Economic modeling to understand the relationship between clinicians and outcomes of mechanically ventilated patients

项目摘要 每年有多达一百万美国人接受机械通气（MV），并且受到高比率的呼吸道感染。 发病率和死亡率。尽管有高质量的证据证明治疗可以改善预后， 遵守最佳做法和病人的结果仍然是可变的。MV患者几乎得到普遍护理 在重症监护室（ICU），其中跨专业团队护理-包括医生，呼吸系统 治疗师，护士，还有其他人-这是常态。基于团队的护理的好处的证据已经驱动了 国际社会的建议，以支持这一模式;然而，最佳做法的团队 合作仍然未知。我们已经表明，医生有助于变异的结果，MV 患者，其他研究表明，特定的护理因素与患者 结果。然而，以前的研究没有考虑到整个团队在评估个人 成员更好地理解ICU团队不同成员的角色和贡献 将告知实践和干预措施，可以改善这一高风险人群的患者结局。的 因此，本提案的主要目的是更好地理解医生的相对贡献， 护士和呼吸治疗师单独或作为团队对接受MV的患者的结果进行评估。我们将 使用“增值模型”（VAM），这是一种为实证个人绩效而开发的经济方法 当多个人在一个过程中工作时进行测量。VAM已经过验证，可用于测量 急性护理护士的表现，是唯一适合这个问题，因为它占了多个 影响个体患者结局的临床医生。我们将通过四个目标来实现我们的目标。第一、 我们将应用VAM在广泛的MV人群中模拟临床医生和患者急性之间的关系 患者，以便估计每个专业组内的相对临床医生表现。二是 比较医生，护士和呼吸治疗师在VAM的基础上表现的贡献， 广泛的患者和经济结果。我们将量化临床医生绩效的贡献 相对于使用omega统计量的患者因素，一种估计相对方差的方法， 多变量模型中的不同变量组。第三，我们将评估VAM的性能， 入选退伍军人管理局急性护理国家样本的MV患者人群 医院，并在不接受MV的患者中，评估VAM方法的普遍性，以估计 临床医生表现。第四，我们将确定与更高性能相关的临床医生和团队因素 使用混合方法。这项研究将进一步加深我们对相关临床医生的了解 使用创新的建模技术对患者结局的贡献，将有助于证据基础 对于危重病人的团队护理，将提高团队效率，并将直接导致 未来的工作，以减少护理变异的高风险人群的患者谁接受MV。

PROJECT SUMMARY Up to one million Americans undergo mechanical ventilation (MV) each year and are subject to high rates of morbidity and mortality. Despite the availability of high-quality evidence for therapies that improve outcomes, adherence to best practices and patient outcomes remain variable. MV patients are nearly universally cared for in intensive care units (ICUs), where interprofessional team-based care – involving physicians, respiratory therapists, and nurses, among others – is the norm. Evidence for the benefits of team-based care has driven recommendations from international societies to support this model; however, best practices for team collaboration remain unknown. We have shown that physicians contribute to variability in outcomes of MV patients, and other studies have demonstrated that specific nursing factors are associated with patient outcomes. However, previous studies have not accounted for the entire team in assessment of individual members. An improved understanding of the roles and contributions of different members of an ICU team would inform practices and interventions that could improve patient outcomes in this high risk population. The primary objective of this proposal, therefore, is to better understand the relative contributions of physicians, nurses, and respiratory therapists individually and as teams to outcomes of patients who undergo MV. We will use “value-added modeling” (VAM), an economic approach developed for empirical individual performance measurement when multiple individuals work in a single process. VAM has been validated for measuring performance of acute care nurses and is uniquely suited to this question, in that it accounts for the multiple clinicians that influence outcomes of individual patients. We will achieve our objective through four aims. First, we will apply VAM to model the relationship between clinicians and patient acuity in a broad population of MV patients, in order to estimate relative clinician performance within each professional group. Second, we will compare the contributions of physician, nurse, and respiratory therapist performance based on VAM across a broad range of patient and economic outcomes. We will quantify the contributions of clinician performance relative to patient factors using the omega statistic, a method for estimating the relative variance explained by different variable groups in a multivariable model. Third, we will evaluate the performance of VAM in a second patient population of MV patients admitted to a national sample of Veterans Administration acute care hospitals, and in patients who do not undergo MV, to evaluate the generalizability of VAM methods to estimate clinician performance. Fourth, we will identify clinician and team factors associated with higher performance using a mixed-methods approach. This study will further our understanding of the relative clinician contributions to patient outcomes using innovative modeling techniques, will contribute to the evidence base for team-based care of critically ill patients, will advance the field of team effectiveness, and will directly lead to future work to reduce care variability among the high-risk population of patients who undergo MV.