Bed management considering bed-blocking and elective patient admissions using simulation optimisation

Background: My tertiary-care hospital is a 750-bed hospital with only 17 airborne infection isolation room (AIIR) and negative-pressure rooms to isolate patients who have been diagnosed or are suspected with prevalent diseases like tuberculosis, measles, and chickenpox. On the other hand, only 14 single-patient isolation rooms are available to isolate patients with multidrug-resistant organisms (MDROs) such as CRE (carbapenum-resistant Enterobacter) or colistin-resistant MDROs. Due to the limited number of isolation rooms, the average number of hours to isolate infected patients was ~20 hours, which ultimately directly placed healthcare workers (HCWs) at risk of exposure to infected patients. Methods: Plan-Do-Study-Act (PDSA) quality improvement methodology was utilized to decrease the average number of hours to isolate infected patients and to reduce the exposure of HCWs to communicable diseases. A detailed analysis were performed to identify root causes and their effects at multiple levels. A multidisciplinary team implemented several strategies: coordination with information and technology team to place isolation alerts in the charting system; screening flyers and questions at emergency department triage; close coordination with admission and bed management office; daily morning and evening rounds by infection preventionists in the emergency department; daily morning meeting with microbiology and bed management office to intervene immediately to isolate patients in a timely way; infection preventionist on-call system (24 hours per day, 7 days per week) to provide recommendations for patient placement and cohorting of infected patients wherever possible. Results: In 1 year, a significant reduction was achieved in the number of hours to isolate infected patients, from 20 hours to 4 hours. As a result, HCW exposures to communicable diseases also decreased from 6.7 to 1.5; HCW exposures to TB decreased from 6.0 to 1.9; exposures measles decreased from 4.75 to 1.5; and exposures chickenpox decreased from 7.3 to 1.0. Significant reductions in cost incurred by the organization for the employees who were exposed to these diseases for postexposure prophylaxis also decreased, from ~Rs. 290,000 (~US$3,000) to ~Rs. 59,520 (~US$600). Conclusions: This multidisciplinary approach achieved infection prevention improvements and enhanced patient and HCW safety in a limited-resource setting.Funding: NoneDisclosures: None

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Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.1344 ◽

2020 ◽

pp. 1-7

Author(s):

Stephanie M. Cabral ◽

Katherine E. Goodman ◽

Natalia Blanco ◽

Surbhi Leekha ◽

Larry S. Magder ◽

...

Keyword(s):

Risk Factors ◽

Risk Factor ◽

Risk Adjustment ◽

Community Hospital ◽

Significant Risk ◽

Significant Risk Factor ◽

Bivariate Analysis ◽

Patient Admissions ◽

Clostridioides Difficile ◽

Risk Adjustment Model

Abstract Objective: To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment. Patients: All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018. Methods: Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities. Results: At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change. Conclusions: Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

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