Developing a Pediatric Readmission (PERE) Risk Prediction Tool for Decision Support During Pre-Discharge Planning

项目总结/摘要 儿科潜在可治愈再入院（“PPR”）对年轻人的生活质量造成损害， 病人和他们的家庭，也给照顾儿童的卫生保健系统带来额外的压力。 准确的再入院风险预测算法可以帮助临床医生识别患者的最大风险， PPR的风险，从而有助于更好地识别最需要更多治疗的患者。 密集排放规划。这种有重点的风险评估有助于分配工作人员， 资源不幸的是，现有的PPR风险预测很少明确考虑儿科患者 并且不是高度准确的。随着机器学习和深度学习的最新进展， 对儿科PPR的大范围风险因素进行评估是可能的。为此，我们的主要 目的是开发一套儿科再入院（“PERE”）风险预测算法， 预测儿童出院后三（3）、七（7）和三十（30）天内发生PPR的风险 目前住院。我们将通过利用统计数据科学的最新进展来实现这一目标 算法我们还将开发PERE预测，以便它们可用于评估以下风险： 患者当前住院期间出院计划阶段之前和期间的PPR。在 在人员和资源有限的环境中，PERE风险预测套件将支持 临床医生有一种方法来识别最有可能成为PPR候选人的患者。 增加出院计划相关护理。 相关性 我们将把我们的PERE风险预测工作的重点放在AHRQ的优先人群：儿童。儿童 由于生理发育、沟通能力有限， 能力，以及对护理人员的依赖，以适当地理解指示和管理护理 出院后。1，2在三（3）天、七（7）天和三十（30）天内早期识别PPR风险 在这个脆弱的人群将提醒医院护士和护理管理人员的风险增加， 在出院计划期间可以更充分地照顾特定患者可能面临的问题。通过使用 深度学习和其他新兴的机器学习方法来优化风险预测， 该提案还支持AHRQ的使命，即利用数据和技术来改善医疗保健 质量和患者结局，并促进改善测量、监测和 监测患者风险。该技术将使用来自非常大的数据来实施 儿科患者护理数据库，以开发和验证PERE风险预测。

Project Summary/Abstract Pediatric Potentially Preventable Readmissions (“PPRs”) take a toll on the quality of life of young patients and their families and also place extra strain on health care systems that care for children. Accurate readmission risk prediction algorithms can aid clinicians in identifying patients most at risk for PPRs and thereby facilitate improved identification of patients most in need of more intensive discharge planning. Such focused risk assessments help with allocation of staffing and resources. Unfortunately, existing PPR risk predictions rarely explicitly consider pediatric patients and are not highly accurate. With recent advances in machine learning and deep learning, assessment of a large range of risk factors for pediatric PPRs is possible. To that end, our primary objective is to develop a suite of Pediatric Readmission (“PERE”) risk prediction algorithms to predict risk of a PPR within three (3), seven (7), and thirty (30) days of discharge from a child’s current inpatient stay. We will do this by leveraging recent advances in statistical data science algorithms. We will also develop PERE predictions such that they can be used to assess risk for PPRs prior to and during the discharge planning phases of a patient’s current inpatient stay. In environments of limited staffing and resources, the PERE risk prediction suite will support clinicians with a means of identifying patients most at risk for PPRs who may be candidates for increased discharge planning related care. Relevance We will focus our PERE risk prediction efforts on an AHRQ priority population: Children. Children represent a vulnerable population due to their developing physiology, limited communication abilities, and reliance on a caregiver to appropriately understand instructions and administer care after discharge.1,2 Early identification of risk for PPRs in three (3), seven (7), and thirty (30) days in this vulnerable population will alert hospital nurses and care managers of the increased risk a particular patient may face that can be more fully attended to during discharge planning. By using deep-learning and other emerging machine learning methods to optimize risk prediction, this proposal also supports AHRQ’s mission to harness data and technology to improve health care quality and patient outcomes and to facilitate improved measurement, monitoring, and surveillance of patient risk. This technology will be implemented using data from a very large pediatric patient care database to develop and validate PERE risk predictions.