Background The precision of clinical documentation in trauma and orthopaedic surgery is pivotal, given its profound implications on patient care and medicolegal risks. This study assessed the impact of an autotext template intervention on the adherence of clinical documentation to the neurovascular assessment standards set by the National Institute for Health and Care Excellence (NICE) and the British Orthopaedic Association Standards for Trauma (BOAST). Methods Conducted at a single hospital, this observational study comprised two phases: a retrospective analysis of clinical documentation for 56 fracture patients (n=56) followed by the implementation of an autotext template and subsequent analysis of a new cohort of 57 patients (n=57). The intervention aimed to enhance documentation quality in line with NICE and BOAST guidelines. Results Initial findings revealed a prevalent use of the nonspecific term \"NVI\" (neurovascularly intact), with only 8.5% (n=5) of pre-intervention documents adhering to detailed motor function assessments and a mere 6.8% (n=4) recording limb colour. Post-intervention analysis showed a significant improvement, with 91.23% (n=52) of documents listing nerves (P < 0.001) and 96.49% (n=55) adhering to motor function documentation using the Medical Research Council (MRC) grading scale (P < 0.001). Despite these advancements, the study acknowledges potential limitations such as the Hawthorne effect and the ongoing challenge of staff rotations. Conclusion The autotext template intervention markedly enhanced the adherence to neurovascular assessment documentation standards, as evidenced by the substantial increases in detailed parameter reporting and supported by statistically significant P-values. This advancement highlights the necessity of equipping clinicians with practical tools to uphold high documentation standards amidst challenging clinical conditions. Future investigations should focus on the long-term sustainability of these improvements across varying medical staff cohorts.

Patients with rare diseases commonly suffer from severe symptoms as well as chronic and sometimes life-threatening effects. Not only the rarity of the diseases but also the poor documentation of rare diseases often leads to an immense delay in diagnosis. One of the main problems here is the inadequate coding with common classifications such as the International Statistical Classification of Diseases and Related Health Problems. Instead, the ORPHAcode enables precise naming of the diseases. So far, just few approaches report in detail how the technical implementation of the ORPHAcode is done in clinical practice and for research. We present a concept and implementation of storing and mapping of ORPHAcodes. The Transition Database for Rare Diseases contains all the information of the Orphanet catalog and serves as the basis for documentation in the clinical information system as well as for monitoring Key Performance Indicators for rare diseases at the hospital. The five-step process (especially using open source tools and the DataVault 2.0 logic) for set-up the Transition Database allows the approach to be adapted to local conditions as well as to be extended for additional terminologies and ontologies.

OBJECTIVE: As healthcare providers increasingly focus on emerging issues of diversity, equity and inclusion (DEI) in patient care, less is known about the training in postgraduate year one (PGY1) pharmacy residency on DEI clinical documentation considerations. This pilot project explored whether training, discussion and self-reflection within a peer review activity promoted DEI self-awareness in clinical documentation through a centralized curriculum of a multisite PGY1.
METHODS: Building upon an established peer review of clinical documentation activity, PGY1 pharmacy residents practicing in ambulatory care settings received training on DEI considerations and completed small and large group discussions, a post-activity mixed methods survey with self-reflection prompts, and a three-month follow-up survey.
RESULTS: Twenty-two residents participated in the peer review of clinical documentation activity, DEI training and discussions. Twelve residents completed the post-activity survey with reflection prompts; 6 (50%) reported similar previous DEI training prior to residency. After the DEI training and discussions, 12 (100%) agreed or strongly agreed that their awareness of DEI documentation considerations increased; 10 (83%) would document their submitted notes differently, while one resident was unsure and one would not make changes. Twelve residents completed the follow-up survey three months following the activity. Themes from the free-text responses on key learnings collected post-activity and three-month post (respectively) included: 1) new knowledge, increased self-awareness, and intended action and 2) increased self-awareness and changes in note-making convention.
CONCLUSIONS: Integrating DEI training, discussion, and self-reflection prompts into a peer review clinical documentation activity increased self-awareness and knowledge of DEI considerations and promoted intended changes in patient care documentation for pharmacy residents. Regardless of previous training, residents reported continued self-awareness and changes in documentation conventions continued three months later.

BACKGROUND: Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)-powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows.
OBJECTIVE: This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model\'s performance across different categories.
METHODS: We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system.
RESULTS: Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the \"Objective\" section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05).
CONCLUSIONS: Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model\'s effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.

BACKGROUND: Common goals for procedural sedation are to control pain and ensure the patient is not moving to an extent that is impeding safe progress or completion of the procedure. Clinicians perform regular assessments of the adequacy of procedural sedation in accordance with these goals to inform their decision-making around sedation titration and also for documentation of the care provided. Natural language processing could be applied to real-time transcriptions of audio recordings made during procedures in order to classify sedation states that involve movement and pain, which could then be integrated into clinical documentation systems. The aim of this study was to determine whether natural language processing algorithms will work with sufficient accuracy to detect sedation states during procedural sedation.
METHODS: A prospective observational study was conducted.
METHODS: Audio recordings from consenting participants undergoing elective procedures performed in the interventional radiology suite at a large academic hospital were transcribed using an automated speech recognition model. Sentences of transcribed text were used to train and evaluate several different NLP pipelines for a text classification task. The NLP pipelines we evaluated included a simple Bag-of-Words (BOW) model, an ensemble architecture combining a linear BOW model and a \"token-to-vector\" (Tok2Vec) component, and a transformer-based architecture using the RoBERTa pre-trained model.
RESULTS: A total of 15,936 sentences from transcriptions of 82 procedures was included in the analysis. The RoBERTa model achieved the highest performance among the three models with an area under the ROC curve (AUC-ROC) of 0.97, an F1 score of 0.87, a precision of 0.86, and a recall of 0.89. The Ensemble model showed a similarly high AUC-ROC of 0.96, but lower F1 score of 0.79, precision of 0.83, and recall of 0.77. The BOW approach achieved an AUC-ROC of 0.97 and the F1 score was 0.7, precision was 0.83 and recall was 0.66.
CONCLUSIONS: The transformer-based architecture using the RoBERTa pre-trained model achieved the best classification performance. Further research is required to confirm the that this natural language processing pipeline can accurately perform text classifications with real-time audio data to allow for automated sedation state assessments.
CONCLUSIONS: Automating sedation state assessments using natural language processing pipelines would allow for more timely documentation of the care received by sedated patients, and, at the same time, decrease documentation burden for clinicians. Downstream applications can also be generated from the classifications, including for example real-time visualizations of sedation state, which may facilitate improved communication of the adequacy of the sedation between clinicians, who may be performing supervision remotely. Also, accumulation of sedation state assessments from multiple procedures may reveal insights into the efficacy of particular sedative medications or identify procedures where the current approach for sedation and analgesia is not optimal (i.e. a significant amount of time spent in \"pain\" or \"movement\" sedation states).

BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
BACKGROUND: German Clinical Trials Register DRKS00023343; https://drks.de/search/de/trial/DRKS00023343.

Patients\' online record access (ORA) is growing worldwide. In some countries, including the United States and Sweden, access is advanced with patients obtaining rapid access to their full records on the web including laboratory and test results, lists of prescribed medications, vaccinations, and even the very narrative reports written by clinicians (the latter, commonly referred to as \"open notes\"). In the United States, patient\'s ORA is also available in a downloadable form for use with other apps. While survey studies have shown that some patients report many benefits from ORA, there remain challenges with implementation around writing clinical documentation that patients may now read. With ORA, the functionality of the record is evolving; it is no longer only an aide memoire for doctors but also a communication tool for patients. Studies suggest that clinicians are changing how they write documentation, inviting worries about accuracy and completeness. Other concerns include work burdens; while few objective studies have examined the impact of ORA on workload, some research suggests that clinicians are spending more time writing notes and answering queries related to patients\' records. Aimed at addressing some of these concerns, clinician and patient education strategies have been proposed. In this viewpoint paper, we explore these approaches and suggest another longer-term strategy: the use of generative artificial intelligence (AI) to support clinicians in documenting narrative summaries that patients will find easier to understand. Applied to narrative clinical documentation, we suggest that such approaches may significantly help preserve the accuracy of notes, strengthen writing clarity and signals of empathy and patient-centered care, and serve as a buffer against documentation work burdens. However, we also consider the current risks associated with existing generative AI. We emphasize that for this innovation to play a key role in ORA, the cocreation of clinical notes will be imperative. We also caution that clinicians will need to be supported in how to work alongside generative AI to optimize its considerable potential.

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Introduction Medical students rotate on various clinical disciplines with the same professional goal of learning medical documentation. This study investigated differences between medical student notes on inpatient general and subspecialty pediatric services by comparing note quality, length, and file time. Methods In a single-site, observational cohort study, medical students in the Core Clerkship in Pediatrics (CCP) from July 2020 to June 2021 participated in a note-writing didactic course. We compared notes from medical students completing their inpatient assignment on a general pediatric service to those who completed it on a pediatric subspecialty service. Primary outcomes were note quality measured by Physician Documentation Quality Instrument-9 (PDQI9), note length (measured by line count), and file time (measured by hours to completion since 6 AM on the morning of note initiation). Results We evaluated 84 notes from 84 medical students on the general pediatric services and 50 notes from 49 medical students on the pediatric subspecialty services. Note quality measured by PDQI9 was significantly higher for general pediatric service notes compared to pediatric subspecialty service notes (p = 0.03). General pediatric service notes were significantly shorter (p < 0.001). We found no difference in file time (p = 0.23). Conclusion Medical student notes on pediatric subspecialty services scored significantly lower in quality and were longer compared to general pediatric services, demonstrating the need for a more tailored note-writing curriculum and note template based on service.

To co-design an intervention to reduce the burden of clinical documentation for nurses and midwives.
A clinician-researcher collaboration used an action research approach to co-design an intervention to reduce clinical documentation. The study consisted of three phases: 1) Analysis of pre-intervention data, 2) Evaluation of existing documentation, 3) Intervention co-design and implementation.
A total of 116 documents were reviewed using a three-stage evaluation process, identifying 28 documents that could be discontinued and 33 documents to be modified for the intervention. This resulted in an average of 7 documents for women who had a vaginal birth (decreased from 13), 9 documents for women who had a caesarean (decreased from 18), and 7 documents for newborns (previously 7-10). The minimum number of documents for a mother and baby reduced from 20 pre-intervention to 14 post-intervention.
The collaboration successfully co-designed and implemented an intervention to address the burden of clinical documentation that can be replicated in other healthcare settings.