HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.

Objective: We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Materials and Methods: HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer's Disease Neuroimaging Initiative dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multitask baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline. Results: Out of 634 MCI patients (mean [IQR] age: 72.8 [67-78], 60% male), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all state-of-the-art longitudinal single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC = 0.623 [0.605-0.644]; all P < .05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression. Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected electronic health records (EHR) variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients.

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.

BACKGROUND: Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment. METHODS: This single-centre, prospective, randomised clinical trial enrolled surgical patients aged ≥18 yr. Anaesthesiologists and nurse anaesthetists providing remote telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury (AKI) within 7 days. The primary outcome was area under the receiver operating characteristic curve (AUROC) for clinician predictions of mortality and AKI, comparing AUROCs between assisted and unassisted assessments. RESULTS: We analysed 5071 patients (mean [range] age: 58 [18-100] yr; 52% female) assessed by 89 clinicians. Of these, 98 (2.2%) patients died within 30 days of surgery and 450 (11.1%) patients sustained AKI. Clinician predictions agreed with the models more strongly in the assisted vs unassisted group (weighted kappa 0.75 vs 0.62 for death, mean difference: 0.13 [95% CI 0.10-0.17]; and 0.79 vs 0.54 for AKI, mean difference: 0.25 [95% CI 0.21-0.29]). Clinical prediction of death was similar between the assisted (AUROC 0.793) and unassisted (AUROC 0.780) groups (mean difference: 0.013 [95% CI -0.070 to 0.097]; P=0.76). Prediction of AKI had an AUROC of 0.734 in the assisted group vs 0.688 in the unassisted group (difference 0.046 [95% CI -0.003 to 0.091]; P=0.06). CONCLUSIONS: Clinician performance was not improved by machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. CLINICAL TRIAL REGISTRATION: NCT05042804.

Measuring cognitive effort using tabular transformer-based language models of electronic health record-based audit log action sequences.

OBJECTIVES: To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities. MATERIALS AND METHODS: EHR-based audit logs of attending physicians and advanced practice providers (APPs) from four surgical intensive care units in 2019 were included. Neural language models (LMs) were trained and validated separately for attendings' and APPs' action sequences. Action entropy was calculated as the cross-entropy associated with the predicted probability of the next action, based on prior actions. To validate the measure, a matched pairs study was conducted to assess the difference in action entropy during known high cognitive effort scenarios, namely, attention switching between patients and to or from the EHR inbox. RESULTS: Sixty-five clinicians performing 5 904 429 EHR-based audit log actions on 8956 unique patients were included. All attention switching scenarios were associated with a higher action entropy compared to non-switching scenarios (P < .001), except for the from-inbox switching scenario among APPs. The highest difference among attendings was for the from-inbox attention switching: Action entropy was 1.288 (95% CI, 1.256-1.320) standard deviations (SDs) higher for switching compared to non-switching scenarios. For APPs, the highest difference was for the to-inbox switching, where action entropy was 2.354 (95% CI, 2.311-2.397) SDs higher for switching compared to non-switching scenarios. DISCUSSION: We developed a LM-based metric, action entropy, for assessing cognitive burden associated with EHR-based actions. The metric showed discriminant validity and statistical significance when evaluated against known situations of high cognitive effort (ie, attention switching). With additional validation, this metric can potentially be used as a screening tool for assessing behavioral action phenotypes that are associated with higher cognitive burden. CONCLUSION: An LM-based action entropy metric-relying on sequences of EHR actions-offers opportunities for assessing cognitive effort in EHR-based workflows.

Contributors to Electronic Health Record-Integrated Secure Messaging Use: A Study of Over 33,000 Health Care Professionals.

OBJECTIVES: Electronic health record (EHR)-integrated secure messaging is extensively used for communication between clinicians. We investigated the factors contributing to secure messaging use in a large health care system. METHODS: This was a cross-sectional study that included 14 hospitals and 263 outpatient clinic locations. Data on EHR-integrated secure messaging use over a 1-month period (February 1, 2023, through February 28, 2023) were collected. A multilevel mixed effects model was used to assess the contribution of clinical role, clinical unit (i.e., specific inpatient ward or outpatient clinic), hospital or clinic location (i.e., Hospital X or Outpatient Clinic Building Y), and inpatient versus outpatient setting toward secure messaging use. RESULTS: Of the 33,195 health care professionals who worked during the study period, 20,576 (62%) were secure messaging users. In total, 25.3% of the variability in messaging use was attributable to the clinical unit and 30.5% was attributable to the hospital or clinic location. Compared with nurses, advanced practice providers, pharmacists, and physicians were more likely to use secure messaging, whereas medical assistants, social workers, and therapists were less likely (p < 0.001). After adjusting for other factors, inpatient versus outpatient setting was not associated with secure messaging use. CONCLUSION: Secure messaging was widely used; however, there was substantial variation by clinical role, clinical unit, and hospital or clinic location. Our results suggest that interventions and policies for managing secure messaging behaviors are likely to be most effective if they are not only set at the organizational level but also communicated and tailored toward individual clinical units and clinician workflows.

Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial.

Background: Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods: This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results: Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions: Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration: ClinicalTrials.gov NCT05042804.

Secure Messaging and Telephone Use for Clinician-to-Clinician Communication.

This cohort study investigates the association of use of text-based secured messaging with telephone use among resident physicians.

Effect of Telemedicine Support for Intraoperative Anaesthesia Care on Postoperative Outcomes: The TECTONICS Randomised Clinical Trial.

Background: Novel applications of telemedicine can improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied. Methods: This single centre randomised clinical trial ( clinicaltrials.gov NCT03923699 ) of unselected adult surgical patients was conducted between July 1, 2019 and January 31, 2023. Patients received usual care or decision support from a telemedicine service, the Anesthesiology Control Tower (ACT). The ACT provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews, machine-learning forecasting, and physiologic alerts. ORs were randomised 1:1. Co-primary outcomes of 30-day all-cause mortality, respiratory failure, acute kidney injury (AKI), and delirium were analysed as intention-to-treat. Results: The trial completed planned enrolment with 71927 surgeries (35956 ACT; 35971 usual care). After multiple testing correction, there was no significant effect of the ACT vs. usual care on 30-day mortality [641/35956 (1.8%) vs 638/35971 (1.8%), risk difference 0.0% (95% CI -0.2% to 0.3%), p=0.96], respiratory failure [1089/34613 (3.1%) vs 1112/34619 (3.2%), risk difference -0.1% (95% CI -0.4% to 0.3%), p=0.96], AKI [2357/33897 (7%) vs 2391/33795 (7.1%), risk difference -0.1% (-0.6% to 0.4%), p=0.96], or delirium [1283/3928 (32.7%) vs 1279/3989 (32.1%), risk difference 0.6% (-2.0% to 3.2%), p=0.96]. There were no significant differences in secondary outcomes or in sensitivity analyses. Conclusions: In this large RCT of a novel application of telemedicine-based remote monitoring and decision support using real-time alerts and case reviews, we found no significant differences in postoperative outcomes. Large-scale intraoperative telemedicine is feasible, and we suggest future avenues where it may be impactful.

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PST). The first pilot trial showed promising changes in cognitive control measured by functional neuroimaging and improvements in depression and anxiety symptoms. METHODS: To further validate Lumen in a 3-arm randomized clinical trial, 200 participants with mild-to-moderate depression and/or anxiety will be randomly assigned in a 2:1:1 ratio to receive Lumen-coached PST, human-coached PST as active treatment comparison, or a waitlist control condition where participants can receive Lumen after the trial period. Participants will be assessed at baseline and 18 weeks. The primary aim is to confirm neural target engagement by testing whether compared with waitlist controls, Lumen participants will show significantly greater improvements from baseline to 18 weeks in the a priori neural target for cognitive control, right dorsal lateral prefrontal cortex engaged by the go/nogo task (primary superiority hypothesis). A secondary hypothesis will test whether compared with human-coached PST participants, Lumen participants will show equivalent improvements (i.e., noninferiority) in the same neural target from baseline to 18 weeks. The second aim is to examine (1) treatment effects on depression and anxiety symptoms, psychosocial functioning, and quality of life outcomes, and (2) relationships of neural target engagement to these patient-reported outcomes. CONCLUSIONS: This study offers potential to improve the reach and impact of psychotherapy, mitigating access, cost, and stigma barriers for people with depression and/or anxiety. CLINICALTRIALS: gov #: NCT05603923.

Implementation and prospective performance evaluation of an intraoperative duration prediction model using high throughput real-time data.

Background: Accurate real-time prediction of intraoperative duration can contribute to improved perioperative outcomes. We implemented a data pipeline for extraction of real-time data from nascent anaesthesia records and silently deployed a predictive machine learning (ML) algorithm. Methods: Clinical variables were retrieved from the electronic health record via a third-party clinical decision support platform and contemporaneously ingested into a previously developed ML model. The model was trained using 3 months data, and performance was subsequently evaluated over 10 months using continuous ranked probability score. Results: The ML model made 6 173 435 predictions on 62 142 procedures. Mean continuous ranked probability score for the ML model was 27.19 (standard error 0.016) min compared with 51.66 (standard error 0.029) min for the bias-corrected scheduled duration. Linear regression did not demonstrate performance drift over the testing period. Conclusions: We implemented and silently deployed a real-time ML algorithm for predicting surgery duration. Prospective evaluation showed that model performance was preserved over a 10-month testing period.

Predicting Primary Care Physician Burnout From Electronic Health Record Use Measures.

OBJECTIVE: To evaluate the ability of routinely collected electronic health record (EHR) use measures to predict clinical work units at increased risk of burnout and potentially most in need of targeted interventions. METHODS: In this observational study of primary care physicians, we compiled clinical workload and EHR efficiency measures, then linked these measures to 2 years of well-being surveys (using the Stanford Professional Fulfillment Index) conducted from April 1, 2019, through October 16, 2020. Physicians were grouped into training and confirmation data sets to develop predictive models for burnout. We used gradient boosting classifier and other prediction modeling algorithms to quantify the predictive performance by the area under the receiver operating characteristics curve (AUC). RESULTS: Of 278 invited physicians from across 60 clinics, 233 (84%) completed 396 surveys. Physicians were 67% women with a median age category of 45 to 49 years. Aggregate burnout score was in the high range (≥3.325/10) on 111 of 396 (28%) surveys. Gradient boosting classifier of EHR use measures to predict burnout achieved an AUC of 0.59 (95% CI, 0.48 to 0.77) and an area under the precision-recall curve of 0.29 (95% CI, 0.20 to 0.66). Other models' confirmation set AUCs ranged from 0.56 (random forest) to 0.66 (penalized linear regression followed by dichotomization). Among the most predictive features were physician age, team member contributions to notes, and orders placed with user-defined preferences. Clinic-level aggregate measures identified the top quartile of clinics with 56% sensitivity and 85% specificity. CONCLUSION: In a sample of primary care physicians, routinely collected EHR use measures demonstrated limited ability to predict individual burnout and moderate ability to identify high-risk clinics.

Perioperative mental health intervention for depression and anxiety symptoms in older adults study protocol: design and methods for three linked randomised controlled trials.

INTRODUCTION: Preoperative anxiety and depression symptoms among older surgical patients are associated with poor postoperative outcomes, yet evidence-based interventions for anxiety and depression have not been applied within this setting. We present a protocol for randomised controlled trials (RCTs) in three surgical cohorts: cardiac, oncological and orthopaedic, investigating whether a perioperative mental health intervention, with psychological and pharmacological components, reduces perioperative symptoms of depression and anxiety in older surgical patients. METHODS AND ANALYSIS: Adults ≥60 years undergoing cardiac, orthopaedic or oncological surgery will be enrolled in one of three-linked type 1 hybrid effectiveness/implementation RCTs that will be conducted in tandem with similar methods. In each trial, 100 participants will be randomised to a remotely delivered perioperative behavioural treatment incorporating principles of behavioural activation, compassion and care coordination, and medication optimisation, or enhanced usual care with mental health-related resources for this population. The primary outcome is change in depression and anxiety symptoms assessed with the Patient Health Questionnaire-Anxiety Depression Scale from baseline to 3 months post surgery. Other outcomes include quality of life, delirium, length of stay, falls, rehospitalisation, pain and implementation outcomes, including study and intervention reach, acceptability, feasibility and appropriateness, and patient experience with the intervention. ETHICS AND DISSEMINATION: The trials have received ethics approval from the Washington University School of Medicine Institutional Review Board. Informed consent is required for participation in the trials. The results will be submitted for publication in peer-reviewed journals, presented at clinical research conferences and disseminated via the Center for Perioperative Mental Health website. TRIAL REGISTRATION NUMBERS: NCT05575128, NCT05685511, NCT05697835, pre-results.

National Multi-Institutional Validation of a Surgical Transfusion Risk Prediction Model.

BACKGROUND: Accurate estimation of surgical transfusion risk is important for many aspects of surgical planning, yet few methods for estimating are available for estimating such risk. There is a need for reliable validated methods for transfusion risk stratification to support effective perioperative planning and resource stewardship. STUDY DESIGN: This study was conducted using the American College of Surgeons NSQIP datafile from 2019. S-PATH performance was evaluated at each contributing hospital, with and without hospital-specific model tuning. Linear regression was used to assess the relationship between hospital characteristics and area under the receiver operating characteristic (AUROC) curve. RESULTS: A total of 1,000,927 surgical cases from 414 hospitals were evaluated. Aggregate AUROC was 0.910 (95% CI 0.904 to 0.916) without model tuning and 0.925 (95% CI 0.919 to 0.931) with model tuning. AUROC varied across individual hospitals (median 0.900, interquartile range 0.849 to 0.944), but no statistically significant relationships were found between hospital-level characteristics studied and model AUROC. CONCLUSIONS: S-PATH demonstrated excellent discriminative performance, although there was variation across hospitals that was not well-explained by hospital-level characteristics. These results highlight the S-PATH's viability as a generalizable surgical transfusion risk prediction tool.

A Perioperative Mental Health Intervention for Depressed and Anxious Older Surgical Patients: Results From a Feasibility Study.

OBJECTIVES: The perioperative period is challenging and stressful for older adults. Those with depression and/or anxiety have an increased risk of adverse surgical outcomes. We assessed the feasibility of a perioperative mental health intervention composed of medication optimization and a wellness program following principles of behavioral activation and care coordination for older surgical patients. METHODS: We included orthopedic, oncologic, and cardiac surgical patients aged 60 and older. Feasibility outcomes included study reach, the number of patients who agreed to participate out of the total eligible; and intervention reach, the number of patients who completed the intervention out of patients who agreed to participate. Intervention efficacy was assessed using the Patient Health Questionnaire for Anxiety and Depression (PHQ-ADS). Implementation potential and experiences were collected using patient surveys and qualitative interviews. Complementary caregiver feedback was also collected. RESULTS: Twenty-three out of 28 eligible older adults participated in this study (mean age 68.0 years, 65% women), achieving study reach of 82% and intervention reach of 83%. In qualitative interviews, patients (n = 15) and caregivers (complementary data, n = 5) described overwhelmingly positive experiences with both the intervention components and the interventionist, and reported improvement in managing depression and/or anxiety. Preliminary efficacy analysis indicated improvement in PHQ-ADS scores (F = 12.13, p <0.001). CONCLUSIONS: The study procedures were reported by participants as feasible and the perioperative mental health intervention to reduce anxiety and depression in older surgical patients showed strong implementation potential. Preliminary data suggest its efficacy for improving depression and/or anxiety symptoms. A randomized controlled trial assessing the intervention and implementation effectiveness is currently ongoing.

Guidance for reporting analyses of metadata on electronic health record use.

INTRODUCTION: Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. RECOMMENDATIONS: In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata-or measures of EHR use derived from them-to foster clarity, standardization, and reproducibility in this emerging and critical area of research.

Association Between User Interaction and Treatment Response of a Voice-Based Coach for Treating Depression and Anxiety: Secondary Analysis of a Pilot Randomized Controlled Trial.

BACKGROUND: The quality of user interaction with therapeutic tools has been positively associated with treatment response; however, no studies have investigated these relationships for voice-based digital tools. OBJECTIVE: This study evaluated the relationships between objective and subjective user interaction measures as well as treatment response on Lumen, a novel voice-based coach, delivering problem-solving treatment to patients with mild to moderate depression or anxiety or both. METHODS: In a pilot trial, 42 adults with clinically significant depression (Patient Health Questionnaire-9 [PHQ-9]) or anxiety (7-item Generalized Anxiety Disorder Scale [GAD-7]) symptoms or both received Lumen, a voice-based coach delivering 8 problem-solving treatment sessions. Objective (number of conversational breakdowns, ie, instances where a participant's voice input could not be interpreted by Lumen) and subjective user interaction measures (task-related workload, user experience, and treatment alliance) were obtained for each session. Changes in PHQ-9 and GAD-7 scores at each ensuing session after session 1 measured the treatment response. RESULTS: Participants were 38.9 (SD 12.9) years old, 28 (67%) were women, 8 (19%) were Black, 12 (29%) were Latino, 5 (12%) were Asian, and 28 (67%) had a high school or college education. Mean (SD) across sessions showed breakdowns (mean 6.5, SD 4.4 to mean 2.3, SD 1.8) decreasing over sessions, favorable task-related workload (mean 14.5, SD 5.6 to mean 17.6, SD 5.6) decreasing over sessions, neutral-to-positive user experience (mean 0.5, SD 1.4 to mean 1.1, SD 1.3), and high treatment alliance (mean 5.0, SD 1.4 to mean 5.3, SD 0.9). PHQ-9 (Ptrend=.001) and GAD-7 scores (Ptrend=.01) improved significantly over sessions. Treatment alliance correlated with improvements in PHQ-9 (Pearson r=-0.02 to -0.46) and GAD-7 (r=0.03 to -0.57) scores across sessions, whereas breakdowns and task-related workload did not. Mixed models showed that participants with higher individual mean treatment alliance had greater improvements in PHQ-9 (ß=-1.13, 95% CI -2.16 to -0.10) and GAD-7 (ß=-1.17, 95% CI -2.13 to -0.20) scores. CONCLUSIONS: The participants had fewer conversational breakdowns and largely favorable user interactions with Lumen across sessions. Conversational breakdowns were not associated with subjective user interaction measures or treatment responses, highlighting how participants adapted and effectively used Lumen. Individuals experiencing higher treatment alliance had greater improvements in depression and anxiety. Understanding treatment alliance can provide insights on improving treatment response for this new delivery modality, which provides accessibility, flexibility, comfort with disclosure, and cost-related advantages compared to conventional psychotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT04524104; https://clinicaltrials.gov/study/NCT04524104.

Characterizing the Patterns of Electronic Health Record-Integrated Secure Messaging Use: Cross-Sectional Study.

BACKGROUND: Communication among health care professionals is essential for the delivery of safe clinical care. Secure messaging has rapidly emerged as a new mode of asynchronous communication. Despite its popularity, relatively little is known about how secure messaging is used and how such use contributes to communication burden. OBJECTIVE: This study aims to characterize the use of an electronic health record-integrated secure messaging platform across 14 hospitals and 263 outpatient clinics within a large health care system. METHODS: We collected metadata on the use of the Epic Systems Secure Chat platform for 6 months (July 2022 to January 2023). Information was retrieved on message volume, response times, message characteristics, messages sent and received by users, user roles, and work settings (inpatient vs outpatient). RESULTS: A total of 32,881 users sent 9,639,149 messages during the study. Median daily message volume was 53,951 during the first 2 weeks of the study and 69,526 during the last 2 weeks, resulting in an overall increase of 29% (P=.03). Nurses were the most frequent users of secure messaging (3,884,270/9,639,149, 40% messages), followed by physicians (2,387,634/9,639,149, 25% messages), and medical assistants (1,135,577/9,639,149, 12% messages). Daily message frequency varied across users; inpatient advanced practice providers and social workers interacted with the highest number of messages per day (median 19). Conversations were predominantly between 2 users (1,258,036/1,547,879, 81% conversations), with a median of 2 conversational turns and a median response time of 2.4 minutes. The largest proportion of inpatient messages was from nurses to physicians (972,243/4,749,186, 20% messages) and physicians to nurses (606,576/4,749,186, 13% messages), while the largest proportion of outpatient messages was from physicians to nurses (344,048/2,192,488, 16% messages) and medical assistants to other medical assistants (236,694/2,192,488, 11% messages). CONCLUSIONS: Secure messaging was widely used by a diverse range of health care professionals, with ongoing growth throughout the study and many users interacting with more than 20 messages per day. The short message response times and high messaging volume observed highlight the interruptive nature of secure messaging, raising questions about its potentially harmful effects on clinician workflow, cognition, and errors.

Identifying and Addressing Barriers to Implementing Core Electronic Health Record Use Metrics for Ambulatory Care: Virtual Consensus Conference Proceedings.

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS: We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care. CONCLUSION: Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop's outputs offers promise for overcoming barriers to implementing EHR use metrics.

An Intraoperative Telemedicine Program to Improve Perioperative Quality Measures: The ACTFAST-3 Randomized Clinical Trial.

Importance: Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed. Objective: To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures. Design, Setting, and Participants: This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US. A total of 26 254 adult surgical patients were randomized to receive either usual intraoperative care (control group; n = 12 980) or usual care augmented by telemedicine decision support (intervention group; n = 13 274). Data were initially analyzed from April 22 to May 19, 2021, with updates in November 2022 and February 2023. Intervention: Patients received either usual care (medical direction from the anesthesia care team) or intraoperative anesthesia care monitored and augmented by decision support from the Anesthesiology Control Tower (ACT), a real-time, live telemedicine intervention. The ACT incorporated remote monitoring of operating rooms by a team of anesthesia clinicians with customized analysis software. The ACT reviewed alerts and electronic health record data to inform recommendations to operating room clinicians. Main Outcomes and Measures: The primary outcomes were avoidance of postoperative hypothermia (defined as the proportion of patients with a final recorded intraoperative core temperature >36 °C) and hyperglycemia (defined as the proportion of patients with diabetes who had a blood glucose level ≤180 mg/dL on arrival to the postanesthesia recovery area). Secondary outcomes included intraoperative hypotension, temperature monitoring, timely antibiotic redosing, intraoperative glucose evaluation and management, neuromuscular blockade documentation, ventilator management, and volatile anesthetic overuse. Results: Among 26 254 participants, 13 393 (51.0%) were female and 20 169 (76.8%) were White, with a median (IQR) age of 60 (47-69) years. There was no treatment effect on avoidance of hyperglycemia (7445 of 8676 patients [85.8%] in the intervention group vs 7559 of 8815 [85.8%] in the control group; rate ratio [RR], 1.00; 95% CI, 0.99-1.01) or hypothermia (7602 of 11 447 patients [66.4%] in the intervention group vs 7783 of 11 672 [66.7.%] in the control group; RR, 1.00; 95% CI, 0.97-1.02). Intraoperative glucose measurement was more common among patients with diabetes in the intervention group (RR, 1.07; 95% CI, 1.01-1.15), but other secondary outcomes were not significantly different. Conclusions and Relevance: In this randomized clinical trial, anesthesia care quality measures did not differ between groups, with high confidence in the findings. These results suggest that the intervention did not affect the targeted care practices. Further streamlining of clinical decision support and workflows may help the intraoperative telemedicine program achieve improvement in targeted clinical measures. Trial Registration: ClinicalTrials.gov Identifier: NCT02830126.

Anesthesia Clinical Workload Estimated From Electronic Health Record Documentation vs Billed Relative Value Units.

Importance: Accurate measurements of clinical workload are needed to inform health care policy. Existing methods for measuring clinical workload rely on surveys or time-motion studies, which are labor-intensive to collect and subject to biases. Objective: To compare anesthesia clinical workload estimated from electronic health record (EHR) audit log data vs billed relative value units. Design, Setting, and Participants: This cross-sectional study of anesthetic encounters occurring between August 26, 2019, and February 9, 2020, used data from 8 academic hospitals, community hospitals, and surgical centers across Missouri and Illinois. Clinicians who provided anesthetic services for at least 1 surgical encounter were included. Data were analyzed from January 2022 to January 2023. Exposure: Anesthetic encounters associated with a surgical procedure were included. Encounters associated with labor analgesia and endoscopy were excluded. Main Outcomes and Measures: For each encounter, EHR-derived clinical workload was estimated as the sum of all EHR actions recorded in the audit log by anesthesia clinicians who provided care. Billing-derived clinical workload was measured as the total number of units billed for the encounter. A linear mixed-effects model was used to estimate the relative contribution of patient complexity (American Society of Anesthesiology [ASA] physical status modifier), procedure complexity (ASA base unit value for the procedure), and anesthetic duration (time units) to EHR-derived and billing-derived workload. The resulting ß coefficients were interpreted as the expected effect of a 1-unit change in each independent variable on the standardized workload outcome. The analysis plan was developed after the data were obtained. Results: A total of 405 clinicians who provided anesthesia for 31 688 encounters were included in the study. A total of 8 288 132 audit log actions corresponding to 39 131 hours of EHR use were used to measure EHR-derived workload. The contributions of patient complexity, procedural complexity, and anesthesia duration to EHR-derived workload differed significantly from their contributions to billing-derived workload. The contribution of patient complexity toward EHR-derived workload (ß = 0.162; 95% CI, 0.153-0.171) was more than 50% greater than its contribution toward billing-derived workload (ß = 0.106; 95% CI, 0.097-0.116; P < .001). In contrast, the contribution of procedure complexity toward EHR-derived workload (ß = 0.033; 95% CI, 0.031-0.035) was approximately one-third its contribution toward billing-derived workload (ß = 0.106; 95% CI, 0.104-0.108; P < .001). Conclusions and Relevance: In this cross-sectional study of 8 hospitals, reimbursement for anesthesiology services overcompensated for procedural complexity and undercompensated for patient complexity. This method for measuring clinical workload could be used to improve reimbursement valuations for anesthesia and other specialties.