Hospital Performance Measures Research Articles

Hospital-Acquired Conditions after Liver Transplantation

Hospital-acquired conditions (HACs) are used to define hospital performance measures. Patient comorbidity may influence HAC development. The National Inpatient Sample database was used to investigate HACs for the patients who underwent liver transplantation. Multivariate analysis was used to identify HAC risk factors. We found a total of 13,816 patients who underwent liver transplantation during 2002-2014. Of these, 330 (2.4%) had a report of HACs. Most frequent HACs were vascular catheter-associated infection [220 (1.6%)], falls and trauma [66 (0.5%), catheter-associated UTI [24 (0.2%)], and pressure ulcer stage III/IV [22 (0.2%)]. Factors correlating with HACs included extreme loss function (AOR: 52.13, P < 0.01) and major loss function (AOR: 8.11, P = 0.04), hepatopulmonary syndrome (AOR: 3.39, P = 0.02), portal hypertension (AOR: 1.49, P = 0.02), and hospitalization length of stay before transplant (AOR: 1.01, P < 0.01). The rate of HACs for liver transplantation is three times higher than the reported overall rate of HACs for GI procedures. Multiple patient factors are associated with HACs, and HACs may not be a reliable measure to evaluate hospital performance. Vascular catheter-associated infection is the most common HAC after liver transplantation.

Utility of 90-Day Mortality vs 30-Day Mortality as a Quality Metric for Transcatheter and Surgical Aortic Valve Replacement Outcomes

Questions have recently arisen as to whether 30-day mortality is a reasonable metric for understanding institutional practice differences after transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR). To examine the utility of 30-day vs 90-day mortality after TAVR and SAVR as a mortality quality metric. This nationally representative, multicenter, cohort study analyzed data from Medicare beneficiaries undergoing TAVR and SAVR procedures from January 1, 2012, to December 31, 2015. Concomitant coronary artery bypass grafting and other heart valve or other major open-heart procedures were excluded. Hospitals that performed fewer than 50 TAVR or 70 SAVR procedures per year were excluded to ensure reliable estimates and to reduce the risks of inflated results because of small institutional sample sizes. Data were analyzed from October 2018 to August 2019. Hospitals were ranked into top- (10%), middle- (80%), and bottom-performing (10%) groups based on their 4-year mean 30-day mortality. Changes in hospital performance rankings at 90 days and 1 year and correlation of 30- and 90-day mortality with 1-year mortality were examined. A total of 30 329 TAVR admissions at 184 hospitals and 26 021 SAVR admissions at 191 hospitals were evaluated. For TAVR, 40 hospitals (21.7%) changed performance rankings at 90 days: 13 (48.1%) in the top-performing group and 8 (29.6%) in the bottom-performing group. At 1 year, 56 hospitals (30.4%), which included 21 (77.8%) in the top-performing group and 12 (44.4%) in the bottom-performing group, changed rankings. Similar findings were observed for SAVR, with an overall 90-day conversion rate of 17.3% and a 1-year rate of 30.3%. These findings persisted after adjusting for the differences in patient risk profiles among the 3 groups. Capturing 90-day events was also more robustly informative regarding expected 1-year outcomes after both TAVR and SAVR, largely owing to the observed plateau in the instantaneous hazard observed beyond this point. The findings suggest that evaluation of hospital performance based on 30-day mortality may underestimate outcomes and therefore substantially misrepresent institutional performance after TAVR and SAVR compared with 90-day mortality, even after risk adjustment. Although 30-day mortality has been validated, 90-day mortality may be a more reliable outcome metric for measuring hospital performance and capturing procedure-related mortality.

Associations Between Nonclinical Services and Patient-Experience Outcomes in US Acute Care Hospitals.

Background:Patient experience is an important measure of hospital quality and performance. Since the passage of the Affordable Care Act, patient experiences with their care encounters are embedded into the framework of payment incentives. However, drivers of patient experience in the context of the supportive, nonclinical, services that relate to patient care have not been as well understood.Aims:To assess the role of organizational factors on patient experience.Methods:This cross-sectional analysis integrates hospital patient-experience scores from Hospital Consumer Assessment of Healthcare Providers and Systems, and Centers for Medicaid and Medicare Service data from 2013 to 2015 (N = 3392). Based on hospitals with “top-box” responses, the aggregate proportion of hospital patients responding “always” on a Likert scale represented a top-box hospital. Domains were split at the mean for analysis (above average = 1). Multivariable logistic regression models for each domain were analyzed against hospital factors and services, including offering a patient education center, patient-enabling services, and language services.Results:Most hospitals reported a full-time hospitalist (64.4%) and a patient education center (60.4%), while fewer provided enabling/support services (33.7%). In multivariable models, small and medium hospitals performed better compared to the largest hospitals (300+ beds; P < .0001). Structurally, medium and small hospitals reported significantly greater odds of top-box patient-experience versus large hospitals. Across all domains, only hospitals with patient education centers returned better performance (adjusted odds ratio: 1.27-1.64; P = .0002-.0166).Discussion/Conclusion:Patient education centers provide relevant information at the point of service and may improve overall patient experience of care. Given the growing reliance on accountable care delivery models, opportunities to partner with community health education partners may be profitable.

Performance measurement of public hospitals through the integration of SWOT and balanced scorecard

Performance measurement is an essential factor for an organization to control the implementation of the strategy. The existing performance measurement system only evaluates financial performance. This study aims to propose the Key Performance Indicators (KPI) and measuring hospital performance with SWOT and BSC integration. Stages of research are determining internal and external factors with SWOT analysis, formulating alternative strategies, translating strategies into BSC perspective on strategic maps, determining performance indicators (KPI), KPI validation with AHP to obtain indicator weights for each view, and measuring hospital performance. Based on the results of the study, performance indicators (KPI) for hospitals based on the Balanced Scorecard perspective are six financial perspective indicators, two customer perspective indicators, four internal process perspective indicators, and five growth and learning perspective indicators. The internal process perspective is 90.43%, which means that the performance of the hospital is very healthy with the AA category, and learning 68.89% means that the performance of the hospital is very healthy with category A.

6131Association between hospital care quality and readmission among Japanese patients with heart failure. From JROAD-DPC study

Abstract Background Measuring the process of care has become a widely used practice to improve a quality of care. Recently, some studies have demonstrated poor to no correlation between 30-day readmission rates and quality of care for heart failure (HF) among hospitalized HF patients. However the investigation about relationships of care quality for HF and 1 year outcome is limited. Purpose To investigate the relationship between quality of care in each hospitals and readmission among HF patients in Japan. Methods From Japanese Registry of All cardiac and vascular diseases (JROAD-DPC) database in 2014, 84,325 HF patients hospitalized to 741 certificated hospitals by Japanese Circulation Society were analyzed. A primary endpoint was readmission for HF in one year. Five performance measures were defined as prescription rate of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB), prescription rate of beta blocker and prescription rate of spironolactone, measurement rate of echocardiography and measurement rate of B-type natriuretic peptide (BNP) during hospitalization. For each of the five measures, a composite score was created by giving points ranging from 1 to 4 from the lower quartile of rates, with the score ranging from 5 to 20 points. Hazard ratios (HR) indicating the effects of the performance measures were estimated using Cox proportional hazard models. Covariates included age, gender, Charson score, and NYHA class. Results In Japanese HF patients (age; 78.1 years old, man 52%), the HF readmission rate in one year was 14,520 (17.2%). The readmission rate decreased with higher quartiles of prescription rate in each medications and performance rates. The highest quartile of each measurements was significantly lower risk for readmission compared to the lowest quartile (ACE/ARB, adjusted HR 0.87 [95% CI, 0.83–0.91], p&lt;0.001; beta-blocker, 0.83 [0.79–0.88], p&lt;0.001; spironolactone, 0.88 [0.83–0.92], p&lt;0.001; echocardiography, 0.90 [0.86–0.94], p&lt;0.001; BNP, 0.92 [0.87–0.96], p&lt;0.001). Kaplan-Meier curves showed that readmission rates were better among higher composite score, compared to lower composite score (Log-rank test=p&lt;0.001). (Figure) Higher composite scores were associated with statistically significant risk reduction of 23% for HF readmission (HR 0.77, 95% CI [0.73–0.81], p&lt;0.001). Figure 1 Conclusion The hospital performance measures were associated with a significant risk reduction of readmission in Japanese patients with HF.

What is full capacity protocol, and how is it implemented successfully?

BackgroundFull capacity protocol (FCP) is an internationally recognized intervention designed to address emergency department (ED) crowding. Despite FCP international recognition and positive effects on hospital performance measures, many hospitals, even the most crowded ones, have not implemented FCP. We conducted this study to identify the core components of FCP, explore the key barriers and facilitators associated with the FCP implementation, and provide practical recommendations on how to overcome those barriers.MethodsTo identify the core components of FCP, we used a non-experimental approach. We conducted semi-structured interviews with key informants (e.g., division chiefs, medical directors) involved in the implementation of FCP. We used the Consolidated Framework for Implementation Research (CFIR) to guide data collection and analysis. We used a template analysis approach to determine the relevance of the CFIR constructs to implementing the FCP. We analyzed the responses to the interview questions about FCP definition and FCP key principles, compared different hospitals’ FCP official documents, and consulted with the original FCP developer. We then used an adaptation framework to categorize the core components of FCP into three main groups. Finally, we summarized practical recommendations for each barrier based on information provided by the interviewees.ResultsA total of 32 interviews were conducted. We observed that FCP has evolved from the idea of transferring boarded patients from ED hallways to inpatient hallways to a practical hospital-wide intervention with several components and multiple levels. The key determinant of successful FCP implementation was collaboration with inpatient nursing staff, as they were often reluctant to have patients boarded in inpatient hallways. Other determinants of successful FCP implementation were reaching consensus about the criteria for activation of each FCP level and actions in each FCP level, modifying the electronic health records system, restructuring the inpatient units to have adequate staffing and resources, complying with external regulations and policies such as fire marshal guidelines, and gaining hospital leaders’ support.ConclusionsThe key determinant in implementing FCP is creating a supportive and cooperative hospital culture and encouraging key stakeholders, including inpatient nursing staff, to acknowledge that crowding is a hospital-wide problem that requires a hospital-wide response.

Efektywność szpitala publicznego – rozważania o istocie i problemach badawczych

&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt;Interesariusze szpitali publicznych są zainteresowani przede wszystkim wynikami leczenia, ale kierownik organizacji, organ zarządzający szpitalem, kontrahenci, instytucje fiansowe, pracownicy i instytucje&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; kontrolne oczekują zbiorów informacji obejmujących dane fiansowe i niefiansowe. Przetworzone dane&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; stają się użytecznymi informacjami, wykorzystywanymi m.in. do analizy poziomu zadowolenia pacjentów,&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; podstawowych wskaźników ekonomicznych oraz efektów leczenia i pozostałych działań. Dane są przy&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt;datne również w ocenie wykonywania zadań przez szpital publiczny. Wszystkie informacje umożliwiają&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; kompleksową ocenę działalności jednostki, a tym samym ocenę jej skuteczności, co oznacza, że organizacja&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; może zastosować holistyczne podejście do pomiaru wyników działań medycznych i innych.&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; Celem artykułu było przedstawienie istoty, rodzajów i problemów badawczych efektywności publicznego szpitala. Do jego osiągnięcia wykorzystano krytyczny przegląd literatury. W opracowaniu&lt;span style="font-family: TimesNewRomanPSMT; font-size: 10pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; &lt;span style="font-family: TimesNewRomanPSMT; font-size: 7pt; color: #231f20; font-style: normal; font-variant: normal;"&gt;&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt;opisano także koncepcję efektywności i wskazano jej typy. Wyniki badań dostarczyły odpowiedzi na&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; pytania badawcze dotyczące: zakresu pomiaru efektywności szpitala, rodzajów efektywności oraz badań&lt;span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"&gt; nad efektywnością.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;

Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data

Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement. To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures. This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018. The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes. There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers. Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

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

BackgroundStandardized 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. AimTo 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. Data12,900,687 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. MethodsPopulation-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. ResultsWhen 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. ConclusionsComparisons 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.

Tips for calculating and displaying risk-standardized hospital outcomes in Stata

A major challenge of outcomes research is measuring hospital performance using readily available administrative data. When the outcome measure is mortality or morbidity, rates are adjusted to account for preexisting conditions that may confound their assessment. However, the concept of “risk-adjusted” outcomes is frequently misunderstood. In this article, we try to clarify things, and we describe Stata tools for appropriately calculating and displaying risk-standardized outcome measures. We offer practical guidance and illustrate the application of these tools to an example based on real data (30-day mortality following acute myocardial infarction in Latvia).

The impact of disease severity adjustment on hospital standardised mortality ratios: Results from a service-wide analysis of ischaemic stroke admissions using linked pre-hospital, admissions and mortality data.

BackgroundAdministrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment.MethodsWe linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia’s largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates.ResultsThe performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2–7).ConclusionsRankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance.

Assessing whether cancer stage is needed to evaluate measures of hospital surgical performance.

While public reports of hospital-level surgical quality measures are becoming increasingly common in health care, a comprehensive national assessment of surgical quality across multiple cancer sites has yet to be developed. Fee-for-service (FFS) Medicare claims present a potential resource from which to measure outcomes following cancer surgery given the national scope of patients and providers. However, due to the administrative nature of the data, clinical cancer information such as stage is not recorded. Leveraging the Surveillance, Epidemiology, and End Results (SEER) registry linked to FFS Medicare claims to analyse outcomes for patients whom we ultimately know stage information, we determined whether Medicare claims are suitable for measuring provider quality following cancer surgery by assessing the extent to which the lack of stage information modifies assessments of provider performance. We identified patients aged 66 and older undergoing cancer surgery between 2011 and 2013 from SEER-Medicare. We compared the changes in the risk-standardized rates (RSRs), decile rankings, and c-statistics with and without risk adjustment for cancer stage for three measures of hospital performance: 30-day mortality, surgical complications, and unplanned readmissions. The RSR changed by at most 11.4% for mortality and by less than 4% for complications and readmissions, indicating that measures of hospital performance were stable with and without adjustment for stage. The relative performance of hospitals was also stable, as demonstrated by fewer than 20% of hospitals changing decile rank. The c-statistic declined by less than 2% across all measures, indicating that model fit was not substantially worsened without this information. These findings support the use of FFS Medicare claims for hospital-level analyses of short-term outcomes following cancer surgery. Quality reporting based on these analyses can be used to help patients choose among hospitals and for evaluating policies to improve surgical cancer care.

Usefulness of bounce-back admission in monitoring the quality of practice in the emergency department.

Background: Recently, unscheduled readmissions after discharge from the emergency department (ED) (bounce-back admissions, BBAs) have been monitored as a hospital performance measure in countries other than Japan. It has been suggested that BBAs may be caused by errors in diagnoses or treatments.Purpose: This retrospective cohort study aimed to evaluate BBAs and improve the quality of medical care in the ED of Showa University Hospital by analyzing the data of adult patients (≥18 years) with index visits to the ED of Showa University Hospital between June 2011 and May 2013 (n=15,069).Patients and methods: Patients were registered and followed up for unscheduled admissions to this hospital within 7 days. In order to understand the reasons for BBAs, individual diagnoses upon BBA were compared to the corresponding diagnoses upon discharge.Results: Among the 11,669 discharged patients, 180 patients were admitted within 3 days after discharge (3-day BBAs), and 257 were admitted within 7 days after discharge (7-day BBAs). The main diagnoses upon admission (BBA) were pneumonia or exacerbation of chronic obstructive pulmonary disease (COPD) or asthma (n=40, 16%), cholecystitis or cholangitis (n=21, 8.2%), and urinary tract infection (n=16, 6.2%). Among the 7-day BBA cases, 117 patients had similar and 110 patients had different diagnoses upon discharge and admission; in the remaining 30 cases, the results could not be ascertained owing to incomplete diagnostic data. In the cases of pneumonia, exacerbation of COPD or asthma, and colitis or enterocolitis, there was a significantly higher “similar” diagnoses than “different”, while the reverse was true for cases of stroke, ileus or bowel obstruction, and meningitis. These results were shared with the ED staff, and similar surveillances were periodically conducted. The frequency of admission within 7 days after discharge continuously declined from 2013 to 2016.Conclusion: Analyzing the discharge and admission diagnoses may help ED staff to understand the reasons for common errors in order to follow the plan-do-check-act cycle of medical care in the ED.

The Quest for Quality and Performance Indicators in Mass Disasters

Introduction:Indicators are used as a benchmark for the quality of disaster response. Desirable attributes of indicators include precision, clear definition, improvement opportunity, unbiased, flexibility, and validity. Due to a lack of universally acceptable, objective indicators, it is currently difficult to gauge improvements in mass casualty preparedness within a hospital.Aim:To describe existing indicators relevant to hospital disaster response, and to explore the use of two new indicators (decanting and chain of command).Methods:A structured literature search in indexed databases was used to identify articles related to the measurement of hospital performance in mass casualties using a matrix technique and snowballing. Relevant websites of disaster management organizations were also reviewed and local disaster management experts were interviewed. Proposed indicators were compared against attributes and some (triage time by category, notification time, time to adequate staff response, preventable deaths, decanting times and chain of command for intensive care unit, and emergency department) were tested and measured in two exercises involving more than 90 staff each, held at two Southeast Queensland hospitals in 2017 and 2018.Results:Over 50 proposed indicators, including indicators within large sets, were identified. Measurement of some indicators was found to be highly subjective. The decanting and chain-of-command indicators emerged as most useful. Intensive Care Unit required 40 mins to decant beds by 50%, while ED required 25 mins to decant beds by 80%. With regards to the chain of command, ED and triage staff performed best, with 66.7% correctly identifying their immediate supervisor. Overall, staff members were able to correctly identify immediate supervisor better compared to team leaders (59.3% and 40% respectively).Discussion:There is a need to narrow down, simplify, and objectify indicators for mass casualty performance. Baseline measurements from actual disasters will provide important comparative data.

Analysis of hospital performance measurement using performance prism method

A good performance measurement should measure all aspects of the stakeholders, including investors, customer, employees, suppliers, regulators and communities. Performance prisms are one of performance measurement methods that assess all aspects of stakeholders. A hospital which has established in Makassar is such organization that used conventional performance measurement. It conducted performance measurement based on only the financial aspect, while the others less noticed. This study applies performance prism that supported by Analytical Hierarchy Process (AHP) as weighting system and integrated of objectives matrix (OMAX) and traffic light systems as scoring systems. The results obtain 99 KPIs consists of 17 investor aspects, 35 customer aspects, 16 employee aspects, 15 supplier aspects and 16 regulator and community aspects. Overall performance index is 7.9 in the yellow category that needs some improvements.

Abstract WP372: International Comparison of Patient Characteristics and Quality of Care for Ischemic Stroke

Background and Purpose: Adherence to evidence-based guidelines is an important indicator of hospital stroke care quality; yet, there is lack of assessment of patient characteristics and performance measures in acute ischemic stroke for most world regions. Methods: We analyzed data of 19,604 acute ischemic stroke patients in China National Stroke Registry and 194,876 patients in GWTG-Stroke registry in US between 2012 to 2013 and compare their baseline characteristics and hospital performance measures. Results: Compared with US, Chinese patients were younger and had lower prevalence of comorbidities except for history of stroke/TIA or smoking. NIHSS was similar (China vs. US: median 4 [IQR 2-7] vs. 4 [1-10]). Chinese patients were more likely to have delays from last-known-well to hospital arrival (median 1,318 minutes [330-3,209] vs. 644 [142-2,055]), less likely to receive thrombolytic therapy (2.5% vs. 8.1%), and were more likely to experience treatment delays (door-to-needle time 95 minutes [72-112] vs. 62 [49-85]). Adherence to early and discharge antithrombotics, smoking cessation counseling, and dysphagia screening were relatively high (e.g.&gt;80%) in both countries. However, large gaps existed in administration of intravenous thrombolytics within 3 hours (18.3% vs. 83.6%), door-to-needle time ≤60 minutes (14.6% vs. 48.0%), deep vein thrombosis prophylaxis (65.0% vs. 97.8%), anticoagulation for atrial fibrillation (21.0% vs. 94.4%), lipid treatment for low-density lipoprotein &gt; 100 mg/dL (66.3% vs. 95.8%), and rehabilitation assessment (58.8% vs. 97.4%). Door-to-CT ≤25 minutes was relatively low in both countries (26.4% vs. 27.9%). Conclusions: We found significant differences in clinical characteristics and gaps in adherence for certain performance measures between China and US. Additional efforts are needed for continued improvements in acute stroke care and secondary prevention in both nations, especially China.

Benchmarking Danish hospitals on mortality and readmission rates after cardiovascular admission.

ObjectiveThe aim of this study was to examine hospital performance measures that account more comprehensively for unique mixes of patients’ characteristics.DesignNationwide cohort registry-based study within a population-based health care system.ParticipantsIn this study, 331,513 patients discharged with a primary cardiovascular diagnosis from 1 of 26 Danish hospitals during 2011–2015 were included. Data covering all Danish hospitals were drawn from the Danish National Patient Registry and the Danish National Health Service Prescription Database.Main outcome measuresThirty-day post-admission mortality rates, 30-day post-discharge readmission rates, and the associated numbers needed to harm were measured.MethodsFor each index hospital, we used a non-parametric logistic regression model to compute propensity scores. Propensity score weighted patients treated at other hospitals collectively resembled patients treated at the index hospital in terms of age, sex, primary discharge diagnosis, diagnosis history, medications, previous cardiac procedures, and comorbidities. Outcomes for the weighted patients treated at other hospitals formed benchmarks for the index hospital. Doubly robust regression formally tested whether the outcomes of patients at the index hospital differed from the outcomes of the patients used to form the benchmarks. For each index hospital, we computed the false discovery rate, ie, the probability of being incorrect if we claimed the hospital differed from its benchmark.ResultsFive hospitals exceeded their benchmark for 30-day mortality rates, with the number needed to harm ranging between 55 and 137. Seven hospitals exceeded their benchmark for readmission, with the number needed to harm ranging from 22 to 71. Our benchmarking approach flagged fewer hospitals as outliers compared with conventional regression methods.ConclusionConventional methods flag more hospitals as outliers than our benchmarking approach. Our benchmarking approach accounts more thoroughly for differences in hospitals’ patient case mix, reducing the risk of false-positive selection of suspected outliers. A more comprehensive system of hospital performance measurement could be based on this approach.

A clinical efficiency index for the measurement of hospital performance: development and validation

A clinical efficiency index for the measurement of hospital performance: development and validation

Childbirth-specific patient-reported outcomes as predictors of hospital satisfaction

Under value-based payment programs, patient-reported experiences and outcomes can impact hospital and physician revenue. To enable obstetrical providers to improve the childbirth experience, a framework for understanding what women expect and desire during childbirth is needed. The purpose of this study was to identify key predictors of childbirth hospital satisfaction with the use of the Childbirth Experiences Survey. This study builds on a larger effort that used Patient-Reported Outcomes Management Information System methods to develop a childbirth-specific preliminary patient-reported experiences and outcomes item bank. These efforts led to the development of an antepartum and postpartum survey (Childbirth Experiences Survey Parts 1 and 2). All phases of the study were conducted with the participation of a community-based research team. We conducted a prospective observational study using national survey response panels that was organized through Nielsen to identify women's antepartum values and preferences for childbirth (Childbirth Experiences Survey Part 1). Eligible participants were pregnant women in the United States (English or Spanish speaking) who were ≥18 years old and ≥20 weeks pregnant. Women were recontacted and invited to participate in a postpartum follow-up survey to collect information about their childbirth patient-reported experiences and outcomes, which included childbirth satisfaction (Childbirth Experiences Survey Part 2). In bivariate analyses, we tested whether predisposing conditions (eg, patient characteristics or previous experiences), values and preferences, patient-reported experiences and outcomes, and the "gaps" between values and preferences and patient-reported experiences and outcomes were predictors of women's satisfaction with hospital childbirth services. Multivariable logistic regression models were fitted to examine the simultaneous effect of predictors on hospital satisfaction, which were adjusted for key predisposing conditions. From 500 women who anticipated a vaginal delivery at the time of the antepartum survey, who labored before delivery, and who answered the postpartum survey, key findings included the following responses: (1) the strongest predictors of women's satisfaction with hospital childbirth services were items in the domains of staff communication, compassion, empathy, and respect, and (2) 23 childbirth-specific patient-reported experiences and outcomes were identified. Examples of these patient-reported experiences and outcomes (such as being told about progress in labor and being involved in decisions regarding labor pain management) appeared especially relevant to women who experienced childbirth. A final model that predicted women's satisfaction with hospital childbirth services included a total of 8 items that could be optimized by doctors, midwives, and hospitals. These included the patient's report of how well she coped with labor pain, whether the hospital provided adequate space and food for their support person, and whether she received practical support for feeding the newborn infant. This study identified 23 childbirth-specific patient-reported experiences and outcomes that were predictors of childbirth hospital satisfaction. The implementation of the Childbirth Experiences Survey Parts 1 and 2 in a multihospital setting may lead to the development of childbirth hospital performance measures and strategies for improvement of the childbirth experience.

Predictors of 30-Day Readmission After Total Knee Arthroplasty: Analysis of 566,323 Procedures in the United Kingdom

BackgroundAll-cause 30-day readmission after total knee arthroplasty (TKA) is currently used as a measure of hospital performance in the United States and elsewhere. Readmissions from surgical causes may more accurately reflect preventability and costs. However, little is known about whether predictors of each type of readmission differ. MethodsAll primary TKAs recorded in England’s National Health Service administrative database from 2006 to 2015 were included. Multilevel logistic regression analysis was used to describe the effects of patient-related factors on 30-day readmission risk using 3 different readmission metrics: all-cause, surgical (defined using International Classification of Disease-10 primary admission diagnoses), and those resulting in return to theater (RTT). ResultsIn total, 566,323 procedures were recorded. The comorbidity with the highest odds ratio (OR) for all types of readmission was psychoses (RTT OR 2.52, P < .001). Obesity was a strong independent predictor of RTT (OR 1.36, P < .001) and had the highest population attributable fraction of any comorbidity (4.7%). Unicompartmental arthroplasty was associated with a significantly lower risk of all types of readmission when compared with TKA, with the effect being most pronounced for surgical readmission (OR 0.66, P < .001). RTT in the index episode increased the risk of RTT readmission (OR 2.80, P < .001), as did any emergency admission to hospital in the preceding 12 months (for >2 emergency admissions, all-cause OR 2.38, P < .001). Length of stay either more than or less than 2 days was associated with an increased risk of all-cause and surgical readmission but not RTT readmission. ConclusionPatient-related predictors of surgical and RTT readmission following TKA differ from those for all-cause readmission, but only the latter metric is in widespread use.