A two-stage estimation of hospital quality using mortality outcome measures: an application using hospital administrative data

An econometric approach to aggregating multiple cardiovascular outcomes in German hospitals

OBJECTIVE

Development of an aggregate quality index to evaluate hospital performance in cardiovascular events treatment.

METHODS

We applied a two-stage regression approach using an accelerated failure time model based on variance weights to estimate hospital quality over four cardiovascular interventions: elective coronary bypass graft, elective cardiac resynchronization therapy, and emergency treatment for acute myocardial infarction. Mortality and readmissions were used as outcomes. For the estimation we used data from a statutory health insurer in Germany from 2005 to 2016.

RESULTS

The precision-based weights calculated in the first stage were higher for mortality than for readmissions. In general, teaching hospitals performed better in our ranking of hospital quality compared to non-teaching hospitals, as did private not-for-profit hospitals compared to hospitals with public or private for-profit ownership.

DISCUSSION

The proposed approach is a new method to aggregate single hospital quality outcomes using objective, precision-based weights. Likelihood-based accelerated failure time models make use of existing data more efficiently compared to widely used models relying on dichotomized data. The main advantage of the variance-based weights approach is that the extent to which an indicator contributes to the aggregate index depends on the amount of its variance.

Artificial neural networks and risk stratification in emergency departments

Emergency departments are characterized by the need for quick diagnosis under pressure. To select the most appropriate treatment, a series of rules to support decision-making has been offered by scientific societies. The effectiveness of these rules affects the appropriateness of treatment and the hospitalization of patients. Analyzing a sample of 1844 patients and focusing on the decision to hospitalize a patient after a syncope event to prevent severe short-term outcomes, this work proposes a new algorithm based on neural networks. Artificial neural networks are a non-parametric technique with the well-known ability to generalize behaviors, and they can thus predict severe short-term outcomes with pre-selected levels of sensitivity and specificity. This innovative technique can outperform the traditional models, since it does not require a specific functional form, i.e., the data are not supposed to be distributed following a specific design. Based on our results, the innovative model can predict hospitalization with a sensitivity of 100% and a specificity of 79%, significantly increasing the appropriateness of medical treatment and, as a result, hospital efficiency. According to Garson's Indexes, the most significant variables are exertion, the absence of symptoms, and the patient's gender. On the contrary, cardio-vascular history, hypertension, and age have the lowest impact on the determination of the subject's health status. The main application of this new technology is the adoption of smart solutions (e.g., a mobile app) to customize the stratification of patients admitted to emergency departments (ED)s after a syncope event. Indeed, the adoption of these smart solutions gives the opportunity to customize risk stratification according to the specific clinical case (i.e., the patient's health status) and the physician's decision-making process (i.e., the desired levels of sensitivity and specificity). Moreover, a decision-making process based on these smart solutions might ensure a more effective use of available resources, improving the management of syncope patients and reducing the cost of inappropriate treatment and hospitalization.

On measuring the quality of hospitals

PURPOSE

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

DESIGN/METHODOLOGY/APPROACH

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

FINDINGS

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

PRACTICAL IMPLICATIONS

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

ORIGINALITY/VALUE

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

Multidimensional performance assessment of public sector organisations using dominance criteria

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 best practice and poor performance under relatively weak assumptions about stakeholders' preferences. We use as an example providers of hip replacement surgery in the English National Health Service and estimate multivariate multilevel models to study their performance in terms of length of stay, readmission rates, post-operative patient-reported health status and waiting time. We find substantial correlation between objectives and demonstrate that ignoring the correlation can lead to incorrect assessments of performance.

A Theory of Waiting Time Reporting and Quality Signaling

We develop a theoretical model to study a policy that publicly reports hospital waiting times. We characterize two effects of such a policy: the 'competition effect' that drives hospitals to compete for patients by increasing service rates and reducing waiting times and the 'signaling effect' that allows patients to distinguish a high-quality hospital from a low-quality one. While for a low-quality hospital both effects help reduce waiting time, for a high-quality hospital, they act in opposite directions. We show that the competition effect will outweigh the signaling effect for the high-quality hospital, and consequently, both hospitals' waiting times will be reduced by the introduction of the policy. This result holds in a policy environment where maximum waiting time targets are not binding. Copyright © 2015 John Wiley & Sons, Ltd.

Applying risk adjusted cost-effectiveness (RAC-E) analysis to hospitals: estimating the costs and consequences of variation in clinical practice

Cost-effectiveness analysis is well established for pharmaceuticals and medical technologies but not for evaluating variations in clinical practice. This paper describes a novel methodology--risk adjusted cost-effectiveness (RAC-E)--that facilitates the comparative evaluation of applied clinical practice processes. In this application, risk adjustment is undertaken with a multivariate matching algorithm that balances the baseline characteristics of patients attending different settings (e.g., hospitals). Linked, routinely collected data are used to analyse patient-level costs and outcomes over a 2-year period, as well as to extrapolate costs and survival over patient lifetimes. The study reports the relative cost-effectiveness of alternative forms of clinical practice, including a full representation of the statistical uncertainty around the mean estimates. The methodology is illustrated by a case study that evaluates the relative cost-effectiveness of services for patients presenting with acute chest pain across the four main public hospitals in South Australia. The evaluation finds that services provided at two hospitals were dominated, and of the remaining services, the more effective hospital gained life years at a low mean additional cost and had an 80% probability of being the most cost-effective hospital at realistic cost-effectiveness thresholds. Potential determinants of the estimated variation in costs and effects were identified, although more detailed analyses to identify specific areas of variation in clinical practice are required to inform improvements at the less cost-effective institutions.