The Volume-Outcome Relationship: From Luft to Leapfrog

Department of Thoracic and Cardiovascular Surgery, Lahey Clinic, Burlington, Massachusetts 01805, USA.
The Annals of Thoracic Surgery (Impact Factor: 3.85). 04/2003; 75(3):1048-58. DOI: 10.1016/S0003-4975(02)04308-4
Source: PubMed


Numerous reports have documented a volume-outcome relationship for complex medical and surgical care, although many such studies are compromised by the use of discharge abstract data, inadequate risk adjustment, and problematic statistical methodology. Because of the volume-outcome association, and because valid outcome measurements are unavailable for many procedures, volume-based referral strategies have been advocated as an alternative approach to health-care quality improvement. This is most appropriate for procedures with the greatest outcome variability between low-volume and high-volume providers, such as esophagectomy and pancreatectomy, and for particularly high-risk subgroups of patients. Whenever possible, risk-adjusted outcome data should supplement or supplant volume standards, and continuous quality improvement programs should seek to emulate the processes of high-volume, high-quality providers. The Leapfrog Group has established a minimum volume requirement of 500 procedures for coronary artery bypass grafting. In view of the questionable basis for this recommendation, we suggest that it be reevaluated.

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    • "Numerous studies have reported a positive association between high-volume physicians and better outcomes, but the debate for the threshold of a composite patient safety score for U.S. hospitals, which the Leapfrog Group has established 38, has still not ended; or perhaps the existing findings encourage patients to prefer facilities with better-than-expected outcomes and away from those with worse-than-expected outcomes. Moreover, despite studies 39 that have confirmed the volume-outcome relationship, more appropriate statistical tools are suggested to clarify some unsatisfactory situations. "
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    ABSTRACT: Background and objects: We explored the relationship between hospital/surgeon volume and postoperative severe sepsis/graft-failure (including death). Methods: The Taiwan National Health Insurance Research Database claims data for all patients with end-stage renal disease patients who underwent kidney transplantation between January 1, 1999, and December 31, 2007, were reviewed. Surgeons and hospitals were categorized into two groups based on their patient volume. The two primary outcomes were severe sepsis and graft failure (including death). The logistical regressions were done to compute the Odds ratios (OR) of outcomes after adjusting for possible confounding factors. Kaplan-Meier analysis was used to calculate the cumulative survival rates of graft failure after kidney transplantation during follow-up (1999-2008). Results: The risk of developing severe sepsis in a hospital in which surgeons do little renal transplantation was significant (odds ratio [OR]; p = 0.0115): 1.65 times (95% CI: 1.12-2.42) higher than for a hospital in which surgeons do many. The same trend was true for hospitals with a low volume of renal transplantations (OR = 2.39; 95% CI: 1.62-3.52; p < 0.0001). The likelihood of a graft failure (including death) within one year for the low-volume surgeon group was 3.1 times higher than for the high-volume surgeon group (p < 0.0001); the trend was similar for hospital volume. Female patients had a lower risk than did male patients, and patients ≥ 55 years old and those with a higher Charlson comorbidity index score, had a higher risk of severe sepsis. Conclusions: We conclude that the risk of severe sepsis and graft failure (including death) is higher for patients treated in hospitals and by surgeons with a low volume of renal transplantations. Therefore, the health authorities should consider exporting best practices through educational outreach and regulation and then providing transparent information for public best interest.
    International journal of medical sciences 06/2014; 11(9):918-24. DOI:10.7150/ijms.8850 · 2.00 Impact Factor
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    • "Although numerous studies have documented a volume-outcome relationship, literature to quantify the strength of this relationship is not available for all diagnosis groups. It is presumed that the association depends on the level of complexity of the intervention and the level of co-operation between different specialties [31,33]. Therefore based on the complexity of the diagnosis groups we divided the diagnosis groups in four categories and (somewhat arbitrarily) gauged volume-outcome relationship in QALYs relative to the diagnosis group neoplasms: high (the same relationship as the group neoplasms), intermediate (50% of neoplasms), low (5% of neoplasms), or no volume-outcome relationship. "
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    ABSTRACT: Background The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the ‘optimal’ hospital infrastructure would be in the Dutch context. Methods To create optimal societal value we have used a mathematical mixed integer programming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities. Results The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD). Conclusions In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a ‘what-if’ analysis.
    BMC Health Services Research 06/2013; 13(1):220. DOI:10.1186/1472-6963-13-220 · 1.71 Impact Factor
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    • "Moreover, similar to an earlier study in the United States [5], they included both mild and severe cases in the analysis, which implied that volume-based selective referral, if adopted, would be applied to all patients with AP. However, some potential disadvantages of the volume-based policy [11] make us believe that selective referral should be limited to high risk patients, such as SAP cases. The transfer to a distant high-volume hospital is unreasonable for a mild AP patient who would recover within several days without the need of specific treatment other than simple supportive care [10]. "
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    ABSTRACT: Background We investigated the relation between hospital volume and outcome in patients with severe acute pancreatitis (SAP). The determination is important because patient outcome may be improved through volume-based selective referral. Methods In this cohort study, we analyzed 22,551 SAP patients in 2,208 hospital-years (between 2000 and 2009) from Taiwan’s National Health Insurance Research Database. Primary outcome was hospital mortality. Secondary outcomes were hospital length of stay and charges. Hospital SAP volume was measured both as categorical and as continuous variables (per one case increase each hospital-year). The effect was assessed using multivariable logistic regression models with generalized estimating equations accounting for hospital clustering effect. Adjusted covariates included patient and hospital characteristics (model 1), and additional treatment variables (model 2). Results Irrespective of the measurements, increasing hospital volume was associated with reduced risk of hospital mortality after adjusting the patient and hospital characteristics (adjusted odds ratio [OR] 0.995, 95% confidence interval [CI] 0.993-0.998 for per one case increase). The patients treated in the highest volume quartile (≥14 cases per hospital-year) had 42% lower risk of hospital mortality than those in the lowest volume quartile (1 case per hospital-year) after adjusting the patient and hospital characteristics (adjusted OR 0.58, 95% CI 0.40-0.83). However, an inverse relation between volume and hospital stay or hospital charges was observed only when the volume was analyzed as a categorical variable. After adjusting the treatment covariates, the volume effect on hospital mortality disappeared regardless of the volume measures. Conclusions These findings support the use of volume-based selective referral for patients with SAP and suggest that differences in levels or processes of care among hospitals may have contributed to the volume effect.
    BMC Gastroenterology 08/2012; 12(1):112. DOI:10.1186/1471-230X-12-112 · 2.37 Impact Factor
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