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Background Section 507 of the VA MISSION Act of 2018 mandated a 2-year pilot study of medical scribes in the Veterans Health Administration (VHA), with 12 VA Medical Centers randomly selected to receive scribes in their emergency departments or high wait time specialty clinics (cardiology and orthopedics). The pilot began on June 30, 2020, and ended on July 1, 2022. Objective Our objective was to evaluate the impact of medical scribes on provider productivity, wait times, and patient satisfaction in cardiology and orthopedics, as mandated by the MISSION Act. Design Cluster randomized trial, with intent-to-treat analysis using difference-in-differences regression. Patients Veterans using 18 included VA Medical Centers (12 intervention and 6 comparison sites). Intervention Randomization into MISSION 507 medical scribe pilot. Main Measures Provider productivity, wait times, and patient satisfaction per clinic-pay period. Key Results Randomization into the scribe pilot was associated with increases of 25.2 relative value units (RVUs) per full-time equivalent (FTE) ( p  < 0.001) and 8.5 visits per FTE ( p  = 0.002) in cardiology and increases of 17.3 RVUs per FTE ( p  = 0.001) and 12.5 visits per FTE ( p  = 0.001) in orthopedics. We found that the scribe pilot was associated with a decrease of 8.5 days in request to appointment day wait times ( p  < 0.001) in orthopedics, driven by a 5.7-day decrease in appointment made to appointment day wait times ( p < 0.001), and observed no change in wait times in cardiology. We also observed no declines in patient satisfaction with randomization into the scribe pilot. Conclusions Given the potential improvements in productivity and wait times with no change in patient satisfaction, our results suggest that scribes may be a useful tool to improve access to VHA care. However, participation in the pilot by sites and providers was voluntary, which could have implications for scalability and what effects could be expected if scribes were introduced to the care process without buy-in. Cost was not considered in this analysis but is an important factor for future implementation. Trial Registration ClinicalTrials.gov Identifier : NCT04154462.

In 2002, the National Institutes of Health sponsored a meeting concerning methodological challenges of research in psychosocial interventions in Autism Spectrum Disorders. This paper provides a summary of the presentations and the discussions that occurred during this meeting. Recommendations to federal and private agencies included the need for randomized clinical trials of comprehensive interventions for autism as the highest, but not the sole priority. Ongoing working groups were proposed to address psychosocial interventions with a focus on relevant statistics, standardized documentation and methods of diagnosis, development of outcome measures, establishment of standards in research; and the need for innovative treatment designs, including application of designs from other research areas to the study of interventions in ASD.

Relisten is an artificial intelligence (AI)-based software developed by Recog Analytics that improves patient care by facilitating more natural interactions between health care professionals and patients. This tool extracts relevant information from recorded conversations, structuring it in the medical record, and sending it to the Health Information System after the professional's approval. This approach allows professionals to focus on the patient without the need to perform clinical documentation tasks. This study aims to evaluate patient-reported satisfaction and perceived quality of care, assess health care professionals' satisfaction with the care provided, and measure the time spent on entering records into the electronic medical record using this AI-powered solution. This proof-of-concept (PoC) study is conducted as a multicenter trial with the participation of several health care professionals (nurses and physicians) in primary care centers (CAPs). The key outcome measures include (1) patient-reported quality of care (evaluated through anonymous surveys), (2) health care professionals' satisfaction with the care provided (assessed through surveys and structured interviews), and (3) time saved on clinical documentation (determined by comparing the time spent manually writing notes versus reviewing and correcting AI-generated notes). Statistical analyses will be performed for each objective, using independent sample comparison tests according to normality evaluated with the Kolmogorov-Smirnov test and Lilliefors correction. Stratified statistical tests will also be performed to consider the variance between professionals. The protocol has been developed using the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklist. Recruitment began in July 2024, and as of November 2024, a total of 318 patients have been enrolled. Recruitment is expected to be completed by March 2025. Data analysis will take place between April and May 2025, with results expected to be published in June 2025. We expect an improvement in the perceived quality of care reported by patients and a significant reduction in the time spent taking clinical notes, with a saving of at least 30 seconds per visit. Although a high quality of the notes generated is expected, it is uncertain whether a significant improvement over the control group, which is already expected to have high-quality notes, will be demonstrated. ClinicalTrials.gov NCT06618092; https://clinicaltrials.gov/study/NCT06618092. DERR1-10.2196/66232.

Clinical documentation has undergone a change due to the usage of electronic health records. The core element is to capture clinical findings and document therapy electronically. Health care personnel spend a significant portion of their time on the computer. Alternatives to self-typing, such as speech recognition, are currently believed to increase documentation efficiency and quality, as well as satisfaction of health professionals while accomplishing clinical documentation, but few studies in this area have been published to date. This study describes the effects of using a Web-based medical speech recognition system for clinical documentation in a university hospital on (1) documentation speed, (2) document length, and (3) physician satisfaction. Reports of 28 physicians were randomized to be created with (intervention) or without (control) the assistance of a Web-based system of medical automatic speech recognition (ASR) in the German language. The documentation was entered into a browser's text area and the time to complete the documentation including all necessary corrections, correction effort, number of characters, and mood of participant were stored in a database. The underlying time comprised text entering, text correction, and finalization of the documentation event. Participants self-assessed their moods on a scale of 1-3 (1=good, 2=moderate, 3=bad). Statistical analysis was done using permutation tests. The number of clinical reports eligible for further analysis stood at 1455. Out of 1455 reports, 718 (49.35%) were assisted by ASR and 737 (50.65%) were not assisted by ASR. Average documentation speed without ASR was 173 (SD 101) characters per minute, while it was 217 (SD 120) characters per minute using ASR. The overall increase in documentation speed through Web-based ASR assistance was 26% (P=.04). Participants documented an average of 356 (SD 388) characters per report when not assisted by ASR and 649 (SD 561) characters per report when assisted by ASR. Participants' average mood rating was 1.3 (SD 0.6) using ASR assistance compared to 1.6 (SD 0.7) without ASR assistance (P<.001). We conclude that medical documentation with the assistance of Web-based speech recognition leads to an increase in documentation speed, document length, and participant mood when compared to self-typing. Speech recognition is a meaningful and effective tool for the clinical documentation process.

ABSTRACT BACKGROUND Poor communication of tests whose results are pending at hospital discharge can lead to medical errors. OBJECTIVE To determine the adequacy with which hospital discharge summaries document tests with pending results and the appropriate follow-up providers. DESIGN Retrospective study of a randomly selected sample PATIENTS Six hundred ninety-six patients discharged from two large academic medical centers, who had test results identified as pending at discharge through queries of electronic medical records. INTERVENTION AND MEASUREMENTS Each patient’s discharge summary was reviewed to identify whether information about pending tests and follow-up providers was mentioned. Factors associated with documentation were explored using clustered multivariable regression models. MAIN RESULTS Discharge summaries were available for 99.2% of 668 patients whose data were analyzed. These summaries mentioned only 16% of tests with pending results (482 of 2,927). Even though all study patients had tests with pending results, only 25% of discharge summaries mentioned any pending tests, with 13% documenting all pending tests. The documentation rate for pending tests was not associated with level of experience of the provider preparing the summary, patient’s age or race, length of hospitalization, or duration it took for results to return. Follow-up providers’ information was documented in 67% of summaries. CONCLUSION Discharge summaries are grossly inadequate at documenting both tests with pending results and the appropriate follow-up providers.

IntroductionPatients with multimorbidity plus additional impairments (eg, mobility limitations, disability, cognitive impairments or frailty) are at the highest risk for poor healthcare outcomes. Advanced care planning (ACP) provides patients and their surrogates the opportunity to discuss their goals, values and priorities for healthcare—particularly in the context of end-of-life care. ACP discussions promote more person-centred care; however, it is currently underused. There is a tremendous need for systematic, scalable approaches to individualised ACP that promotes patient and family engagement. Here we describe the study protocol for a randomised effectiveness trial of a nurse navigator and informatics intervention designed to improve the documentation and quality of ACP discussions.Methods and analysisThis is a randomised, pragmatic, effectiveness trial; patients aged 65 years and older who have multimorbidity plus impairments in either physical function (eg, mobility limitations or disability) or cognition, and/or frailty within an affiliated Accountable Care Organization were eligible. The electronic health record was used to develop an automatic prescreening system for eligible patients (n=765) and participants were randomised in a 1:1 ratio to either the nurse navigator-led ACP pathway or usual care. Our primary outcomes are documentation of ACP discussions within the EHR along with the quality of ACP discussions. Secondary outcomes include a broad range of ACP actions (eg, usage of ACP billing codes, choosing a surrogate decision-maker and advance directive documentation). Outcomes will be measured over 12 months of follow-up.Ethics and disseminationThis study has been approved by the appropriate Institutional Review Boards and is guided by input from patient and clinical advisory boards. The results of this study will inform a scalable solution to ACP discussions throughout our healthcare system and statewide.Trials registration numberNCT03609658.

To evaluate associations between the electronic environment, clerical burden, and burnout in US physicians. Physicians across all specialties in the United States were surveyed between August and October 2014. Physicians provided information regarding use of electronic health records (EHRs), computerized physician order entry (CPOE), and electronic patient portals. Burnout was measured using validated metrics. Of 6375 responding physicians in active practice, 5389 (84.5%) reported that they used EHRs. Of 5892 physicians who indicated that CPOE was relevant to their specialty, 4858 (82.5%) reported using CPOE. Physicians who used EHRs and CPOE had lower satisfaction with the amount of time spent on clerical tasks and higher rates of burnout on univariate analysis. On multivariable analysis, physicians who used EHRs (odds ratio [OR]=0.67; 95% CI, 0.57-0.79; P<.001) or CPOE (OR=0.72; 95% CI, 0.62-0.84; P<.001) were less likely to be satisfied with the amount of time spent on clerical tasks after adjusting for age, sex, specialty, practice setting, and hours worked per week. Use of CPOE was also associated with a higher risk of burnout after adjusting for these same factors (OR=1.29; 95% CI, 1.12-1.48; P<.001). Use of EHRs was not associated with burnout in adjusted models controlling for CPOE and other factors. In this large national study, physicians' satisfaction with their EHRs and CPOE was generally low. Physicians who used EHRs and CPOE were less satisfied with the amount of time spent on clerical tasks and were at higher risk for professional burnout.

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. 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. 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. 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). 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.

Objective To evaluate the ability of an electronic system created at the University of Washington to accurately document prerecorded VF and pulseless electrical activity (PEA) cardiac arrest scenarios compared with the American Heart Association paper cardiac arrest record. Methods 16 anaesthesiology residents were randomly assigned to view one of two prerecorded, simulated VF and PEA scenarios and asked to document the event with either the paper or electronic system. Each subject then repeated the process with the other video and documentation method. Five types of documentation errors were defined: (1) omission, (2) specification, (3) timing, (4) commission and (5) noise. The mean difference in errors between the paper and electronic methods was analysed using a single factor repeated measures ANOVA model. Results Compared with paper records, the electronic system omitted 6.3 fewer events (95% CI −10.1 to −2.5, p=0.003), which represents a 28% reduction in omission errors. Users recorded 2.9 fewer noise items (95% CI −5.3 to −0.6, p=0.003) when compared with paper, representing a 36% decrease in redundant or irrelevant information. The rate of timing (Δ=−3.2, 95% CI −9.3 to 3.0, p=0.286) and commission (Δ=−4.4, 95% CI −9.4 to 0.5, p=0.075) errors were similar between the electronic system and paper, while the rate of specification errors were about a third lower for the electronic system when compared with the paper record (Δ=−3.2, 95% CI −6.3 to −0.2, p=0.037). Conclusions Compared with paper documentation, documentation with the electronic system captured 24% more critical information during a simulated medical emergency without loss in data quality.

Objective of the study To audit patients’ allergy documentation in a large rural hospital an to make recommendations about accurate drug allergies in hospital settings. Setting a 257 bed large hospital and fully integrated health service in Australia, providing a range of services including; medicine, surgery, aged care, cancer care, mental health, maternity and rehabilitation. Method A retrospective design was used to fulfil the aims of this study. Patient medical records were randomly selected and checked for allergy documentation over a 6 month period. Results A total of 521 patients’ medical records were reviewed. Of all the medical records examined in total, 269 (52%) had no allergy, while 252 (48%) reported some kind of allergy. Overall, only three patients (0.6%) had their allergy details fully and accurately recorded in the three places audited and they are the front cover of the patients’ notes, the admission notes and the drug chart. Conclusion Many preventable medical errors are caused by poor documentation which is often due to lack of drug allergy information. All health professional should be more pro-active in determining the manner of any drug allergy or adverse drug reactions (ADR) along with the extent of the reaction.