Association between survival and number of shocks for pulseless ventricular arrhythmias during pediatric in-hospital cardiac arrest in a national registry

BACKGROUND

Annually 15,200 children suffer an in-hospital cardiac arrest (IHCA) in the US. Ventricular fibrillation or pulseless ventricular tachycardia (VF/pVT) is the initial rhythm in 10-15% of these arrests. We sought to evaluate the association of number of shocks and early dose escalation with survival for initial VF/pVT in pediatric IHCA.

METHODS

Using 2000-2020 data from the American Heart Association's (AHA) Get with the Guidelines®-Resuscitation (GWTG-R) registry, we identified children >48 hours of life and ≤18 years who had an IHCA from initial VF/pVT and received defibrillation.

RESULTS

There were 251 subjects (37.7%) who received a single shock and 415 subjects (62.3%) who received multiple shocks. Baseline and cardiac arrest characteristics did not differ between those who received a single shock versus multiple shocks except for duration of arrest and calendar year. The median first shock dose was consistent with AHA dosing recommendations and not different between those who received a single shock versus multiple shocks. Survival was improved for those who received a single shock compared to multiple shocks. However, no difference in survival was noted between those who received 2, 3, or ≥4 shocks. Of those receiving multiple shocks, no difference was observed with early dose escalation.

CONCLUSIONS

In pediatric IHCA, most patients with initial VF/pVT require more than one shock. No distinctions in patient or pre-arrest characteristics were identified between those who received a single shock versus multiple shocks. Subjects who received a single shock were more likely to survive to hospital discharge even after adjusting for duration of resuscitation.

The segregation of physician networks providing care to black and white patients with heart disease: Concepts, measures, and empirical evaluation

Black-White disparities in cardiac care may be related to physician referral network segregation. We developed and tested new geographic physician network segregation measures. We used Medicare claims to identify Black and White Medicare heart disease patients and map physician networks for 169 hospital referral regions (HRRs) with over 1000 Black patients. We constructed two network segregation indexes ranging from 0 (integration) to 100 (total segregation): Dissimilarity (the unevenness of Black and White patient distribution across physicians [Dn]) and Absolute Clustering (the propensity of Black patients' physicians to have closer ties with each other than with other physicians [ACLn]). We employed conditional logit models to estimate the probability of using the best (lowest mortality) geographically available hospital for Black and White patients undergoing coronary artery bypass grafting (CABG) surgery in 126 markets with sufficient sample size at increasing levels of network segregation and for low vs. high HRR Black patient population. Physician network segregation was lower than residential segregation (Dissimilarity 21.9 vs. 48.7, and Absolute Clustering 4.8 vs. 32.4) and positively correlated with residential segregation (p < .001). Network segregation effects differed by race and HRR Black patient population. For White patients, higher network segregation was associated with a higher probability of using the best available hospitals in HRRs with few black patients but unchanged (ACLn) or lower (Dn) probability of best hospital use in HRRs with many Black patients. For Black patients, higher network segregation was not associated with a substantial change in the probability of best hospital use regardless of the HRR Black patient population size. Measuring physician network segregation is feasible and associated with nuanced effects on Black-White differences in high-quality hospital use for heart disease. Further work is needed to understand underlying mechanisms and potential uses in health equity policy.

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis

INTRODUCTION

Accurate estimation of an expected discharge date (EDD) early during hospitalization impacts clinical operations and discharge planning.

METHODS

We conducted a retrospective study of patients discharged from six general medicine units at an academic medical center in Boston, MA from January 2017 to June 2018. We retrieved all EDD entries and patient, encounter, unit, and provider data from the electronic health record (EHR), and public weather data. We excluded patients who expired, discharged against medical advice, or lacked an EDD within the first 24 h of hospitalization. We used generalized estimating equations in a multivariable logistic regression analysis to model early EDD accuracy (an accurate EDD entered within 24 h of admission), adjusting for all covariates and clustering by patient. We similarly constructed a secondary multivariable model using covariates present upon admission alone.

RESULTS

Of 3917 eligible hospitalizations, 890 (22.7%) had at least one accurate early EDD entry. Factors significantly positively associated (OR > 1) with an accurate early EDD included clinician-entered EDD, admit day and discharge day during the work week, and teaching clinical units. Factors significantly negatively associated (OR < 1) with an accurate early EDD included Elixhauser Comorbidity Index ≥ 11 and length of stay of two or more days. C-statistics for the primary and secondary multivariable models were 0.75 and 0.60, respectively.

CONCLUSIONS

EDDs entered within the first 24 h of admission were often inaccurate. While several variables from the EHR were associated with accurate early EDD entries, few would be useful for prospective prediction.

The association between bed occupancy rates and hospital quality in the English National Health Service

We study whether hospitals that exhibit systematically higher bed occupancy rates are associated with lower quality in England over 2010/11-2017/18. We develop an economic conceptual framework to guide our empirical analysis and run regressions to inform possible policy interventions. First, we run a pooled OLS regression to test if high bed occupancy is associated with, and therefore acts as a signal of, lower quality, which could trigger additional regulation. Second, we test whether this association is explained by exogenous demand-supply factors such as potential demand, and unavoidable costs. Third, we include determinants of bed occupancy (beds, length of stay, and volume) that might be associated with quality directly, rather than indirectly through bed occupancy. Last, we use a within-between random-effects specification to decompose these associations into those due to variations in characteristics between hospitals and variations within hospitals. We find that bed occupancy rates are positively associated with overall and surgical mortality, negatively associated with patient-reported health gains, but not associated with other indicators. These results are robust to controlling for demand-supply shifters, beds, and volume. The associations reduce by 12%-25% after controlling for length of stay in most cases and are explained by variations in bed occupancy between hospitals.

JUE Insight: Is hospital quality predictive of pandemic deaths? Evidence from US counties

In the large literature on the spatial-level correlates of COVID-19, the association between quality of hospital care and outcomes has received little attention to date. To examine whether county-level mortality is correlated with measures of hospital performance, we assess daily cumulative deaths and pre-crisis measures of hospital quality, accounting for state fixed-effects and potential confounders. As a measure of quality, we use the pre-pandemic adjusted five-year penalty rates for excess 30-day readmissions following pneumonia admissions for the hospitals accessible to county residents based on ambulance travel patterns. Our adjustment corrects for socio-economic status and down-weighs observations based on small samples. We find that a one-standard-deviation increase in the quality of local hospitals is associated with a 2% lower death rate (relative to the mean of 20 deaths per 10,000 people) one and a half years after the first recorded death.

Decreasing boarders in the emergency department by reducing clerical work in the discharge process of in-hospital patients in Brazil - an interrupted time-series analysis

BACKGROUND

Emergency Department (ED) boarding is related to in-hospital patients' discharge since no beds will be available for receiving ED patients if there is a delay for patients in the yard leaving the hospital. New techniques implemented in hospital institutions, such as digital signatures to facilitate clerical work improve these processes. We evaluated the impact of expediting patients' discharge after medical orders with the number of patients with an unplanned hospital admission from the Hospital Out Clinic directed to ED for waiting for an available bed in a public tertiary hospital in Brazil.

METHODS

We conducted a quasi-experimental study before and after an intervention. It consisted of an encrypted digital signature to reduce clerical work and expedite the patient's release from the institution after medical discharge. We used an interrupted time-series analysis based on administrative data (number of hospital discharges, bed turnover, the time between medical discharge, and the time the patient effectively left the hospital) from 2011 to 2020.

RESULTS

We enrolled 210,496 patients admitted to the hospital from January 2011 to December 2020. Of those, 69,897(33%) composed the group after the intervention. There was no difference between the groups' gender, age distribution, the proportion of surgical patients, or in-hospital stay (≤ 7 or > 7 days). The interrupted time series analysis for the time from medical order to effectively hospital discharge showed an immediate change in level (Coefficient β2 -3.6 h-95% confidence interval -3.9;-3.4), but no a difference in the slope of the behavior of the post-intervention curve (β3 0.0005 coefficient-95% confidence interval -0.0040;0.0050). For the number of patients directed to ED, we observed no immediate change in level (Coefficient β2 -0.84 patients-95% confidence interval -0.33;0.16), but a difference in the slope of the behavior of the post-intervention curve (β3 0.0005 coefficient-95% confidence interval -0.0040;0.0050).

CONCLUSION

Reducing clerical work and expediting patient discharge was associated with decreased potential boarders to ED.

The weekend effect in stroke mortality: evidence from Austrian acute care hospitals

Many studies provide evidence for the so-called weekend effect by demonstrating that patients admitted to hospital during weekends show less favourable outcomes such as increased mortality, compared with similar patients admitted during weekdays. The underlying causes for this phenomenon are still discussed controversially. We analysed factors influencing weekend effects in inpatient care for acute stroke in Austria. The study analysed secondary datasets from all 130 public acute care hospitals in Austria between 2010 and 2014 (Austrian DRG Data). The study cohort included 86,399 patient cases admitted with acute ischaemic stroke. By applying multivariate regression analysis, we tested whether patient, treatment or hospital characteristics drove in-hospital mortality on weekends and national holidays. We found that the risk to die after an admission at weekend was significantly higher compared to weekdays, while the number of admissions following stroke was significantly lower. Adjustment for patient, treatment and hospital characteristics substantially reduced the weekend effect in mortality but did not eliminate it. We conclude that the observed weekend effect could be explained either by lower quality of health care or higher severity of stroke admissions at the weekend. In depth analyses supported the hypothesis of higher stroke severity in weekend patients as seen in other studies. While DRG data is useful to analyse stroke treatment and outcomes, adjustment for case mix and severity is essential.

Predictive modeling of in-hospital mortality following elective surgery

INTRODUCTION

The specific healthcare macroenvironment factors contributing to in-hospital mortality following elective surgery remain nuanced. We hypothesize an accurate global elective surgical mortality model can be created.

METHODS

FL AHCA and Hospital Compare (2016-2019) were queried for in-hospital mortality following elective surgeries. Stepwise logistic regression with 47 patient and hospital factors was followed by gradient boosting machine (GBM) modeling describing the relative influence on risk for in-hospital mortality. Deceased and surviving patients were matched (1:2) to perform univariate analysis and logistic regression of significant factors.

RESULTS

A total of 511,897 admissions, 2,266 patient deaths and 162 Florida hospitals were included. GBM factors (AUC 0.94) included post-operative patient and hospital factors. In the final regression model, patient age older than 70 years of age and hospital 5-star rating were significant (OR 2.87, 0.47, respectively). Hospitals rated 5-stars were protective of mortality.

CONCLUSION

In-patient mortality following elective surgery is influenced by patient and hospital level factors. Efforts should be made to mitigate these risks or enhance those that are protective.

Physician investment in hospitals: Specialization, selection, and quality in cardiac care

Physician ownership of hospitals involves several competing economic forces. Physician-owners may be incentivized to "cherry-pick" and treat profitable patients at their facilities. However, physician-owned hospitals are often specialized and may provide higher-quality care for well-matched patients. Using multiple identification approaches, I document no significant mortality improvement for cardiac patients treated at physician-owned hospitals. Using aggregate data on ownership to infer physician-owner preferences in a hospital choice model, my results rule out significant cherry-picking within physician-owners' patient populations. However, both facility location and a healthier overall patient population among physician-owners drive advantageous selection of patients into physician-owned hospitals.

Avoidable Hospitalizations And Observation Stays: Shifts In Racial Disparities

Racial disparities in hospitalization rates for ambulatory care-sensitive conditions are concerning and may signal differential access to high-quality ambulatory care. Whether racial disparities are improving as a result of better ambulatory care versus artificially narrowing because of increased use of observation status is unclear. Using Medicare data for 2011-15, we sought to determine whether black-white disparities in avoidable hospitalizations were improving and evaluated the degree to which changes in observations for ambulatory care-sensitive conditions may be contributing to changes in these gaps. We found that while the racial gap in avoidable hospitalizations due to such conditions has decreased, that seems to be explained by a concomitant increase in the gap of avoidable observation stays. This suggests that changes from inpatient admissions to observation status seem to be driving the reduction in racial disparities in avoidable hospitalizations, rather than changes in the ambulatory setting.

Measurement matters: changing penalty calculations under the hospital acquired condition reduction program (HACRP) cost hospitals millions

BACKGROUND

Since October 2014, the Centers for Medicare and Medicaid Services has penalized 25% of U.S. hospitals with the highest rates of hospital-acquired conditions under the Hospital Acquired Conditions Reduction Program (HACRP). While early evaluations of the HACRP program reported cumulative reductions in hospital-acquired conditions, more recent studies have not found a clear association between receipt of the HACRP penalty and hospital quality of care. We posit that some of this disconnect may be driven by frequent scoring updates. The sensitivity of the HACRP penalties to updates in the program's scoring methodology has not been independently evaluated.

METHODS

We used hospital discharge records from 14 states to evaluate the association between changes in HACRP scoring methodology and corresponding shifts in penalty status. To isolate the impact of changes in scoring methods over time, we used FY2018 hospital performance data to calculate total HAC scores using FY2015 through FY2018 CMS scoring methodologies.

RESULTS

Comparing hospital penalty status based on various HACRP scoring methodologies over time, we found a significant overlap between penalized hospitals when using FY 2015 and 2016 scoring methodologies (95%) and between FY 2017 and 2018 methodologies (46%), but substantial differences across early vs later years. Only 15% of hospitals were eligible for penalties across all four years. We also found significant changes in a hospital's (relative) ranking across the various years, indicating that shifts in penalty status were not driven by small changes in HAC scores clustered around the penalty threshold.

CONCLUSIONS

HACRP penalties have been highly sensitive to program updates, which are generally announced after performance periods are concluded. This disconnect between performance and penalties calls into question the ability of the HACRP to improve patient safety as intended.

Hospital-Acquired Condition Reduction Program Is Not Associated With Additional Patient Safety Improvement

In 2013 the Centers for Medicare and Medicaid Services announced that it would begin levying penalties against hospitals with the highest rates of hospital-acquired conditions through the Hospital-Acquired Condition Reduction Program. Whether the program has been successful in improving patient safety has not been independently evaluated. We used clinical registry data on rates of hospital-acquired conditions in 2010-18 from a large surgical collaborative in Michigan to estimate the impact of the policy. While rates of all such conditions declined from 133.4 per 1,000 discharges in the pre-program period to 122.2 in the post-program period, greater improvements were observed for nontargeted measures. We conclude that the program did not improve patient safety in Michigan beyond existing trends. These findings raise questions about whether the program will lead to improvements in patient safety as intended.

Access, Quality, And Financial Performance Of Rural Hospitals Following Health System Affiliation

More than 100 rural hospitals have closed since 2010. Some rural hospitals have affiliated with health systems to improve their financial performance and potentially avoid closure, but the effects of affiliation on rural hospitals and their patients are unclear. To examine the relationship between affiliation and performance, we compared rural hospitals that affiliated with a health system in the period 2008-17 and a propensity score-weighted set of nonaffiliating rural hospitals on twelve measures of structure, utilization, financial performance, and quality. Following health system affiliation, rural hospitals experienced a significant reduction in on-site diagnostic imaging technologies, the availability of obstetric and primary care services, and outpatient nonemergency visits, as well as a significant increase in operating margins (by 1.6-3.6 percentage points from a baseline of -1.6 percent). Changes in patient experience scores, readmissions, and emergency department visits were similar for affiliating and nonaffiliating hospitals. While joining health systems may improve rural hospitals' financial performance, affiliation may reduce access to services for patients in rural areas.

Financial Integration's Impact On Care Delivery And Payment Reforms: A Survey Of Hospitals And Physician Practices

Health systems continue to grow in size. Financial integration-the ownership of hospitals or physician practices-often has anticompetitive effects that contribute to the higher prices for health care seen in the US. To determine whether the potential harms of financial integration are counterbalanced by improvements in quality, we surveyed nationally representative samples of hospitals (n = 739) and physician practices (n = 2,189), stratified according to whether they were independent or were owned by complex systems, simple systems, or medical groups. The surveys included nine scales measuring the level of adoption of diverse, quality-focused care delivery and payment reforms. Scores varied widely across hospitals and practices, but little of this variation was explained by ownership status. Quality scores favored financially integrated systems for four of nine hospital measures and one of nine practice measures, but in no case favored complex systems. Greater financial integration was generally not associated with better quality.

Using Information Technology To Improve COVID-19 Care At New York City Health + Hospitals

As the coronavirus disease 2019 (COVID-19) pandemic surged in New York City, the city's public hospital system, New York City Health + Hospitals, recognized that innovative technological solutions were needed to respond to the crisis. Our health system recently transitioned to a unified enterprisewide electronic medical record across all of our hospitals. This accelerated our ability to implement a series of technological solutions to the crisis. We engaged in focused efforts to improve staff efficiency, including rapid medical screening exams for low-acuity patients, use of "SmartNotes," and improved vital sign monitoring. We standardized patient workup using specialty-specific order sets, created dashboards to give insight into enterprisewide bed availability and facilitate transfers from the hardest-hit hospitals, and improved the patient experience by using tablets to connect patients with loved ones. The technology bridged divides between different hospital systems across New York City to encourage the sharing of data and improve patient care. By rapidly expanding its use of information technology, NYC Health + Hospitals was able to respond to the COVID-19 surge and is now better positioned to work in a more integrated fashion in the future.

U.S. hospital performance methodologies: a scoping review to identify opportunities for crossing the quality chasm

Background

Hospital performance quality assessments inform patients, providers, payers, and purchasers in making healthcare decisions. These assessments have been developed by government, private and non-profit organizations, and academic institutions. Given the number and variability in available assessments, a knowledge gap exists regarding what assessments are available and how each assessment measures quality to identify top performing hospitals. This study aims to: (a) comprehensively identify current hospital performance assessments, (b) compare quality measures from each methodology in the context of the Institute of Medicine’s (IOM) six domains of STEEEP (safety, timeliness, effectiveness, efficiency, equitable, and patient-centeredness), and (c) formulate policy recommendations that improve value-based, patient-centered care to address identified gaps.

Methods

A scoping review was conducted using a systematic search of MEDLINE and the grey literature along with handsearching to identify studies that provide assessments of US-based hospital performance whereby the study cohort examined a minimum of 250 hospitals in the last two years (2017–2019).

Results

From 3058 unique records screened, 19 hospital performance assessments met inclusion criteria. Methodologies were analyzed across each assessment and measures were mapped to STEEEP. While safety and effectiveness were commonly identified measures across assessments, efficiency, and patient-centeredness were less frequently represented. Equity measures were also limited to risk- and severity-adjustment methods to balance patient characteristics across populations, rather than stand-alone indicators to evaluate health disparities that may contribute to community-level inequities.

Conclusions

To further improve health and healthcare value-based decision-making, there remains a need for methodological transparency across assessments and the standardization of consensus-based measures that reflect the IOM’s quality framework. Additionally, a large opportunity exists to improve the assessment of health equity in the communities that hospitals serve.

Shelter-In-Place Orders Reduced COVID-19 Mortality And Reduced The Rate Of Growth In Hospitalizations

Most states enacted shelter-in-place orders when mitigating the coronavirus disease 2019 (COVID-19) pandemic. Emerging evidence indicates that these orders have reduced COVID-19 cases. Using data starting at different dates in March and going through May 15, 2020, we examined the effects of shelter-in-place orders on daily growth rates of both COVID-19 deaths and hospitalizations, using event study models. We found that shelter-in-place orders reduced both the daily mortality growth rate nearly three weeks after their enactment and the daily growth rate of hospitalizations two weeks after their enactment. After forty-two days from enactment, the daily mortality growth rate declined by up to 6.1 percentage points. Projections suggest that as many as 250,000-370,000 deaths were possibly averted by May 15 in the forty-two states plus Washington, D.C., that had statewide shelter-in-place orders. The daily hospitalization growth rate examined in nineteen states with shelter-in-place orders and three states without them that had data on hospitalizations declined by up to 8.4 percentage points after forty-two days. This evidence suggests that shelter-in-place orders have been effective in reducing the daily growth rates of COVID-19 deaths and hospitalizations.

Electronic Health Information Exchange At Discharge From Inpatient Psychiatric Care In Acute Care Hospitals

To address the complex health care needs of patients with mental illness-who commonly have co-occurring medical conditions and substance use disorders-it is critically important for providers to use electronic health records (EHRs) for health information exchange (HIE) when patients are transferred from inpatient psychiatric units in acute care hospitals. Efficient and timely HIE is necessary to ensure that patients receive adequate and informed follow-up care. This study examined the percentage of inpatient psychiatric units that reported using EHRs for HIE at transfers of care and hospital characteristics associated with that use. We linked national data from the Inpatient Psychiatric Facility Quality Reporting Program of the Centers for Medicare and Medicaid Services, the American Hospital Association Annual Survey, and state mental health privacy laws. In 2016 the use of electronic HIE upon transfer from psychiatric units lagged behind the corresponding overall use rates from acute care hospitals (56.3 percent versus 88 percent), with wide variation across states. Hospital size and accountable care organization participation were associated with electronic HIE, but a state's having mental health privacy laws more stringent than the Health Insurance Portability and Accountability Act did not. Given these results, policy efforts to incentivize the use of electronic HIE in psychiatric settings should be strengthened.

Improving The Accuracy Of Hospital Quality Ratings By Focusing On The Association Between Volume And Outcome

The Centers for Medicare and Medicaid Services (CMS) uses hierarchical modeling to stabilize its hospital quality star ratings by shrinking the performance of low-volume hospitals toward the performance of average hospitals. Responding to criticism that the methodology may distort the performance of low-volume hospitals, a CMS expert panel recommended that the agency consider using "shrinkage targets" to more accurately classify hospital quality performance. To test the "shrinkage targets" approach, we created two parallel sets of performance measures. We found that there was moderate-to-substantial agreement between the standard CMS approach and the approach based on shrinkage targets in hospital star ratings for all but the lowest-volume hospitals. These findings suggest that the standard CMS risk-adjustment methodology does not distort the star ratings of hospitals as long as case volumes exceed the current cutoff (twenty-five cases) used by CMS for public reporting.

Neighborhood Racial And Economic Polarization, Hospital Of Delivery, And Severe Maternal Morbidity

Recent national and state legislation has called attention to stark racial/ethnic disparities in maternal mortality and severe maternal morbidity (SMM), the latter of which is defined as having a life-threatening condition or life-saving procedure during childbirth. Using linked New York City birth and hospitalization data for 2012-14, we examined whether racial and economic spatial polarization is associated with SMM rates, and whether the delivery hospital partially explains the association. Women in ZIP codes with the highest concentration of poor blacks relative to wealthy whites experienced 4.0 cases of SMM per 100 deliveries, compared with 1.7 cases per 100 deliveries among women in the neighborhoods with the lowest concentration (risk difference = 2.4 cases per 100). Thirty-five percent of this difference was attributable to the delivery hospital. Women in highly polarized neighborhoods were most likely to deliver in hospitals located in similarly polarized neighborhoods. Housing policy that targets racial and economic spatial polarization may address a root cause of SMM, while hospital quality improvement may mitigate the impact of such polarization.

Local Referral of High-Risk Pancreatectomy Patients to Improve Surgical Outcomes and Minimize Travel Burden

BACKGROUND

Referring patients to high-quality hospitals for complex procedures may improve outcomes. This is most feasible within small geographic areas. However, access to specialized surgical procedures may be an implementation barrier. We sought to determine the availability of high-quality hospitals performing pancreatectomy and the potential benefit and travel burden of referral within small geographic areas.

METHODS

We identified elderly Medicare beneficiaries undergoing pancreatectomy between 2012 and 2014. Hospitals were stratified into quintiles of quality based on postoperative complication rates. Patient risk was assessed by modeling the predicted risk of developing a postoperative complication. The geographic unit of analysis was Metropolitan Statistical Area (MSA). Hospitals were categorized into MSA by zip code. Travel distance was calculated using patient and hospital zip code.

RESULTS

Among high-risk patients, 40.7% received care at the lowest-quality hospitals even though 80% had a high-quality hospital in the same MSA. Shifting these patients from low- to high-quality hospitals would decrease serious complications from 46.6 to 21.9% (P < 0.001) and mortality from 10.9 to 8.9% (P = 0.047). Three quarters of high-risk patients treated at low-quality hospitals could reach a high-quality hospital by extending their travel < 5 miles, and nearly 60% traveled farther to a low-quality hospital than was necessary to reach a high-quality hospital.

CONCLUSIONS

High-risk pancreatectomy patients often receive care at low-quality hospitals despite the availability of high-quality hospitals in the area or within an acceptable distance. Referral of high-risk patients to high-quality hospitals within small geographic areas may be an effective strategy to improve outcomes following pancreatic surgery.

Clinician-Directed Performance Improvement: Moving Beyond Externally Mandated Metrics

Pay-for-performance and public reporting programs have successfully focused hospitals' attention on quality. But they have also had unintended consequences, including encouraging a narrowing of focus to mandated metrics, and their effectiveness in improving outcomes remains uncertain. Moreover, they have not successfully engaged clinicians. To offset these deficiencies, a community hospital in Santa Fe, New Mexico, developed a clinician-led quality improvement program that was parallel to its traditional quality program. Called Clinician-Directed Performance Improvement (CDPI), the program is designed to give clinicians the protected time, support, and training to select and conduct performance improvement projects. Since its implementation in September 2015, CDPI has been associated with large improvements in quality and physician engagement and has generated net savings. This article discusses the program, its results, and policy implications.

Comparative evaluation of operating room terminal cleaning by two methods: Focused multivector ultraviolet (FMUV) versus manual-chemical disinfection

BACKGROUND

This non-randomized comparative observational study evaluated the performance of a standard manual-chemical disinfection process with an automated process employing focused multivector ultraviolet (FMUV) light technology during operating room (OR) terminal cleaning.

METHODS

An Association of periOperative Registered Nurses terminal cleaning protocol was modified to incorporate the use of automated FMUV technology that allows workers to occupy the room during operation. This modified protocol was compared with a standard manual-chemical cleaning and disinfection protocol. Equipment surfaces were pre-sampled before and after terminal cleaning. A total of 165 objects were sampled in each process using a 5-point multisided sampling method.

RESULTS

The parallel process employing FMUV reduced the active microbial burden by 96.5% from baseline (P < .0001), which was over 2.5 times better than the standard process. The standard terminal manual-chemical disinfection process reduced the active microbial burden on sampled objects by 38.4% from baseline (P < .0001).

CONCLUSIONS

The data demonstrates that the performance of standard manual-chemical disinfection alone is variable in a live clinical setting even under the most ideal conditions. By comparison, automated FMUV treatment incorporated in a parallel process consistently produced thorough and significant reductions of microbial contamination levels on all visibly clean patient care equipment.

Do variations in hospital admission rates bias comparisons of standardized hospital mortality rates? A population-based cohort study

BACKGROUND

Standardized mortality rates are routinely used as measures of hospital performance and quality. Such metrics may, however, be biased if hospital admission thresholds differ and patient severity is not fully measured.

AIM

To examine whether comparisons of hospital mortality rates suffer from selection bias due to variations in hospital admission rates, using the example of variations by day of the week.

DATA

12,900,687 emergency department attendances and 3,418,446 unplanned admissions to all acute non-specialist hospitals of the National Health Service in England between 1 April 2013 and 28 February 2014.

METHODS

Population-based retrospective cohort study. Mortality within 30 days of attendance is modelled as a function of weekend or weekday attendance and hospital-level predictors of admission rates using patient-level risk-adjusted probit and bivariate Heckman selection models. Robustness is supported by the use of different hospital-level predictors.

RESULTS

When examining only the admitted population, patients admitted to hospital at weekends have a 0.206 percentage point higher risk of death within 30 days compared to patients admitted during the week. However, patients attending emergency departments at weekends have a 1.390 percentage point lower probability of being admitted to hospital. Once this selection bias is accounted for, the weekend effect in mortality is reduced by two-thirds to a 0.068 percentage point increase in the risk of death.

CONCLUSIONS

Comparisons of standardized hospital mortality rates following unplanned admissions can be biased by variations in emergency department admission rates, leading to incorrect conclusions about quality. The use of mortality as a performance measure could therefore lead to misleading comparisons if admission rates vary and illness severity is not fully controlled for. Accounting for sample selection bias and dependence between admission and mortality rates is vital if accurate comparisons of hospital performance are to be made.

Association of Hospital Employee Satisfaction with Patient Safety and Satisfaction within Veterans Affairs Medical Centers

BACKGROUND

Employee satisfaction is thought to impact performance. However, which aspects of employee satisfaction matter most is unknown. We utilized data from the Veterans Affairs Medical Centers(VAMC) via their Strategic Analytics for Improvement and Learning program to examine the association between organizational satisfaction as well as job-specific satisfaction with measures of patient safety, patient satisfaction, and hospital rating.

METHODS

The correlation between employee satisfaction with their organization and with their specific job were examined across indicators of patient care using Pearson and Spearman's correlation. Employee satisfaction data were obtained from the All Employee Survey.

RESULTS

We found that employee job-specific satisfaction does not correlate with patient outcomes, whereas higher satisfaction with the organization is associated with improved patient safety (ρ = -0.19, P < .05) and correlates with all aspects of patient satisfaction ("top box" ratings of hospital [r = 0.30, P < 0.005], primary care [r = 0.25, P < 0.005], and specialty care [r = 0.14, P < 0.005]). Further, employees are more satisfied with their job and organization when they work at a VAMC with a higher Star rating.

CONCLUSION

Employee organizational satisfaction and job-specific satisfaction are distinct metrics, and it is higher organizational satisfaction that is associated with improved patient care.

On measuring the quality of hospitals

PURPOSE

Researchers, policymakers and hospital managers often encounter numerous quality measures when assessing hospital quality. The purpose of this paper is to address the challenge of summarising, interpreting and comparing multiple quality measures across different quality dimensions by proposing a simple method of constructing a composite quality index. The method is applied to hospital administrative data to demonstrate its use in analysing hospital performance.

DESIGN/METHODOLOGY/APPROACH

Logistic and fixed effects regression analyses are applied to secondary admitted patient data from all hospitals in the state of Victoria, Australia for the period 2000/2001-2011/2012.

FINDINGS

The derived composite quality index was used to rank hospital performance and to assess changes in state-wide average hospital quality over time. Further regression analyses found private hospitals, day hospitals and non-acute hospitals were associated with higher composite quality, while small hospitals were associated with lower quality.

PRACTICAL IMPLICATIONS

The method will enable policymakers and hospital managers to better monitor the performance of hospitals. It allows quality to be related to other attributes of hospitals such as size and volume, and enables policymakers and managers to focus on hospitals with relevant characteristics such that quantity and quality changes can be better understood, monitored and acted upon.

ORIGINALITY/VALUE

A simple method of constructing a composite quality is an indispensable practical tool in tracking the quality of hospitals when numerous measures are used to capture different aspects of quality. The derived composite quality can be used to summarise hospital performance and to identify factors associated with quality via regression analyses.

Volume-outcome relationship and minimum volume regulations in the German hospital sector - evidence from nationwide administrative hospital data for the years 2005-2007

BACKGROUND

This paper analyses the volume-outcome relationship and the effects of minimum volume regulations in the German hospital sector.

METHODS

We use a full sample of administrative data from the unselected, complete German hospital population for the years 2005 to 2007. We apply regression methods to analyze the association between volume and hospital quality. We measure hospital quality with a binary variable, which indicates whether the patient has died in hospital. Using simulation techniques we examine the impact of the minimum volume regulations on the accessibility of hospital services.

RESULTS

We find a highly significant negative relationship between case volume and mortality for complex interventions at the pancreas and oesophagus as well as for knee replacement. For liver, kidney and stem cell transplantation as well as for CABG we could not find a strong association between volume and quality. Access to hospital care is only moderately affected by minimum volume regulations.

CONCLUSION

The effectiveness of minimum volume regulations depends on the type of intervention. Depending on the type of intervention, quality gains can be expected at the cost of slightly decreased access to care.

Returns to specialization: Evidence from the outpatient surgery market

Technological changes in medicine have created new opportunities to provide surgical care in lower cost, specialized facilities. This paper examines patient outcomes in ambulatory surgery centers (ASCs), which were developed as a low-cost alternative to outpatient surgery in hospitals. Because we are concerned that selection into ASCs may bias estimates of facility quality, we use predicted changes in federally set Medicare facility payment rates as an instrument for ASC utilization to estimate the effect of location of treatment on patient outcomes. We find that patients treated in an ASC are less likely to be admitted to a hospital or visit an emergency room a short time after outpatient surgery. The findings in this paper indicate that factors other than patient and physician heterogeneity contribute to the observed returns to specialization in the ASC market.

Public release of hospital quality data for referral practices in Germany: results from a cluster-randomised controlled trial

OBJECTIVE

To evaluate the impact of different dissemination channels on the awareness and usage of hospital performance reports among referring physicians, as well as the usefulness of such reports from the referring physicians' perspective.

DATA SOURCES/STUDY SETTING

Primary data collected from a survey with 277 referring physicians (response rate = 26.2%) in Nuremberg, Germany (03-06/2016).

STUDY DESIGN

Cluster-randomised controlled trial at the practice level. Physician practices were randomly assigned to one of two conditions: (1) physicians in the control arm could become aware of the performance reports via mass media channels (Mass Media, [Formula: see text]=132, [Formula: see text]=147); (2) physicians in the intervention arm also received a printed version of the report via mail (Mass and Special Media, [Formula: see text]=117; [Formula: see text]=130).

PRINCIPAL FINDINGS

Overall, 68% of respondents recalled hospital performance reports and 21% used them for referral decisions. Physicians from the Mass and Special Media group were more likely to be aware of the performance reports (OR 4.16; 95% CI 2.16-8.00, p < .001) but not more likely to be influenced when referring patients into hospitals (OR 1.73; 95% CI 0.72-4.12, p > .05). On a 1 (very good) to 6 (insufficient) scale, the usefulness of the performance reports was rated 3.67 (±1.40). Aggregated presentation formats were rated more helpful than detailed hospital quality information.

CONCLUSIONS

Hospital quality reports have limited impact on referral practices. To increase the latter, concerns raised by referring physicians must be given more weight. Those principally refer to the underlying data, the design of the reports, and the lack of important information.

Does adding clinical data to administrative data improve agreement among hospital quality measures?

BACKGROUND

Hospital performance measures based on patient mortality and readmission have indicated modest rates of agreement. We examined if combining clinical data on laboratory tests and vital signs with administrative data leads to improved agreement with each other, and with other measures of hospital performance in the nation's largest integrated health care system.

METHODS

We used patient-level administrative and clinical data, and hospital-level data on quality indicators, for 2007-2010 from the Veterans Health Administration (VA). For patients admitted for acute myocardial infarction (AMI), heart failure (HF) and pneumonia we examined changes in hospital performance on 30-d mortality and 30-d readmission rates as a result of adding clinical data to administrative data. We evaluated whether this enhancement yielded improved measures of hospital quality, based on concordance with other hospital quality indicators.

RESULTS

For 30-d mortality, data enhancement improved model performance, and significantly changed hospital performance profiles; for 30-d readmission, the impact was modest. Concordance between enhanced measures of both outcomes, and with other hospital quality measures - including Joint Commission process measures, VA Surgical Quality Improvement Program (VASQIP) mortality and morbidity, and case volume - remained poor.

CONCLUSIONS

Adding laboratory tests and vital signs to measure hospital performance on mortality and readmission did not improve the poor rates of agreement across hospital quality indicators in the VA.

INTERPRETATION

Efforts to improve risk adjustment models should continue; however, evidence of validation should precede their use as reliable measures of quality.

Health care public reporting utilization - user clusters, web trails, and usage barriers on Germany's public reporting portal Weisse-Liste.de

BACKGROUND

Quality of care public reporting provides structural, process and outcome information to facilitate hospital choice and strengthen quality competition. Yet, evidence indicates that patients rarely use this information in their decision-making, due to limited awareness of the data and complex and conflicting information. While there is enthusiasm among policy makers for public reporting, clinicians and researchers doubt its overall impact. Almost no study has analyzed how users behave on public reporting portals, which information they seek out and when they abort their search.

METHODS

This study employs web-usage mining techniques on server log data of 17 million user actions from Germany's premier provider transparency portal Weisse-Liste.de (WL.de) between 2012 and 2015. Postal code and ICD search requests facilitate identification of geographical and treatment area usage patterns. User clustering helps to identify user types based on parameters like session length, referrer and page topic visited. First-level markov chains illustrate common click paths and premature exits.

RESULTS

In 2015, the WL.de Hospital Search portal had 2,750 daily users, with 25% mobile traffic, a bounce rate of 38% and 48% of users examining hospital quality information. From 2013 to 2015, user traffic grew at 38% annually. On average users spent 7 min on the portal, with 7.4 clicks and 54 s between clicks. Users request information for many oncologic and orthopedic conditions, for which no process or outcome quality indicators are available. Ten distinct user types, with particular usage patterns and interests, are identified. In particular, the different types of professional and non-professional users need to be addressed differently to avoid high premature exit rates at several key steps in the information search and view process. Of all users, 37% enter hospital information correctly upon entry, while 47% require support in their hospital search.

CONCLUSIONS

Several onsite and offsite improvement options are identified. Public reporting needs to be directed at the interests of its users, with more outcome quality information for oncology and orthopedics. Customized reporting can cater to the different needs and skill levels of professional and non-professional users. Search engine optimization and hospital quality advocacy can increase website traffic.

Importance of Statistical Evidence in Estimating Valid DEA Scores

Data Envelopment Analysis (DEA) allows healthcare scholars to measure productivity in a holistic manner. It combines a production unit's multiple outputs and multiple inputs into a single measure of its overall performance relative to other units in the sample being analyzed. It accomplishes this task by aggregating a unit's weighted outputs and dividing the output sum by the unit's aggregated weighted inputs, choosing output and input weights that maximize its output/input ratio when the same weights are applied to other units in the sample. Conventional DEA assumes that inputs and outputs are used in different proportions by the units in the sample. So, for the sample as a whole, inputs have been substituted for each other and outputs have been transformed into each other. Variables are assigned different weights based on their marginal rates of substitution and marginal rates of transformation. If in truth inputs have not been substituted nor outputs transformed, then there will be no marginal rates and therefore no valid basis for differential weights. This paper explains how to statistically test for the presence of substitutions among inputs and transformations among outputs. Then, it applies these tests to the input and output data from three healthcare DEA articles, in order to identify the effects on DEA scores when input substitutions and output transformations are absent in the sample data. It finds that DEA scores are badly biased when substitution and transformation are absent and conventional DEA models are used.