Abstract:Public sector organisations pursue multiple objectives and serve a number of stakeholders. But stakeholders are rarely explicit about the valuations they attach to different objectives, nor are these valuations likely to be identical. This complicates the assessment of their performance because no single set of weights can be chosen legitimately to aggregate outputs into unidimensional composite scores. We propose the use of dominance criteria in a multidimensional performance assessment framework to identify … Show more
“…Both increasing average age and the associated prevalence of multiple chronic conditions in contemporary patient populations can complicate the assessment of healthcare management, so that methods that can accommodate multi-dimensional outcomes would be useful. However, we are aware of only one study that has provided a strategy to rank providers in a multi-dimensional setting, by proposing a dominance criterion [5]. Thus, methods for assessing multiple healthcare outcomes are required.…”
Background: Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. Methods: Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion. Results: We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death). Conclusions: The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance.
“…Both increasing average age and the associated prevalence of multiple chronic conditions in contemporary patient populations can complicate the assessment of healthcare management, so that methods that can accommodate multi-dimensional outcomes would be useful. However, we are aware of only one study that has provided a strategy to rank providers in a multi-dimensional setting, by proposing a dominance criterion [5]. Thus, methods for assessing multiple healthcare outcomes are required.…”
Background: Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. Methods: Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion. Results: We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death). Conclusions: The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance.
“…However, no diagnostic information is presented from the principal component analysis to support the assumption of unidimensionality and the use of a single principal component. In other studies, quality indicators have been shown to be multidimensional . Based on their analysis, Lieberthal and Comer concluded thatpatient satisfaction is a poor measure of quality….…”
Section: Introductionmentioning
confidence: 99%
“…In other studies, quality indicators have been shown to be multidimensional. [18][19][20][21][22] Based on their analysis, Lieberthal and Comer concluded that patient satisfaction is a poor measure of quality…. the best hospitals were not the ones that were the quietest or that had the most responsive clinicians.…”
Objective
To evaluate the dimensionality of hospital quality indicators treated as unidimensional in a prior publication.
Data Source/Study Design
Pooled cross‐sectional 2010‐2011 Hospital Compare data (10/1/10 and 10/1/11 archives) and the 2012 American Hospital Association Annual Survey.
Data Extraction
We used 71 indicators of structure, process, and outcomes of hospital care in a principal component analysis of Ridit scores to evaluate the dimensionality of the indicators. We conducted an exploratory factor analysis using only the indicators in the Centers for Medicare & Medicaid Services' Hospital Value‐Based Purchasing.
Principal Findings
There were four underlying dimensions of hospital quality: patient experience, mortality, and two clinical process dimensions.
Conclusions
Hospital quality should be measured using a variety of indicators reflecting different dimensions of quality. Treating hospital quality as unidimensional leads to erroneous conclusions about the performance of different hospitals.
“…Given the challenge of finding suitable instrumental variables, we avoid the causal identification problem and motivated by the methodology used in studies that have assessed performance in physical health care [22,23], we analyse costs and outcomes using two separate equations and allow for a correlation in responses. As in previous studies [14,21] we measure quality in terms of an outcome measurethe Health of the Nation Outcome Scale (HoNOS).…”
Section: Introductionmentioning
confidence: 99%
“…As in previous studies [14,21] we measure quality in terms of an outcome measurethe Health of the Nation Outcome Scale (HoNOS). Following earlier studies [18,[21][22][23] we use multi-level modelling which allows us to examine the correlation in residual responses at provider-level to provide insight into the relationship between costs and outcomes and whether a potential trade-off exists.…”
Provider payment systems for mental health care that incentivize cost control and quality improvement have been a policy focus in a number of countries. In England, a new prospective provider payment system is being introduced to mental health that should encourage providers to control costs and improve outcomes. The aim of this research is to investigate the relationship between costs and outcomes to ascertain whether there is a trade-off between controlling costs and improving outcomes. The main data source is the Mental Health Minimum Data Set (MHMDS) for the years 2011/12 and 2012/13. Costs are calculated using NHS reference cost data while outcomes are measured using the Health of the Nation Outcome Scales (HoNOS). We estimate a bivariate multi-level model with costs and outcomes simultaneously. We calculate the correlation and plot the pairwise relationship between residual costs and outcomes at the provider level. After controlling for a range of demographic, need, social, and treatment variables, residual variation in costs and outcomes remains at the provider level. The correlation between residual costs and outcomes is negative, but very small, suggesting that cost-containment efforts by providers should not undermine outcome-improving efforts under the new payment system.
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