Improving management of hyponatraemia by increasing urine testing in the emergency department.

Hyponatraemia on hospital admission is associated with increased length of stay, healthcare expenditures and mortality. Urine studies collected before fluid or diuretic administration are essential to diagnose the underlying cause of hyponatraemia, thereby empowering admitting teams to employ the appropriate treatment. A multidisciplinary quality improvement (QI) team led by internal medicine residents performed a QI project from July 2020 through June 2021 to increase the rate of urine studies collected before fluid or diuretic administration in the emergency department (ED) in patients admitted with moderate to severe hyponatraemia. We implemented two plan-do-study-act (PDSA) cycles to address this goal. In PDSA Cycle #1, we displayed an educational poster in employee areas of the ED and met with nursing staff at their monthly meetings to communicate the project and answer questions. We also obtained agreement from ED attending physicians and nursing leaders to support the project. In PDSA Cycle #2, we implemented a structural change in the nursing triage process to issue every patient who qualified for bloodwork with a urine specimen container labelled with a medical record number on registration so that the patient could provide a sample at any point, including while in the waiting area. After PDSA Cycle #1, urine specimen collection increased from 34.5% to 57.5%. After PDSA Cycle #2, this increased further to 59%. We conclude that a combination of educational and structural changes led to a significant increase in urine specimen collection before fluid or diuretic administration among patients presenting with moderate-to-severe hyponatraemia in the ED.

Reduction in Health Care Facility-Onset Clostridioides difficile Infection: A Quality Improvement Initiative.

OBJECTIVE: To reduce health care facility-onset (HCFO) Clostridioides difficile infection (CDI) incidence by improving diagnostic stewardship and reducing the inappropriate testing of C difficile assays. PATIENTS AND METHODS: A multidisciplinary team conducted a quality improvement initiative from January 1, 2020, through March 31, 2021. Clostridioides difficile infection and inappropriate testing were identified via electronic health records using predefined criteria related to stool quantity/caliber, confounding medications, and laboratory data. An intervention bundle was designed including (1) provider education, (2) implementation of an appropriate testing algorithm, (3) expert review of C difficile orders, and (4) batch testing of assays to facilitate review and cancellation if inappropriate. RESULTS: Compared with a baseline period from January to September 2020, implementation of our intervention bundle from December 2020 to March 2021 resulted in an 83.6% reduction in inappropriate orders tested and a 41.7% reduction in HCFO CDI incidence. CONCLUSION: A novel prevention bundle improved C difficile diagnostic stewardship and HCFO CDI incidence by reducing testing of inappropriate orders. Such initiatives targeting HCFO CDI may positively affect patient safety and hospital reimbursement.

Hemolytic Uremic Syndrome With Severe Neurologic Complications in an Adult.

The case of a female presenting with Shiga toxin-producing Escherichia coli and hemolytic uremic syndrome highlights a severe neurologic complication that can be associated with these conditions.

Disparities in patient record completeness with respect to the health care utilization project.

Patient data completeness is an important characteristic in maintaining accurate health records and providing the highest standard of care. Furthermore, finding discrepancies in care based on different subpopulation parameters is important to identify areas of underlying systemic issues in order to address concerns and alleviate those discrepancies. In this project, the investigators use the Data Completeness Analysis Package to find trends in patient record completeness using Healthcare Cost and Utilization Project's State Inpatient Database for the state of Florida, specifically focusing on finding discrepancies among subpopulations along the variables of age, race, and gender. The results from testing Data Completeness Analysis Package with State Inpatient Database show a variety of patterns that provides insights to the health care delivery in Florida.

A New Paradigm to Analyze Data Completeness of Patient Data.

BACKGROUND: There is a need to develop a tool that will measure data completeness of patient records using sophisticated statistical metrics. Patient data integrity is important in providing timely and appropriate care. Completeness is an important step, with an emphasis on understanding the complex relationships between data fields and their relative importance in delivering care. This tool will not only help understand where data problems are but also help uncover the underlying issues behind them. OBJECTIVES: Develop a tool that can be used alongside a variety of health care database software packages to determine the completeness of individual patient records as well as aggregate patient records across health care centers and subpopulations. METHODS: The methodology of this project is encapsulated within the Data Completeness Analysis Package (DCAP) tool, with the major components including concept mapping, CSV parsing, and statistical analysis. RESULTS: The results from testing DCAP with Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) data show that this tool is successful in identifying relative data completeness at the patient, subpopulation, and database levels. These results also solidify a need for further analysis and call for hypothesis driven research to find underlying causes for data incompleteness. CONCLUSION: DCAP examines patient records and generates statistics that can be used to determine the completeness of individual patient data as well as the general thoroughness of record keeping in a medical database. DCAP uses a component that is customized to the settings of the software package used for storing patient data as well as a Comma Separated Values (CSV) file parser to determine the appropriate measurements. DCAP itself is assessed through a proof of concept exercise using hypothetical data as well as available HCUP SID patient data.