conclusion In the study hospital, over one-third of physicians' time was spent interacting with the EHR, environment, with many aspects of used systems far from optimal and no convenient way for physicians to address issues as they occur at the point of care. A tool facilitating easier identification and registration of issues, as they occur, may aid in generating a more complete overview of limitations in the EHR environment.


2017 ◽  
Vol 08 (02) ◽  
pp. 369-380 ◽  
Author(s):  
Christopher Aakre ◽  
Mikhail Dziadzko ◽  
Mark Keegan ◽  
Vitaly Herasevich

Summary Objectives: Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records. Methods: We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings. Results: We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable. Conclusion: Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments – partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study. Citation: Aakre C, Dziadzko M, Keegan MT, Herasevich V. Automating clinical score calculation within the electronic health record: A feasibility assessment. Appl Clin Inform 2017; 8: 369–380 https://doi.org/10.4338/ACI-2016-09-RA-0149


2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.


2012 ◽  
Author(s):  
Robert Schumacher ◽  
Robert North ◽  
Matthew Quinn ◽  
Emily S. Patterson ◽  
Laura G. Militello ◽  
...  

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