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Abstract Number: 96
IDENTIFICATION AND ANALYSIS OF CONTEXT-SPECIFIC STIGMATIZING LANGUAGE IN CLINICAL NOTES
SHM Converge 2023
Background: Stigmatizing language in clinical notes can negatively impact physician attitudes, propagate bias, affect prescribing behaviors, and exacerbate healthcare disparities, yet remains prevalent even in the Open Notes era. Prior analyses of stigmatizing terms in clinical notes are limited by the lack of context in which terms are used and multiple meanings of certain words [...]
Abstract Number: 178
ADMINISTRATIVE CODING VS LABORATORY DIAGNOSIS OF INPATIENT HYPOGLYCEMIA
SHM Converge 2023
Background: Hypoglycemia is common and potentially life-threatening for diabetic patients, often iatrogenic from diabetes treatments. It is important to be able to accurately study rates of hypoglycemia when evaluating the inpatient treatment of diabetes at a systems-level. Discharge diagnosis codes show promise as a tool in the surveillance of hypoglycemic events in large administrative databases [...]
Abstract Number: 237
REDUCTION OF UNNECESSARY MAGNESIUM LABORATORY TESTING AT STANFORD MEDICAL CENTER
SHM Converge 2023
Background: Lab testing is critical for clinical decision-making and used throughout healthcare systems. Overuse of lab testing is known to be commonplace and contributes to shortages in lab testing materials. While practices exist at medical institutions that aim to limit lab test overuse, it is unclear which interventions are effective. Methods: We aimed to reduce [...]
Abstract Number: 253
A NOVEL ELECTRONIC TOOL FOR MALNUTRITION DOCUMENTATION
SHM Converge 2023
Background: Many hospitalized adults arrive with pre-existing malnutrition, which is associated with high risk of adverse outcomes. As such, malnutrition is a common variable in cost and quality risk adjustment models based on hospital discharge diagnosis codes. Hospital dietitians assess patients for malnutrition and generally document the details of their evaluations. However, coding professionals cannot [...]
Abstract Number: 394
REAL-TIME PERFORMANCE OF A MACHINE-LEARNING MODEL FOR ADMISSION PREDICTION
SHM Converge 2023
Background: To mitigate COVID-19 related hospital crowding, our hospital internal medicine (HIM) department collaborated with our emergency department (ED) to launch a service in which an HIM team was placed in the ED to assist with triage. The goal was to expedite patients with a clear need for admission, while diverting others who could be [...]
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