Polanczyk Carisi A, Ruschel Karen B, Castilho Fabio Morato, Ribeiro Antonio L
National Institute of Science and Technology for Health Technology Assessment (IATS), CNPq, 2350 Ramiro Barcelos, room 21507, Porto Alegre, RS, 90035-903, Brazil.
Graduate Program in Cardiology and Cardiovascular Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Curr Heart Fail Rep. 2019 Feb;16(1):1-6. doi: 10.1007/s11897-019-0417-0.
This paper reviews performance measure in health, their importance, and methodologic issues, focusing on metrics for health failure patients. Quality measures are instruments to assess structural aspects or processes of care aiming to guarantee that optimal patient outcomes are achieved. As heart failure is a chronic condition in which established therapies reduce mortality and hospital admissions, there are quite a lot of initiatives that aim to monitor for quality of care and to coordinate the disease management.
Several performance measures were validated for these patients, from process of care (left ventricular function assessment and use of ACEi/ARBs and beta-blockers) to health outcomes (hospital mortality and readmissions). In the early years, studies demonstrated a relationship between quality measurements and health outcomes. Nonetheless, more recent ones based on large databases of patients' medical records have shown that traditional indicators explain only a small fraction of health and patient reported- and perceived outcomes. Public reporting of quality measures and payment conditioned to the quality of care provided were not able to show benefit in terms of hard outcomes. Data science and big data methods are promising in providing actionable knowledge for quality improvement, with real-time data that could support decision-making. Heart failure is a chronic condition that has proven to be useful for measuring medical and healthcare quality. Evidence-based indicators have already reached high rates of adherence and are currently poorly correlated with outcomes. Using real-life data and based on the patient's perspective can be useful tools to improve these indicators.
本文回顾了健康领域的绩效衡量指标、其重要性及方法学问题，重点关注心力衰竭患者的指标。质量衡量指标是用于评估医疗结构方面或过程的工具，旨在确保实现最佳患者结局。由于心力衰竭是一种慢性病，现有疗法可降低死亡率和住院率，因此有许多举措旨在监测医疗质量并协调疾病管理。
针对这些患者，多项绩效衡量指标得到验证，从医疗过程（左心室功能评估以及使用血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂和β受体阻滞剂）到健康结局（医院死亡率和再入院率）。早期，研究表明质量衡量与健康结局之间存在关联。然而，基于大型患者病历数据库的最新研究表明，传统指标仅能解释健康及患者报告和感知结局的一小部分。公开报告质量衡量指标以及根据所提供医疗质量进行支付，在硬结局方面并未显示出益处。数据科学和大数据方法有望为质量改进提供可操作的知识，通过实时数据支持决策制定。心力衰竭是一种慢性病，已被证明对衡量医疗和医疗保健质量有用。循证指标已达到较高的依从率，目前与结局的相关性较差。使用实际生活数据并基于患者视角可能是改进这些指标的有用工具。
