Documentation Tasks Research Articles

Initial Findings of a Mixed-Methods Study to Reduce Nursing Documentation-Related Burden.

Amidst the current healthcare workforce crisis, nurses continue to experience burnout, with one contributor being the growing amount of required documentation and other electronic health record (EHR) tasks that must be completed. This study aims to identify ways in which nurses can be better supported through consideration for the burden that EHR systems may present due to increasing documentation requirements and areas of inefficient use. This study leverages nursing engagement to ensure the needs of nurses are identified and EHR efficiency is improved. Practical strategies and EHR system improvements will be proposed based on the recommendations and guidance of nursing staff.

New Era in Cultural Heritage Preservation: Cooperative Aerial Autonomy for Fast Digitalization of Difficult-to-Access Interiors of Historical Monuments

Digital documentation of large interiors of historical buildings is an exhausting task since most of the areas of interest are beyond typical human reach. We advocate the use of autonomous teams of multi-rotor Unmanned Aerial Vehicles (UAVs) to speed up the documentation process by several orders of magnitude while allowing for a repeatable, accurate, and condition-independent solution capable of precise collision-free operation at great heights. The proposed multi-robot approach allows for performing tasks requiring dynamic scene illumination in large-scale real-world scenarios, a process previously applicable only in small-scale laboratory-like conditions. Extensive experimental analyses range from single-UAV imaging to specialized lighting techniques requiring accurate coordination of multiple UAVs. The system's robustness is demonstrated in more than two hundred autonomous flights in fifteen historical monuments requiring superior safety while lacking access to external localization. This unique experimental campaign, cooperated with restorers and conservators, brought numerous lessons transferable to other safety-critical robotic missions in documentation and inspection tasks.

ISPO introduction program for independent smallholders: Initial steps towards mandatory ISPO 2025

The government, through Peraturan Presiden RI Nomor 44 Tahun 2020, mandates Indonesian Sustainable Palm Oil (ISPO) certification for smallholder farmers within five years of its enactment. However, by December 2022, only 32 farmer groups had achieved ISPO certification. The Gunung Ambat Farmers Group, which has already received funds from the PSR program, is one of the independent farmer groups with the potential to acquire ISPO accreditation. Yet, members' understanding of this certification remains low due to a lack of socialization or training from the government or other entities. This initiative aims to promote the regulation and provide training on the concepts and requirements for ISPO certification. Activities include administering questionnaires to assess knowledge, conducting training sessions, engaging in puzzle games, and practicing the documentation of farming activities. The results indicate significant improvements in farmers' understanding before and after the training. Initially, farmers had no knowledge of ISPO certification; however, post-training, over 70 percent of participants understood the certification, with an average of 35 percent comprehending its principles and criteria. Participants are also encouraged to document their farming progress, enhancing their skills in farming business documentation and administrative tasks.

Beginner Friendly Drawing Pad Using AI

This project introduces an innovative solution designed to empower individuals with disabilities and enhance drawing efficiency for professionals. Through the utilization of AI-powered hand gesture recognition, the drawing pad allows for intuitive sketching by simply using gestures, marking a significant advancement in visual expression for those who are dumb. Professionals stand to benefit greatly from the streamlined drawing process, as the system translates gestures into precise depictions with minimal manual input, ultimately increasing productivity. Furthermore, the project addresses the time-consuming nature of documentation tasks by enabling swift note-taking via gestures, catering to users seeking efficient methods of documentation. Additionally, the system serves as a versatile tool for educators, facilitating interactive teaching through gesture-based drawing, thereby transforming the teaching profession by making lessons more engaging and accessible.

Potential of Large Language Models in Health Care: Delphi Study.

A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques.

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient-doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.

The Implementation of an Electronic Medical Record in a German Hospital and the Change in Completeness of Documentation: Longitudinal Document Analysis.

Electronic medical records (EMR) are considered a key component of the health care system's digital transformation. The implementation of an EMR promises various improvements, for example, in the availability of information, coordination of care, or patient safety, and is required for big data analytics. To ensure those possibilities, the included documentation must be of high quality. In this matter, the most frequently described dimension of data quality is the completeness of documentation. In this regard, little is known about how and why the completeness of documentation might change after the implementation of an EMR. This study aims to compare the completeness of documentation in paper-based medical records and EMRs and to discuss the possible impact of an EMR on the completeness of documentation. A retrospective document analysis was conducted, comparing the completeness of paper-based medical records and EMRs. Data were collected before and after the implementation of an EMR on an orthopaedical ward in a German academic teaching hospital. The anonymized records represent all treated patients for a 3-week period each. Unpaired, 2-tailed t tests, chi-square tests, and relative risks were calculated to analyze and compare the mean completeness of the 2 record types in general and of 10 specific items in detail (blood pressure, body temperature, diagnosis, diet, excretions, height, pain, pulse, reanimation status, and weight). For this purpose, each of the 10 items received a dichotomous score of 1 if it was documented on the first day of patient care on the ward; otherwise, it was scored as 0. The analysis consisted of 180 medical records. The average completeness was 6.25 (SD 2.15) out of 10 in the paper-based medical record, significantly rising to an average of 7.13 (SD 2.01) in the EMR (t178=-2.469; P=.01; d=-0.428). When looking at the significant changes of the 10 items in detail, the documentation of diet (P<.001), height (P<.001), and weight (P<.001) was more complete in the EMR, while the documentation of diagnosis (P<.001), excretions (P=.02), and pain (P=.008) was less complete in the EMR. The completeness remained unchanged for the documentation of pulse (P=.28), blood pressure (P=.47), body temperature (P=.497), and reanimation status (P=.73). Implementing EMRs can influence the completeness of documentation, with a possible change in both increased and decreased completeness. However, the mechanisms that determine those changes are often neglected. There are mechanisms that might facilitate an improved completeness of documentation and could decrease or increase the staff's burden caused by documentation tasks. Research is needed to take advantage of these mechanisms and use them for mutual profit in the interests of all stakeholders. German Clinical Trials Register DRKS00023343; https://drks.de/search/de/trial/DRKS00023343.

Back to Babies: Reducing Documentation Time in the NICU

Abstract Background There is no accepted best practice for generation and content of daily progress notes in the neonatal intensive care unit (NICU). Objectives This study aimed to implement a consistent documentation standard process for a neonatology provider group at a level IV tertiary care NICU. The primary aim was to improve timeliness of daily progress note completion. Secondary aims were to maintain or improve clinician satisfaction, reduce variability, and reduce attending neonatologist electronic medical record (EMR) documentation tasks. Methods We formed a work group including advanced practice providers (APPs) and physicians from the NICU that met over 6 months to define the ideal NICU documentation content, map the workflow for documentation, identify gaps in EMR content, and create solutions for each gap. Baseline assessment included a change readiness survey to identify barriers to workflow change and a review of neonatologist signature timestamp to determine time to note completion. Twenty random progress notes were sampled weekly for 6 months prior to implementation of new workflow as well as 6 months postimplementation. Average time to note completion was compared in the pre- and postintervention groups. Results In total, 962 notes were sampled, 481 each in the pre- and postintervention states. Twenty neonatologists were captured in the preintervention state, 24 in the postintervention state, 18 were captured in both samples. Final note completion time mean improved from 10 hours and 32 minutes (from starting note to final sign) to 8 hours and 40 minutes (p &lt; 0.01). Those sampled in both epochs improved from 10 hours and 6 minutes to 8 hours and 30 minutes (p &lt; 0.05). Conclusion Progress notes generated by neonatologists are completed earlier than those generated by an APP with a Neonatologist addendum. Specialty-specific education and training are critical to high satisfaction in large EMR workflow transitions.

The epistemic future in exported grammars: a comparison between two Italo-Romance speech communities

Abstract The analysis of differences and analogies that the (synthetic) epistemic future exhibits in two varieties of the Italo‑Romance dialect of Verbicaro contributes to uncover uncharted aspects of its morphosyntax and uses. The case study presented in this paper is based on different degrees of competence of the speakers of the indigenous variety of north-western Calabria as well as the corresponding exported variety spoken in São Paulo (Brazil). The grammar and use of the epistemic future is fully captured if the analysis of the data is pursued alongside an assessment of the acquisitional history of the speakers. Based on the sociolinguistic account of three different groups of speakers of the exported Verbicarese (monolingual Verbicarese L1, bilingual Verbicarese‑Portuguese, Verbicarese heritage speakers), I provide a structural interpretation of the development of the synthetic epistemic future in both the exported and the indigenous variety. More specifically, I show how glaring parallels can be established between the three relevant groups of speakers of the exported speech community and three distinct stages of evolution of the epistemic future in the indigenous variety. This study also serves as a documentation task for the epistemic future which is rapidly disappearing in Verbicarese.

Improved oral anticancer medication (OAM) adherence/toxicity assessment documentation.

409 Background: Based on national OAM guidelines and to improve patient safety, most OAM programs monitor toxicity/adherence within 10-14 days following OAM initiation. At our institution, only 50% of toxicity/adherence documentation tasks were completed by day 10 of cycle 1 after initiating OAM. We aimed to increase toxicity/adherence documentation tasks to at least 75% by day 10 of cycle 1 following a newly prescribed OAM in collaboration with Hematology/Oncology Pharmacy Association/American Society of Clinical Oncology 2022 quality training program (QTP). Methods: We engaged a multidisciplinary team to gather/interpret data, create process map, and identify barriers. Outcome measure was documentation of toxicity/adherence by day 10 of cycle 1. Included all cancer patients initiating OAM treatment plan. Excluded those on intravenous/oral regimens and gynecology/oncology patients. Process measure was percentage of 9 crucial tasks identified as necessary to appropriately document toxicity/adherence. Balance measures included provider visits, emergency department visits, telephone/electronic medical record OAM-related messages, and nursing satisfaction. A cause-and-effect diagram, pareto chart and prioritization matrix were used to narrow focus of improvement changes. Two plan-do-study-act (PDSA) cycles were conducted from January 2023-June 2023. All data were analyzed using Statistical Process Control Charts with 3-sigma control limits. Results: Original high-impact, easy-to-implement intervention (re-training staff) delayed due to institutional-directed intervention (education documentation retraining due to The Joint Commission survey). Education documentation retraining did not significantly improve toxicity/adherence task completion rate (50% to 53%) by improved provider and emergency department visits (16.7% and 12.5% to 0%) (see Table 1). Nursing turnover, lack of formal OAM process training and institutional buy-in for OAM program/training likely contributed to results. Re-training supported with institutional buy-in completed. PDSA cycle 2 data collection is ongoing; preliminary results show no change in task completion rate. Conclusions: Education documentation and retraining alone is insufficient to improve OAM toxicity/adherence documentation rates. Other interventions are needed to attain our goal.[Table: see text]

Impact of Electronic Medical Record-connected Questionnaire on Efficient Nursing Documentation: A Study of Usability and Efficacy.

Documentation tasks comprise a large percentage of nurses' workloads. Nursing records were partially based on a report from the patient. However, it is not a verbatim transcription of the patient's complaints but a type of medical record. Therefore, to reduce the time spent on nursing documentation, it is necessary to assist in the appropriate conversion or citation of patient reports to professional records. However, few studies have been conducted on systems for capturing patient reports in electronic medical records (EMR). In addition, there have been no reports on whether such a system reduces the time spent on nursing documentation. This study aims to develop a patient self-reporting system that appropriately converts data to nursing records and evaluate its effect on reducing the documenting burden for nurses. An electronic medical record-connected questionnaire and a preadmission nursing questionnaire were administered. The questionnaire responses entered by the patients were quoted in the patient profile for inpatient assessment in the nursing system. To clarify its efficacy, this study examined whether the use of the electronic questionnaire system saved nurses' time entering the patient profile admitted between August and December 2022. It also surveyed the usability of the electronic questionnaire between April and December 2022. In the usability survey, 78% of patients answered an electronic questionnaire. Of these, 88% felt it was easy to use and 85% were willing to use it again. An electronic questionnaire was used in 1,326 of 2,425 admission cases (use group). The Input Time for the patient profile was significantly shorter in the use group than in the no-use group (P<.001). Stratified analyses showed that nurses took 13-18% less time to enter patient profiles within the use group in the internal medicine wards (P<.001) and in the patients with less-independent activities of daily living (ADL) (P<.001), even though there was no difference in the amount of information. By contrast, there was no difference in the time to entry in the surgical wards (P=.50) and in the patients with independent ADL (P=.20), but there was a greater amount of information in the use group. The study developed and implemented a system in which self-reported patient data were captured in the hospital information network and quoted in the nursing system. This system contributes to improving the efficiency of nurses' task recordings.

Development of BrIM-Based Bridge Maintenance System for Existing Bridges

Globally, bridges are rapidly aging, and traditional maintenance approaches face significant challenges in terms of efficiency and cost. To overcome these challenges, considerable research has been conducted to introduce enhanced bridge management systems (BMSs) based on bridge information modeling (BrIM) from various perspectives. However, most studies have highlighted the advantages of BrIM, while neglecting the practical issues that potential users may encounter on existing bridges. The primary problem is digitizing existing bridges that have not yet adopted BrIM. The universal applicability of BrIM should be carefully considered from the perspective of national maintenance authorities managing thousands of bridges, because modeling based on commercial software is expected to be time-consuming and costly. Therefore, in this study, information and functional requirements were derived from interviews with stakeholders, including bridge owners, managers, and site inspectors. Based on this understanding, a data-driven modeling approach using basic bridge information was implemented, and an inventory code system was integrated to efficiently manage and utilize the data. Moreover, mapping and deep learning-based vectorization were considered for managing inspection information, and features for bridge assessment, dashboards, and reporting were incorporated to support decision-making. The developed BrIM demonstrated the potential for enhancing maintenance efficiency through a case study. Particularly, significant improvements were observed in mandatory documentation tasks, along with their investigation and analysis, as required by regulations. Additionally, efficient modeling and data management were achieved for the existing bridge.

Stressors, Resources, and Strain Associated with Digitization Processes of Medical Staff Working in Neurosurgical and Vascular Surgical Hospital Wards: A Multimethod Study.

The digitization of German hospitals is proceeding continuously, leading to the implementation of new digital technologies, such as electronic health records (EHRs) or other technologies, used for the purpose of medical documentation tasks. Even though the replacement of paper documentation through digitized documentation in general promises to come along with plenty of benefits, the daily utilization of technologies might also lead to stresses and strains among the medical staff, eventually possibly leading to the development of different negative work and health-related outcomes. This study, therefore, aims at identifying persisting digitization-associated stressors and resources among medical hospital staff, examining their influences on different work and health-related outcomes, and finally, identifying potential needs for preventive measures. A quantitative study in the form of an online questionnaire survey was conducted among physicians working in the medical field of neuro- and vascular surgery in German hospitals. The study was carried out between June and October 2022 utilizing an online questionnaire based on several standardized scales, such as the technology acceptance model (TAM) and the technostress model, as well as on several scales from the Copenhagen Psychosocial Questionnaire (COPSOQ). The study found medium levels of technostress among the participating physicians (n = 114), as well as low to medium levels of persisting resources. The queried physicians, on average, reported low levels of burnout symptoms, generally described their health status as good, and were mostly satisfied with their job. Despite the prevalence of technostress and the low levels of resources among the surveyed physicians, there is little awareness of the problem of digital stress, and preventive measures have not been widely implemented yet in the clinics, indicating a needs gap and the necessity for the strategic and quality-guided implementation of measures to effectively prevent digital stress from developing.

DIGITISATION OF RETREATING INDUSTRIAL HERITAGE; MODELLING THE DECOMMISSIONING OF THE COAL WASHERIES OF ONLLWYN

Abstract. Digitisation for the purposes of recording cultural heritage and its condition is conventionally associated with the task of documentation for conservation. Occasionally emergency recording will anticipate the potential imminent destruction of heritage at risk. By contrast here, although the heritage status of the site whilst proposed in the 1990s, it was never secured yet the closure and eventual dismemberment of the buildings is a plan that is already underway. The coal washeries of Onllwyn sit within a vast landscape that is now most widely recognised as a National Park. During the last century, however, it was a thriving industrial site. The assembly of buildings used to wash and sort coal prior to distribution were recorded using a terrestrial laser scanner and an aerial drone in August 2022 shortly prior to the closure of a site that had been part of a changing industrial landscape since the mid-nineteenth century. As part of a wide agreement to build a historic narrative for a once large industrial site with a planned closure a comprehensive historical review has been built. This included the acquisition of historic maps but also of numerous historical aerial and terrestrial photographs as well as the collation of films and oral histories. Here pointclouds generated from terrestrial laser scans, photogrammetry from drone imagery and photogrammetry from historical aerial images have been combined in an attempt to create a navigable digital backdrop to the decommissioning of a vast industrial landscape as it anticipates a new future. The aim of the models created is to provide a virtual spatial platform to co-locate memories of a community life that is left centred around a lost place of work.

Creating an Optimal Learning Environment in the Operating Room: Lessons From the Soccer Field

For trainees, the operating room (OR) often represents an educational sanctuary, where for a few hours they can focus on their craft, rather than on phone calls, pages, and the never-ending task of electronic health record documentation. The OR provides a unique opportunity for unfettered one-on-one time with an attending surgeon at their side, where they can learn the art of surgery without interruption. It is vitally important to maximize learning in the OR, yet evidence suggests that it is not always an ideal educational environment. Considering the recent excitement over the World Cup soccer tournament (full disclosure: the senior author is an Argentine immigrant and soccer fan), in this article, we provide evidence-based ideas and suggestions on how to optimize learning in the OR using some analogies from the soccer field.

Administrative burden in Swiss nursing homes and its association with care workers’ outcomes—a multicenter cross-sectional study

BackgroundCare workers in nursing homes often perform tasks that are rather related to organizational or management activities than ‘direct patient care’. ‘Indirect care activities’, such as documentation or other administrative tasks are often considered by care workers as a burden, as they increase overall workload and keep them away from caring for residents. So far, there is little investigation into what kind of administrative tasks are being performed in nursing homes, by which type of care workers, and to which extent, nor how administrative burden is associated with care workers’ outcomes.PurposeThe objective of this study was to describe care workers’ administrative burden in Swiss nursing homes and to explore the association with four care worker outcomes (i.e., job dissatisfaction, emotional exhaustion, intention to leave the current job and the profession).MethodsThis multicenter cross-sectional study used survey data from the Swiss Nursing Homes Human Resources Project 2018. It included a convenience sample of 118 nursing homes and 2′207 care workers (i.e., registered nurses, licensed practical nurses) from Switzerland’s German- and French-speaking regions. Care workers completed questionnaires assessing the administrative tasks and burden, staffing and resource adequacy, leadership ability, implicit rationing of nursing care and care worker characteristics and outcomes. For the analysis, we applied generalized linear mixed models, including individual-level nurse survey data and data on unit and facility characteristics.ResultsOverall, 73.9% (n = 1′561) of care workers felt strongly or rather strongly burdened, with one third (36.6%, n = 787) reporting to spend 2 h or more during a "normal" day performing administrative tasks. Ratings for administrative burden ranged from 42.6% (n = 884; ordering supplies and managing stocks) to 75.3% (n = 1′621; filling out the resident’s health record). One out of four care workers (25.5%, n = 561) intended to leave the profession, whereby care workers reporting higher administrative task burden (OR = 1.24; 95%CI: 1.02–1.50) were more likely to intend to leave the profession.ConclusionThis study provides first insights on care workers’ administrative burden in nursing homes. By limiting care workers’ burdensome administrative tasks and/or shifting such tasks from higher to lower educated care workers or administrative personnel when appropriate, nursing home managers could reduce care workers’ workload and improve their job satisfaction and retention in the profession.

Use of Virtual Interactive Patient Encounters to Prepare First-Year Medical Students for Clinical Practice.

With the dissolution of the Step 2 Clinical Skills exam, medical programs have a greater responsibility to teach and assess clinical skills in the preclerkship years. Clinical teaching this early has traditionally been avoided because of insufficient integration with biomedical sciences, curricular time constraints, and concerns about overwhelming novice learners with clinical learning objectives. To overcome these barriers, the authors created a clinical framework for the biomedical science curriculum by integrating a series of virtual interactive patient (VIP) videos. Matriculating first-year medical students were enrolled in a clinically integrated biomedical science course that used VIP videos to teach and assess clinical skills. The VIP videos were enhanced with interactive pop-in windows, and at the conclusion of each video, students performed a clinical documentation task and received immediate feedback. The authors implemented 7 VIP cases during fall 2021 in which first-year medical students wrote the patient care plan, problem representation statement, or clinical reasoning assessment. Student responses were independently evaluated by course faculty using a 4-level scoring scale. The authors calculated the pooled mean scores for each documentation task and examined student feedback. Seven VIP encounters were assigned to 124 students (mean response rate, 98.5%). Pooled mean scores on the clinical documentation tasks showed that most students were able to achieve levels 3 or 4 when writing the patient care plan (97 [82%] to 113 [94%]), addressing social determinants of health (80 [67%]), writing an accurate problem representation statement (113 [91%] to 117 [94%]), and performing clinical reasoning skills (48 [40%] to 95 [82%]). VIP encounters were feasible to produce, effective at integrating course content, successful at assessing student clinical documentation skills, and well received. The authors will continue to produce, implement, and study the VIP as an integrating learning tool in undergraduate medical education.

An EHR-based method to structure, standardize, and automate clinical documentation tasks for pharmacists to generate extractable outcomes.

As the recognition of team-based care grows and pharmacists increase in patient care interventions, it is important that tools to track clinical services are easily accessible and well-integrated into workflow for all providers. We describe and discuss feasibility and implementation of data tools in an electronic health record to evaluate a pragmatic clinical pharmacy intervention focused on deprescribing in aged adults delivered at multiple clinical sites in a large academic health system. Of the data tools utilized, we were able to demonstrate clear documentation frequency of certain phrases during the intervention period for 574 patients receiving opioids and 537 patients receiving benzodiazepines. Although clinical decision support and documentation tools exist, they are underutilized or cumbersome to integrate into primary health care and strategies, such as employed, are a solution. This communication incorporates the importance of clinical pharmacy information systems in research design.

PD27-07 AUTOMATED OPERATIVE REPORTS FOR ROBOTIC RADICAL PROSTATECTOMY USING AN ARTIFICIAL INTELLIGENCE PLATFORM

You have accessJournal of UrologyCME1 Apr 2023PD27-07 AUTOMATED OPERATIVE REPORTS FOR ROBOTIC RADICAL PROSTATECTOMY USING AN ARTIFICIAL INTELLIGENCE PLATFORM Abhinav Khanna, Alenka Antolin, Maya Zohar, Omri Bar, Danielle Ben-Ayoun, Alexander Krueger, Igor Frank, R. Houston Thompson, Paras Shah, Vidit Sharma, Stephen A. Boorjian, Tamir Wolf, Dotan Asselmann, and Matthew Tollefson Abhinav KhannaAbhinav Khanna More articles by this author , Alenka AntolinAlenka Antolin More articles by this author , Maya ZoharMaya Zohar More articles by this author , Omri BarOmri Bar More articles by this author , Danielle Ben-AyounDanielle Ben-Ayoun More articles by this author , Alexander KruegerAlexander Krueger More articles by this author , Igor FrankIgor Frank More articles by this author , R. Houston ThompsonR. Houston Thompson More articles by this author , Paras ShahParas Shah More articles by this author , Vidit SharmaVidit Sharma More articles by this author , Stephen A. BoorjianStephen A. Boorjian More articles by this author , Tamir WolfTamir Wolf More articles by this author , Dotan AsselmannDotan Asselmann More articles by this author , and Matthew TollefsonMatthew Tollefson More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003305.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The manual creation of operative reports is a tedious documentation task that increases administrative burden among surgeons, which is a primary driver of physician burnout. Using pre-templated notes may reduce this burden but can lead to documentation inaccuracies. Additionally, surgical operative reports are subjective and lack transparency. We aim to develop an artificial intelligence (AI) tool for automatically generating operative reports based on surgical video alone. METHODS: A previously developed computer vision AI algorithm was employed to automatically detect major steps of robotic radical prostatectomy, including pelvic lymph node dissection, Space of Retzius dissection, anterior bladder neck transection, posterior bladder neck transection, seminal vesicle and posterior dissection, lateral/pedicle and apical dissection, urethral transection, vesicourethral anastomosis, and final inspection/extraction. Each step was mapped to pre-specified text, which was then compiled into a narrative operative report based on AI recognition of surgical steps. Accuracy of the AI-generated operative reports was assessed by comparing to operative reports documented in the medical record (human). All discrepancies between AI and human operative reports were adjudicated by independent video review performed by a fellowship-trained urologic oncologist. RESULTS: A total of 117 cases from a single tertiary referral center were included. There was concordance between human and AI operative reports in 107 cases, suggesting that the AI reproduces major components of the human operative report with 91.5% accuracy. Discrepancies between human and AI operative reports were identified in 10 cases, of which 9 were clinically significant. These included 8 discrepancies in lymph node dissection and 1 discrepancy in anterior bladder neck transection. Upon expert video review, the human was inaccurate in 3 discrepancies (2.6%), while the AI was inaccurate in 6 discrepancies (5.1%). CONCLUSIONS: To our knowledge, this is the first report of AI-powered automated creation of operative reports, which achieve high accuracy as compared to human operative reports for major surgical steps. This novel tool has potential to reduce documentation burden, improve operative report accuracy, promote surgical transparency, and decrease subjectivity in surgical documentation. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e744 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abhinav Khanna More articles by this author Alenka Antolin More articles by this author Maya Zohar More articles by this author Omri Bar More articles by this author Danielle Ben-Ayoun More articles by this author Alexander Krueger More articles by this author Igor Frank More articles by this author R. Houston Thompson More articles by this author Paras Shah More articles by this author Vidit Sharma More articles by this author Stephen A. Boorjian More articles by this author Tamir Wolf More articles by this author Dotan Asselmann More articles by this author Matthew Tollefson More articles by this author Expand All Advertisement PDF downloadLoading ...

Underwater 3D Scanning System for Cultural Heritage Documentation

Three-dimensional capturing of underwater archeological sites or sunken shipwrecks can support important documentation purposes. In this study, a novel 3D scanning system based on structured illumination is introduced, which supports cultural heritage documentation and measurement tasks in underwater environments. The newly developed system consists of two monochrome measurement cameras, a projection unit that produces aperiodic sinusoidal fringe patterns, two flashlights, a color camera, an inertial measurement unit (IMU), and an electronic control box. The opportunities and limitations of the measurement principles of the 3D scanning system are discussed and compared to other 3D recording methods such as laser scanning, ultrasound, and photogrammetry, in the context of underwater applications. Some possible operational scenarios concerning cultural heritage documentation are introduced and discussed. A report on application activities in water basins and offshore environments including measurement examples and results of the accuracy measurements is given. The study shows that the new 3D scanning system can be used for both the topographic documentation of underwater sites and to generate detailed true-scale 3D models including the texture and color information of objects that must remain under water.