Pôle Information Médicale Evaluation Recherche, Hospices Civils de Lyon, Lyon, France
Health Services and Performance Research lab (HESPER EA7425), Université Claude Bernard Lyon 1, Lyon, France.
BMJ Qual Saf. 2019 Jun;28(6):459-467. doi: 10.1136/bmjqs-2018-008039. Epub 2018 Oct 26.
Quality improvement and epidemiology studies often rely on database codes to measure performance or impact of adjusted risk factors, but how validity issues can bias those estimates is seldom quantified.
To evaluate whether and how much interhospital administrative coding variations influence a typical performance measure (adjusted mortality) and potential incentives based on it.
National cross-sectional study comparing hospital mortality ranking and simulated pay-for-performance incentives before/after recoding discharge abstracts using medical records.
Twenty-four public and private hospitals located in France PARTICIPANTS: All inpatient stays from the 78 deadliest diagnosis-related groups over 1 year.
Elixhauser and Charlson comorbidities were derived, and mortality ratios were computed for each hospital. Thirty random stays per hospital were then recoded by two central reviewers and used in a Bayesian hierarchical model to estimate hospital-specific and comorbidity-specific predictive values. Simulations then estimated shifts in adjusted mortality and proportion of incentives that would be unfairly distributed by a typical pay-for-performance programme in this situation.
Positive and negative predictive values of routine coding of comorbidities in hospital databases, variations in hospitals' mortality league table and proportion of unfair incentives.
A total of 70 402 hospital discharge abstracts were analysed, of which 715 were recoded from full medical records. Hospital comorbidity-level positive predictive values ranged from 64.4% to 96.4% and negative ones from 88.0% to 99.9%. Using Elixhauser comorbidities for adjustment, 70.3% of hospitals changed position in the mortality league table after correction, which added up to a mean 6.5% (SD 3.6) of a total pay-for-performance budget being allocated to the wrong hospitals. Using Charlson, 61.5% of hospitals changed position, with 7.3% (SD 4.0) budget misallocation.
Variations in administrative data coding can bias mortality comparisons and budget allocation across hospitals. Such heterogeneity in data validity may be corrected using a centralised coding strategy from a random sample of observations.
质量改进和流行病学研究通常依赖于数据库代码来衡量调整后的风险因素的绩效或影响，但很少有量化的方法来确定有效性问题如何会产生偏差。
评估医院间行政编码差异对典型绩效指标（调整后死亡率）和基于该指标的潜在激励措施的影响程度。
全国性的病例对照研究，比较使用病历重新编码出院摘要前后医院死亡率排名和模拟按绩效付费激励措施。
法国 24 家公立和私立医院。
一年内 78 个最致命的诊断相关组的所有住院患者。
使用 Elixhauser 和 Charlson 共病进行分析，并计算每个医院的死亡率比值。然后，每个医院随机抽取 30 名住院患者，由两名中央审查员重新编码，并使用贝叶斯层次模型估计医院特异性和共病特异性预测值。模拟然后估计在这种情况下，典型的按绩效付费计划中调整后死亡率和不公平分配的激励措施比例的变化。
医院数据库中常规共病编码的阳性和阴性预测值、医院死亡率排名表的变化以及不公平激励措施的比例。
共分析了 70402 份医院出院摘要，其中 715 份从完整病历中重新编码。医院共病水平的阳性预测值范围为 64.4%至 96.4%，阴性预测值范围为 88.0%至 99.9%。使用 Elixhauser 共病进行调整后，70.3%的医院在死亡率排名表中位置发生了变化，这导致总绩效付费预算中错误分配给错误医院的金额增加了 6.5%（标准差为 3.6）。使用 Charlson 共病时，61.5%的医院位置发生了变化，预算分配错误为 7.3%（标准差为 4.0）。
行政数据编码的差异会影响医院间的死亡率比较和预算分配。可以使用从观察到的随机样本进行集中编码策略来纠正数据有效性方面的这种异质性。
