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Comparing High- and Low-Performing Hospitals Using Risk Adjusted Excess Mortality and Cost Inefficiency
Abstract
This study examines characteristics associated with high- and low-performing hospitals, where performance is defined in terms of both mortality outcomes and efficiency. In particular, we use data for Florida hospitals in 1999-2001 to classify hospitals into performance groups based on both risk-adjusted excess mortality and cost efficiency. The results indicate that hospitals in the high-performing group were more likely to be for-profit, had higher occupancy rates, had proportionately more Medicare and proportionately fewer Medicaid and self-pay patients, used fewer patient-care personnel per admission, and had higher operating margins than all other hospitals. Hospitals in the low-performing group, on the other hand, were less likely to be for-profit, had more beds, used more patient-care personnel per admission, had lower pay per patient-care personnel, had higher average costs, and had lower operating margins than all other hospitals. Interestingly, managed care presence, measured by proportion of HMO-PPO admissions, was not a significant factor in differentiating hospital performance groups.
... The characteristic associated with the highest level of evidence for a positive association was use of computerized physician order entry (CPOE) (level B) [12,13,17,23]. There was some evidence (level C) to support a positive association between performance and: network membership [20,21,24], service size [16, 20, 21, 24, 29, 31, 34 -36], nursing leadership [10,33], hospital volume [7,29] and nursing workforce design [8,16,20,21,27,36] and patient health outcomes as already described. ...
... There were 20 papers with 18 studies that investigated the associations between healthcare characteristics and financial performance. This included 2 moderate quality systematic reviews [12,37] and 16 observational studies (12 USA [14,15,20,21,23,27,32,(38)(39)(40)(41)(42)(43)(44), 2 UK [32,45], 2 Canada [34,46]). The financial performance outcomes reported included profitability (10 studies) and efficiency (11 studies). ...
... Multidimensional performance assessment. Nine studies (three in the UK and six in the USA), including one moderate-quality systematic review without meta-analysis [64] and eight (three high-quality [21,32,65,66], three moderate [20,28,65] and two low [27,61] quality) observational studies investigated a variety of hospital characteristics in relation to performance based on multidimensional outcomes. Overall, there was low-level evidence for associations between hospital characteristics and multidimensional performance, with the exception of financial incentives. ...
/st> The objective of this review was to critically appraise the literature relating to associations between high-level structural and operational hospital characteristics and improved performance.
/st> The Cochrane Library, MEDLINE (Ovid), CINAHL, proQuest and PsychINFO were searched for articles published between January 1996 and May 2010. Reference lists of included articles were reviewed and key journals were hand searched for relevant articles.
and data extraction Studies were included if they were systematic reviews or meta-analyses, randomized controlled trials, controlled before and after studies or observational studies (cohort and cross-sectional) that were multicentre, comparative performance studies. Two reviewers independently extracted data, assigned grades of evidence according to the Australian National Health and Medical Research Council guidelines and critically appraised the included articles. Data synthesis Fifty-seven studies were reported within 12 systematic reviews and 47 observational articles. There was heterogeneity in use and definition of performance outcomes. Hospital characteristics investigated were environment (incentives, market characteristics), structure (network membership, ownership, teaching status, geographical setting, service size) and operational design (innovativeness, leadership, organizational culture, public reporting and patient safety practices, information technology systems and decision support, service activity and planning, workforce design, staff training and education). The strongest evidence for an association with overall performance was identified for computerized physician order entry systems. Some evidence supported the associations with workforce design, use of financial incentives, nursing leadership and hospital volume.
/st> There is limited, mainly low-quality evidence, supporting the associations between hospital characteristics and healthcare performance. Further characteristic-specific systematic reviews are indicated.
... In this study, we consider two measures of operational performance in hospitals. One measure is time-related -average length of stay (Ashby et al., 2000;Glick et al., 2003), and the other is quality related -risk-adjusted mortality rate (McKay and Deily, 2005;Gowrisankaran and Town, 2003). Below we develop our conceptual model in more detail to explore how both contextual and operation performance factors may be related to hospital cost performance. ...
... Another frequently used measure of operational effectiveness in hospitals is the risk-adjusted mortality rate (Rogowski et al., 2004;McKay and Deily, 2005;Gowrisankaran and Town, 2003). In this paper, we employ the risk-adjusted mortality rate as an indicator of hospital operational performance (Rogowski et al., 2004;McKay and Deily, 2005;Gowrisankaran and Town, 2003). ...
... Another frequently used measure of operational effectiveness in hospitals is the risk-adjusted mortality rate (Rogowski et al., 2004;McKay and Deily, 2005;Gowrisankaran and Town, 2003). In this paper, we employ the risk-adjusted mortality rate as an indicator of hospital operational performance (Rogowski et al., 2004;McKay and Deily, 2005;Gowrisankaran and Town, 2003). Two different perspectives lead to two different expectations with respect to mortality rate and cost. ...
The purpose of this paper is to examine empirically how operational performance and contextual factors contribute to differences in overall patient care costs across different hospitals.
Administrative data are employed from a sample of hospitals in New York State to construct measures of contextual factors, operational performance, and cost per patient. Operational performance and cost variables are adjusted to account for case mix differences across hospitals. Hierarchical regression is used to analyze the effects of contextual and operational variables on cost performance.
Increased length of stay, increased patient volume, and educational mission were associated with higher cost per patient. Mortality performance was associated with lower cost per patient. However, it was not found that location, size, or ownership status had a significant relationship with cost performance.
This paper identifies several significant relationships between contextual and operational variables and hospital costs. From a managerial perspective, these findings highlight the fact that some drivers of cost in hospitals are under the control of managers. One of the primary cost drivers in the study is length of stay, which implies that there is significant room for improvement in healthcare performance through a focus on operational excellence.
For researchers, the present study highlights the relative importance of operational versus contextual factors, with respect to cost performance in hospitals. The results of this study also provide direction for additional research into the role operational performance might play in determining the overall organizational performance in a hospital.
... Hospitals are not only supposed to treat as many patients as they can, given the resources made available to them, they are also required to improve quality of care. Also, a trade-off between quantity and quality per stay is plausible, in the sense that resources allocated to quality improvement may prevent the hospital to admit more patients or that, in order to achieve a given level of quality, the hospital might keep their patients a bit longer but it doesn't increase their RIWs (Valdmanis et al., 2008;Mckay and Deily, 2005). ...
... Others estimate quality efficiency and quantity efficiency separately (Nayar and Ozcan, 2008). The former exclude quality of care from the production function and the latter ignores the trade-off between quantity and quality per stay (Valdmanis et al., 2008, Mckay andDeily, 2005). In our study, we included HSMR as an output in the production function. ...
Objective
To evaluate the technical efficiency of acute inpatient care at the pan‐Canadian level and to explore the factors associated with inefficiency—why hospitals are not on their production frontier.
Data Sources/Study Setting
Canadian Management Information System (MIS) database (CMDB) and Discharge Abstract Database (DAD) for the fiscal year of 2012–2013.
Study Design
We use a nonparametric approach (data envelopment analysis) applied to three peer groups (teaching, large, and medium hospitals, focusing on their acute inpatient care only). The double bootstrap procedure (Simar and Wilson 2007) is adopted in the regression.
Data Collection/Extraction Methods
Information on inpatient episodes of care (number and quality of outcomes) was extracted from the DAD. The cost of the inpatient care was extracted from the CMDB.
Principal Findings
On average, acute hospitals in Canada are operating at about 75 percent efficiency, and this could thus potentially increase their level of outcomes (quantity and quality) by addressing inefficiencies. In some cases, such as for teaching hospitals, the factors significantly correlated with efficiency scores were not related to management but to the social composition of the caseload. In contrast, for large and medium nonteaching hospitals, efficiency related more to the ability to discharge patients to postacute care facilities. The efficiency of medium hospitals is also positively related to treating more clinically noncomplex patients.
Conclusions
The main drivers of efficiency of acute inpatient care vary by hospital peer groups. Thus, the results provide different policy and managerial implications for teaching, large, and medium hospitals to achieve efficiency gains.
... Firstly, we seek to test whether better process management at patient level provides higher level of outcome quality for the individual patient. Secondly, we follow the approach by McKay and Deily (2005) in profiling into highand low-performing departments according to a number of structure and process quality indicators (15). The purpose is to explore whether there are differences in these features of highand low-performing departments. ...
... Firstly, we seek to test whether better process management at patient level provides higher level of outcome quality for the individual patient. Secondly, we follow the approach by McKay and Deily (2005) in profiling into highand low-performing departments according to a number of structure and process quality indicators (15). The purpose is to explore whether there are differences in these features of highand low-performing departments. ...
Objective: The purpose is to explore whether better structure and process management provide better outcome quality for the individual patient and among hospital departments. Methods: Using patient level data in which 4,202 patients across seven vascular departments are pooled we estimate fixed effect logit models for three outcome quality measures; 30 day mortality, death after discharge and wound complications. First, we estimate the association between three process quality measures and the outcome quality for the individual patient. We then profile high- and lowperforming departments with respect to structural and process quality measures to explore whether more or less successful departments are characterised by specific features. Results: For the individual patient our results show that for death after discharge a higher length of stay reduces the risk of dying. At departmental level, our results suggest that staffing decisions may also be an important factor. However, additional research is needed in order to learn more about how structure and process indicators are associated with high-performance. Conclusions: Differences in outcome quality occur due to differences in the needs of patients treated, but also due to differences in how hospital departments organise care.
... html? redir ect=/ Hospi tal-Value-Based-Purch asing/ 2 Of particular relevance to this study are SFA studies, with a health care focus, that have used panel data; these include: Chirikos (1998), Chirikos and Sear (2000), Deily et al. (2000), Li and Rosenman (2001), Rosko (2001Rosko ( , 2004, Sari (2003), McKay and Deily (2005), Deily and McKay (2006), Furukawa et al. (2010), Mutter (2010, 2014). 3 For excellent reviews of the SFA literature, see: Kumbhakar and Lovell (2000), Greene (2008) and Belotti (2012). ...
The use of stochastic frontier models for inference on hospital efficiency is complicated by the inability to fully control for quality differences across hospitals. Additionally, the potential existence of cross-sectional dependence due to the presence of unobserved common factors leads to endogeneity problems that can bias both cost function and efficiency estimates. Using a panel consisting of 1518 hospitals for the years 1996–2013 (T = 18), I adopt techniques for dealing with long, cross-sectionally dependent panel data in order to estimate cost parameters and hospital specific efficiency. In particular, I employ the estimation technique proposed by Bai (Econometrica 77(4):1229–1279, 2009), which assumes that the unobservable heterogenous effects have a factor structure. I find evidence of considerable scale economies and that hospital cost inefficiencies have been increasing during the period of 1996–2013, and that the growth in expenditures is, in part, driven by spending that increases patient satisfaction, but that does not significantly contribute to improved patient health outcomes.
... Numerous studies have attempted to identify determinants of a hospital's profitability, and it has been widely believed that a hospital's proportions of Medicare and Medicaid services are inversely associated with the profitability due to their low reimbursement rate. This belief is well supported by the majority of studies: a negative association between Medicare program and hospital profitability [5][6][7][8]; and a negative association between Medicaid program and hospital profitability [5,[7][8][9][10][11]. However, some studies have reported mixed results for the relationship between government health programs and their effects on a hospital's profitability or cost-efficiency [8,11]. ...
... Thus, the CMI serves as a good proxy for patients' disease severity. The CMI has been successfully applied to models of hospital variance in morbidity and performance [26,40,44,45]. Furthermore, we included turnover and the percentage of patients admitted as emergency cases to adjust for differences in workload intensity that could not be sufficiently captured by the CMI alone [38,[46][47][48][49][50]. ...
Objective
To identify which unit types are most sensitive to nurse staffing levels.
Data sources/study setting
Collection of secondary data took place from March to July 2016. For our study, we analyzed administrative hospital claims data and self-reported structural data from hospitals in Germany. We used 26,502,579 admissions nested in 13,089 units in 3680 hospitals from 2012 to 2014.
Study design
We used regression analysis to examine the relationship between 11 established nursing-sensitive outcomes (NSOs) and nurse-to-patient ratios on a unit level. Nurse-to-patient ratios were our key explanatory variable. We conducted separate OLS regressions for each NSO in each unit type using linear and non-linear terms.
Data collection/extraction methods
We linked hospital claims data with self-reported structural data from hospitals from 2012 to 2014.
Principal findings
We identified 15 unit types with at least one significant NSO. The effect of potential understaffing on NSOs depends on the unit type.
Conclusions
Our study indicates that the relationship between nurse staffing levels and NSOs varies greatly depending on the unit type concerning both significance and magnitude. Future research might consider performing analyses on unit level instead of hospital level.
... Olesen and Petersen (1995) emphasized that high service quality requires increased costs and resources. Carey and Burgess (1999) and McKay and Deily (2005) studied the relationship between cost and service quality on a sample of US Department of Veterans Affairs hospitals during the period 1988-1993, and the authors showed that the investment costs are positively associated with service quality. Deflorio et al. (2010) introduced performance indicators in freight routing problems to estimate the effect of transportation cost on the service quality offered. ...
Service quality is one of the crucial factors for service providers to improve on their performance and enhance competitiveness in service operations. Service providers have been investing significant financial, material, and human resources to enrich service quality with an aim to promote profit generation. This study presents a two-stage network DEA model employing the relations among costs, service quality, and profit in evaluating performance. It also shows how efficiently costs contribute to performance in favor of service quality and how efficiently service quality provision contributes to performance in favor of profit generation. This study also demonstrates a case study involving 32 Korean university hospitals with real-world data and provides rich experiment results to address the effectiveness and applicability of the proposed model incorporating service quality in the service operations industry.
... 16 Organizational ownership was categorized as government, for-profit, and not-for profit, and was utilized to provide indications of financial and quality performance. 17 Finally, physician surgical volume was included to provide indications concerning physician experience relating to the surgery. Within the dataset, a sizable proportion of physicians only provided 1 surgery for endometrial cancer during the time period, and, as such, the distribution was segmented into physicians providing 1 versus physicians providing more than 1 surgery for endometrial cancer. ...
Objective:
The goal of this research was to analyze if disparities in route of hysterectomy for endometrial cancer exist in Florida.
Materials and Methods:
In this retrospective cohort study, Florida inpatient and ambulatory surgery databases (2014-2016) were examined to find cases of patients with endometrial cancer who underwent hysterectomy in the state. Logistic regression models were used to compare patient- and hospital-level factors associated with having minimally invasive surgery (MIS) versus open surgery, and complications in patients having open hysterectomy versus MIS.
Results:
Overall, 6513 patients met the inclusion criteria. MIS was performed in 81.4% of cases. The odds of using a minimally invasive approach to hysterectomy (vaginal, robotic, or laparoscopic) were significantly lower for black women (odds ratio [OR]: 0.41; 95% confidence interval [CI]: 0.34-0.50) as well as for other non-white patients (OR: 0.64; 95% CI: 0.49-0.84). Patients with Medicaid (OR: 0.42; 95% CI: 0.30-0.59) or Medicare managed care (OR: 0.73; 95% CI: 0.59-0.91), or who received care at a teaching hospital (OR: 0.82; 95% CI: 0.68-0.98) or government hospital (OR: 0.50; 95% CI: 0.38-0.65) were also less likely to receive MIS. Patients receiving care at a high-volume (OR: 1.69; 95% CI: 1.30-2.20) or medium-volume (OR: 3.11; 95% CI: 2.37-4.08) hospital, or patients who were located in the Central (OR: 1.71; 95% CI: 1.17-2.48) or Peninsula (OR: 1.73; 95% CI: 1.17-2.56) regions, compared to the Florida Panhandle, had greater odds of receiving MIS.
Conclusions:
Although Florida has a high adoption of MIS for treating endometrial cancer, disparities persist. Efforts of state-level entities should focus on improving access to minimally invasive hysterectomy for racial minorities with endometrial cancer.
... Finally, we have studied for the first time the relationship between costs and mortality for HCU and non-HCU across Ontario LHINs to explore health system performance from the efficiency angle. Efforts to examine the relationship between healthcare spending and outcomes, most often mortality, have been made globally applying various approaches [16,30,[59][60][61][62][63][64][65]. The approach taken in our study builds on previous research that conducted hospital profiling [57,58,66]. ...
Background
Senior high cost health care users (HCU) are a priority for many governments. Little research has addressed regional variation of HCU incidence and outcomes, especially among incident HCU. This study describes the regional variation in healthcare costs and mortality across Ontario’s health planning districts [Local Health Integration Networks (LHIN)] among senior incident HCU and non-HCU and explores the relationship between healthcare spending and mortality.
Methods
We conducted a retrospective population-based matched cohort study of incident senior HCU defined as Ontarians aged ≥66 years in the top 5% most costly healthcare users in fiscal year (FY) 2013. We matched HCU to non-HCU (1:3) based on age, sex and LHIN. Primary outcomes were LHIN-based variation in costs (total and 12 cost components) and mortality during FY2013 as measured by variance estimates derived from multi-level models. Outcomes were risk-adjusted for age, sex, ADGs, and low-income status. In a cost-mortality analysis by LHIN, risk-adjusted random effects for total costs and mortality were graphically presented together in a cost-mortality plane to identify low and high performers.
Results
We studied 175,847 incident HCU and 527,541 matched non-HCU. On average, 94 out of 1000 seniors per LHIN were HCU (CV = 4.6%). The mean total costs for HCU in FY2013 were 12 times higher that of non-HCU ($29,779 vs. $2472 respectively), whereas all-cause mortality was 13.6 times greater (103.9 vs. 7.5 per 1000 seniors).
Regional variation in costs and mortality was lower in senior HCU compared with non-HCU. We identified greater variability in accessing the healthcare system, but, once the patient entered the system, variation in costs was low. The traditional drivers of costs and mortality that we adjusted for played little role in driving the observed variation in HCUs’ outcomes. We identified LHINs that had high mortality rates despite elevated healthcare expenditures and those that achieved lower mortality at lower costs. Some LHINs achieved low mortality at excessively high costs.
Conclusions
Risk-adjusted allocation of healthcare resources to seniors in Ontario is overall similar across health districts, more so for HCU than non-HCU. Identified important variation in the cost-mortality relationship across LHINs needs to be further explored.
Electronic supplementary material
The online version of this article (10.1186/s12877-018-0952-7) contains supplementary material, which is available to authorized users.
... Ownership is used as a control variable, as ownership has been demonstrated to play an important role in organizational financial and quality performance. 24 A binary variable indicating whether the hospital is part of a system is included as hospitals that are part of a system may be more likely to adhere to system mandates than they are to respond to market forces. 25 Teaching status is evaluated as a binary variable indicating whether medical education occurs at the hospital. ...
To help influence the health care environment as well as the flow of resources into and out of hospitals, the Centers for Medicare & Medicaid Services has implemented a performance incentive initiative called the Hospital Value-Based Purchasing (HVBP) program. As such, this study utilizes the lens of Resource Dependency Theory to evaluate the effect of the external environment on hospital performance as measured by the HVBP program. This study utilizes data from the 2014 American Hospital Association (AHA) Annual Survey database, 2014 Area Health Resource File (AHRF), the 2014 Medicare Final Rule Standardizing File, and the 2014 Medicare Hospital Compare database. The associations between external environment and hospital performance are assessed through multiple regression analysis. Hospital performance scores in the HVBP program are sensitive to environmental factors; however, not all domains are influenced to the same degree. It would seem that hospitals do not have either the same ability or motivation to make changes in each of the value-based purchasing domains. Ultimately, the findings from this study indicate that environmental forces do play a role in hospitals' performance in the HVBP program.
... Therefore, these DRG-based assessments of specialty medical service performance allow hospital managers to objectively understand their specialty's service performance, including their strengths and weaknesses, providing scientific and objective assistance and guidance for improving service quality. [2,[25][26][27] It is noteworthy to mention that complicated case-mix systems such as DRGs are based on good medical information systems and valid data. [28][29][30] In other words, the validity of applying DRGs as a tool for risk-adjustment and healthcare management is seriously dependent on the coding quality of the discharge data. ...
Diagnosis-related groups (DRGs) have been receiving increasing attention in health service research in China. In the present study, we used the 2014 Beijing-Diagnosis Related Groups (BJ-DRGs) to evaluate the inpatient service performance of the clinical subspecialty “major operation of the digestive system” of a cancer specialist hospital.
The research hospital is one of 16 public municipal hospitals overseen by the Beijing Health Bureau (“16 hospitals”). Discharge data collected between 2008 and 2015 were drawn from the front pages of the medical records of these hospitals. After the data were reported to the Beijing Public Health Information Centre, as well as being grouped using the BJ-DRGs. We evaluated the service performance of this subspecialty in terms of capacity, efficiency, and service quality, based on the BJ-DRGs risk adjustment tool.
From 2008 to 2015, the total weight of the subspecialty in the research hospital increased annually. In 2015, the cases in this hospital accounted for 50.27% of the total in 16 hospitals. The time consumption index was 0.91, whereas the charge consumption index was 1.24, which was 24% higher than the average in16 hospitals. The mortality rates of the middle–low risk groups (GB15 and GB25) in the research hospital and the 16 hospitals were zero, while the mortality rates for the middle–high risk groups (GB11 and GB23) in the research hospital were significantly lower than those in 16 hospitals.
The service capacity of the subspecialty steadily increased in the research hospital. However, the hospital must offer more attention to complex digestive disease cases (GB11/GB23) and strictly control hospitalization expenses, while maintaining the advantages of service efficiency and quality.
... 19 Ownership is reported as for-profit, federal government, and not-for-profit and is utilized to provide indication of financial and quality performance. 20 Rurality, system status, and teaching status are all reported as binary variables, where 0 indicates urban, not a part of a system, or nonteaching. System status indicates whether the organization is part of a larger system and provides indication as to the resources available to the organization. ...
Under the Hospital-Acquired Condition Reduction Program (HACRP), introduced by the Affordable Care Act, the Centers for Medicare and Medicaid must reduce reimbursement by 1% for hospitals that rank among the lowest performing quartile in regard to hospital-acquired conditions (HACs). This study seeks to determine whether Accredited Cancer Program (ACP) hospitals (as defined by the American College of Surgeons) score differently on the HACRP metrics than nonaccredited cancer program hospitals. This study uses data from the 2014 American Hospital Association Annual Survey database, the 2014 Area Health Resource File, the 2014 Medicare Final Rule Standardizing File, and the FY2017 HACRP database (Medicare Hospital Compare Database). The association between ACPs, HACs, and market characteristics is assessed through multinomial logistic regression analysis. Odds ratios and 95% confidence intervals are reported. Accredited cancer hospitals have a greater risk of scoring in the Worse outcome category of HAC scores, vs Middle or Better outcomes, compared with nonaccredited cancer hospitals. Despite this, they do not have greater odds of incurring a payment reduction under the HACRP measurement system. While ACP hospitals can likely improve scores, questions concerning the consistency of the message between ACP hospital quality and HACRP quality need further evaluation to determine potential gaps or issues in the structure or measurement. ACP hospitals should seek to improve scores on domain 2 measures. Although ACP hospitals do likely see more complex patients, additional efforts to reduce surgical site infections and related HACs should be evaluated and incorporated into required quality improvement efforts. From a policy perspective, policy makers should carefully evaluate the measures utilized in the HACPR.
... Organizational size is defined through the number of staffed beds and reported as a categorical variable (fewer than 100, 100-199, and more than 200 staffed beds). We also use ownership, which has been demonstrated to play an important role in organizations' performance and quality (McKay & Deily, 2005), as a variable. Hospitals that provide medical education often provide greater quality and patient safety than nonteaching hospitals (Shahian et al., 2012). ...
Executive summary:
The hospital value-based purchasing (HVBP) program of the Centers for Medicare & Medicaid Services challenges hospitals to deliver high-quality care or face a reduction in Medicare payments. How do different organizational structures and market characteristics enable or inhibit successful transition to this new model of value-based care? To address that question, this study employs an institutional theory lens to test whether certain organizational structures and market characteristics mediate hospitals' ability to perform across HVBP domains.Data from the 2014 American Hospital Association Annual Survey Database, Area Health Resource File, the Medicare Hospital Compare Database, and the association between external environment and hospital performance are assessed through multiple regression analysis. Results indicate that hospitals that belong to a system are more likely than independent hospitals to score highly on the domains associated with the HVBP incentive arrangement. However, varying and sometimes counterintuitive market influences bring different dimensions to the HVBP program. A hospital's ability to score well in this new value arrangement may be heavily based on the organization's ability to learn from others, implement change, and apply the appropriate amount of control in various markets.
... Examples for reexamination include findings that for-profit hospitals are more responsive to changes in profitability (Horwitz, 2005); operate at a lower cost (Jiang et al., 2006;McKay & Deily, 2005); improve financial performance following ownership conversions (Joynt et al., 2014); and conflicting negligible differences in profitability (Becker & Sloan, 1985). ...
... 27 Organizational ownership is categorized as nonfederal government, for profit, and not for profit and is utilized to provide an indication of financial and quality performance. 28 System status is a binary variable and indicates if the organization is part of a larger system and provides an indication into the resources available to the organization. 29 Teaching status has been demonstrated to correlate with patient safety scores and therefore is an important control variable for this study. ...
The Affordable Care Act of 2010 introduced a Hospital Value-Based Purchasing Total Performance Score for payment purposes and to evaluate hospital quality of care. In fiscal year 2016, Total Performance Score was composed of (1) Clinical Processes of Care, (2) Patient Experience of Care, (3) Outcome, and (4) Efficiency domains. The objective of this study was to examine the association between the Patient Experience of Care and Outcome domains. The Donabedian model of structure, process, and outcome was used as a conceptual framework for this study. Data from the 2015-2016 Area Health Resource File, the 2016 American Hospital Association database, and the 2016 Hospital Value-Based Purchasing were used. Univariate, bivariate, and multivariate analyses were conducted to examine the impact of patient experience on outcome of care and hospitals. From a sample of 1866 hospitals across the United States, patient experience was significantly and positively associated with patient outcome. In addition, for-profit hospitals, hospitals with more beds, nonteaching hospitals, and hospitals located in less competitive markets were found to have a significant association with better outcomes. The study's findings are important as policy makers consider additional or alternative indicators that may better represent and encourage higher quality of care within acute care hospitals.
... The coefficients for ownership status (for-profit and non-federal government providers) are negative. This suggests that for-profit hospitals are associated with higher cost efficiency when compared to non-profit providers, which is in line with the Property Rights Theory [35,44,[48][49][50]. The negative coefficients for Medicaid and Medicare shares of admissions suggest that the reimbursement policies of public payers are positively associated with cost efficiency. ...
The importance of increasing cost efficiency for community hospitals in the United States has been underscored by the Great Recession and the ever-changing health care reimbursement environment. Previous studies have shown mixed evidence with regards to the relationship between linking hospitals' reimbursement to quality of care and cost efficiency. Moreover, current evidence suggests that not only inherently financially disadvantaged hospitals (e.g., safety-net providers), but also more financially stable providers, experienced declines to their financial viability throughout the recession. However, little is known about how hospital cost efficiency fared throughout the Great Recession. This study contributes to the literature by using stochastic frontier analysis to analyze cost inefficiency of Washington State hospitals between 2005 and 2012, with controls for patient burden of illness, hospital process of care quality, and hospital outcome quality. The quality measures included in this study function as central measures for the determination of recently implemented pay-for-performance programs. The average estimated level of hospital cost inefficiency before the Great Recession (10.4 %) was lower than it was during the Great Recession (13.5 %) and in its aftermath (14.1 %). Further, the estimated coefficients for summary process of care quality indexes for three health conditions (acute myocardial infarction, pneumonia, and heart failure) suggest that higher quality scores are associated with increased cost inefficiency.
... Therefore, it is likely that lower levels of operational efficiency (higher costs) and higher levels of healthcare quality are positively associated. Stressing the importance of this relationship, McKay and Deily (2005) call for more research on the trade-off between operational efficiency and service quality in healthcare. Below we review previous studies that have examined the efficiency-quality relationship. ...
The influence of IT investment on hospital efficiency and quality are of great interest to healthcare executives as well as insurers. Few studies have examined how IT investments influence both efficiency and quality or whether there is an optimal IT investment level that influences both in the desired direction. Decision makers in healthcare wonder if there are tradeoffs between their pursuit of hospital operational efficiency and quality. Our study involving a 2-stage double bootstrap DEA analysis of 187 US hospitals over two years found direct effects of IT investment upon service quality and a moderating effect of quality upon operational efficiency. Further, our findings indicate a U-shaped relationship between IT investments and operational efficiency suggesting that IT investments have diminishing returns beyond a certain point.
... Urban vs. rural population is based upon the metropolitan vs. nonmetropolitan designation as found in the 2013 Rural-Urban Continuum Codes [30]. Ownership will also be used as a control variable, as ownership has been demonstrated to play an important role in organizations performance and quality [31]. ...
To determine if the Value-Based Purchasing Performance Scoring system correlates with hospital acquired condition quality indicators.
This study utilizes the following secondary data sources: the American Hospital Association (AHA) annual survey and the Centers for Medicare and Medicaid (CMS) Value-Based Purchasing and Hospital Acquired Conditions databases.
Zero-inflated negative binomial regression was used to examine the effect of CMS total performance score on counts of hospital acquired conditions. Hospital structure variables including size, ownership, teaching status, payer mix, case mix, and location were utilized as control variables.
The secondary data sources were merged into a single database using Stata 10.
Total performance scores, which are used to determine if hospitals should receive incentive money, do not correlate well with quality outcome in the form of hospital acquired conditions.
Value-based purchasing does not appear to correlate with improved quality and patient safety as indicated by Hospital Acquired Condition (HAC) scores. This leads us to believe that either the total performance score does not measure what it should, or the quality outcome measurements do not reflect the quality of the total performance scores measure.
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... Patient satisfaction surveys are used to assess perceptions of the interactions themselves; while alternatively, more clinical measures such as LOS give some indication of the extent to which a process-based approach to quality management is in place. In this paper, we employ multiple outcome measures of hospital quality, including mortality, potentially preventable complications (PPCs) and LOS, in order to assess how these hospitals perform along different dimensions (Rogowski et al., 2004;McKay and Deily, 2005;Gowrisankaran and Town, 2003;Fuller et al., 2009;Milstein, 2009). ...
Research into hospital quality performance typically considers a single dimension of quality at a time (e.g., West et al., 2002; McFadden et al., 2004). But as both hospitals and payers are aware, quality is multidimensional and needs to be measured more holistically to capture top performers. Data envelopment analysis (DEA) is a useful tool that typically looks at economic or cost data to determine the most efficient organisations in a group (with few exceptions). Using data from cardiology units in a sample of hospitals, this paper presents results from the use of DEA to study multiple quality metrics simultaneously in a geographically clustered group of hospitals to determine the best performers. This type of analysis might be useful for a hospital payer or a government agency that wants to reward hospitals for greater quality performance, but might otherwise be using a single dimension. Even those organisations that use multiple quality measures must face the problem of how to combine these different dimensions into one comprehensive quality measure. Our results highlight the usefulness of this technique in this situation and demonstrate how this technique can identify organisations that might otherwise be overlooked as high performers using traditional, single-dimension methods.
... The β cj estimates can be interpreted as the department specific contribution to the cost level, since it explains the risk-adjusted costs, having taken all of the above mentioned variables, including patient case-mix but excluding quality, into account [6]. This type of unexplained deviation from expected costs is also referred to as the department level of inefficiency [9,16,25,26]. The department fixed effects β cj are interpreted as risk-adjusted costs and used in the department level analysis. ...
An increasing focus on hospital productivity has rendered a need for more thorough knowledge of cost drivers in hospitals, including a need for quantification of the impact of age, case-mix and other characteristics of patients, as well as establishment of the cost-quality relationship.
The aim of this study is to identify cost drivers for vascular surgery in Danish hospitals with a specific view to quality of the treatment: Is higher quality associated with increased costs, when all other cost drivers are accounted for?
We analyse cost drivers in a register-based study, using patient level data from three sources: The Vascular Register, the hospital cost database, and the National Patient Register with added DRG-information. The analysis follows a multilevel set-up, where cost drivers at patient level are analysed in a set of general linear regression models including complications and mortality as quality measures. At the hospital level of the analysis, we analyse deviations of observed costs from risk-adjusted costs and compare these to deviations of observed quality from risk-adjusted quality.
We find, not surprisingly, that a number of patient characteristics, including case-mix and severity, have a major impact on treatment costs. At patient level, both complications and mortality are associated with increased costs. At hospital department level, results are not straightforward, but could indicate a U-shaped association.
We conclude that the relation between costs and quality is not straightforward, at least not at department level. Our results indicate, albeit vaguely, a U-shaped relation between quality, in terms of fewer surgical complications than expected, and costs at department level, since our results suggest that increasing costs for vascular departments are associated with increased quality when costs are high and decreased quality when costs are low. For mortality however, we have not been able to establish a clear relation to costs.
... Mortality rate is simply the number of deaths divided by the total number of cases. In this paper, we employ mortality rate to assess quality, which has often been used as an indicator of quality performance (Rogowski et al. 2004;McKay and Deily 2005;Gowrisankaran and Town 2003). ...
Focus in healthcare has been heralded as the next frontier in improving its efficiency and efficacy (Herzlinger 2004). Focus
takes several different forms, ranging from standalone specialty centers to a hospital that places a strategic emphasis on
a clinical area. We adopt this latter perspective and define focus as a disproportionate emphasis on a particular clinical
area in a hospital. We use secondary data from hospitals providing cardiology care in New York State to examine the relationship
between focus and performance. We develop two measures of focus. Proportional focus is defined to be the proportion of cases treated in a particular clinical specialty. Expertise focus is defined to be specific evidence that a hospital has taken action to build expertise in treating diseases in that specialty.
We operationalize hospital performance along cost and quality dimensions, and we use hierarchical regression to examine the
impact of focus on performance. Our results indicate that proportional focus, but not expertise focus, is associated with
better cost performance. Quality performance, on the other hand, was associated only with the interaction between proportional
focus and expertise focus, which means that only hospitals exhibiting higher levels of both proportional and expertise focus
achieve better quality performance. These findings support the notion that not only is focus important in healthcare, but
also that researchers and practitioners need to recognize that relationships are contingent on the performance and focus measures
used and thus, findings may not be generalizable from one metric to another.
... 12,13 Based on these factors, different multimarker risk-prediction models have been developed to increase prediction accuracy and precision. 14,15 However, gaps in our knowledge of the underlying pathophysiologic mechanisms pose difficulties in interpreting the currently available excess of data. Furthermore, data in this respect are not always consistent. ...
Patients with heart failure (HF) are hospitalized over a million times annually in the United States. Hospitalization marks a fundamental change in the natural history of HF, leading to frequent subsequent rehospitalizations and a significantly higher mortality compared with nonhospitalized patients. Three-fourths of all HF hospitalizations are due to exacerbation of symptoms in patients with known HF. One-half of hospitalized HF patients experience readmission within 6 months. Preventing HF hospitalization and rehospitalization is important to improve patient outcomes and curb health care costs. To implement cost-effective strategies to contain the HF hospitalization epidemic, optimal schemes to identify high-risk individuals are needed. In this review, we describe the risk factors that have been associated with hospitalization risk in HF and the various multimarker risk prediction schemes developed to predict HF rehospitalization. We comment on areas that represent gaps in our knowledge or difficulties in interpretation of the current literature, representing opportunities for future research. We also discuss issues with using HF readmission rate as a quality indicator.
... The empirical literature that examines the differences in hospital performance by ownership type reflects the ambiguity in the theoretical literature. Studies measuring efficiency using SFA have found that NFP hospitals are more efficient than FP hospitals (Folland & Hofler, 2001; Koop, Osiewalski, & Steel, 1997; McKay, Deily, & Dorner, 2002 Rosko, 1999 Rosko, , 2001a Rosko, , 2004 Rosko, Proenca, Zinn, & Bazzoli, 2007; Zuckerman et al., 1994) and that FP hospitals are more efficient than NFP hospitals (Li & Rosenman, 2001; McKay & Deily, 2005; Rosko, 2001b; Sari, 2003). Mutter and Rosko (2008) used rigorous and preferred strategies identified by Shen, Eggleston, Lau, and Schmid (2007) for assessing differences in hospital performance by ownership type as well as the approach recommended by Rosko and Mutter (2008) for using SFA to estimate efficiency in the hospital industry. ...
This article focuses on the lessons learned from stochastic frontier analysis studies of U.S. hospitals, of which at least 27 have been published. A brief discussion of frontier techniques is provided, but a technical review of the literature is not included because overviews of estimation issues have been published recently. The primary focus is on the correlates of hospital inefficiency. In addition to examining the association of market pressures and hospital inefficiency, the authors also examined the relationship between inefficiency and hospital behavior (e.g., hospital exits) and inefficiency and other measures of hospital performance (e.g., outcome measures of quality). The authors found that consensus is emerging on the relationship of some factors to hospital efficiency; however, further research is needed to better understand others. The application of stochastic frontier analysis to specific policy issues is in its infancy; however, the methodology holds promise for being useful in certain contexts.
Pasca konflik (1987-2005) dan tsunami (2004), masyarakat Aceh memiliki tingkat kualitas kesehatan yang sangat memprihatinkan. Sejak tahun 2010, pemerintah Aceh telah meluncurkan dan melaksanakan program Jaminan Kesehatan Aceh (JKA), bertujuan untuk meningkatkan tingkat kesehatan masyarakat Aceh dan sebagai investasi pemerintah di bidang kesehatan untuk meningkatkan Indek Pembangunan Manusia (IPM) Aceh. Dalam pelaksanaannya, dana dalam jumlah yang besar telah dialokasikan untuk program JKA yang bersumber dari Anggaran Pendapatan Belanja Aceh (APBA) ternyata belum berbanding lurus dengan kualitas pelayanan JKA yang diberikan. Banyak pasien JKA yang mengeluh terhadap pelayanan program JKA. Puskesmas dan RSUD sebagai mitra pelayanan kesehatan program JKA belum mampu memberikan pelayanan kesehatan secara optimal. Studi ini bertujuan untuk menganalisis tingkat efisiensi dan tingkat kepuasaan masyarakat terhadap pelaksanaan program JKA. Sampel pada penelitian ini adalah Puskesmas Rawat Inap Tingkat Pertama (RITP) dan peserta JKA yang pernah atau sedang mendapatkan pelayanan kesehatan di Puskesmas RITP di wilayah timur Provinsi NAD. Alasan pengambilan sampel ini karena Puskesmas adalah pintu masuk pelayanan kesehatan program JKA, akan tetapi sebagian besar pasien JKA harus antri untuk mendapatkan pelayanan kesehatan di RSUD (Rumah Sakit Umum Daerah). Jumlah pasien yang sangat banyak di RSUD menyebabkan sebagain besar pasien JKA tidak tertangani di RSUD sehingga membuat mereka kecewa terhadap pelayanan kesehatan JKA. Kombinasi pendekatan non-parametric (data envelopment analysis) dan statistik deskriptif digunakan untuk menjawab permasalahan efisiensi dan kepuasan masyarakat terhadap pelaksanaan program JKA. Hasil analisis menunjukkan bahwa 81 persen atau 26 Puskesmas RITP yang melayani program JKA di wilayah timur Provinsi NAD menunjukkan nilai efisien secara teknis. Dari 26 Puskesmas tersebut, 5 Puskesmas juga telah mampu melaksanakan program JKA dengan efisien secara skala. Efisien secara teknis berarti bahwa manajemen Puskesmas tersebut telah mampu menangani permasalahan lokalnya dengan menggunakan peralatan medis dan teknologi yang memadai. Efisien dalam skala berarti Puskesmas RITP telah mampu memberi pelayanan kepada pasien sesuai dengan input yang mereka miliki. Berdasarkan laporan pelaksanaan program JKA tahun 2012, realisasi biaya RITL (Rawat Inap Tingkat Lanjutan) yang pelayanannya di RSUD sebesar 37%, sedangkan realisasi biaya RITP yang pelayanannya di Puskesmas RITP hanya sebesar 2%. Jelas bahwa pasien rawat inap banyak dilayani di RSUD dibandingkan Puskesmas RITP. Hasil analisis Indeks Kemampuan Masyarakat (IKM) Aceh berada pada kategori C. Alokasi biaya yang efisien diikuti dengan perbaikan kualitas secara simultan adalah hal yang sulit untuk dicapai, adanya trade off antara kualitas dengan efisiensi.
Risk is inherent at all levels of hospital management such as determining healthcare service priorities, purchasing new medical equipment, patient safety, clinical governance, etc. The effectiveness of an audit process in reducing risk is a critical success factor in hospital management. Since hospital data is becoming increasingly larger, the data may be too large for auditors to handle. Consequently, they need to learn a new skill and knowledge to face the digital transformation era. The era of intelligent audit technology has arrived. In the future, auditors can use big data analysis and technology to get the assistance of advanced audit analysis tools. This paper introduces a smart audit case using diagnosis-related group (DRG) data. It explains how to use computer-assisted audit techniques (CAATs) to develop the predictions of DRGs as a starting point, triggering students to analyze the editing of DRG codes in depth by using a machine-learning model to pre-audit the accuracy of inpatient DRGs’ drop point in Health Insurance Declaration forms.
O objetivo deste estudo é analisar as características das publicações internacionais na temática avaliação do desempenho hospitalar (ADH), por meio de um fragmento da literatura, na busca de geração de conhecimento, identificação de lacunas e possibilidade de contribuições. Esta pesquisa utiliza uma abordagem qualitativa e o instrumento selecionado para identificar e selecionar o fragmento de literatura para análise e reflexão foi o Knowledge Development Process – Constructivist (ProKnow-C). O portfólio bibliográfico com 43 artigos identificou os autores e os núcleos de pesquisas de destaque e os periódicos com maior número de publicações. Quanto aos métodos mais utilizados na ADH, destacam-se os que se baseiam na teoria das fronteiras Eficientes, classificados como: Data Envelopment Analysis (DEA - 53,85%), Stochastic Frontier Analysis (SFA - 12,82%), comparação entre DEA e SFA (7,69%) e Partinal Frontier Analysis (2,56%). Quanto ao emprego ou dimensões analisadas, 38,46% dos artigos destinam-se aos gestores. A partir das análises geradas foi possível identificar lacunas, referentes a mecanismos de avaliação do desempenho voltados para as dimensões que extrapolem as métricas. Para geração e aplicação de conhecimento nesta área, sugere-se a adequação da ADH as especificidades destas organizações, considerando o ambiente e a finalidade do sistema ao qual se insere.
Objective:
The aim of this study was to explore the relationship between a hospital's Magnet recognition status, tenure, and its performance in the Hospital Value-Based Purchasing (HVBP) program.
Background:
Previous studies have sought to determine associations between quality of care provided in inpatient setting and the Magnet Recognition Program; however, no study has done so using the most recent (FY2017) iteration of the HVBP program, nor determined the influence a hospital's Magnet designation tenure has on HVBP scores.
Method:
This study used a cross-sectional study design of 2686 hospitals using propensity score matching to reduce bias and improve comparability.
Results:
Magnet-designated hospitals were associated with higher total performance, process of care and patient experience of care scores, and lower efficiency score. No association was identified between the length of time hospitals have been Magnet designated.
Conclusion:
Findings suggest non-Magnet status hospitals need to consider implementing the principles of Magnet into their culture or participation in the Magnet Recognition Program to provide higher quality of care.
Background
Multidisciplinary care has been shown to improve outcomes for patients, and interprofessional collaboration has been demonstrated to be beneficial for providers. In the field of surgery, although a large number of multidisciplinary care teams have been described, no study to date has examined whether or not these team-based interventions are generally cost-effective. This is the first systematic review to examine cost savings attributable to multidisciplinary care across all surgical fields.
Methods
A comprehensive literature review of articles published on cost outcomes associated with multidisciplinary surgical teams was performed. Selected articles reported on cost outcomes directly attributable to a collaborative intervention. Cost savings were totaled on a per-patient basis. Each article was also reviewed to determine whether the authors ultimately recommended the team-based intervention described.
Results
A total of 1421 articles were identified in the initial query, of which 43 met inclusion criteria. Thirty-nine studies (91%) reported multidisciplinary care to be cost effective, with an average cost savings among all studies of $5815 per patient. No significant differences in the amount of savings achieved were found between different intervention subtypes. All studies ultimately recommended (40) or gave mixed reviews (3) of multidisciplinary care, regardless of whether cost savings were achieved.
Conclusion
Multidisciplinary surgical care is beneficial not only in terms of patient and provider outcomes, but also in reference to its cost-effectiveness. Well-designed multidisciplinary teams tend to optimize perioperative care for all involved parties. Efforts to improve surgical care should employ multidisciplinary teams to promote both quality and cost-effective care.
Inpatient volume has long been believed to be a contributing factor to ambulance diversion, which can lead to delayed treatment and poorer outcomes. We examined the extent to which both daily inpatient and emergency department (ED) volumes at specified hospitals, and diversion levels (that is, the number of hours ambulances were diverted on a given day) at their nearest neighboring hospitals, were associated with diversion levels in the period 2005-12. We found that a 10 percent increase in patient volume was associated with a sevenfold greater increase in diversion hours when the volume increase occurred among inpatients (5 percent) versus ED visitors (0.7 percent). When the next-closest ED experienced mild, moderate, or severe diversion, the study hospital's diversion hours increased by 8 percent, 23 percent, and 44 percent, respectively. These findings suggest that efforts focused on managing inpatient volume and flow might reduce diversion more effectively than interventions focused only on ED dynamics.
Executive summary:
As part of the provisions of the 2010 Affordable Care Act, the Centers for Medicare & Medicaid Services adjusts payments made to hospitals through its Hospital Value-Based Purchasing (HVBP) program. In light of the increasing aim to improve efficiency, healthcare organizations are exploring innovative strategies for care delivery, including the use of hospitalists. Supporters of the hospitalist model suggest use of these specialists offers several advantages over the traditional model of physician care in the inpatient setting, including improved coordination, reduced costs, and improved quality indicator scores. This study explores the effect of hospitalists on hospitals' scores in the four domains of the fiscal year 2016 HVBP program: clinical process of care, patient experience of care (PEOC), outcome, and efficiency. Data from the 2015 HVBP database, the 2015 Medicare Final Rule Standardizing File, and the 2015 American Hospital Association database were used for the analysis. The study used multivariate regression analysis in Stata 12. Results from this study suggest that hospitals employing a higher percentage of hospitalists see related improvement in their overall total performance score. In light of improvements within the PEOC, outcome, and efficiency scores, it would appear that hospitalists are primarily providing linking services, which helps provide better coordination of care that is otherwise lacking in more traditionally fragmented approaches to care.
Introduction:
Hospital Value-Based Purchasing (HVBP) is an initiative that rewards acute-care hospitals with incentive payments for the quality of care they provide. A hospital's trauma certification has the potential to influence HVBP scores as attaining the certification provides indication of the service quality offered by the hospital. As such, this study focuses on hospitals' level of trauma certification attainment through the American College of Surgeons and whether this certification is associated with greater HVBP.
Methods:
A retrospective review of the 2015 HVBP database, 2015 Area Health Resources Files (AHRF) database, and the 2015 American Hospital Association (AHA) database is utilized, and propensity score matching was employed to determine the association between level of trauma certification and scores on HVBP dimensions.
Results:
Results reveal trauma certification is associated with lower HVBP domain scores when compared to hospitals without trauma certification. In addition, hospitals with a greater degree of trauma specialization were associated with lower total performance score and efficiency domain scores.
Conclusions:
Although payers attempt to connect hospital reimbursements with quality and outcomes, unintended consequences may occur. In response to these results, HVBP risk adjustment and scoring methods should receive further scrutiny.
In this paper, we extend the 4-random-component closed skew-normal stochastic frontier model by including exogenous determinants of hospital persistent (long-run) and transient (short-run) inefficiency, separated from unobserved heterogeneity. We apply this new model to a dataset composed by 133 Italian hospitals during the period 2008–2013. We show that average total inefficiency is about 23%, higher than previous estimates; hence, a model where the different types of inefficiency and hospital unobserved characteristics are not confounded allows us to get less biased estimates of hospital inefficiency. Moreover, we find that transient efficiency is more important than persistent efficiency, as it accounts for 60% of the total one. Last, we find that ownership (for-profit hospitals are more transiently inefficient and less persistently inefficient than not-for-profit ones, whereas public hospitals are less transiently inefficient than not-for-profit ones), specialization (specialized hospitals are more transiently inefficient than general ones; i.e., there is evidence of scope economies in short-run efficiency), and size (large-sized hospitals are better than medium and small ones in terms of transient inefficiency) are determinants of both types of inefficiency, although we do not find any statistically significant effect of multihospital systems and teaching hospitals.
Objective:
To examine the relationship between nurse staffing patterns and patients' experience of care in hospitals with a particular focus on staffing flexibility.
Data sources/study setting:
The study sample comprised U.S. general hospitals between 2010 and 2012. Nurse staffing data came from the American Hospital Association Annual Survey, and patient experience data came from the Medicare Hospital Consumer Assessment of Healthcare Providers and Systems.
Study design:
An observational research design was used entailing a pooled, cross-sectional data set. Regression models were estimated using generalized estimating equation (GEE) and hospital fixed effects. Nurse staffing patterns were assessed based on both levels (i.e., ratio of full-time equivalent nurses per 1,000 patient days) and composition (i.e., skill mix-percentage of registered nurses; staffing flexibility-percentage of part-time nurses).
Principal findings:
All three staffing variables were significantly associated with patient experience in the GEE analysis, but only staffing flexibility was significant in the fixed-effects analysis. A higher percentage of part-time nurses was positively associated with patient experience. Multiplicative and nonlinear effects for the staffing variables were also observed.
Conclusions:
Among three staffing variables, flexibility was found to be the most important relative to patient experience. Unobserved hospital characteristics appear to underlie patient experience as well as certain nurse staffing patterns.
In this study, the technical efficiency of outpatient service production of hospitals is analyzed by using the Stochastic Frontier Analysis (SFA) with different model specifications. The purpose of the study is to present the effects of different SFA model specifications on the distribution of efficiency score and/or production function parameter estimates. In the analysis, the data of 429 Turkish MoH hospitals for the years 2012, 2013 and 2014 is used. The results of this paper suggest that different SFA specification, i.e. using Cobb-Douglas or Translog production technology and/or using an error component model or technical efficiency effects model, shifts the production function parameter estimates and the mean efficiency scores. On the other hand, the efficiency scores estimated by different model specifications are found to be highly correlated both in magnitude and rank order.
Introduction In the light of apparently inexorable rises in health-care expenditure, the cost effectiveness of the health system has become a dominant concern for many policy-makers. Do the funders of the health system (taxpayers, insurees, employers or patients) get good value for money? Productivity measurement is a fundamental requirement for securing providers’ accountability to their payers and ensuring that health system resources are spent wisely. Productivity measurement spans a wide range - from the cost effectiveness of individual treatments or practitioners to the productivity of a whole system. Whatever level of analysis is used, a fundamental challenge is the need to attribute both the consumption of resources (costs) and the outcomes achieved (benefits) to the organizations or individuals under scrutiny. The diverse methods used include direct measurement of the costs and benefits of treatment; complex econometric models that yield measures of comparative efficiency; and attempts to introduce health system outcomes into national accounts. Productivity analysis can be considered via two broad questions: (i) how are resources being used? and (ii) is there scope for better utilization of these resources? These questions can be considered for the whole health system and for organizations within it but most applied research at system level tends to concentrate on the first question. The second question is the primary concern of organizational studies. This chapter begins with an outline of the fundamental concepts required for productivity analysis, distinguishing productivity from efficiency. This is followed by a discussion of the challenges associated with applying these concepts in the health sector in which it is particularly difficult to define and measure outputs and to determine the relationship between health-care resources (inputs) and outputs.
Prior research has examined relationships between individual managerial variables and quality performance in hospitals, but there has been little research on how quality performance depends on interactions between these variables. These decisions, however, are often interrelated, with management facing choices between particular investments that may be limited by their availability at a given time. Using data from the population of hospitals in New York State, the authors explore how quality performance, measured by the risk-adjusted mortality rate, may be affected by capital investment, staffing level, and compensation level, both individually and in combination. Their findings show that the combination of capital investment and compensation level is significantly related to risk-adjusted mortality rates. There are significant interactions between these two variables with respect to quality performance, indicating that managers should consider these issues simultaneously in their decision-making process.
Background:
Hospital incentive payments are increasingly becoming tied to quality. However, the U.S. health care system continues to face rising health care costs and scarce workforce resources, making improving quality a challenge. Patient satisfaction and process quality are two areas of quality tied to reimbursement. Both are associated with positive health outcomes, but little is known about the relationship between the two.
Purposes:
The purpose of this study is to determine if there is an association between process quality and patient satisfaction in a representative sample of U.S. hospitals.
Methodology/approach:
We utilize a pooled cross-sectional study design with year fixed effects from 2009 to 2011. We linked the Hospital Compare data set and the American Hospital Association Annual Survey of Hospitals (AHA) data set. We use a method prescribed by the Joint Commission to determine hospital-level process quality in three areas: heart failure, acute myocardial infarction, and pneumonia treatment. We then use regression models to measure the relationship between process quality and two measures of overall hospital patient satisfaction.
Findings:
After we control for hospital-level characteristics and year, we find that patient satisfaction is positively associated with all three areas of hospital process quality (p < .01). For example, acute myocardial infarction process quality was positively associated with whether patients "would definitely recommend the hospital" (B = 0.75, p < .01). Process quality areas were moderately and positively correlated (p < .01), and on average, patient satisfaction scores have increased over time (p < .01).
Practice implications:
Our findings of an association between process quality and patient satisfaction suggest that focusing on process quality does not have negative implications for patient satisfaction. As performance in different process quality areas is only moderately correlated, managers should continually monitor all areas. The trend of increased patient satisfaction over time, perhaps because of industry pressures, should be investigated further.
This paper investigates regional hospital efficiency in China. As hospital operations naturally increase along with an undesirable output of patient mortality, which may induce medical disputes and ligations, this study adopts data envelopment analysis to evaluate and compare the efficiency scores obtained with and without considering the undesirable output. On the basis of province-level panel data over 2002–2008 and considering the risk-adjusted undesirable output, empirical estimates indicate that hospital efficiency is moderate, but increases gradually from 0.6881 to 0.8159. Importantly, without considering the undesirable output, the average efficiency score is overestimated and the efficiency ranking across provinces changes considerably. An efficiency gap is found between coastal and non-coastal regions, but this gap’s drop is mainly contributed by the fast efficiency improvement of the western regions. However, the central regions continue to achieve a significantly lower efficiency score than the eastern and western regions. Moreover, the initiation of the New Rural Cooperative Medical System has overall enhanced hospital efficiency in China, especially for the non-coastal regions.
In Brazilian health insurance sector, the fee-for-service model still remains the major payment method for health services, and predominates in the relationship between hospitals and private health insurance companies. After the creation of Health Insurance Qualification Program (HIQP), which focuses on the quality of the assistance given to consumers, the health insurance companies will be evaluated by health care performance indicators, established by this program. The present study discusses the impact of this pattern on the relationship between health insurance companies and hospitals, by analyzing data from interviews carried through with 18 health insurance managers, regarding the use - in hospital management - of performance indicators compatible to those adopted by HIQP. According to the managers perception, only three hospitals use this sort of indicators, two of them which are hospitals managed by the health insurance companies. The alignment of interests between health plans organizations and health care providers, at the HIQP proposed template, will imply changes in payment models between these market players, towards the inclusion of performance and quality of assistance given to users by providers, as components of wage determination.
Although differences in nurse staffing have been associated with individual hospital characteristics in the literature, there have been no studies on how these factors may influence nurse staffing changes made after the mandated nurse-to-patient ratios in acute care hospitals in California. The aim of this study was to determine if changes in medical-surgical nurse staffing (registered nurses and registry nurses) were associated with particular hospital characteristics. Researchers found the baseline level of nurse staffing was the variable most associated with change in mean productive hours per patient day from FY 2000 to FY 2006. Those hospitals with nurse staffing below 4.0 mean productive medical-surgical RN and registry hours per patient day in FY 2000 had a significantly larger, positive change in mean productive hours than did hospitals with average mean productive hours (approximately 5 hours) in FY 2000. Hospitals staffing above 6.0 mean productive hours per patient day in FY 2000 changed their staffing less than did hospitals with average mean productive hours.
Payers are known to influence the adoption of health information technology (HIT) among hospitals. However, previous studies examining the relationship between payer mix and HIT have not focused specifically on electronic health record systems (EHRs). Using data from the Nationwide Inpatient Sample and the American Hospital Association Annual Survey, we examine how Medicare, Medicaid, commercial insurance, and managed care caseloads are associated with EHR adoption in hospitals. Overall, we found a weak relationship between payer mix and EHR adoption. Medicare and, separately, Medicaid volumes were not associated with EHR adoption. Furthermore, commercial insurance volume was not associated with EHR adoption; however, a hospital located in the third quartile of managed care caseloads had a decreased likelihood of EHR adoption. We did not find empirical evidence to support the hypothesis that payer generosity and other indirect mechanisms influence EHR adoption in hospitals. The direct incentives embedded in the Health Information Technology for Economic and Clinical Health Act may have a positive influence on EHR adoption--especially for hospitals with high Medicare and/or Medicaid caseloads. However, it is still uncertain whether the available incentives will offset the barriers many hospitals face in achieving meaningful use of EHRs.
Background:
Hospitals facing financial uncertainty have sought to reduce nurse staffing as a way to increase profitability. However, nurse staffing has been found to be important in terms of quality of patient care and nursing-related outcomes. Nurse staffing can provide a competitive advantage to hospitals and as a result of better financial performance, particularly in more competitive markets.
Purpose:
In this study, we build on the Resource-Based View of the Firm to determine the effect of nurse staffing on total profit margin in more competitive and less competitive hospital markets in Florida.
Methodology/approach:
By combining a Florida statewide nursing survey with the American Hospital Association Annual Survey and the Area Resource File, three separate multivariate linear regression models were conducted to determine the effect of nurse staffing on financial performance while accounting for market competitiveness. The analysis was limited to acute care hospitals.
Findings:
Nurse staffing levels had a positive association with financial performance (β = 3.3, p = .02) in competitive hospital markets, but no significant association was found in less competitive hospital markets.
Practice implications:
Hospitals in more competitive hospital markets should reconsider reducing nursing staff, as these cost-cutting measures may be inefficient and negatively affect financial performance.
This study is unique because it uses multiple regression and data envelopment analysis (DEA) to evaluate teaching hospital quality. The results support the premise that teaching hospital leadership through the effective allocation of resources can improve the quality of care. This study has managerial implications by demonstrating the positive correlation between HMO market penetration and improved clinical quality outcomes. This would suggest that improved efficiency caused by limited HMO reimbursement and tight utilization controls encourage hospitals to cut waste as well as improve their clinical care processes. Additionally, our research found that teaching hospitals with higher levels of long-term debt also had improved quality. This shows that increased investments in facilities and advanced technology at teaching hospitals can lead to enhanced quality.
The advent of the value-based purchasing (VBP) concept in which efficiency and quality are considered prompted an interest in exploring approaches that incorporate both. This study examined a hospital classification method, Centers for Excellence in Efficiency and Quality (CEEQs), for coronary revascularization procedures. The results identified select hospitals (two [out of 33] in coronary artery bypass graft surgery (CABG) and seven in percutaneous coronary intervention (PCI)) that can be classified as CEEQs. Furthermore, an investigation of hospitals' efficiency and quality revealed great variation in efficiency among high-quality hospitals. The study demonstrated the possibility of employing service- or disease-specific approaches to VBP and pay-for-performance (P4P) programs.
The error term in the stochastic frontier model is of the form (v–u), where v is a normal error term representing pure randomness, and u is a non-negative error term representing technical inefficiency. The entire (v–u) is easily estimated for each observation, but a previously unsolved problem is how to separate it into its two components, v and u. This paper suggests a solution to this problem, by considering the expected value of u, conditional on (v–u). An explicit formula is given for the half-normal and exponential cases.
To compare quality of care measured by explicit criteria, implicit review, and sickness-adjusted outcomes at different types of hospitals.
Further analysis of data retrospectively abstracted from medical records to evaluate the effects of prospective payment on quality of care for hospitalized Medicare patients.
Hospitals in five states were sampled to represent the national Medicare admissions along many dimensions.
A total of 14,008 elderly patients with one of the following five diseases: congestive heart failure, acute myocardial infarction, pneumonia, stroke, or hip fracture. These patients were randomly sampled from those with these diseases in 297 hospitals in two time periods, 1981 to 1982 and 1985 to 1986.
Explicit criteria, implicit review, and mortality within 30 days of admission adjusted for sickness at admission.
Quality of care ratings for hospital types are similar using explicit criteria, implicit review, and outcomes adjusted for sickness at admission. Quality differences between types of hospitals were large, with the lowest group estimated to have four percentage points higher mortality than major teaching hospitals in a cohort of patients with average mortality of 16%. Quality varies from state to state, but teaching, larger, and more urban hospitals have better quality in general than nonteaching, small, and rural hospitals. Hospital quality persists over time, but small nonteaching hospitals narrowed the gap with better quality hospitals between 1981 and 1986.
The different measures led to consistent and plausible relationships between quality and hospital characteristics. Thus, valid information about hospital quality can be obtained. We need to develop ways to use such information to improve care.
This study explores a new approach to estimating the cost of inpatient and outpatient services provided by hospitals. Data from a nationwide survey of non-federal, short-term, U.S. hospitals are used to make cost estimates based on a multiple-output cost function. The results provide information on the structure of hospital costs, and include estimates of the marginal and average incremental cost of outpatient care. Because of the innovative specification of the cost function, the study is of interest for its methodology as well as empirical results.
Growing evidence suggests that nurse staffing affects the quality of care in hospitals, but little is known about whether the educational composition of registered nurses (RNs) in hospitals is related to patient outcomes.
To examine whether the proportion of hospital RNs educated at the baccalaureate level or higher is associated with risk-adjusted mortality and failure to rescue (deaths in surgical patients with serious complications).
Cross-sectional analyses of outcomes data for 232 342 general, orthopedic, and vascular surgery patients discharged from 168 nonfederal adult general Pennsylvania hospitals between April 1, 1998, and November 30, 1999, linked to administrative and survey data providing information on educational composition, staffing, and other characteristics.
Risk-adjusted patient mortality and failure to rescue within 30 days of admission associated with nurse educational level.
The proportion of hospital RNs holding a bachelor's degree or higher ranged from 0% to 77% across the hospitals. After adjusting for patient characteristics and hospital structural characteristics (size, teaching status, level of technology), as well as for nurse staffing, nurse experience, and whether the patient's surgeon was board certified, a 10% increase in the proportion of nurses holding a bachelor's degree was associated with a 5% decrease in both the likelihood of patients dying within 30 days of admission and the odds of failure to rescue (odds ratio, 0.95; 95% confidence interval, 0.91-0.99 in both cases).
In hospitals with higher proportions of nurses educated at the baccalaureate level or higher, surgical patients experienced lower mortality and failure-to-rescue rates.
Objective.
—To compare quality of care measured by explicit criteria, implicit review, and sickness-adjusted outcomes at different types of hospitals.Design.
—Further analysis of data retrospectively abstracted from medical records to evaluate the effects of prospective payment on quality of care for hospitalized Medicare patients.Setting.
—Hospitals in five states were sampled to represent the national Medicare admissions along many dimensions.Patients.
—A total of 14008 elderly patients with one of the following five diseases: congestive heart failure, acute myocardial infarction, pneumonia, stroke, or hip fracture. These patients were randomly sampled from those with these diseases in 297 hospitals in two time periods, 1981 to 1982 and 1985 to 1986.Outcome Measures.
—Explicit criteria, implicit review, and mortality within 30 days of admission adjusted for sickness at admission.Results.
—Quality of care ratings for hospital types are similar using explicit criteria, implicit review, and outcomes adjusted for sickness at admission. Quality differences between types of hospitals were large, with the lowest group estimated to have four percentage points higher mortality than major teaching hospitals in a cohort of patients with average mortality of 16%. Quality varies from state to state, but teaching, larger, and more urban hospitals have better quality in general than nonteaching, small, and rural hospitals. Hospital quality persists overtime, but small nonteaching hospitals narrowed the gap with better quality hospitals between 1981 and 1986.Conclusions.
—The different measures led to consistent and plausible relationships between quality and hospital characteristics. Thus, valid information about hospital quality can be obtained. We need to develop ways to use such information to improve care.(JAMA. 1992;268:1709-1714)
This study examined the impact of managed care and other environmental factors on hospital inefficiency in 1631 US hospitals during the period 1990–1996. A panel, stochastic frontier regression model was used to estimate inefficiency parameters and inefficiency scores.
The results suggest that mean estimated inefficiency decreased by about 28% during the study period. Inefficiency was negatively associated with health maintenance organization (HMO) penetration and industry concentration. It was positively related with Medicare share and for-profit ownership status. Copyright
This study examines variation in severity-adjusted Medicare hospital mortality rates across nine U.S. census regions. The extent to which regional variation is reduced by controlling for differences in hospital resources and structure, county-level population characteristics, and the level of federal SuperPRO-identified hospital quality problems is estimated. Hospital resources, population characteristics, and SuperPro process quality scores are significant predictors of hospital mortality rates, but they do not explain the important, highly significant regional differences observed after controlling for hospital case-mix severity.
This study assessed the relationship between the Health Care Financing Administration adjusted mortality rate for a hospital and the errors in care found by the peer review process. The three data sets used were: (1) the 1987-1988 completed reviews from 38 peer review organizations (PROs) of 4,132 hospitals and 2,035,128 patients; (2) all 1987 hospital mortality rates for Medicare patients as adjusted by HCFA for patient mix; and (3) the 1986 American Hospital Association Survey. The PRO data were used to compute the percentage of cases reviewed from each hospital confirmed by a reviewing physician to have a quality problem. The average percentage of confirmed problems was 3.73 percent with state rates ranging from 0.03 percent to 38.5 percent. The average within-state correlation between the problem rate and the adjusted mortality rate for all PROs was .19 (p < .0001), but the correlations were much higher for relatively homogeneous groups of hospitals, .42 for public hospitals and .36 for hospitals in large metropolitan statistical areas (MSAs). These results suggest that the HCFA adjusted hospital mortality rate and the PRO-confirmed problem rate are related methods to compare hospitals on the basis of quality of care. Both methods may compare quality better if used within a group of homogenous hospitals.
Using a sample of 3,000 urban hospitals, this article examines the contributions of selected hospital characteristics to variations in hospital technical efficiencies, while it accounts for multiple products and inputs, and controls for local environmental variations. Four hospital characteristics are examined: hospital size, membership in a multihospital system, ownership, and payer mix (managed care contracts, percent Medicare, and percent Medicaid). Ownership and percent Medicare are consistently found to be related significantly to hospital efficiency. Within the ownership variable, government hospitals tend to be more efficient and for-profit hospitals less efficient than other hospitals. Higher percentages of Medicare payment are negatively related to efficiency. While not consistently significant across all five of the MSA size categories in which the analyses are conducted, possession of managed care contracts, membership in a multihospital system, and size all are consistently related positively to hospital technical efficiency. These variables are also all significant when the hospitals are examined in a combined analysis. Percent Medicaid was not significant in any of the analyses. Implications for policy and the need for methodological work are discussed.
The average level of cost inefficiency in New York nursing homes is estimated at 29%, based on a two-year panel of 164 Skilled Nursing Facilities and 443 combination Skilled and Health Related Facilities. The stochastic frontier cost function is fit to the data utilizing the composed error model, wherein statistical noise and allocative and technical inefficiency are jointly estimated. There is no change in efficiency between 1987 and 1990, and it does not vary between for-profit and not-for-profit homes. Excessive managerial and supervisory personnel and diseconomies of size are linked to inefficient operation. Chronic excess demand is suggested as a cause of the high level of inefficiency.
This paper uses a stochastic frontier multiproduct cost function to derive hospital-specific measures of inefficiency. The cost function includes direct measures of illness severity, output quality, and patient outcomes to reduce the likelihood that the inefficiency estimates are capturing unmeasured differences in hospital outputs. Models are estimated using data from the AHA Annual Survey, Medicare Hospital Cost Reports, and MEDPAR. We explicitly test the assumption of output endogeneity and reject it in this application. We conclude that inefficiency accounts for 13.6 percent of total hospital costs. This estimate is robust with respect to model specification and approaches to pooling data across distinct groups of hospitals.
Reports on hospital quality performance are being produced with increasing frequency by state agencies, commercial data vendors, and health care purchasers. Risk-adjusted mortality rate is the most commonly used measure of quality in these reports. The purpose of this study was to determine whether risk-adjusted mortality rates are valid indicators of hospital quality performance.
Based on an analytical model of random measurement error, sensitivity and predictive error of mortality rate indicators of hospital performance were estimated.
The following six parameters were shown to determine accuracy: (1) mortality risks of patients who receive good quality care and (2) of those who receive poor quality care, (3) proportion of patients (across all hospitals) who receive poor quality care, (4) proportion of hospitals considered to be "poor quality," (5) patients' relative risk of receiving poor quality care in "good quality" and in "poor quality" hospitals, and (6) number of patients treated per hospital. Using best available values for model parameters, analyses demonstrated that in nearly all situations, even with perfect risk adjustment, identifying poor quality hospitals on the basis of mortality rate performance is highly inaccurate. Of hospitals that delivered poor quality care, fewer than 12% were identified as high mortality rate outliers, and more than 60% of outliers were actually good quality hospitals.
Under virtually all realistic assumptions for model parameter values, sensitivity was less than 20% and predictive error was greater than 50%. Reports that measure quality using risk-adjusted mortality rates misinform the public about hospital performance.
HMOs have been shown to have an effect on the care provided directly to their enrollees. They may also influence the care provided to individuals in fee-for-service plans through a spill-over effect. The objective of this study was to investigate the associations among HMO market penetration, HMO and hospital competition, and the quality of care received by Medicare fee-for-service patients measured by risk-adjusted hospital mortality rates.
The 1990 data for 1,927 hospitals in 134 metropolitan statistical areas (with five or more hospitals) included Medicare fee-for-service risk-adjusted mortality rates from the Medicare Hospital Information Reports, hospital characteristics from the American Hospital Association annual survey, and HMO market penetration and competition calculated from InterStudy and Group Health Association of America data.
Statistical regression techniques were used to identify the associations between HMO market penetration, competition, and risk-adjusted mortality, controlling for other hospital characteristics and region.
Higher HMO market penetration and to a lesser degree increased HMO competition were associated with better mortality outcomes for fee-for-service Medicare enrollees. Competition between hospitals did not exhibit a significant association.
HMOs may have a spill-over effect on quality of care received by individuals enrolled in fee-for-service plans. These findings may be explained by a positive effect on local practice styles or a preferential selection by HMOs for areas with better hospital care.
Outcomes performance measures are increasingly important in health care. The Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) continues to rely on structure and process measures based on accepted good practice. One of the first tasks in moving to a more outcomes-oriented approach is to compare the two measurement approaches. This article compares seven non-federal general hospital performance measures derived from Medicare against Joint Commission scores. Joint Commission measures are generally not correlated with outcome measures. The few significant correlations that appear are often counterintuitive. We conclude that a potentially serious disjuncture exists between the outcomes measures and Joint Commission evaluations.
Leading healthcare provider organizations now use a "balanced scorecard" of performance measures, expanding information reviewed at the governance level to include financial, customer, and internal performance information, as well as providing an opportunity to learn and grow to provide better strategic guidance. The approach, successfully used by other industries, uses competitor data and benchmarks to identify opportunities for improved mission achievement. This article evaluates one set of nine multidimensional hospital performance measures derived from Medicare reports (cash flow, asset turnover, mortality, complications, length of inpatient stay, cost per case, occupancy, change in occupancy, and percent of revenue from outpatient care). The study examines the content validity, reliability and sensitivity, validity of comparison, and independence and concludes that seven of the nine measures (all but the two occupancy measures) represent a potentially useful set for evaluating most U.S. hospitals. This set reflects correctable differences in performance between hospitals serving similar populations, that is, the measures reflect relative performance and identify opportunities to make the organization more successful.
To examine the changes in licensed nursing staff in Pennsylvania hospitals from 1991 to 1997, and to assess the relationship of licensed nursing staff with patient adverse events in hospitals.
A convenience sample of all Pennsylvania, acute-care, hospitals, 1991 to 1997.
The study first describes the percentage change of licensed nursing staff categories in Pennsylvania hospitals from 1991 to 1997. Second, random effects Poisson regressions are used to assess the association of the numbers and proportions of licensed nurses with yearly iatrogenic lung collapse, pressure sores, falls, pneumonia, posttreatment infections, and urinary tract infections. Controls are the yearly number of patients, hospital acuity, and other hospital characteristics.
Secondary data containing patient- and hospital-level measures from three sources were recoded to establish the incidence of adverse events, aggregated to the hospital level, and merged to form one data set. PRINCIPAL FUNDING: Licensed nurses' acuity-adjusted patient load increased from 1991 to 1997. Licensed nurse/total nursing staff declined from 1994 to 1997. Greater incidence of nearly all adverse events occurred in hospitals with fewer licensed nurses. Greater incidence of decubitus ulcers and pneumonia occurred in hospitals with a lower proportion of licensed nurses.
This study suggests that licensed nurses' patient load began increasing in the 1990s. Adequate licensed nurse staffing is important in minimizing the incidence of adverse events in hospitals. Ensuring adequate licensed nurse staffing should be an area of major concern to hospital management. Improved measures of nurse staffing and patient outcomes, and further studies are suggested.
This article explores the current state of the creation and use of evidence by managers for cost containment in hospitals. We assert that hospitals do not know enough about what things cost, and until they get evidence on costs, it is not likely that much can be done to narrow the chasm between common practice and best practice. Part of the problem is that managers do not seek out available evidence that exists, and part of the problem is a lack of sufficient research efforts to generate evidence for managers to use. The article strives to help direct future efforts by researchers and managers in the area of evidence-based cost containment research by presenting a framework for priorities that managers and researchers can use to increase the amount of research done to generate evidence and to increase the use of evidence by health care managers.
To evaluate previous research findings of the relationship between nurse staffing and quality of care by examining the effects of change in registered nurse staffing on change in quality of care.
Secondary data from the American Hospital Association (AHA)(nurse staffing, hospital characteristics), InterStudy and Area Resource Files (ARF) (market characteristics), Centers for Medicare and Medicaid Services (CMS) (financial performance), and Healthcare Cost and Utilization Project (HCUP) (quality measures-in-hospital mortality ratio and the complication ratios for decubitus ulcers, pneumonia, and urinary tract infection, which were risk-adjusted using the Medstat disease staging algorithm).
Data from a longitudinal cohort of 422 hospitals were analyzed from 1990-1995 to examine the relationships between nurse staffing and quality of care.
A generalized method of moments estimator for dynamic panel data was used to analyze the data. Principal Findings. Increasing registered nurse staffing had a diminishing marginal effect on reducing mortality ratio, but had no consistent effect on any of the complications. Selected hospital characteristics, market characteristics, and financial performance had other independent effects on quality measures.
The findings provide limited support for the prevailing notion that improving registered nurse (RN) staffing unconditionally improves quality of care.
The authors consider the association between productive efficiency and clinical quality in institutional long-term care for the elderly. Cross-sectional data were collected from 122 wards in health-centre hospitals and residential homes in Finland in 2001. Productive efficiency was measured in terms of technical efficiency, which was defined as the unit's distance from the (best practice) production frontier. The analysis employed stochastic production frontier estimation, where technical inefficiency in the production function was specified to be a function of ward characteristics and clinical quality of care. Several quality indicators based on the Resident Assessment Instrument, such as prevalence of pressure ulcers and depression with no treatment, were used in the analysis. The results did not reveal systematic association between technical efficiency and clinical quality of care. However, the prevalence of pressure ulcers, indicating poor quality of care was associated with technical efficiency, a fact which highlights the importance of including quality measures in the assessment of efficiency in long-term care.
Medstat Disease Staging 2 Software, Version 4.13: Reference Guide
- Medstat Group
Medstat Group. Medstat Disease Staging 2 Software, Version
4.13: Reference Guide. 2002.
Structural versus Outcomes Measures in Hospitals: A Comparison of Joint Commission and Medicare Outcomes Scores in Hospitals
''Structural versus Outcomes Measures in Hospitals: A
Comparison of Joint Commission and Medicare Outcomes Scores in Hospitals.'' Quality Management Health
Care 10, (2002): 29–38.
- Unruh