Open notes invite patients and families to read ambulatory visit notes through the patient portal. Little is known about the extent to which they identify and speak up about perceived errors. ...Understanding the barriers to speaking up can inform quality improvements.
To describe patient and family attitudes, experiences, and barriers related to speaking up about perceived serious note errors.
Mixed method analysis of a 2016 electronic survey of patients and families at 2 northeast US academic medical centers. Participants had active patient portal accounts and at least 1 note available in the preceding 12 months.
6913 adult patients (response rate 28%) and 3672 pediatric families (response rate 17%) completed the survey. In total, 8724/9392 (93%) agreed that reporting mistakes improves patient safety. Among 8648 participants who read a note, 1434 (17%) perceived ≥1 mistake. 627/1434 (44%) reported the mistake was serious and 342/627 (56%) contacted their provider. Participants who self-identified as Black or African American, Asian, "other," or "multiple" race(s) (OR 0.50; 95% CI (0.26,0.97)) or those who reported poorer health (OR 0.58; 95% CI (0.37,0.90)) were each less likely to speak up than white or healthier respondents, respectively. The most common barriers to speaking up were not knowing how to report a mistake (61%) and avoiding perception as a "troublemaker" (34%). Qualitative analysis of 476 free-text suggestions revealed practical recommendations and proposed innovations for partnering with patients and families.
About half of patients and families who perceived a serious mistake in their notes reported it. Identified barriers demonstrate modifiable issues such as establishing clear mechanisms for reporting and more challenging issues such as creating a supportive culture. Respondents offered new ideas for engaging patients and families in improving note accuracy.
Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.
We trained logistic ...regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment.
The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user.
Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings.
EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.
Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an ...individualized approach to risk assessment and prophylaxis. We conducted electronic surveys asking clinician and healthcare informaticians about their perspectives on AI/ML for VTE prevention and management. Of 101 respondents to the informatician survey, most were 40 years or older, male, clinicians and data scientists, and had performed research on AI/ML. Of the 607 US-based respondents to the clinician survey, most were 40 years or younger, female, physicians, and had never used AI to inform clinical practice. Most informaticians agreed that AI/ML can be used to manage VTE (56.0%). Over one-third were concerned that clinicians would not use the technology (38.9%), but the majority of clinicians believed that AI/ML probably or definitely can help with VTE prevention (70.1%). The most common concern in both groups was a perceived lack of transparency (informaticians 54.4%; clinicians 25.4%). These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation.
This post hoc analysis of PIONEER I and II randomized clinical trials assesses whether receiving adalimumab is associated with decreased hematologic abnormalities and increased clinical improvement ...in patients with hidradenitis suppurativa.
Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive ...patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking.
To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models.
A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models).
A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination.
The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.
This retrospective review evaluated the causes of severe eosinophilia (≥5000 eosinophils/L). Higher eosinophilia levels are more likely to cause tissue damage and may reflect disease severity.
We ...reviewed 193 cases of patients seen at Beth Israel Deaconess Medical Center in Boston, Massachusetts, and at the University of Vermont Medical Center in Burlington, Vermont, between January 2015 to May 2020 who had a peak absolute eosinophil count of at least 5000/μL.
Thirty-nine percent of cases were attributable to a hematologic or oncologic cause. These cases had the highest mean peak absolute eosinophil count at 11,698/μL. Twenty percent of cases were secondary to drug reactions, of which 90% took place in an inpatient setting. Three percent of cases were from helminthic infection, the majority of which were in returning travelers.
In our region of study, hematologic and oncologic cases are important causes of severe eosinophilia, drug reactions are a common etiology in the inpatient setting, and infections are a rare cause.
Background
COVID‐19 infection delays therapy and in‐person evaluation for oncology patients, but clinic clearance criteria are not clearly defined.
Methods
We conducted a retrospective review of ...oncology patients with COVID‐19 at a tertiary care center during the Delta and Omicron waves and compared clearance strategies.
Results
Median clearance by two consecutive negative tests was 32.0 days (Interquartile Range IQR 22.0–42.5, n = 153) and was prolonged in hematologic malignancy versus solid tumors (35.0 days for hematologic malignancy, 27.5 days for solid tumors, p = 0.01) and in patients receiving B‐cell depletion versus other therapies. Median clearance by single negative test was reduced to 23.0 days (IQR 16.0–33.0), with recurrent positive rate 25.4% in hematologic malignancy versus 10.6% in solid tumors (p = 0.02). Clearance by a predefined waiting period required 41 days until an 80% negative rate.
Conclusions
COVID‐19 clearance remains prolonged in oncology patients. Single‐negative test clearance can balance delays in care with risk of infection in patients with solid tumors.
We conducted a retrospective review of oncology patients with COVID‐19 infection at a single tertiary care center during the Delta and Omicron waves and compared clearance strategies. Median clearance as defined by two consecutive negative tests was 32 days, and was prolonged in hematologic malignancy and in patients who received B‐cell depletion. Single‐negative test clearance showed high recurrence in patients with hematologic malignancy but may be appropriate for patients with solid tumors.
Spontaneous tumor lysis syndrome (STLS) secondary to metastatic pancreatic adenocarcinoma is a rare clinical phenomenon. An 86-year-old woman with a history of pancreatic cysts presented to the ...emergency department with progressive fatigue, transaminitis, elevated lactate dehydrogenase, and acute kidney injury of unclear etiology. Abdominal imaging and celiac lymph node biopsy were consistent with metastatic pancreatic adenocarcinoma. Her clinical status deteriorated requiring intensive care unit transfer, and her laboratory results were found to be consistent with STLS. Despite treatment, she entered multisystem organ failure and died shortly after. This case adds to the literature of STLS in pancreatic adenocarcinomas.