Vanderbilt University School of Medicine, Nashville, TN, US.
Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, US.
J Med Syst. 2021 Nov 16;46(1):2. doi: 10.1007/s10916-021-01793-w.
Discharge planning is a vital tool in managing hospital capacity, which is essential for maintaining hospital throughput for surgical postoperative admissions. Early discharge planning has been effective in reducing length of stay and hospital readmissions. Between 2014 and 2017, Vanderbilt University Medical Center (VUMC) implemented a tool in the electronic health record (EHR) requiring providers to input the patient's estimated discharge date on each hospital day. We hypothesized discharge estimates would be more accurate, on average, for surgical patients compared to non-surgical patients because treatment plans are known in advance of surgical admissions. We also analyzed the data to identify factors associated with more accurate discharge estimates. In this retrospective observational study, via an analysis of covariance (ANCOVA) approach, we identified factors associated with more accurate discharge estimates for admitted adult patients at VUMC. The primary outcome was the difference between estimated and actual discharge date, and the primary exposure of interest was whether the patient underwent surgery while admitted to the hospital. A total of 304,802 date of discharge estimate entries from 68,587 inpatient encounters met inclusion criteria. After controlling for measured confounding, we found the discharge estimates were more precise as the difference between estimated and actual discharge date narrowed; for each additional day closer to discharge, prediction accuracy improved by .67 days (95% confidence interval [CI], 0.66 to 0.67; p < 0.001), on average. No difference was observed on the primary outcome in patients undergoing surgery compared with non-surgical treatment (0.02 days; 95% CI, 0.00 to 0.03; p = 0.111). Faculty members were found to perform best among all clinicians in predicting estimated discharge date with a 0.24-day better accuracy (95% CI, 0.20 to 0.27; p < 0.001), on average, than other staff. Weekend and holiday, specific clinical teams, staff types, and discharge dispositions were associated with the variability in estimated versus actual discharge date (p < 0.001). Given the widespread variation in current efforts to improve discharge planning and the recommended approach of assigning a discharge date early in the hospital stay, understanding provider estimated discharge dates is an important tool in hospital capacity management. While we did not determine a difference in discharge estimates among surgical and non-surgical patients, we found estimates were more accurate as discharge came nearer and identified notable trends in provider inputs and patient factors. Assessing factors that impact variability in discharge accuracy can allow hospitals to design targeted interventions to improve discharge planning and reduce unnecessary hospital days.
出院计划是管理医院容量的重要工具，这对于维持外科术后入院的医院吞吐量至关重要。早期出院计划已被证明可有效缩短住院时间和减少医院再入院率。2014 年至 2017 年期间，范德堡大学医学中心（Vanderbilt University Medical Center，VUMC）在电子健康记录（Electronic Health Record，EHR）中实施了一项工具，要求医务人员在每个住院日输入患者的预计出院日期。我们假设与非外科患者相比，外科患者的出院估计会更准确，因为外科入院前已知治疗计划。我们还分析了数据，以确定与更准确的出院估计相关的因素。在这项回顾性观察研究中，我们通过协方差分析（Analysis of Covariance，ANCOVA）方法，确定了与 VUMC 住院成年患者更准确的出院估计相关的因素。主要结果是估计和实际出院日期之间的差异，主要暴露因素是患者在住院期间是否接受了手术。共有 304802 项来自 68587 次住院就诊的出院日期估计条目符合纳入标准。在控制了测量性混杂因素后，我们发现随着估计和实际出院日期之间的差异缩小，出院估计变得更加精确；每提前一天接近出院，预测准确性提高 0.67 天（95%置信区间[Confidence Interval，CI]，0.66 至 0.67；p<0.001）。与非手术治疗相比，接受手术的患者在主要结局方面没有差异（0.02 天；95%CI，0.00 至 0.03；p=0.111）。与其他医务人员相比，教员在预测估计的出院日期方面表现最佳，平均准确率提高了 0.24 天（95%CI，0.20 至 0.27；p<0.001）。周末和节假日、特定临床团队、医务人员类型和出院处置与预计与实际出院日期的差异相关（p<0.001）。鉴于目前改善出院计划的努力存在广泛差异，以及建议在住院期间尽早分配出院日期的做法，了解医务人员对出院日期的估计是医院容量管理的重要工具。虽然我们没有确定手术和非手术患者之间的出院估计存在差异，但我们发现随着出院日期的临近，估计变得更加准确，并确定了医务人员输入和患者因素方面的显著趋势。评估影响出院准确性差异的因素可以使医院设计有针对性的干预措施，以改善出院计划并减少不必要的住院天数。
