Hospital Performance Metrics Research Articles

Evaluating the Implementation of the Connect for Health Pediatric Weight Management Program

Adoption of primary care interventions to reduce childhood obesity is limited. Progress in reducing obesity prevalence and eliminating disparities can be achieved by implementing effective childhood obesity management interventions in primary care settings. To examine the extent to which implementation strategies supported the uptake of research evidence and implementation of the Connect for Health pediatric weight management program. This quality improvement study took place at 3 geographically and demographically diverse health care organizations with substantially high numbers of children living in low-income communities in Denver, Colorado; Boston, Massachusetts; and Greenville, South Carolina, from November 2019 to April 2022. Participants included pediatric primary care clinicians and staff and families with children aged 2 to 12 years with a body mass index (BMI) in the 85th percentile or higher. Pediatric weight management program with clinician-facing tools (ie, clinical decision support tools) and family-facing tools (ie, educational handouts, text messaging program, community resource guide) along with implementation strategies (ie, training and feedback, technical assistance, virtual learning community, aligning with hospital performance metrics) to support the uptake. Primary outcomes were constructs from the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) Framework examined through parent, clinician, and leadership surveys and electronic health record data to understand the number of children screened and identified, use of the clinical decision support tools, program acceptability, fidelity to the intervention and implementation strategies, and program sustainability. The program screened and identified 18 333 children across 3 organizations (Denver Health, 8480 children [46.3%]; mean [SD] age, 7.97 [3.31] years; 3863 [45.5%] female; Massachusetts General Hospital (MGH), 6190 children [33.8%]; mean [SD] age, 7.49 [3.19] years; 2920 [47.2%] female; Prisma Health, 3663 children [20.0%]; mean [SD] age, 7.33 [3.15] years; 1692 [46.2%] female) as having an elevated BMI. The actionable flagging system was used for 8718 children (48%). The reach was equitable, with 7843 children (92.4%) from Denver Health, 4071 children (65.8%) from MGH, and 1720 children (47%) from Prisma Health being from racially and ethnically minoritized groups. The sites had high fidelity to the program and 6 implementation strategies, with 4 strategies (67%) used consistently at Denver Health, 6 (100%) at MGH, and 5 (83%) at Prisma Health. A high program acceptability was found across the 3 health care organizations; for example, the mean (SD) Acceptability of Intervention Measure score was 3.72 (0.84) at Denver Health, 3.82 (0.86) at MGH, and 4.28 (0.68) at Prisma Health. The implementation strategies were associated with 7091 (39%) uses of the clinical decision support tool. The mean (SD) program sustainability scores were 4.46 (1.61) at Denver Health, 5.63 (1.28) at MGH, and 5.54 (0.92) at Prisma Health. These findings suggest that by understanding what strategies enable the adoption of scalable and implementation-ready programs by other health care organizations, it is feasible to improve the screening, identification, and management of children with overweight or obesity and mitigate existing disparities.

The Influence of Patient and Surgeon Characteristics on Press Ganey Provider Ratings in Plastic Surgery.

Although patient satisfaction ratings are increasingly used as hospital and provider performance metrics, these ratings may be affected by factors extraneous to surgeon performance. The aim of this study was to assess whether outpatient Press Ganey ratings for plastic surgery providers were tied more to provider or patient characteristics. All Consumer Assessment of Healthcare Providers and Systems Clinician & Group Survey (CG-CAHPS) responses for plastic surgery providers from 2017 to 2023 from a single institution were analyzed. Ordered logistic regression models were used to identify characteristics associated with provider ratings. In total, 6442 surveys from 4594 patients representing 29 plastic surgery providers were analyzed. Across the patient cohort, 23.2% (N = 1492) rated plastic surgery providers lower than a 10/10. After adjustment, provider characteristics including provider gender, patient-provider gender concordance, and years in practice were not associated with overall provider ratings (all P > .05). In contrast, patient characteristics were associated with provider ratings. Older patient age (odds ratio [OR], 1.02; P < .001) was associated with higher ratings. Finally, self-reported overall health and mental health were associated with lower provider ratings (both P < .05); specifically, mental health categories lower than "Excellent" were associated with significantly lower provider ratings (overall mental health "Very Good': OR, 0.57; "Good": OR, 0.53; "Fair": OR, 0.59; "Poor": OR, 0.55; all P < .05). Press Ganey ratings for plastic surgeons are influenced by patient characteristics, including age and mental health. Because satisfaction ratings are increasingly used as performance metrics in plastic surgery, adjusted rating models may be needed to more accurately reflect surgeon performance.

Factors affecting the Health Care Insurance Inclusion and Saudi Hospital Management Operation Efficiency

The Saudi healthcare scene has seen significant changes, with an increased emphasis on improving the quality and availability of medical treatments. Given the critical role that hospitals play in the delivery of healthcare services, it is critical to understand the relationship between management strategies and operational efficiency. It is difficult to transform a weak existing program into a large project of national health care insurance in a country that lacks a stable and implanted national health care insurance program, such as KSA. This inclusion, while highly helpful for human health care, poses a significant challenge for health care management and may have an impact on Management Efficiency, which is heavily influenced by variables such as Management Staff Satisfaction. Plan for Facility Availability, Management Cost Reduction, and Patient Safety Improvement After healthcare insurance reforms, healthcare professionals were required to have compensatory shifts, which healthcare administrators were required to supply. Using well-established theories of organizational management and healthcare economics, this study will enhance hospital management practices in terms of operational efficiency results. Furthermore, the study looks into the extent to which the availability of health insurance functions as a moderator in this relationship. The availability of health care insurance influences patients' ability to obtain medical services and has the potential to influence the efficacy of hospital management practices. Quantitative research was employed to achieve these objectives. Quantitative data is gathered through surveys and historical hospital performance metrics derived from interviews and focus group conversations with key players in Saudi Arabia's healthcare system.

Near real-time prediction of urgent care hospital performance metrics using scalable random forest algorithm: A multi-site development

While previous studies have shown the potential value of predictive modelling for emergency care, few models have been implemented for producing near real-time predictions across various demand, utilisation and performance metrics. In this study, 33 independent Random Forest (RF) algorithms were developed to forecast 11 urgent care metrics over a 24-hour period across three hospital sites in an Integrated Care System (ICS) in South West England. Metrics included: ambulance handover delay; emergency department occupancy; and patients awaiting admission. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE) were used to assess the performance of RF and compare it to two alternative models: naïve baseline (NB) and Auto-Regressive Integrated Moving Average (ARIMA). Using these measures, RF outperformed NB and ARIMA in 76% (N = 25/33) of urgent care metrics according to SMAPE, 88% (N = 29/33) according to MAE and 91% (N = 30/33) according to RMSE. The RFs developed in this study have been implemented within the local ICS, providing predictions on an hourly basis that can be accessed by local healthcare planners and managers.

Evaluating post-earthquake functionality and surge capacity of hospital emergency departments using discrete event simulation

Past earthquakes have illustrated the impacts of reduced hospital functionality due to physical damage resulting in a health service deficit immediately after a major seismic event. In this article, a methodology was developed to quantify the deficit in health care anticipated due to a loss of functionality of a hospital emergency department (ED) and a surge in demand due to regional damage in an earthquake scenario. Earthquake-induced patient arrivals were calculated using multi-severity casualty estimation for the catchment area of the hospital. The surge in patients (demand) was then compared to the ability of the hospital to treat patients (capacity) based on anticipated functionality. Nonlinear response history analysis of the hospital building was performed using simplified structural models, and the structural and non-structural component damage was estimated based on FEMA P-58. Expected damage was linked to the post-earthquake functionality of the ED service areas on each floor by incorporating the fault-tree analysis method. Finally, discrete event simulation was used to evaluate the ED surge capacity, providing hospital performance metrics, such as wait times (WTs) and length of stay (LOS) for patients of ranging acuity. A case study of a hospital in the City of Vancouver subjected to an Mw9.0 Cascadia Subduction Zone scenario earthquake was presented. Emergency rooms (ERs) were identified as the ED bottleneck during the emergency response. The mean ER WT exceeded its limit of 2 h and reached up to 17 h in the most unfavorable simulation. Likewise, the mean LOS nearly doubled from 6.5 to 12 h, also exceeding the established target of 10 h. The deployment of field hospitals for less severe patients as an emergency plan to mitigate the ED overcrowding was also analyzed to demonstrate that the methodology can be used as a decision support tool to improve healthcare disaster planning.

Phase-Type Survival Trees to Model a Delayed Discharge and Its Effect in a Stroke Care Unit

The problem of hospital patients’ delayed discharge or ‘bed blocking’ has long been a challenge for healthcare managers and policymakers. It negatively affects the hospital performance metrics and has other severe consequences for the healthcare system, such as affecting patients’ health. In our previous work, we proposed the phase-type survival tree (PHTST)-based analysis to cluster patients into clinically meaningful patient groups and an extension of this approach to examine the relationship between the length of stay in hospitals and the destination on discharge. This paper describes how PHTST-based clustering can be used for modelling delayed discharge and its effects in a stroke care unit, especially the extra beds required, additional cost, and bed blocking. The PHTST length of stay distribution of each group of patients (each PHTST node) is modelled separately as a finite state continuous-time Markov chain using Coxian-phase-type distributions. Delayed discharge patients waiting for discharge are modelled as the Markov chain, called the ‘blocking state’ in a special state. We can use the model to recognise the association between demographic factors and discharge delays and their effects and identify groups of patients who require attention to resolve the most common delays and prevent them from happening again. The approach is illustrated using five years of retrospective data of patients admitted to the Belfast City Hospital with a stroke diagnosis.

Ninety-Day Risk-Standardized Home Time as a Performance Metric for Cardiac Surgery Hospitals in the United States.

Assessing hospital performance for cardiac surgery necessitates consistent and valid care quality metrics. The association of hospital-level risk-standardized home time for cardiac surgeries with other performance metrics such as mortality rate, readmission rate, and annual surgical volume has not been evaluated previously. The study included Medicare beneficiaries who underwent isolated or concomitant coronary artery bypass graft, aortic valve, or mitral valve surgery from January 1, 2013, to October 1, 2019. Hospital-level performance metrics of annual surgical volume, 90-day risk-standardized mortality rate, 90-day risk-standardized readmission rate, and 90-day risk-standardized home time were estimated starting from the day of surgery using generalized linear mixed models with a random intercept for the hospital. Correlations between the performance metrics were assessed using the Pearson correlation coefficient. Patient-level clinical outcomes were also compared across hospital quartiles by 90-day risk-standardized home time. Last, the temporal stability of performance metrics for each hospital during the study years was also assessed. Overall, 919 698 patients (age 74.2±5.8 years, 32% women) were included from 1179 hospitals. Median 90-day risk-standardized home time was 71.2 days (25th-75th percentile, 66.5-75.6), 90-day risk-standardized readmission rate was 26.0% (19.5%-35.7%), and 90-day risk-standardized mortality rate was 6.0% (4.0%-8.8%). Across 90-day home time quartiles, a graded decline was observed in the rates of in-hospital, 90-day, and 1-year mortality, and 90-day and 1-year readmission. Ninety-day home time had a significant positive correlation with annual surgical volume (r=0.31; P<0.001) and inverse correlation with 90-day risk-standardized readmission rate (r=-0.40; P <0.001) and 90-day risk-standardized mortality rate (r=-0.60; P <0.001). Use of 90-day home time as a performance metric resulted in a meaningful reclassification in performance ranking of 22.8% hospitals compared with annual surgical volume, 11.6% compared with 90-day risk-standardized mortality rate, and 19.9% compared with 90-day risk-standardized readmission rate. Across the 7 years of the study period, 90-day home time demonstrated the most temporal stability of the hospital performance metrics. Ninety-day risk-standardized home time is a feasible, comprehensive, patient-centered metric to assess hospital-level performance in cardiac surgery with greater temporal stability than mortality and readmission measures.

The Impact Of Acute Heart Failure Related Length Of Stay On The 30-day All-Cause Readmission Rate.

The Impact Of Acute Heart Failure Related Length Of Stay On The 30-day All-Cause Readmission Rate.

Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

PurposeAbundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.Design/methodology/approachAs the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.FindingsThe empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.Practical implicationsWith these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.Originality/valueTo address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Hospital Performance for Gastrointestinal Bleeding Mortality, Length of Stay, and Complication Rates in the USA.

Hospitals are held accountable for quality metrics, through public reporting programs and by payers. However, little is known about hospital performance in GIB nationally. A retrospective longitudinal analysis utilizing Vizient's database was performed to identify GIB hospitalizations across 349 hospitals from 2016 to 2018. The primary outcome was risk-adjusted mortality; secondary outcomes included risk-adjusted length of stay and complication rate. Trends in performance were characterized using quintiles, with analysis of concordance within hospitals and across hospitals over time. Pearson's correlation coefficients were performed to assess the relationship among metrics. 28.1% of hospitals had a steadily improving risk-adjusted mortality index from 2016 to 2018, while 15.5% were steadily worsening in mortality. For LOS, 25.2% of hospitals were improving, while 22.4% deteriorated. For complication rate, 22.9% of hospitals steadily improved, while 19.2% of hospitals deteriorated. Although many hospitals improved substantially in one outcome, they did not necessarily improve in all outcomes. Of the 98 hospitals that steadily improved in mortality from 2016 to 2018, only 8 out of 98 steadily improved in all three outcomes (8.3%). Across all 3years, mortality was weakly correlated with LOS (r = 0.22, p < 0.001), but not with the rate of complications (r = 0.08, p = 0.12). Hospital performance metrics for GIB, such as mortality, length of stay, and complication rate, are weakly correlated and thus likely measure different aspects of care. While many hospitals improved over time, few hospitals improved in all three metrics. Additionally, many hospitals are deteriorating over time, and further research is needed to determine which care processes are associated with better outcomes.

County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.

The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to be associated with disparate health outcomes. Associations between county-level SES factors and CMS’s risk-adjusted 30-day acute myocardial infarction (AMI) mortality rates are explored for n = 2462 hospitals using a variety of sources for county-level SES information. Upon performing multiple imputation, a stepwise backward elimination model selection approach using Akaike’s information criteria was used to identify the optimal model. The resulting model, comprised of 14 predictors mostly at the county level, provides an additional 8% explanatory power to capture the variability in 30-day risk-standardized AMI mortality rates, which already account for patient-level clinical differences. SES factors may be an important feature for inclusion in future risk-adjustment models, which will have system and policy implications for distributing resources to hospitals, such as reimbursements. It also serves as a stepping stone to identify and address long-standing SES-related inequities.

Association Between Hospital Performance Metrics and Market Share

Association Between Hospital Performance Metrics and Market Share

Integrated care pathways in neurosurgery: A systematic review.

Integrated care pathways (ICPs) are a pre-defined framework of evidence based, multidisciplinary practice for specific patients. They have the potential to enhance continuity of care, patient safety, patient satisfaction, efficiency gains, teamwork and staff education. In order to inform the development of neurosurgical ICPs in the future, we performed a systematic review to aggregate examples of neurosurgical ICP, to consider their impact and design features that may be associated with their success. Electronic databases MEDLINE, EMBASE, and CENTRAL were searched for relevant literature published from date of inception to July 2020. Primary studies reporting details of neurosurgical ICPs, across all pathologies and age groups were eligible for inclusion. Patient outcomes in each case were also recorded. Twenty-four studies were included in our final dataset, from the United States, United Kingdom, Italy, China, Korea, France, Netherlands and Switzerland, and a number of sub-specialties. 3 for cerebrospinal fluid diversion, 1 functional, 2 neurovascular, 1 neuro-oncology, 2 paediatric, 2 skull base, 10 spine, 1 for trauma, 2 miscellaneous (other craniotomies). All were single centre studies with no regional or national examples. Thirteen were cohort studies while 11 were case series which lacked a control group. Effectiveness was typically evaluated using hospital or professional performance metrics, such as length of stay (n = 11, 45.8%) or adverse events (n = 17, 70.8%) including readmission, surgical complications and mortality. Patient reported outcomes, including satisfaction, were evaluated infrequently (n = 3, 12.5%). All studies reported a positive impact. No study reported how the design of the ICP was informed by published literature or other methods. ICPs have been successfully developed across numerous neurosurgical sub-specialities. However, there is often a lack of clarity over their design and weaknesses in their evaluation, including an underrepresentation of the patient's perspective.

Smart Hospital Management System

Information and Communication Technologies (ICTs) are commonly using in healthcare organizations worldwide. There are different kinds of healthcare applications developed in android Smartphone’s which help patients and their caregivers to reduce time and cost efficiency. Hospitals are the largest and most complex organizations where health care is provided. Safe and effective patient care services in hospitals depend on the efficient decisions made by hospital executives. The main task of hospital executives is to ensure the hospital can provide high quality patient care and services. This Android application used for displaying hospital performance metrics on a daily basis. This application allows hospital executives to review and monitor hospital operational data with ease of access and in a portable manner. Thus, reducing the effort of the hospital executives to perform their tasks. In this research work, an application is developed that locates the nearest hospital. The System is designed for Any Hospital to replace their existing manual, paper-based system. The new system is to control the following information; List of Hospitals, bed availability, Book Appointment, List of Doctors, Facilities and Book Ambulance. With the help of this application, a patient can find the nearest hospital according to specialized consultant availability.

Textbook outcomes in hepatobiliary and pancreatic surgery

The concept of textbook outcome (TO) has recently gained popularity in surgical research and has been used to evaluate the quality or success of different surgical procedures, including hepatopancreatobiliary (HPB) operations. TO consists of individual outcome parameters that each reflect different domains of care including structure, process, and individual outcomes; in turn, the composite TO metric represents the optimal course after a surgical episode. TO can be used to assess patient-level outcomes, hospital performance, center designation and quality metrics. In addition to being an outcome measurement, TO may also be linked to healthcare costs. Future efforts should be directed towards establishing a universal definition of TO in HPB surgery so that surgeons and hospitals can assess and compare outcomes, identify shortcomings and improve real world patient outcomes.

Unintended Consequences of Health Care Reform: Impact of Changes in Payor Mix on Patient Safety Indicators.

To evaluate the impact of the Affordable Care Act's Medicaid expansion on patient safety metrics at the hospital level by expansion status, across varying levels of safety-net burden, and over time. Medicaid expansion has raised concerns over the influx of additional medically and socially complex populations on hospital systems. Whether increases in Medicaid and uninsured payor mix impact hospital performance metrics remains largely unknown. We sought to evaluate the effects of expansion on Centers for Medicare and Medicaid Services-endorsed Patient Safety Indicators (PSI-90). Three hundred fifty-eight hospitals were identified using State Inpatient Databases (2012-2015) from 3 expansions (KY, MD, NJ) and 2 nonexpansion (FL, NC) states. PSI-90 scores were calculated using Agency for Healthcare Research and Quality modules. Hospital Medicaid and uninsured patients were categorized into safety-net burden (SNB) quartiles. Hospital-level, multivariate linear regression was performed to measure the effects of expansion and change in SNB on PSI-90. PSI-90 decreased (safety improved) over time across all hospitals (-5.2%), with comparable reductions in expansion versus nonexpansion states (-5.9% vs -4.7%, respectively; P = 0.441) and across high SNB hospitals within expansion versus nonexpansion states (-3.9% vs -5.2%, P = 0.639). Pre-ACA SNB quartile did not predict changes in PSI-90 post-ACA. However, when hospitals increased their SNB by 5%, they incurred significantly more safety events in expansion relative to nonexpansion states (+1.87% vs -14.0%, P = 0.013). Despite overall improvement in patient safety, increased SNB was associated with increased safety events in expansion states. Accordingly, Centers for Medicare and Medicaid Services measures may unintentionally penalize hospitals with increased SNB following Medicaid expansion.

Risk factors, resource use, and cost of postoperative low cardiac output syndrome

ObjectiveLow cardiac output syndrome complicates recovery after cardiac surgery. We examined the incidence and risk factors for low cardiac output syndrome and its association with postoperative mortality, morbidity, resource use, and cost. MethodsThis cross-sectional retrospective observational study examined patients having cardiac surgery captured in the Premier Healthcare Database. Low cardiac output syndrome was defined as the requirement for postoperative mechanical circulatory support and/or hemodynamic instability requiring prolonged inotropic support. Incidence, risk factors, and association of low cardiac output syndrome with postoperative outcomes, including mortality, hospital and intensive care unit length of stay, hospital readmission, and cost at 30 days, 90 days, and 6 months, were examined. ResultsAmong 59,810 patients from 164 hospitals having cardiac surgery between July 1, 2012, and June 30, 2014, low cardiac output syndrome developed in 6067 (10.1%) patients. Patients presenting in cardiogenic shock or systolic (± diastolic) heart failure were at greatest risk. Risk-adjusted in-hospital mortality was 12-fold greater with low cardiac output syndrome (odds ratio, 12.0; 95% confidence interval, 10.6-13.5). Risk-adjusted hospital costs (2019$; median [Q1, Q3]) were $64,041 [21,439] in patients who developed low cardiac output syndrome versus $48,086 [16,098] without; P < .001. Increased costs were driven by longer risk-adjusted hospital stay (10.1 [4.5] vs 8.5 [3.8] days); P < .001, intensive care unit (5.5 [2.5] vs 3.3 [1.5] days; P < .001) stay, and all-cause 30-day adjusted hospital readmission rates (mean [SD] 16.6 [8.2]% vs 13.9 [7.2]%; P < .001). ConclusionsCardiac surgical patients who develop postoperative low cardiac output syndrome suffer greater mortality and have greater resource use, health care costs, and all-cause readmission, which informs perioperative decision making, and impacts hospital performance metrics and federal priority to reduce health care costs.

Exact Science and the Art of Approximating Quality in Hospital Performance Metrics

Exact Science and the Art of Approximating Quality in Hospital Performance Metrics

Nonparametric change point estimation for survival distributions with a partially constant hazard rate.

We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g.mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction.

Hospital cultural competency as a systematic organizational intervention

Cultural competency or the ongoing capacity of health care systems to provide for high-quality care to diverse patient populations (National Quality Forum, 2008) has been proposed as an organizational strategy to address disparities in quality of care, patient experience, and workforce representation. But far too many health care organizations still do not treat cultural competency as a business imperative and driver of strategy. The aim of the study was to examine the impact of a systematic, multifaceted, and organizational level cultural competency initiative on hospital performance metrics at the organizational and individual levels. This demonstration project employs a pre-post control group design. Two hospital systems participated in the study. Within each system, two hospitals were selected to serve as the intervention and control hospitals. Executive leadership (C-suite) and all staff at one general medical/surgical nursing unit at the intervention hospitals experienced a systematic, planned cultural competency intervention. Assessments and interventions focused on three organizational level competencies of cultural competency (diversity leadership, strategic human resource management, and patient cultural competency) and three individual level competencies (diversity attitudes, implicit bias, and racial/ethnic identity status). In addition, we evaluated the impact of the intervention on diversity climate and workforce diversity. Overall performance improvement was greater in each of the two intervention hospitals than in the control hospital within the same health care system. Both intervention hospitals experienced improvements in the organizational level competencies of diversity leadership and strategic human resource management. Similarly, improvements were observed in the individual level competencies for diversity attitudes and implicit bias for Blacks among the intervention hospitals. Furthermore, intervention hospitals outperformed their respective control hospitals with respect to diversity climate. A focused and systematic approach to organizational change when coupled with interventions that encourage individual growth and development may be an effective approach to building culturally competent health care organizations.