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.

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.

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.

Classifying clinical work settings using EHR audit logs: a machine learning approach.

OBJECTIVES: We used electronic health record (EHR)-based raw audit logs to classify the work settings of anesthesiology physicians providing care in both surgical intensive care units (ICUs) and operating rooms. STUDY DESIGN: Observational study. METHODS: Attending anesthesiologists who worked at least 1 shift in 1 of 4 surgical ICUs in calendar year 2019 were included. Time-stamped EHR-based audit log events for each week were used to create event frequencies and represented as a term frequency-inverse document frequency matrix. Primary classification outcome of interest was a physician's clinical work setting. Performance of multiple supervised machine learning classifiers were evaluated. RESULTS: A total of 24 attending physicians were included; physicians performed a median (IQR) of 2545 (906-5071) EHR-based actions per week and worked a median (IQR) of 5 (3-7) weeks in a surgical ICU. A random forest classifier yielded the best discriminative performance (mean [SD] area under receiver operating characteristic curve, 0.88 [0.05]; mean [SD] area under precision-recall curve, 0.72 [0.13]). Model explanations illustrated that clinical activities related to signing of clinical notes, printing handoff data, and updating diagnosis information were associated with the positive prediction of working in a surgical ICU setting. CONCLUSIONS: A random forest classifier using a frequency-based feature engineering approach successfully predicted work settings of physicians with multiple clinical responsibilities with high accuracy. These findings highlight opportunities for using audit logs for automated assessment of clinician activities and their work settings, thereby affording the ability to accurately assess context-specific work characteristics (eg, workload).

Interactions in Ternary Mixtures of MnO2, Al2O3, and Natural Organic Matter (NOM) and the Impact on MnO2 Oxidative Reactivity.

Our previous work reported that Al2O3 inhibited the oxidative reactivity of MnO2 through heteroaggregation between oxide particles and surface complexation of the dissolved Al ions with MnO2 (S. Taujale and H. Zhang, "Impact of interactions between metal oxides to oxidative reactivity of manganese dioxide" Environ. Sci. Technol. 2012, 46, 2764-2771). The aim of the current work was to investigate interactions in ternary mixtures of MnO2, Al2O3, and NOM and how the interactions affect MnO2 oxidative reactivity. For the effect of Al ions, we examined ternary mixtures of MnO2, Al ions, and NOM. Our results indicated that an increase in the amount of humic acids (HAs) increasingly inhibited Al adsorption by forming soluble Al-HA complexes. As a consequence, there was less inhibition on MnO2 reactivity than by the sum of two binary mixtures (MnO2+Al ions and MnO2+HA). Alginate or pyromellitic acid (PA)-two model NOM compounds-did not affect Al adsorption, but Al ions increased alginate/PA adsorption by MnO2. The latter effect led to more inhibition on MnO2 reactivity than the sum of the two binary mixtures. In ternary mixtures of MnO2, Al2O3, and NOM, NOM inhibited dissolution of Al2O3. Zeta potential measurements, sedimentation experiments, TEM images, and modified DLVO calculations all indicated that HAs of up to 4 mg-C/L increased heteroaggregation between Al2O3 and MnO2, whereas higher amounts of HAs completely inhibited heteroaggregation. The effect of alginate is similar to that of HAs, although not as significant, while PA had negligible effects on heteroaggregation. Different from the effects of Al ions and NOMs on MnO2 reactivity, the MnO2 reactivity in ternary mixtures of Al2O3, MnO2, and NOM was mostly enhanced. This suggests MnO2 reactivity was mainly affected through heteroaggregation in the ternary mixtures because of the limited availability of Al ions.