Kawazoe Yoshimasa, Shimamoto Kiminori, Shibata Daisaku, Shinohara Emiko, Kawaguchi Hideaki, Yamamoto Tomotaka
Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
JMIR Med Inform. 2022 Jul 27;10(7):e37913. doi: 10.2196/37913.
Falls may cause elderly people to be bedridden, requiring professional intervention; thus, fall prevention is crucial. The use of electronic health records (EHRs) is expected to provide highly accurate risk assessment and length-of-stay data related to falls, which may be used to estimate the costs and benefits of prevention. However, no studies to date have investigated the extent to which hospital stays could be shortened through fall avoidance resulting from the use of prediction tools.
We first estimated the extended length of hospital stay caused by falls among elderly inpatients. Next, we developed a model that predicts falls using clinical text as input and evaluated its accuracy. Finally, we estimated the potentially shortened hospital stay that would be made possible by appropriate interventions based on the prediction model.
Patients aged 65 years or older were selected as subjects, and the EHRs of 1728 falls and 70,586 nonfalls were subjected to analysis. The extended-stay lengths were estimated using propensity score matching of 49 associated variables. Bidirectional encoder representations from transformers and bidirectional long short-term memory methods were used to predict falls from clinical text. The estimated length of stay and the outputs of the prediction model were used to determine stay reductions.
The extended length of hospital stay due to falls was estimated to be 17.8 days (95% CI 16.6-19.0), which dropped to 8.6 days when there were unobserved covariates at an odds ratio of 2.0. The accuracy of the prediction model was as follows: area under the receiver operating characteristic curve, 0.851; F-value, 0.165; recall, 0.737; precision, 0.093; and specificity, 0.839. When assuming interventions with 25% or 100% effectiveness against cases where the model predicted a fall, the stay reduction was estimated at 0.022 and 0.099 days/day, respectively.
The accuracy of the prediction model using clinical text is considered to be higher than the prediction accuracy of conventional assessments. However, our model's precision remained low at 9.3%. This may be due, in part, to the inclusion of cases in which falls did not occur because of preventative interventions during hospitalization. Nonetheless, it is estimated that interventions for cases when falls were predicted will reduce medical costs by 886 Yen/day (~US $6.50/day) of intervention, even if the preventative effect is 25%. Limitations include the fact that these results cannot be extrapolated to short- or long-term hospitalization cases, and that this was a single-center study.
跌倒可能导致老年人卧床不起，需要专业干预；因此，预防跌倒至关重要。电子健康记录（EHRs）的使用有望提供与跌倒相关的高度准确的风险评估和住院时间数据，这些数据可用于估计预防的成本和效益。然而，迄今为止，尚无研究调查通过使用预测工具避免跌倒可缩短住院时间的程度。
我们首先估计老年住院患者跌倒导致的延长住院时间。接下来，我们开发了一个以临床文本为输入来预测跌倒的模型，并评估其准确性。最后，我们估计基于预测模型进行适当干预可能缩短的住院时间。
选择65岁及以上的患者作为研究对象，对1728例跌倒患者和70586例未跌倒患者的电子健康记录进行分析。使用49个相关变量的倾向得分匹配来估计延长的住院时间。使用来自变换器的双向编码器表示和双向长短期记忆方法从临床文本中预测跌倒。估计的住院时间和预测模型的输出用于确定住院时间的缩短情况。
跌倒导致的延长住院时间估计为17.8天（95%可信区间16.6 - 19.0），当存在未观察到的协变量且比值比为2.0时，该时间降至8.6天。预测模型的准确性如下：受试者工作特征曲线下面积为0.851；F值为0.165；召回率为0.737；精确率为0.093；特异性为0.839。当假设针对模型预测跌倒的情况进行有效性为25%或100%的干预时，估计住院时间缩短分别为0.022天/天和0.099天/天。
使用临床文本的预测模型的准确性被认为高于传统评估的预测准确性。然而，我们模型的精确率仍然较低，为9.3%。这可能部分归因于纳入了因住院期间的预防干预而未发生跌倒的病例。尽管如此，据估计，即使预防效果为25%，对预测跌倒的病例进行干预也将使医疗成本每天降低886日元（约合6.50美元/天）。局限性包括这些结果不能外推到短期或长期住院病例，且这是一项单中心研究。
