Opportunities to improve the diagnosis and treatment of primary hyperparathyroidism: retrospective cohort study.

Background: Although primary hyperparathyroidism (PHPT) is readily diagnosed biochemically and can be cured with low-risk surgery, it is often underrecognized and undertreated. Our objectives were to characterize, within our health system, how often patients with hypercalcemia were evaluated for PHPT and how often patients with PHPT underwent definitive treatment with parathyroidectomy. Methods: Ambulatory patients aged 18 years or older seen at our health system between January 2018 and June 2023 with chronic hypercalcemia were identified from the medical record. After excluding causes of secondary hyperparathyroidism, the proportion of patients with parathyroid hormone (PTH) tests was calculated. Among patients with biochemical evidence of PHPT, the proportion of patients who underwent parathyroidectomy was calculated. Multivariable logistic regression was used to identify factors associated with an evaluation for PHPT and, separately, with parathyroidectomy. Results: Of 7,675 patients with chronic hypercalcemia, 3,323 (43.3%) had a PTH test obtained within 6 months. An age between 40-49 vs. <30 years [(odds ratio (OR) =3.2; 95% confidence interval (CI): 1.8-5.6; P<0.001], a serum calcium level between 11.6-12.0 vs. <11.0 mg/dL (OR =3.9; 95% CI: 3.2-4.7; P<0.001), and osteoporosis (OR =3.1; 95% CI: 2.7-3.5; P<0.001) were associated with an evaluation for PHPT. Among those with PTH levels, 1,327 (39.9%) had PHPT but only 916 (69.0%) were recognized. Three hundred and forty-five (26.0%) patients with PHPT underwent parathyroidectomy. An increasing number of surgical indications was associated with parathyroidectomy (P<0.001), though overall rates remained less than 40%. Among indications for surgery, including age and serum total calcium level, only osteoporosis was associated with parathyroidectomy (OR =2.0; 95% CI: 1.4-2.8; P<0.001). Conclusions: In this study, more than half of patients with chronic hypercalcemia were not evaluated for PHPT. Among patients with biochemical evidence of PHPT, one-third were unrecognized and only one-in-four received curative treatment. Opportunities to improve the management of PHPT exist within our large integrated health system.

Single-cell transcriptomics identifies aberrant glomerular angiogenic signalling in the early stages of WT1 kidney disease.

WT1 encodes a podocyte transcription factor whose variants can cause an untreatable glomerular disease in early childhood. Although WT1 regulates many podocyte genes, it is poorly understood which of them are initiators in disease and how they subsequently influence other cell-types in the glomerulus. We hypothesised that this could be resolved using single-cell RNA sequencing (scRNA-seq) and ligand-receptor analysis to profile glomerular cell-cell communication during the early stages of disease in mice harbouring an orthologous human mutation in WT1 (Wt1R394W/+). Podocytes were the most dysregulated cell-type in the early stages of Wt1R394W/+ disease, with disrupted angiogenic signalling between podocytes and the endothelium, including the significant downregulation of transcripts for the vascular factors Vegfa and Nrp1. These signalling changes preceded glomerular endothelial cell loss in advancing disease, a feature also observed in biopsy samples from human WT1 glomerulopathies. Addition of conditioned medium from murine Wt1R394W/+ primary podocytes to wild-type glomerular endothelial cells resulted in impaired endothelial looping and reduced vascular complexity. Despite the loss of key angiogenic molecules in Wt1R394W/+ podocytes, the pro-vascular molecule adrenomedullin was upregulated in Wt1R394W/+ podocytes and plasma and its further administration was able to rescue the impaired looping observed when glomerular endothelium was exposed to Wt1R394W/+ podocyte medium. In comparative analyses, adrenomedullin upregulation was part of a common injury signature across multiple murine and human glomerular disease datasets, whilst other gene changes were unique to WT1 disease. Collectively, our study describes a novel role for altered angiogenic signalling in the initiation of WT1 glomerulopathy. We also identify adrenomedullin as a proangiogenic factor, which despite being upregulated in early injury, offers an insufficient protective response due to the wider milieu of dampened vascular signalling that results in endothelial cell loss in later disease. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Dual Task Ball Balancing Test and Its Association With Cognitive Function: Algorithm Development and Validation.

BACKGROUND: Dual task paradigms are thought to offer a quantitative means to assess cognitive reserve and the brain's capacity to allocate resources in the face of competing cognitive demands. The most common dual task paradigms examine the interplay between gait or balance control and cognitive function. However, gait and balance tasks can be physically challenging for older adults and may pose a risk of falls. OBJECTIVE: We introduce a novel, digital dual-task assessment that combines a motor-control task (the "ball balancing" test), which challenges an individual to maintain a virtual ball within a designated zone, with a concurrent cognitive task (the backward digit span task [BDST]). METHODS: The task was administered on a touchscreen tablet, performance was measured using the inertial sensors embedded in the tablet, conducted under both single- and dual-task conditions. The clinical use of the task was evaluated on a sample of 375 older adult participants (n=210 female; aged 73.0, SD 6.5 years). RESULTS: All older adults, including those with mild cognitive impairment (MCI) and Alzheimer disease-related dementia (ADRD), and those with poor balance and gait problems due to diabetes, osteoarthritis, peripheral neuropathy, and other causes, were able to complete the task comfortably and safely while seated. As expected, task performance significantly decreased under dual task conditions compared to single task conditions. We show that performance was significantly associated with cognitive impairment; significant differences were found among healthy participants, those with MCI, and those with ADRD. Task results were significantly associated with functional impairment, independent of diagnosis, degree of cognitive impairment (as indicated by the Mini Mental State Examination [MMSE] score), and age. Finally, we found that cognitive status could be classified with >70% accuracy using a range of classifier models trained on 3 different cognitive function outcome variables (consensus clinical judgment, Rey Auditory Verbal Learning Test [RAVLT], and MMSE). CONCLUSIONS: Our results suggest that the dual task ball balancing test could be used as a digital cognitive assessment of cognitive reserve. The portability, simplicity, and intuitiveness of the task suggest that it may be suitable for unsupervised home assessment of cognitive function.

Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis.

OBJECTIVE: Racial and ethnic differences in presentation and outcomes have been reported in systemic sclerosis (SSc) and SSc-interstitial lung disease (ILD). However, prior studies have limited diversity. We aim to evaluate if there are racial/ethnic differences associated with ILD, time intervals between SSc and ILD and with emergency department (ED) visit or hospitalization rates. METHODS: Clinical and sociodemographic variables were extracted for 756 patients with SSc from longitudinal health records in an integrated health-system. Logistic regression models analyzed the association of covariates with ILD and age at SSc-ILD. Healthcare outcomes were analyzed with complementary log-log regression models. RESULTS: Overall, 33.7% of patients in the cohort had an ILD code, with increased odds for Asian (odds ratio [OR], 2.60; 95% confidence interval [CI], 1.29-5.28; p=0.008) compared with White patients. The predicted age in years of SSc-ILD was younger for Hispanic (estimate, -6.5; 95% CI, -13--0.21; p = 0.04) and Black/African American patients (-10; 95% CI -16--4.9; p < 0.001) compared with White patients. Black/African American patients were more likely to have an ILD code before an SSc code (59% compared with 20.6% of White patients), and the shortest interval from SSc to ILD (3 months). Black/African American (HR, 2.59; 95% CI 1.47-4.49; p = 0.001) and Hispanic patients (HR 2.29; 95% CI 1.37- 3.82; p = 0.002) had higher rates of an ED visit. CONCLUSION: We found that odds of SSc-ILD differed by racial/ethnic group, minoritized patients had earlier age of presentation, and greater rates of an ED visit.

Factors Associated with Completeness of Sex and Gender Fields in Electronic Health Records.

Purpose: Our purpose was to understand the completeness of sex and gender fields in electronic health record (EHR) data and patient-level factors associated with completeness of those fields. In doing so, we aimed to inform approaches to EHR sex and gender data collection. Methods: This was a retrospective observational study using 2016-2021 deidentified EHR data from a large health care system. Our sample included adults who had an encounter at any of three hospitals within the health care system or were enrolled in the health care system's Accountable Care Organization. The sex and gender fields of interest were gender identity, sex assigned at birth (SAB), and legal sex. Patient characteristics included demographics, clinical features, and health care utilization. Results: In the final study sample (N = 3,473,123), gender identity, SAB, and legal sex (required for system registration) were missing for 75.4%, 75.8%, and 0.1% of individuals, respectively. Several demographic and clinical factors were associated with having complete gender identity and SAB. Notably, the odds of having complete gender identity and SAB were greater among individuals with an activated patient portal (odds ratio [OR] = 2.68; 95% confidence interval [CI] = 2.66-2.70) and with more outpatient visits (OR = 4.34; 95% CI = 4.29-4.38 for 5+ visits); odds of completeness were lower among those with any urgent care visits (OR = 0.80; 95% CI = 0.78-0.82). Conclusions: Missingness of sex and gender data in the EHR was high and associated with a range of patient factors. Key features associated with completeness highlight multiple opportunities for intervention with a focus on patient portal use, primary care provider reporting, and urgent care settings.

A Calculated Risk: Evaluation of QTc Drug-Drug Interaction (DDI) Clinical Decision Support (CDS) Alerts and Performance of the Tisdale Risk Score Calculator.

INTRODUCTION: A risk factor for a potentially fatal ventricular arrhythmia Torsade de Pointes is a prolongation in the heart rate-corrected QT interval (QTc) ≥ 500 milliseconds (ms) or an increase of ≥ 60 ms from a patient's baseline value, which can cause sudden cardiac death. The Tisdale risk score calculator uses clinical variables to predict which hospitalized patients are at the highest risk for QTc prolongation. OBJECTIVE: To determine the rate of overridden QTc drug-drug interaction (DDI)-related clinical decision support (CDS) alerts per patient admission and the prevalence by Tisdale risk score category of these overridden alerts. Secondary outcome was to determine the rate of drug-induced QTc prolongation (diQTP) associated with overrides. METHODS: Our organization's enterprise data warehouse was used to retrospectively access QTc DDI alerts presented for patients aged ≥ 18 years who were admitted to Brigham and Women's Hospital during 2022. The QTc DDI CDS alerts were included if shown to a physician, fellow, resident, physician assistant, or nurse practitioner when entering the order in inpatient areas for patients with a length of stay of at least 2 days. Variables collected for the Tisdale calculator included age, sex, whether patient was on a loop diuretic, potassium level, admission QTc value, admitting diagnosis of acute myocardial infarction, sepsis, or heart failure, and number of QTc-prolonging drugs given to the patient. RESULTS: A total of 2649 patients with 3033 patient admissions had 18,432 QTc DDI alerts presented that were overridden. An average of 3 unique QTc DDI alerts were presented per patient admission and the alerts were overridden an average of 6 times per patient admission. Overall, 6% of patient admissions were low risk (score ≤ 6), 64% moderate risk (score 7-10), and 30% high risk (score ≥ 11) of QTc prolongation. The most common QTc DDI alerts overridden resulting in an diQTP were quetiapine and propofol (11%) and amiodarone and haloperidol (7%). The diQTP occurred in 883 of patient admissions (29%) and was more frequent in those with higher risk score, with 46% of patient admissions with diQTP in high risk, 23% in moderate risk, and 8% in low risk. CONCLUSION: Use of the Tisdale calculator to assess patient-specific risk of QT prolongation combined with CDS may improve overall alert quality and acceptance rate, which may decrease the diQTP rate.

In Vivo Genomic Supercoiling Mapping Using Psora-seq.

Supercoiling is a fundamental property of DNA that governs all strand opening reactions, including DNA replication, transcription, and homologous recombination. However, traditional genomic supercoiling assays are difficult and lack sensitivity. Building on prior assays using the DNA intercalator psoralen, we developed a supercoil mapping assay that is robust and sensitive to a wide range of supercoiling while requiring only commercially available reagents and common laboratory equipment. This method, psoralen affinity purification with genomic sequencing (Psora-seq), utilizes biotinylated psoralen and streptavidin-conjugated magnetic beads to facilitate efficient pull-down of psoralen-bound DNA, followed by deep sequencing to identify and quantify supercoiling at 1 kb resolution. Psora-seq overcomes two major bottlenecks associated with existing psoralen pull-down assays, inefficient photo-binding of psoralen-bound molecules, and poor recovery of cross-linked DNA.

Frequency and preventability of adverse drug events in the outpatient setting.

BACKGROUND: Limited data exist regarding adverse drug events (ADEs) in the outpatient setting. The objective of this study was to determine the incidence, severity, and preventability of ADEs in the outpatient setting and identify potential prevention strategies. METHODS: We conducted an analysis of ADEs identified in a retrospective electronic health records review of outpatient encounters in 2018 at 13 outpatient sites in Massachusetts that included 13 416 outpatient encounters in 3323 patients. Triggers were identified in the medical record including medications, consultations, laboratory results, and others. If a trigger was detected, a further in-depth review was conducted by nurses and adjudicated by physicians to examine the relevant information in the medical record. Patients were included in the study if they were at least 18 years of age with at least one outpatient encounter with a physician, nurse practitioner or physician's assistant in that calendar year. Patients were excluded from the study if the outpatient encounter occurred in outpatient surgery, psychiatry, rehabilitation, and paediatrics. RESULTS: In all, 5% of patients experienced an ADE over the 1-year period. We identified 198 ADEs among 170 patients, who had a mean age of 60. Most patients experienced one ADE (87%), 10% experienced two ADEs and 3% experienced three or more ADEs. The most frequent drug classes resulting in ADEs were cardiovascular (25%), central nervous system (14%), and anti-infective agents (14%). Severity was ranked as significant in 85%, 14% were serious, 1% were life-threatening, and there were no fatal ADEs. Of the ADEs, 22% were classified as preventable and 78% were not preventable. We identified 246 potential prevention strategies, and 23% of ADEs had more than one prevention strategy possibility. CONCLUSIONS: Despite efforts to prioritise patient safety, medication-related harms are still frequent. These results underscore the need for further patient safety improvement in the outpatient setting.

Deep survival analysis for interpretable time-varying prediction of preeclampsia risk.

OBJECTIVE: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, this assumption is rarely true for most diseases, as the underlying factors have complex, non-linear, and time-varying relationships. This concern is especially relevant for pregnancy, where the risk for pregnancy-related complications, such as preeclampsia, varies across gestation. Recently, deep learning survival models have shown promise in addressing the limitations of classical models, as the novel models allow for non-proportional risk handling, capturing nonlinear relationships, and navigating complex temporal dynamics. METHODS: We present a methodology to model the temporal risk of preeclampsia during pregnancy and investigate the associated clinical risk factors. We utilized a retrospective dataset including 66,425 pregnant individuals who delivered in two tertiary care centers from 2015 to 2023. We modeled the preeclampsia risk by modifying DeepHit, a deep survival model, which leverages neural network architecture to capture time-varying relationships between covariates in pregnancy. We applied time series k-means clustering to DeepHit's normalized output and investigated interpretability using Shapley values. RESULTS: We demonstrate that DeepHit can effectively handle high-dimensional data and evolving risk hazards over time with performance similar to the Cox Proportional Hazards model, achieving an area under the curve (AUC) of 0.78 for both models. The deep survival model outperformed traditional methodology by identifying time-varied risk trajectories for preeclampsia, providing insights for early and individualized intervention. K-means clustering resulted in patients delineating into low-risk, early-onset, and late-onset preeclampsia groups-notably, each of those has distinct risk factors. CONCLUSION: This work demonstrates a novel application of deep survival analysis in time-varying prediction of preeclampsia risk. Our results highlight the advantage of deep survival models compared to Cox Proportional Hazards models in providing personalized risk trajectory and demonstrating the potential of deep survival models to generate interpretable and meaningful clinical applications in medicine.

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Importance: Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs. Objective: To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS. Design: One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups. Setting: Two large U.S. health systems. Participants: Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. Intervention: The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Main Outcomes and Measures: Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission. Results: A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. Conclusions and Relevance: A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. Trial Registration: ClinicalTrials.gov Identifier: NCT03911687.

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.

Background: Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. Objective: This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. Methods: The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient's OUD risk level, which was then compared to the ML application's risk assignments. Results: A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. Conclusions: A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.

Straight to the point: evaluation of a Point of Care Information (POCI) resource in answering disease-related questions.

Objective: To evaluate the ability of DynaMedex, an evidence-based drug and disease Point of Care Information (POCI) resource, in answering clinical queries using keyword searches. Methods: Real-world disease-related questions compiled from clinicians at an academic medical center, DynaMedex search query data, and medical board review resources were categorized into five clinical categories (complications & prognosis, diagnosis & clinical presentation, epidemiology, prevention & screening/monitoring, and treatment) and six specialties (cardiology, endocrinology, hematology-oncology, infectious disease, internal medicine, and neurology). A total of 265 disease-related questions were evaluated by pharmacist reviewers based on if an answer was found (yes, no), whether the answer was relevant (yes, no), difficulty in finding the answer (easy, not easy), cited best evidence available (yes, no), clinical practice guidelines included (yes, no), and level of detail provided (detailed, limited details). Results: An answer was found for 259/265 questions (98%). Both reviewers found an answer for 241 questions (91%), neither found the answer for 6 questions (2%), and only one reviewer found an answer for 18 questions (7%). Both reviewers found a relevant answer 97% of the time when an answer was found. Of all relevant answers found, 68% were easy to find, 97% cited best quality of evidence available, 72% included clinical guidelines, and 95% were detailed. Recommendations for areas of resource improvement were identified. Conclusions: The resource enabled reviewers to answer most questions easily with the best quality of evidence available, providing detailed answers and clinical guidelines, with a high level of replication of results across users.

Telehealth Experience Among Patients With Limited English Proficiency.

This cross-sectional study assesses the implication of patients' English language skills for telehealth use and visit experience.

Patterns of Social Needs Predict Quality-of-Life and Healthcare Utilization Outcomes in Patients from a Large Hospital System.

BACKGROUND: Unmet social needs (SNs) often coexist in distinct patterns within specific population subgroups, yet these patterns are understudied. OBJECTIVE: To identify patterns of social needs (PSNs) and characterize their associations with health-related quality-of-life (HRQoL) and healthcare utilization (HCU). DESIGN: Observational study using data on SNs screening, HRQoL (i.e., low mental and physical health), and 90-day HCU (i.e., emergency visits and hospital admission). Among patients with any SNs, latent class analysis was conducted to identify unique PSNs. For all patients and by race and age subgroups, compared with no SNs, we calculated the risks of poor HRQoL and time to first HCU following SNs screening for each PSN. PATIENTS: Adult patients undergoing SNs screening at the Mass General Brigham healthcare system in Massachusetts, United States, between March 2018 and January 2023. MAIN MEASURES: SNs included: education, employment, family care, food, housing, medication, transportation, and ability to pay for household utilities. HRQoL was assessed using the Patient-Reported Outcomes Measurement Information System Global-10. KEY RESULTS: Six unique PSNs were identified: "high number of social needs," "food and utility access," "employment needs," "interested in education," "housing instability," and "transportation barriers." In 14,230 patients with HRQoL data, PSNs increased the risks of poor mental health, with risk ratios ranging from 1.07(95%CI:1.01-1.13) to 1.80(95%CI:1.74-1.86). Analysis of poor physical health yielded similar findings, except that the "interested in education" showed a mild protective effect (0.97[95%CI:0.94-1.00]). In 105,110 patients, PSNs increased the risk of 90-day HCU, with hazard ratios ranging from 1.09(95%CI:0.99-1.21) to 1.70(95%CI:1.52-1.90). Findings were generally consistent in subgroup analyses by race and age. CONCLUSIONS: Certain SNs coexist in distinct patterns and result in poorer HRQoL and more HCU. Understanding PSNs allows policymakers, public health practitioners, and social workers to identify at-risk patients and implement integrated, system-wide, and community-based interventions.

Virtual Scribes and Physician Time Spent on Electronic Health Records.

Importance: Time on the electronic health record (EHR) is associated with burnout among physicians. Newer virtual scribe models, which enable support from either a real-time or asynchronous scribe, have the potential to reduce the burden of the EHR and EHR-related documentation. Objective: To characterize the association of use of virtual scribes with changes in physicians' EHR time and note and order composition and to identify the physician, scribe, and scribe response factors associated with changes in EHR time upon virtual scribe use. Design, Setting, and Participants: Retrospective, pre-post quality improvement study of 144 physicians across specialties who had used a scribe for at least 3 months from January 2020 to September 2022, were affiliated with Brigham and Women's Hospital and Massachusetts General Hospital, and cared for patients in the outpatient setting. Data were analyzed from November 2022 to January 2024. Exposure: Use of either a real-time or asynchronous virtual scribe. Main Outcomes: Total EHR time, time on notes, and pajama time (5:30 pm to 7:00 am on weekdays and nonscheduled weekends and holidays), all per appointment; proportion of the note written by the physician and team contribution to orders. Results: The main study sample included 144 unique physicians who had used a virtual scribe for at least 3 months in 152 unique scribe participation episodes (134 [88.2%] had used an asynchronous scribe service). Nearly two-thirds of the physicians (91 physicians [63.2%]) were female and more than half (86 physicians [59.7%]) were in primary care specialties. Use of a virtual scribe was associated with significant decreases in total EHR time per appointment (mean [SD] of 5.6 [16.4] minutes; P < .001) in the 3 months after vs the 3 months prior to scribe use. Scribe use was also associated with significant decreases in note time per appointment and pajama time per appointment (mean [SD] of 1.3 [3.3] minutes; P < .001 and 1.1 [4.0] minutes; P = .004). In a multivariable linear regression model, the following factors were associated with significant decreases in total EHR time per appointment with a scribe use at 3 months: practicing in a medical specialty (-7.8; 95% CI, -13.4 to -2.2 minutes), greater baseline EHR time per appointment (-0.3; 95% CI, -0.4 to -0.2 minutes per additional minute of baseline EHR time), and decrease in the percentage of the note contributed by the physician (-9.1; 95% CI, -17.3 to -0.8 minutes for every percentage point decrease). Conclusions and Relevance: In 2 academic medical centers, use of virtual scribes was associated with significant decreases in total EHR time, time spent on notes, and pajama time, all per appointment. Virtual scribes may be particularly effective among medical specialists and those physicians with greater baseline EHR time.

The Safety of Outpatient Health Care : Review of Electronic Health Records.

BACKGROUND: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. OBJECTIVE: To measure AEs in the outpatient setting. DESIGN: Retrospective review of the electronic health record (EHR). SETTING: 11 outpatient sites in Massachusetts in 2018. PATIENTS: 3103 patients who received outpatient care. MEASUREMENTS: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. RESULTS: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care-associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, -0.05 [CI, -0.09 to -0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. LIMITATION: Retrospective EHR review may miss AEs. CONCLUSION: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. PRIMARY FUNDING SOURCE: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.

Barriers and enablers for externally and internally driven implementation processes in healthcare: a qualitative cross-case study.

BACKGROUND: Quality in healthcare is a subject in need of continuous attention. Quality improvement (QI) programmes with the purpose of increasing service quality are therefore of priority for healthcare leaders and governments. This study explores the implementation process of two different QI programmes, one externally driven implementation and one internally driven, in Norwegian nursing homes and home care services. The aim for the study was to identify enablers and barriers for externally and internally driven implementation processes in nursing homes and homecare services, and furthermore to explore if identified enablers and barriers are different or similar across the different implementation processes. METHODS: This study is based on an exploratory qualitative methodology. The empirical data was collected through the 'Improving Quality and Safety in Primary Care - Implementing a Leadership Intervention in Nursing Homes and Homecare' (SAFE-LEAD) project. The SAFE-LEAD project is a multiple case study of two different QI programmes in primary care in Norway. A large externally driven implementation process was supplemented with a tracer project involving an internally driven implementation process to identify differences and similarities. The empirical data was inductively analysed in accordance with grounded theory. RESULTS: Enablers for both external and internal implementation processes were found to be technology and tools, dedication, and ownership. Other more implementation process specific enablers entailed continuous learning, simulation training, knowledge sharing, perceived relevance, dedication, ownership, technology and tools, a systematic approach and coordination. Only workload was identified as coincident barriers across both externally and internally implementation processes. Implementation process specific barriers included turnover, coping with given responsibilities, staff variety, challenges in coordination, technology and tools, standardizations not aligned with work, extensive documentation, lack of knowledge sharing. CONCLUSION: This study provides understanding that some enablers and barriers are present in both externally and internally driven implementation processes, while other are more implementation process specific. Dedication, engagement, technology and tools are coinciding enablers which can be drawn upon in different implementation processes, while workload acted as the main barrier in both externally and internally driven implementation processes. This means that some enablers and barriers can be expected in implementation of QI programmes in nursing homes and home care services, while others require contextual understanding of their setting and work.