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

项目总结 每年有多达一百万的美国人接受机械通风(MV)，并受到高比率的 发病率和死亡率。尽管有高质量的证据证明治疗可以改善结果， 对最佳实践和患者结果的坚持仍然是可变的。MV患者几乎得到了普遍的照顾 在重症监护病房(ICU)，在那里有医生参与的跨专业团队护理，呼吸系统 治疗师和护士等-是常态。基于团队的护理好处的证据推动了 国际社会为支持这一模式提出的建议；然而，团队的最佳实践 协作仍是未知数。我们已经证明，医生对MV治疗结果的可变性有贡献 患者，以及其他研究表明，特定的护理因素与患者相关 结果。然而，以往的研究并没有考虑到整个团队对个体的评估 会员。更好地理解ICU团队中不同成员的角色和贡献 将为在这一高危人群中改善患者结局的做法和干预措施提供信息。这个 因此，这项建议的主要目标是更好地了解医生的相对贡献， 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。我们会 使用“增值模型”(VAM)，这是一种为经验性个人表现而开发的经济方法 当多个人在一个过程中工作时的测量。VAM已被验证可用于测量 急诊护理护士的表现，并特别适合这个问题，因为它解释了多重 影响个别患者预后的临床医生。我们将通过四个目标实现我们的目标。第一, 我们将应用VAM在广泛的MV人群中对临床医生和患者视力之间的关系进行建模 患者，以评估每个专业组内相对临床医生的表现。第二，我们将 比较基于VAM的医生、护士和呼吸治疗师在 广泛的患者和经济结果。我们将量化临床医生表现的贡献。 相对于使用omega统计量的患者因素，估计相对方差的方法如下所述 多变量模型中的不同变量组。第三，我们将在一秒钟内评估VAM的性能 全国退伍军人管理局急性护理抽样入院MV患者人数 医院，以及在没有接受MV的患者中，评估VAM方法的普适性来估计 临床医生的表现。第四，我们将确定与更高绩效相关的临床医生和团队因素 使用混合方法的方法。这项研究将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果的贡献，将有助于证据库 对于以团队为基础的危重病人护理，将提升团队领域的效能，并将直接导致 未来的工作是减少接受MV的高危人群中的护理可变性。

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.