Comparative Effectiveness of Risk-adjusted Cumulative Sum and Periodic Evaluation for Monitoring Hospital Perioperative Mortality.

BACKGROUND: National surgical quality improvement (QI) programs use periodic, risk-adjusted evaluation to identify hospitals with higher than expected perioperative mortality. Rapid, accurate identification of poorly performing hospitals is critical for avoiding potentially preventable mortality and represents an opportunity to enhance QI efforts. METHODS: Hospital-level analysis using Veterans Affairs (VA) Surgical Quality Improvement Program data (2011-2016) to compare identification of hospitals with excess, risk-adjusted 30-day mortality using observed-to-expected (O-E) ratios (ie, current gold standard) and cumulative sum (CUSUM) with V-mask. Various V-mask slopes and radii were evaluated-slope of 2.5 and radius of 1.0 was used as the base case. RESULTS: Hospitals identified by CUSUM and quarterly O-E were identified midway into a quarter [median 47 days; interquartile range (IQR): 24-61 days before quarter end] translating to a median of 129 (IQR: 60-187) surgical cases and 368 (IQR: 145-681) postoperative inpatient days occurring after a CUSUM signal, but before the quarter end. At hospitals identified by CUSUM but not O-E, a median of 2 deaths within a median of 5 days triggered a signal. In some cases, these clusters extended beyond CUSUM identification date with as many as 8 deaths undetected using O-E. Sensitivity and negative predictive values for CUSUM relative to O-E were 71.9% (95% confidence interval: 66.2%-77.1%) and 95.5% (94.4%-96.4%), respectively. CONCLUSIONS: CUSUM evaluation identifies hospitals with clusters of mortality in excess of expected more rapidly than periodic analysis. CUSUM represents an analytic tool national QI programs could utilize to provide participating hospitals with data that could facilitate more proactive implementation of local interventions to help reduce potentially avoidable perioperative mortality.

Relationship Between Perioperative Outcomes Used for Profiling Hospital Noncardiac Surgical Quality.

BACKGROUND: Risk-adjusted morbidity and mortality are commonly used by national surgical quality improvement (QI) programs to measure hospital-level surgical quality. However, the degree of hospital-level correlation between mortality, morbidity, and other perioperative outcomes (like reoperation) collected by contemporary surgical QI programs has not been well-characterized. MATERIALS AND METHODS: Veterans Affairs (VA) Surgical Quality Improvement Program (VASQIP) data (2015-2016) were used to evaluate hospital-level correlation in performance between risk-adjusted 30-d mortality, morbidity, major morbidity, reoperation, and 2 composite outcomes (1- mortality, major morbidity, or reoperation; 2- mortality or major morbidity) after noncardiac surgery. Correlation between outcomes rates was evaluated using Pearson's correlation coefficient. Correlation between hospital risk-adjusted performance rankings was evaluated using Spearman's correlation. RESULTS: Based on a median of 232 [IQR 95-331] quarterly surgical cases abstracted by VASQIP, statistical power for identifying 30-d mortality outlier hospitals was estimated between 3.3% for an observed-to-expected ratio of 1.1 and 45.7% for 3.0. Among 230,247 Veterans who underwent a noncardiac operation at 137 VA hospitals, there were moderate hospital-level correlations between various risk-adjusted outcome rates (highest r = 0.40, mortality and composite 1; lowest r = 0.32, mortality and morbidity). When hospitals were ranked based on performance, there was low-to-moderate correlation between rankings on the various outcomes (highest ρ = 0.47, mortality and composite 1; lowest ρ = 0.37, mortality and major morbidity). CONCLUSIONS: Modest hospital-level correlations between perioperative outcomes suggests it may be difficult to identify high (or low) performing hospitals using a single measure. Additionally, while composites of currently measured outcomes may be an efficient way to improve analytic sample size (relative to evaluations based on any individual outcome), further work is needed to understand whether they provide a more robust and accurate picture of hospital quality or whether evaluating performance across a portfolio of individual measures is most effective for driving QI.

Case Sampling for Evaluating Hospital Postoperative Morbidity in US Surgical Quality Improvement Programs.

Importance: US surgical quality improvement (QI) programs use data from a systematic sample of surgical cases, rather than universal review of all cases, to assess and compare risk-adjusted hospital postoperative complication rates. Given decreasing postoperative complication rates over time and the types of cases eligible for abstraction, it is unclear whether case sampling is robust for identifying hospitals with higher than expected complications. Objective: To compare the assessment of hospital 30-day complication rates derived from sampling strategy used by some US surgical QI programs relative to universal review of all cases. Design, Setting, and Participants: This US hospital-level analysis took place from January 1, 2016, through September 30, 2020. Data analysis was performed from July 1, 2022, through December 21, 2022. Quarterly, risk-adjusted, 30-day complication observed to expected (O-E) ratios were calculated for each hospital using the sample (n = 502 730) and universal review (n = 1 725 364). Outlier hospitals (ie, those with higher than expected mortality) were identified using an O-E ratio significantly greater than 1.0. Patients 18 years and older who underwent a noncardiac operation at US Department of Veterans Affairs (VA) hospitals with a record in the VA Surgical Quality Improvement Program (systematic sample) and the VA Corporate Data Warehouse surgical domain (100% of surgical cases) were included. Main Outcome Measure: Thirty-day complications. Results: Most patients in both the representative sample and the universal sample were men (90.2% vs 91.2%) and White (74.7% vs 74.5%). Overall, 30-day complication rates were 7.6% and 5.3% for the sample and universal review cohorts, respectively (P < .001). Over 2145 hospital quarters of data, hospitals were identified as an outlier in 15.0% of quarters using the sample and 18.2% with universal review. Average hospital quarterly complication rates were 4.7%, 7.2%, and 7.4% for outliers identified using the sample only, universal review only, and concurrent identification in both data sources, respectively. For nonsampled cases, average hospital quarterly complication rates were 7.0% at outliers and 4.4% at nonoutliers. Among outlier hospital quarters in the sample, 54.2% were concurrently identified with universal review. For those identified with universal review, 44.6% were concurrently identified using the sample. Conclusion: In this observational study, case sampling identified less than half of hospitals with excess risk-adjusted postoperative complication rates. Future work is needed to ascertain how to best use currently collected data and whether alternative data collection strategies may be needed to better inform local QI efforts.

An innovative approach to aligning healthcare with what matters most to patients: A hybrid type 1 trial protocol of patient priorities care for older adults with multiple chronic conditions.

BACKGROUND: Providing healthcare for older adults with multiple chronic conditions (MCC) is challenging. Polypharmacy and complex treatment plans can lead to high treatment burden and risk for adverse events. For clinicians, managing the complexities of patients with MCC leaves little room to identify what matters and align care options with patients' health priorities. New care approaches are needed to navigate these challenges. In this clinical trial, we evaluate implementation and effectiveness outcomes of an innovative, structured, patient-centered care approach (Patient Priorities Care; PPC) for reducing treatment burden and aligning health care decisions with the health priorities of older adults with MCC. METHODS: This is a multisite, assessor-blind, two-arm, parallel hybrid type 1 randomized controlled trial. We are enrolling 396 older (65+) Veterans with MCC who receive primary care at the Veterans Affairs Medical Center. Veterans are randomly assigned to either PPC or usual care. In the PPC arm, Veterans have a brief telephone call with a study facilitator to identify their personal health priorities. Then, primary care providers use this information to align healthcare with Veteran priorities during their established clinic appointments. Data are collected at baseline and 4-month follow up to assess for changes in treatment burden and use of home and community services. Formative and summative evaluations are also collected to assess for implementation outcomes according to Proctor's implementation framework. CONCLUSIONS: This work has the potential to significantly improve the standard of care by personalizing healthcare and helping patients achieve what is most important to them.

Case Sampling vs Universal Review for Evaluating Hospital Postoperative Mortality in US Surgical Quality Improvement Programs.

Importance: Representative surgical case sampling, rather than universal review, is used by US Department of Veterans Affairs (VA) and private-sector national surgical quality improvement (QI) programs to assess program performance and to inform local QI and performance improvement efforts. However, it is unclear whether case sampling is robust for identifying hospitals with safety or quality concerns. Objective: To evaluate whether the sampling strategy used by several national surgical QI programs provides hospitals with data that are representative of their overall quality and safety, as measured by 30-day mortality. Design, Setting, and Participants: This comparative effectiveness study was a national, hospital-level analysis of data from adult patients (aged ≥18 years) who underwent noncardiac surgery at a VA hospital between January 1, 2016, and September 30, 2020. Data were obtained from the VA Surgical Quality Improvement Program (representative sample) and the VA Corporate Data Warehouse surgical domain (100% of surgical cases). Data analysis was performed from July 1 to December 21, 2022. Main Outcomes and Measures: The primary outcome was postoperative 30-day mortality. Quarterly, risk-adjusted, 30-day mortality observed-to-expected (O-E) ratios were calculated separately for each hospital using the sample and universal review cohorts. Outlier hospitals (ie, those with higher-than-expected mortality) were identified using an O-E ratio significantly greater than 1.0. Results: In this study of data from 113 US Department of Veterans Affairs hospitals, the sample cohort comprised 502 953 surgical cases and the universal review cohort comprised 1 703 140. The majority of patients in both the representative sample and the universal sample were men (90.2% vs 91.1%) and were White (74.7% vs 74.5%). Overall, 30-day mortality was 0.8% and 0.6% for the sample and universal review cohorts, respectively (P < .001). Over 2145 quarters of data, hospitals were identified as an outlier in 11.7% of quarters with sampling and in 13.2% with universal review. Average hospital quarterly 30-day mortality rates were 0.4%, 0.8%, and 0.9% for outlier hospitals identified using the sample only, universal review only, and concurrent identification in both data sources, respectively. For nonsampled cases, average hospital quarterly 30-day mortality rates were 1.0% at outlier hospitals and 0.5% at nonoutliers. Among outlier hospital quarters in the sample, 47.4% were concurrently identified with universal review. For those identified with universal review, 42.1% were concurrently identified using the sample. Conclusions and Relevance: In this national, hospital-level study, sampling strategies employed by national surgical QI programs identified less than half of hospitals with higher-than-expected perioperative mortality. These findings suggest that sampling may not adequately represent overall surgical program performance or provide stakeholders with the data necessary to inform QI efforts.

Continuous Cloud-Based Early Warning Score Surveillance to Improve the Safety of Acutely Ill Hospitalized Patients.

INTRODUCTION: This study sought to evaluate the impact of changes made to the process of continually screening hospitalized patients for decompensation. METHODS: Patients admitted to hospital wards were screened using a cloud-based early warning score (modified National Early Warning Score [mNEWS]). Patient with mNEWS ≥7 triggered a structured response. Outcomes of this quality improvement study during the intervention period from February through August 2018 (1741 patients) were compared with a control population (1,610 patients) during the same months of 2017. RESULTS: The intervention group improved the time to the first lactate order within 24 hours of mNEWS ≥7 (p < .001), the primary outcome, compared with the control group. There was no significant improvement in time to intensive care unit (ICU) transfer, ICU length of stay (LOS), or hospital mortality. Among patients with a lactate ordered within 24 hours, there was a 47% reduction of in-hospital mortality (odds ratio 0.53, 95% confidence interval 0.3-0.89, p = .02) and a 4.7 day reduction in hospital LOS (p < .001) for intervention versus control cohorts. CONCLUSIONS: Cloud-based electronic surveillance can result in earlier detection of clinical decompensation. This intervention resulted in lower hospital LOS and mortality among patients with early detection of and intervention for clinical decompensation.

Using a Logic Model to Design and Evaluate a Quality Improvement Leadership Course.

Strong leadership is an essential factor in the success of quality improvement (QI) initiatives that generate and sustain improvements in patient outcomes. Notably, there is a rising need for frontline clinicians, who are often charged with leading QI efforts, to receive training in blended QI and leadership methods and skills. The Leading Healthcare Improvement (LHI) course is a longitudinal leadership course embedded within the Department of Veterans Affairs Quality Scholars (VAQS) program, a multisite interprofessional QI fellowship program. The LHI course was developed to provide frontline clinicians who are emerging QI leaders with the skills to lead and advance improvement efforts at their institutions. It consists of eight 60-minute online sessions and was implemented and delivered to a cohort of interprofessional fellows at 9 sites during the 2017-2018 academic year.This article describes the use of a logic model as a framework to guide the planning, implementation, and evaluation of the LHI course. The authors developed 5 logic model components: inputs, activities, outputs, short-term outcomes, and long-term outcomes. They defined the short-term outcomes using feedback from fellows and an evaluation of the fellows' abstract submissions to the VAQS Summer Institute. Submissions were reviewed to identify how fellows applied the LHI course concepts to QI projects at their respective sites. The authors also collected preliminary impact data from fellows to determine long-term outcomes.Finally, they used the logic model to inform changes to the LHI course based on the evaluation data they collected and developed plans to measure the impact of the course on learners, patients, and the health care system. The authors conclude with lessons learned to guide others who are implementing similar QI efforts.

What do patients say about their experience with urinary catheters and peripherally inserted central catheters?

BACKGROUND: Little is known about the patient experience with urinary catheters or peripherally inserted central catheters (PICCs). We sought to better understand patient perspectives on having a urinary catheter or a PICC by reviewing open-ended comments made by patients about having either of these 2 devices. METHODS: As part of a larger study, we asked patients about certain catheter-related complications at the time of catheter placement and on days 14, 30, and 70 (PICCs only). In this larger project, we performed a structured assessment that included an open-ended question about other comments (initial interview) or problems (follow-up interview) associated with the device. For the current study, we conducted a descriptive analysis of these open-ended comments, classifying them as positive, negative, or neutral. RESULTS: Positive comments about urinary catheters accounted for 9 of 147 comments (6%), whereas positive comments about PICCs accounted for 10 of 100 comments (10%). Positive comments for both catheter types were mostly related to convenience. More than 80% of comments about both types of devices were negative and fell into the following areas: catheter malfunction; pain, irritation, or discomfort; interference with activities of daily living; provider error; and other. CONCLUSIONS: Our findings underscore the need to optimize the patient experience with placement, ongoing use, and removal of urinary catheters and PICCs.

Protocol to disseminate a hospital-site controlled intervention using audit and feedback to implement guidelines concerning inappropriate treatment of asymptomatic bacteriuria.

BACKGROUND: Antimicrobial stewardship to combat the spread of antibiotic-resistant bacteria has become a national priority. This project focuses on reducing inappropriate use of antimicrobials for asymptomatic bacteriuria (ASB), a very common condition that leads to antimicrobial overuse in acute and long-term care. We previously conducted a successful intervention, entitled "Kicking Catheter Associated Urinary Tract Infection (CAUTI): the No Knee-Jerk Antibiotics Campaign," to decrease guideline-discordant ordering of urine cultures and antibiotics for ASB. The current objective is to facilitate implementation of a scalable version of the Kicking CAUTI campaign across four geographically diverse Veterans Health Administration facilities while assessing what aspects of an antimicrobial stewardship intervention are essential to success and sustainability. METHODS: This project uses an interrupted time series design with four control sites. The two main intervention tools are (1) an evidence-based algorithm that distills the guidelines into a streamlined clinical pathway and (2) case-based audit and feedback to train clinicians to use the algorithm. Our conceptual framework for the development and implementation of this intervention draws on May's General Theory of Implementation. The intervention is directed at providers in acute and long-term care, and the goal is to reduce inappropriate screening for and treatment of ASB in all patients and residents, not just those with urinary catheters. The start-up for each facility consists of centrally-led phone calls with local site champions and baseline surveys. Case-based audit and feedback will begin at a given site after the start-up period and continue for 12 months, followed by a sustainability assessment. In addition to the clinical outcomes, we will explore the relationship between the dose of the intervention and clinical outcomes. DISCUSSION: This project moves from a proof-of-concept effectiveness study to implementation involving significantly more sites, and uses the General Theory of Implementation to embed the intervention into normal processes of care with usual care providers. Aspects of implementation that will be explored include dissemination, internal and external facilitation, and organizational partnerships. "Less is More" is the natural next step from our prior successful Kicking CAUTI intervention, and has the potential to improve patient care while advancing the science of implementation.