BackgroundThe increasing adoption of electronic health records (EHRs) has been associated with a number of unintended negative consequences with provider efficiency and job satisfaction. To address this, there has been a dramatic increase in the use of medical scribes to perform many of the required EHR functions. Despite this rapid growth, little has been published on the training or assessment tools to appraise the safety and efficacy of scribe-related EHR activities. Given the number of reports documenting that other professional groups suffer from a number of performance errors in EHR interface and data gathering, scribes likely face similar challenges. This highlights the need for new assessment tools for medical scribes.ObjectiveThe objective of this study was to develop a virtual video-based simulation to demonstrate and quantify the variability and accuracy of scribes’ transcribed notes in the EHR.MethodsFrom a pool of 8 scribes in one department, a total of 5 female scribes, intent on pursuing careers in health care, with at least 6 months of experience were recruited for our simulation study. We created three simulated patient-provider scenarios. Each scenario contained a corresponding medical record in our simulation instance of our EHR. For each scenario, we video-recorded a standardized patient-provider encounter. Five scribes with at least 6 months of experience both with our EHR and in the specialty of the simulated cases were recruited. Each scribe watched the simulated encounter and transcribed notes into a simulated EHR environment. Transcribed notes were evaluated for interscribe variability and compared with a gold standard for accuracy.ResultsAll scribes completed all simulated cases. There was significant interscribe variability in note structure and content. Overall, only 26% of all data elements were unique to the scribe writing them. The term data element was used to define the individual pieces of data that scribes perceived from the simulation. Note length was determined by counting the number of words varied by 31%, 37%, and 57% between longest and shortest note between the three cases, and word economy ranged between 23% and 71%. Overall, there was a wide inter- and intrascribe variation in accuracy for each section of the notes with ranges from 50% to 76%, resulting in an overall positive predictive value for each note between 38% and 81%.ConclusionsWe created a high-fidelity, video-based EHR simulation, capable of assessing multiple performance indicators in medical scribes. In this cohort, we demonstrate significant variability both in terms of structure and accuracy in clinical documentation. This form of simulation can provide a valuable tool for future development of scribe curriculum and assessment of competency.

Background: The preoperative period has gained recognition as a crucial time to identify and manage preoperative medical conditions for preventing perioperative complications. Consequently, preoperative clinics have now become an essential component of perioperative care at many large hospitals. As the prevalence of preoperative clinics continues to grow, and the field of perioperative medicine progresses, respiratory therapists (RTs) will inevitably find a growing role to participate in preoperative patient optimization to mitigate pulmonary complications. Methods: Keyword searches on perioperative pulmonary complications were conducted on the Medline database via PubMed and identified over 2000 candidate articles for review. Articles were included if they were English only and resulted with one or more of the following search terms; pulmonary complications, postoperative complications, postoperative pulmonary complications (PPCs), prehabilitation, incentive spirometry, smoking cessation, noninvasive ventilation. Preference was given for meta-analyses, randomized controlled trials, and systematic reviews. Publications within the past two decades were given additional preference toward final inclusion. The authors discussed eligible articles in group meetings over the span of multiple years to assess relevance and quality of data for narrowing eligible articles to the final selection of publications for the review. Findings: The following narrative review examines preoperative optimization strategies to prevent PPCs and highlight areas where RTs may play a key role. After examining challenges in defining PPCs, the review examines key risk models available to predict PPCs and their implications for subsequent discussion on preventive measures that RTs may assist with in a multidisciplinary team. Conclusion: RTs can reduce the health care burden of PPCs by assisting fellow perioperative clinicians in providing respiratory care for patients with premorbid conditions. While much of our review focused on pre-existing pulmonary pathologies and both the pharmacological and nonpharmacological optimization of these pathologies, there are other factors contributing to PPCs deserving future exploration.