Uses of Electronic Health Record Data to Measure the Clinical Learning Environment of Graduate Medical Education Trainees: A Systematic Review.

PURPOSE: This study systematically reviews the uses of electronic health record (EHR) data to measure graduate medical education (GME) trainee competencies. METHOD: In January 2022, the authors conducted a systematic review of original research in MEDLINE from database start to December 31, 2021. The authors searched for articles that used the EHR as their data source and in which the individual GME trainee was the unit of observation and/or unit of analysis. The database query was intentionally broad because an initial survey of pertinent articles identified no unifying Medical Subject Heading terms. Articles were coded and clustered by theme and Accreditation Council for Graduate Medical Education (ACGME) core competency. RESULTS: The database search yielded 3,540 articles, of which 86 met the study inclusion criteria. Articles clustered into 16 themes, the largest of which were trainee condition experience (17 articles), work patterns (16 articles), and continuity of care (12 articles). Five of the ACGME core competencies were represented (patient care and procedural skills, practice-based learning and improvement, systems-based practice, medical knowledge, and professionalism). In addition, 25 articles assessed the clinical learning environment. CONCLUSIONS: This review identified 86 articles that used EHR data to measure individual GME trainee competencies, spanning 16 themes and 6 competencies and revealing marked between-trainee variation. The authors propose a digital learning cycle framework that arranges sequentially the uses of EHR data within the cycle of clinical experiential learning central to GME. Three technical components necessary to unlock the potential of EHR data to improve GME are described: measures, attribution, and visualization. Partnerships between GME programs and informatics departments will be pivotal in realizing this opportunity.

Leveraging natural language processing to augment structured social determinants of health data in the electronic health record.

OBJECTIVE: Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH information, detailing aspects such as status, severity, and temporality. This work has two primary objectives: (1) develop a natural language processing information extraction model to capture detailed SDOH information and (2) evaluate the information gain achieved by applying the SDOH extractor to clinical narratives and combining the extracted representations with existing structured data. MATERIALS AND METHODS: We developed a novel SDOH extractor using a deep learning entity and relation extraction architecture to characterize SDOH across various dimensions. In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225 089 patients and 430 406 notes with social history sections and compared the extracted SDOH information with existing structured data. RESULTS: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found extracted SDOH information complements existing structured data with 32% of homeless patients, 19% of current tobacco users, and 10% of drug users only having these health risk factors documented in the clinical narrative. CONCLUSIONS: Utilizing EHR data to identify SDOH health risk factors and social needs may improve patient care and outcomes. Semantic representations of text-encoded SDOH information can augment existing structured data, and this more comprehensive SDOH representation can assist health systems in identifying and addressing these social needs.

Integrating patient voices into the extraction of social determinants of health from clinical notes: ethical considerations and recommendations.

Identifying patients' social needs is a first critical step to address social determinants of health (SDoH)-the conditions in which people live, learn, work, and play that affect health. Addressing SDoH can improve health outcomes, population health, and health equity. Emerging SDoH reporting requirements call for health systems to implement efficient ways to identify and act on patients' social needs. Automatic extraction of SDoH from clinical notes within the electronic health record through natural language processing offers a promising approach. However, such automated SDoH systems could have unintended consequences for patients, related to stigma, privacy, confidentiality, and mistrust. Using Floridi et al's "AI4People" framework, we describe ethical considerations for system design and implementation that call attention to patient autonomy, beneficence, nonmaleficence, justice, and explicability. Based on our engagement of clinical and community champions in health equity work at University of Washington Medicine, we offer recommendations for integrating patient voices and needs into automated SDoH systems.

Screening for Thyroid Disorders Among Resistant Hypertension Patients: Are We Doing Enough?

Objective: To perform a quality assurance study assessing if hypo- and hyperthyroidism are appropriately screened for in patients with resistant hypertension.Design: Data was collected from patients diagnosed with resistant hypertension, defined as being on four or more different classes of anti-hypertensive medications. These patients were filtered to determine if thyroid stimulating hormone (TSH) measurement occurred within 90 days of the addition of a fourth medication class.Setting: Two internal medicine residency clinics in Pittsburgh, PA.Participants: Patients were selected who had a diagnosis of hypertension and were seen in clinic between January 1, 2018 and December 23, 2020.Methods: A single center retrospective review was performed.Results: A total of 1,125 patients were identified as having resistant hypertension. Of these, only 74 patients were found to have a TSH measurement taken within 90 days of having a fourth medication class prescribed. Seven TSH values were found to be abnormal with one patient being diagnosed with hyperthyroidism, demonstrating a screening rate of 6.6%. There were statistically significant differences in age, body mass index, and diastolic blood pressure in those screened versus not.Conclusions: Thyroid disease is under-screened as an etiology for resistant hypertension, particularly given the ease of diagnosis and reversibility of these conditions.

Novel Coronavirus Infection (COVID-19) Related Thrombotic and Bleeding Complications in Critically Ill Patients: Experience from an Academic Medical Center.

INTRODUCTION: Thrombosis and bleeding are recognized complications of the novel coronavirus infection (COVID-19), with a higher incidence described particularly in the critically ill. METHODS: A retrospective review of COVID-19 patients admitted to our intensive care units (ICU) between 1 January 2020 and 31 December 2020 was performed. Primary outcomes included clinically significant thrombotic and bleeding events (according to the ISTH definition) in the ICU. Secondary outcomes included mortality vis-a-vis the type of anticoagulation. RESULTS: The cohort included 144 consecutive COVID-19 patients with a median age of 64 years (IQR 54.5-75). The majority were male (85 (59.0%)) and Caucasian (90 (62.5%)) with a median BMI of 30.5 kg/m2 (IQR 25.7-36.1). The median APACHE score at admission to the ICU was 12.5 (IQR 9.5-22). The coagulation parameters at admission were a d-dimer level of 109.2 mg/mL, a platelet count of 217.5 k/mcl, and an INR of 1.4. The anticoagulation strategy at admission included prophylactic anticoagulation for 97 (67.4%) patients and therapeutic anticoagulation for 35 (24.3%) patients, while 12 (8.3%) patients received no anticoagulation. A total of 29 patients (20.1%) suffered from thrombotic or major bleeding complications. These included 17 thrombus events (11.8%)-8 while on prophylactic anticoagulation (7 regular dose and 1 intermediate dose) and 9 while on therapeutic anticoagulation (p-value = 0.02)-and 19 major bleeding events (13.2%) (4 on no anticoagulation, 7 on prophylactic (6 regular dose and 1 intermediate dose), and 8 on therapeutic anticoagulation (p-value = 0.02)). A higher thrombosis risk among patients who received remdesivir (18.8% vs. 5.3% (p-value = 0.01)) and convalescent serum (17.3% vs. 5.8% (p-value = 0.03%)) was noted, but no association with baseline characteristics (age, sex, race, comorbidity), coagulation parameters, or treatments (steroids, mechanical ventilation) could be identified. There were 10 pulmonary embolism cases (6.9%). A total of 99 (68.8%) patients were intubated, and 66 patients (45.8%) died. Mortality was higher, but not statistically significant, in patients with thrombotic or bleeding complications-58.6% vs. 42.6% (p-value = 0.12)-and higher in the bleeding (21.2%) vs. thrombus group (12.1%), p-value = 0.06. It did not significantly differ according to the type of anticoagulation used or the coagulation parameters. CONCLUSIONS: This study describes a high incidence of thrombotic and bleeding complications among critically ill COVID-19 patients. The findings of thrombotic events in patients on anticoagulation and major bleeding events in patients on no or prophylactic anticoagulation pose a challenging clinical dilemma in the issue of anticoagulation for COVID-19 patients. The questions raised by this study and previous literature on this subject demonstrate that the role of anticoagulation in COVID-19 patients is worthy of further investigation.

Characteristics and Outcomes of COVID-19 Patients Admitted to Intensive Care Units in a Large Health System in Western Pennsylvania.

Importance Despite growing literature, there is still limited understanding of factors that can predict outcomes in coronavirus disease 2019 (COVID-19) patients who require intensive care. Objective To evaluate the characteristics of COVID-19 patients admitted to the intensive care unit (ICU) and identify their associations with outcomes. Background There are limited data on the outcomes in COVID-19 patients in Pennsylvania. Design Retrospective study Setting Intensive care units in an academic health system in Western Pennsylvania. Participants Patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed COVID-19 admitted to ICUs as direct admission or transfers from regular floors between March 1, 2020, and April 30, 2020. Main outcome(s) and measure(s) The primary outcome was inpatient mortality. Secondary outcomes included complications during ICU stay, hospital length of stay, discharge disposition, and the need for oxygen at discharge. Categorical variables are described as frequencies and continuous variables as median with interquartile range (IQR). Regression modeling was used to identify the predictors of inpatient mortality in these patients. P-value <0.05 was considered statistically significant. Analysis was performed using Stata version 15.1 (StataCorp, College Station, Texas). Results The cohort included 58 consecutive patients, with a median age of 62 years (IQR 54-73), 63.8% of which were male. On presentation, constitutional symptoms were the most common (91.4%), followed by lower respiratory tract symptoms (87.9%). Tachypnea (65.5%) and hypoxia (67.2%) were the most common abnormal vital signs at presentation. Common comorbidities were cardiovascular disease (74.1%), obesity (53.5%), and diabetes (39.7%). The median Acute Physiology and Chronic Health Evaluation (APACHE) score on admission to ICU was 11 (IQR 8.5-17.5). The major complications included acute respiratory distress syndrome (ARDS) 50.0%, shock 41.4%, and acute kidney injury 41.4%. The proportion of patients who underwent mechanical ventilation, required vasopressors, or were on renal replacement therapy were 58.6%, 41.4%, and 10.3%, respectively. Overall mortality was 32.8%. Age, Charlson-comorbidity index, tachypnea, lymphopenia at presentation, high APACHE score, shock, ARDS, mechanical ventilation, and steroid use were significantly associated with mortality. Of the patients who survived their ICU stay, 63.2% were discharged home and 44.7% had a new oxygen requirement at discharge. Conclusion and relevance Our study reports high mortality in COVID-19 patients requiring ICU care in Western Pennsylvania. Identifying factors associated with poor prognosis could help risk-stratify these patients. Prospective studies are needed to assess whether early risk stratification and triaging result in improved outcomes.