Journal for Healthcare Quality最新文献

Abstract: Untreated obstructive sleep apnea (OSA) is associated with increased cardiovascular morbidity and mortality, warranting enhanced awareness, screening, and action among healthcare providers to optimize patient outcomes. Attempting to address the potential under detection of OSA, this quality improvement project implemented the STOP-Bang Sleep Apnea Questionnaire at a cardiology clinic, with the goals to stratify risk for OSA in 100% of patients and increase provider referral of high risk patients for sleep studies. The setting was an outpatient cardiology clinic in Pensacola, Florida. The sample included new and existing patients 18 years or older who had one or more of the following diagnoses: hypertension, heart failure, coronary artery disease, atrial fibrillation, or arrhythmia. Methods involved collecting and comparing preintervention sleep study referral rate data with 6-week STOP-Bang questionnaire implementation data. Results demonstrated a 65% (279 of 428 patients seen) screening implementation rate and a modest but statistically significant increase in the overall referral rate from 2.6% preintervention to 5.1% postintervention ( p = .040). Overall, standard use of the STOP-Bang questionnaire may enable higher detection and referral of OSA.

Abstract: Although well-accepted clinical practice guidelines exist for the diagnosis of prosthetic joint infection (PJI), little is known about the quality of diagnosis for PJI. The identification of quality gaps in the diagnosis of PJI would facilitate the development of care structures and processes to shorten time to diagnosis and reduce the significant morbidity, mortality, and economic burden associated with this condition. Hence, we sought to develop valid clinical quality measures to improve the timeliness and accuracy of PJI diagnosis. We convened a nine-member multidisciplinary national panel of PJI experts including orthopedic surgeons, infectious disease specialists, an emergency medicine physician, and a patient previously treated for PJI to review, discuss, and rate the validity of proposed measures using a modification of the RAND-UCLA appropriateness method. In total, 57 permutations of six proposed measures were rated. Populations considered to be at high enough risk for PJI that certain care processes should always be performed were identified by the panel. Among the proposed quality measures, the panel rated five as valid. These novel clinical quality measures could provide insight into care gaps in the diagnosis of PJI.

Objective: To explore the perceived effects of COVID-19 on MARQUIS toolkit implementation and sustainability, challenges faced by hospitals in sustaining medication reconciliation efforts, and the strategies used to mitigate the negative effects of the pandemic.

Data sources and study settings: Primary qualitative data were extracted from a Web-based survey. Data were collected from hospitals that participated in MARQUIS2 ( n = 18) and the MARQUIS Collaborative ( n = 5).

Study design: A qualitative, cross-sectional study was conducted.

Data collection/data extraction: Qualitative data were extracted from a Research Electronic Data Capture survey databased and uploaded into an Excel data analysis template. Two coders independently coded the data with a third coder resolving discrepancies.

Principal findings: Thirty-one team members participated, including pharmacists ( n = 20; 65%), physicians ( n = 9; 29%), or quality-improvement (QI) specialists ( n = 2; 6%) with expertise in medication reconciliation (MedRec) (14; 45%) or QI (10; 32%). Organizational resources were limited, including funding, staffing, and access to pharmacy students. To support program continuation, hospitals reallocated staff and used new MedRec order sets. Telemedicine, workflow adaptations, leadership support, QI team involvement, and ongoing audits and feedback promoted toolkit sustainability.

Conclusions: COVID-19 affected the capacity of hospitals to sustain the MARQUIS toolkit. However, hospitals adapted various strategies to sustain the toolkit.

Background: Many medications are low-risk but must undergo the same ordering process as high-risk medications in the inpatient setting. Nurses identify the need for supportive medications and notify providers. An order panel and policy were developed to allow nurses to order low-risk, supportive medications.

Purpose: The aim of this study was to increase order panel utilization from a 6% to a goal of 15%.

Methods: This was a quality improvement study at a 1000-bed academic medical center. Five plan-do-study-act (PDSA) cycles were implemented. The primary end point was order panel utilization, and secondary end points were individual nursing unit utilization and the number of orders for each medication on the panel.

Results: After each PDSA cycle, order panel utilization improved to 7.8%, 13.2%, 7.5%, 10.2%, and 10.6%, respectively. The units using the order panel most often were general medicine (n = 95, 28%), medical intensive care (n = 71, 21%), and inpatient oncology (n = 40, 12%). The medication most frequently ordered was lanolin alcohols-mineral oil with petrolatum (Eucerin) cream (n = 220, 28%).

Conclusions: Order panel utilization improved from a baseline of 6% to an average of 9.9%. Increasing awareness of the order panel and adding medications will contribute to improvement in order panel utilization in the long-term.

Level of evidence: 4, Descriptive quality improvement project.

Abstract: No previous works have analyzed whether the order in which surgical teams see patients on morning rounds affects discharge efficiency at teaching hospitals. We obtained perioperative urologic surgery timing data at our academic institution from 2014 to 2019. We limited the analysis to routine postoperative day 1 discharges. Univariate and multivariate analyses were performed to determine whether various hospital and patient factors were associated with discharge timing. We analyzed 1,494 patients. Average discharge order time was 11:22 a.m. and hospital discharge 1:24 p.m. Univariate regression revealed earlier discharge order time for patients seen later in rounds by 4 minutes per sequential room cluster (p = .013) and by 12 minutes per cluster when excluding short-stay patients. Multivariate analysis revealed discharge order placement did not vary significantly by rounding order. However, time of hospital discharge did (p < .001), likely due to speed of discharge in the designated short-stay units. Attending physician was the most consistent predictor in variations of discharge timing, with statistical significance across all measured outcomes. Patients seen later in rounding progression received earlier discharge orders, but this relationship does not remain in multivariate modeling or translate to earlier discharge. These findings have helped guide quality improvement efforts focused on discharge efficiency.