Table 1 - uploaded by Paul Hakendorf
Content may be subject to copyright.
Source publication
Background
Methods for the cost-effectiveness analysis of health technologies are now well established, but such methods may also have a useful role in the context of evaluating the effects of variation in applied clinical practice. This study illustrates a general methodology for the comparative analysis of applied clinical practice at alternative...
Contexts in source publication
Context 1
... patient characteristics for the eligible cohort of stroke patients presenting in the year to June 30 2006 are presented in Table 1. The only significant variation in patient casemix concerned socioeconomic disadvan- tage (p < 0.001), with patients at hospital D classified as the most disadvantaged. ...
Similar publications
Comparing outcomes between hospitals requires consideration of patient factors that could account for any observed differences. Adjusting for comorbid conditions is common when studying outcomes following cancer surgery, and a commonly used measure is the Charlson comorbidity index. Other measures of patient health include the ECOG performance stat...
Citations
... Differences between observed and expected values for each intermediate endpoint were considered a measure of hospital performance. Expected outcome values for LOS and patient outcomes were estimated using separate generalised linear statistical models, as applied in previous risk adjusted cost-effectiveness analyses (19,26,27). Briefly, generalised linear models were constructed with backwards stepwise selection using the full range of demographic, socioeconomic, and clinical explanatory variables (comorbidities, admission type, primary and additional diagnoses and procedures) which were extracted from three separate datasets (ISAAC, Register of Births, Deaths and Marriages and area codes). ...
... Following determination of appropriate statistical models, risk adjusted LOS and patient outcome estimates for each patient were generated as the relevant observed minus expected values, which can be interpreted as risk adjusted differences in LOS and patient outcome. This method provides a more intuitive interpretation of hospital performance than 'observed divided by expected' adjusted values as previously reported (26). For the risk adjusted performance evaluation of LOS separate ANOVAs were performed to compare standardised LOS (observed LOS minus expected LOS) between the three key hospitals for each year of analysis. ...
Background: Despite advances in vascular surgery techniques, ageing populations and increasing rates of vascular disease and diabetes have contributed to relatively steady amputation rates. Older amputees have limited life expectancies and often require expensive rehabilitation interventions on top of vascular procedures. Services warrant scrutiny with view to reducing clinical practice variations, improving hospital performance and securing the best patient outcomes. Objective: This study employed a novel methodology to assess 12-month hospital performance associated with provision of lower-limb amputee services at three neighbouring hospitals. Design and Setting: Routinely collected data on individuals having an initial lower-limb amputation from July 2001 to June 2008 at three hospital networks in Adelaide, South Australia were analysed. Observed and expected lengths of stay and patient outcomes were generated, from which relative performance across hospitals were compared. Results: Following amputation we observed a short time-to-death (0.8 years, IQR 0.21-2.12) and 12-month mortality rate of 25% (unadjusted). Risk-adjusted analyses indicated that one hospital with co-located vascular and rehabilitation services had greater performance with lower re-amputation rates and fewer deaths. However, length of stay at this hospital was longer than expected. Conclusion: The risk-adjusted performance analysis provided an approach which could inform further investigations around variation in hospital performance to inform best practice service delivery.
... The cost-effectiveness of clinical interventions (e.g., diagnostic tests, therapies or medicines) is normally assessed using current clinical care as a comparator, with national guidelines as a proxy for current care [1,2]. However, this comparison with guidelines is inadequate when clinical practice differs significantly from guidelines and is particularly problematic when clinical practice differs between hospitals. ...
... However, both documents pay little attention to clinical practice variation and its consequences when performing relevant cost-effectiveness analyses (CEAs). In practice, most cost-effectiveness studies do not take into account possible causes and consequences of clinical practice variation [1,2]. ...
... A few studies have used large databases to investigate practice variation and the impact it has on costs and effects [1,2]. However, their approach is different from ours since we aim to illustrate the importance of investigating possible clinical practice variation and deviation from national guidelines, and the need to perform hospital-level CEAs, which incorporate local hospital conditions when important clinical practice variation exists. ...
The cost-effectiveness of clinical interventions is often assessed using current care as the comparator, with national guidelines as a proxy. However, this comparison is inadequate when clinical practice differs from guidelines, or when clinical practice differs between hospitals. We examined the degree of variation in the way patients with a recent transient ischemic attack (TIA) or minor ischemic stroke are assessed and used the results to illustrate the importance of investigating possible clinical practice variation, and the need to perform hospital-level cost-effectiveness analyses (CEAs) when variation exists.
Semi-structured interviews were conducted with 16 vascular neurologists in hospitals throughout the Netherlands. Questions were asked about the use of initial and confirmatory diagnostic imaging tests to assess carotid stenosis in patients with a recent TIA or minor ischemic stroke, criteria to perform confirmatory tests, and criteria for treatment. We also performed hospital-level CEAs to illustrate the consequences of the observed diagnostic strategies in which the diagnostic test costs, sensitivity and specified were varied according to the local hospital conditions.
56 % (9/16) of the emergency units and 63 % (10/16) of the outpatient clinics use the initial and confirmatory diagnostic tests to assess carotid stenosis in accordance with the national guidelines. Of the hospitals studied, only one uses the recommended criteria for use of a confirmatory test, 38 % (6/16) follow the guidelines for treatment. The most cost-effective diagnostic test strategy differs between hospitals.
If important practice variation exists, hospital-level CEAs should be performed. These CEAs should include an assessment of the feasibility and costs of switching to a different strategy.
... The major next step, however, is that the comparative process analyses displayed in this case study must now be combined with other comparative analyses that focus on the outcomes of the observed differences in patient pathways. In particular, we need to link our study with those that directly evaluate the cost-effectiveness of alternative forms of service provision by employing statistical methods to capture long-run cost and health outcomes for patients, as in, for example, Karnon et al. [2013] and Pham et al. [2012]. By linking these two analysis perspectives (i.e., our case study, which focuses on the process perspective, and those studies that focus on the long-term costeffectiveness of services) through common identifiers, we would then be well positioned to investigate the economic impact and efficiency of practice changes and investments. ...
Business process analysis and process mining, particularly within the health care domain, remain underutilized. Applied research that employs such techniques to routinely collected health care data enables stakeholders to empirically investigate care as it is delivered by different health providers. However, crossorganizational mining and the comparative analysis of processes present a set of unique challenges in terms of ensuring population and activity comparability, visualizing the mined models, and interpreting the results. Without addressing these issues, health providers will find it difficult to use process mining insights, and the potential benefits of evidence-based process improvement within health will remain unrealized. In this article, we present a brief introduction on the nature of health care processes, a review of process mining in health literature, and a case study conducted to explore and learn how health care data and crossorganizational comparisons with process-mining techniques may be approached. The case study applies process-mining techniques to administrative and clinical data for patients who present with chest pain symptoms at one of four public hospitals in South Australia. We demonstrate an approach that provides detailed insights into clinical (quality of patient health) and fiscal (hospital budget) pressures in the delivery of health care. We conclude by discussing the key lessons learned from our experience in conducting business process analysis and process mining based on the data from four different hospitals.
... Indeed, there is significant evidence of variation in the costs and outcomes associated with clinical practice at alternative institutions, which may be partly explained by systems-based factors such as the availability and organization of relevant resources. 3,4 Discrete-event simulation (DES) is a flexible modeling technique that facilitates the representation of queues for resources along a clinical pathway 5 and has also been used to represent disease progression and health-related outcomes in homogeneous patient cohorts. 6 Only one example was found in which a DES has been used to represent health outcomes in the context of constrained resource availability. ...
Background:
Decision-analytic models are routinely used as a framework for cost-effectiveness analyses of health care services and technologies; however, these models mostly ignore resource constraints. In this study, we use a discrete-event simulation model to inform a cost-effectiveness analysis of alternative options for the organization and delivery of clinical services in the ophthalmology department of a public hospital. The model is novel, given that it represents both disease outcomes and resource constraints in a routine clinical setting.
Methods:
A 5-year discrete-event simulation model representing glaucoma patient services at the Royal Adelaide Hospital (RAH) was implemented and calibrated to patient-level data. The data were sourced from routinely collected waiting and appointment lists, patient record data, and the published literature. Patient-level costs and quality-adjusted life years were estimated for a range of alternative scenarios, including combinations of alternate follow-up times, booking cycles, and treatment pathways.
Results:
The model shows that a) extending booking cycle length from 4 to 6 months, b) extending follow-up visit times by 2 to 3 months, and c) using laser in preference to medication are more cost-effective than current practice at the RAH eye clinic.
Conclusions:
The current simulation model provides a useful tool for informing improvements in the organization and delivery of glaucoma services at a local level (e.g., within a hospital), on the basis of expected effects on costs and health outcomes while accounting for current capacity constraints. Our model may be adapted to represent glaucoma services at other hospitals, whereas the general modeling approach could be applied to many other clinical service areas.
In Australia, local health services with allocated budgets manage public hospital services for defined geographical areas. The authors were embedded in a local health service for around 2 years and undertook a range of local level economic evaluations for which three decision contexts were defined: intervention development, post-implementation and prioritisation. Despite difficulties in estimating opportunity costs and in the relevance of portfolio-based prioritisation approaches, economic evaluation added value to local decision-making. Development-focused (ex ante) economic evaluations used expert elicitation and calibration methods to synthesise published evidence with local health systems data to evaluate interventions to prevent hospital acquired complications. The use of economic evaluation facilitated the implementation of interventions with additional resource requirements. Decision analytic models were used alongside the implementation of larger scale, more complex service interventions to estimate counterfactual patient pathways, costs and outcomes, providing a transparent alternative to the statistical analyses of intervention effects, which were subject to high risk of bias. Economic evaluations of more established services had less impact due to data limitations and lesser executive interest. Prioritisation-focused economic evaluations compared costs, outcomes and processes of care for defined patient populations across alternative local health services to identify, understand and quantify the effects of unwarranted variation to inform priority areas for improvement within individual local health services. The sustained use of local level economic evaluation could be supported by embedding health economists in local continuous improvement units, perhaps with an initial focus on supporting the development and evaluation of prioritised new service interventions. Shared resources and critical mass are important, which could be facilitated through groups of embedded economists with joint appointments between different local health services and the same academic institution.
Although multidisciplinary heart failure (HF) clinics are efficacious, it is not known how patient factors or HF clinic structural indicators and process measures have an impact on the cumulative health care costs.
In this retrospective cohort study using administrative databases in Ontario, Canada, we identified 1216 HF patients discharged alive after an acute care hospitalization in 2006 and treated at a HF clinic. The primary outcome was the cumulative 1-year health care costs. A hierarchical generalized linear model with a logarithmic link and gamma distribution was developed to determine patient-level and clinic-level predictors of cost.
The mean 1-year cost was $27,809 (range, $69 to $343,743). There was a 7-fold variation in the mean costs by clinic, from $14,670 to $96,524. Delays in being seen at a HF clinic were a significant patient-level predictor of costs (rate ratio 1.0015 per day; P<0.001). Being treated at a clinic with >3 physicians was associated with lower costs (rate ratio 0.78; P=0.035). Unmeasured patient-level differences accounted for 97.4% of the between-patient variations in cost. The between-clinic variation in costs decreased by 16.3% when patient-level factors were accounted for; it decreased by a further 49.8% when clinic-level factors were added.
From a policy perspective, the wide spectrum of HF clinic structure translates to inefficient care. Greater guidance as to the type of patient seen at a HF clinic, the timeliness of the initial visit, and the most appropriate structure of the HF clinics may potentially result in more cost-effective care.
Objective:
Controlled evaluations are subject to uncertainty regarding their replication in the real world, particularly around systems of service provision. Using routinely collected data, we undertook a risk adjusted cost-effectiveness (RAC-E) analysis of alternative applied models of primary health care for the management of obese adult patients. Models were based on the reported level of involvement of practice nurses (registered or enrolled nurses working in general practice) in the provision of clinical-based activities.
Design and methods:
Linked, routinely collected clinical data describing clinical outcomes (weight, BMI, and obesity-related complications) and resource use (primary care, pharmaceutical, and hospital resource use) were collected. Potential confounders were controlled for using propensity weighted regression analyses.
Results:
Relative to low level involvement of practice nurses in the provision of clinical-based activities to obese patients, high level involvement was associated with lower costs and better outcomes (more patients losing weight, and larger mean reductions in BMI). Excluding hospital costs, high level practice nurse involvement was associated with slightly higher costs. Incrementally, the high level model gets one additional obese patient to lose weight at an additional cost of $6,741, and reduces mean BMI by an additional one point at an additional cost of $563 (upper 95% confidence interval $1,547).
Conclusion:
Converted to quality adjusted life year (QALY) gains, the results provide a strong indication that increased involvement of practice nurses in clinical activities is associated with additional health benefits that are achieved at reasonable additional cost. Dissemination activities and incentives are required to encourage general practices to better integrate practice nurses in the active provision of clinical services.
Using the Institute of Medicine framework that outlines the domains of quality, this article considers four key aspects of health care delivery which have the potential to significantly affect the quality of health care within the pediatric intensive care unit. The discussion covers: performance improvement and how existing methods for reporting, review, and analysis of medical error relate to patient care; team composition and workflow; and the impact of information technologies on clinical practice. Also considered is how protocol-driven and standardized practice affects both patients and the fiscal interests of the health care system.
