Inability of Providers to Predict Unplanned Readmissions
Inability of Providers to Predict Unplanned Readmissions
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DOI：
10.1007/s11606-011-1663-3
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发表时间：
2011-07-01
影响因子：
5.7
通讯作者：
Vidyarthi, Arpana R.
中科院分区：
文献类型：
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作者：
Allaudeen, Nazima;Schnipper, Jeffrey L.;Vidyarthi, Arpana R.
背景：再入院给患者带来了巨大的痛苦和相当大的经济成本。识别再次住院的高危患者是减少再次住院的重要策略。我们的目的是评估医生、病例经理和护士预测他们的老年患者是否会再次入院的准确性，并将他们的预测与标准化的风险工具(重复入院概率，或P-ra)进行比较。方法：加州大学旧金山医学中心是一家拥有550张床位的三级医疗学术医疗中心，从普通医疗服务出院的患者年龄为千分之65，有资格在5周内参加登记。在出院时，照顾每个患者的住院团队成员估计了30天内非计划再次住院的可能性，并预测了可能再次住院的原因。我们还计算了每个患者的P-ra。我们通过电子病历(EMR)审查和与患者/照顾者的电话联系来确定再次入院。通过为每个提供者组和P-Ra创建ROC曲线来确定歧视。结果：164名患者符合登记条件。在这些患者中，有5人在出院后30天内死亡。在其余159名患者中，52名患者(32.7%)再次入院。医生提供者的平均再入院预测与实际再入院率最接近，而病例经理、护士和P-RA都高估了再入院。对于所有提供者群体和P-ra来说，区分重新入院和不再入院的能力很差(AUC从病例经理的0.50下降到实习生的0.59，P-ra的0.56)。结论：本研究发现(1)总体再住院率高于先前报道，可能是因为我们采用了更全面的随访方法，以及(2)无论是提供者还是已发表的算法都不能准确预测哪些患者具有最高的再入院风险。在降低再住院率的压力越来越大的情况下，医院没有准确的预测工具来指导他们的努力。
BACKGROUND: Readmissions cause significant distress to patients and considerable financial costs. Identifying hospitalized patients at high risk for readmission is an important strategy in reducing readmissions. We aimed to evaluate how well physicians, case managers, and nurses can predict whether their older patients will be readmitted and to compare their predictions to a standardized risk tool (Probability of Repeat Admission, or P-ra).METHODS: Patients aged a parts per thousand yen65 discharged from the general medical service at University of California, San Francisco Medical Center, a 550-bed tertiary care academic medical center, were eligible for enrollment over a 5-week period. At the time of discharge, the inpatient team members caring for each patient estimated the chance of unscheduled readmission within 30 days and predicted the reason for potential readmission. We also calculated the P-ra for each patient. We identified readmissions through electronic medical record (EMR) review and phone calls with patients/caregivers. Discrimination was determined by creating ROC curves for each provider group and the P-ra.RESULTS: One hundred sixty-four patients were eligible for enrollment. Of these patients, five died during the 30-day period post-discharge. Of the remaining 159 patients, 52 patients (32.7%) were readmitted. Mean readmission predictions for the physician providers were closest to the actual readmission rate, while case managers, nurses, and the P-ra all overestimated readmissions. The ability to discriminate between readmissions and non-readmissions was poor for all provider groups and the P-ra (AUC from 0.50 for case managers to 0.59 for interns, 0.56 for P-ra). None of the provider groups predicted the reason for readmission with accuracy.CONCLUSIONS: This study found (1) overall readmission rates were higher than previously reported, possibly because we employed a more thorough follow-up methodology, and (2) neither providers nor a published algorithm were able to accurately predict which patients were at highest risk of readmission. Amid increasing pressure to reduce readmission rates, hospitals do not have accurate predictive tools to guide their efforts.