McCray Sally, Barsha Laura, Maunder Kirsty
Dept of Dietetics and Foodservices, Mater Group, Raymond Terrace, South Brisbane, QLD, Australia.
Mater Research Institute, University of Queensland Brisbane, QLD, Australia.
J Hum Nutr Diet. 2022 Dec;35(6):1071-1078. doi: 10.1111/jhn.13026. Epub 2022 May 18.
Routine malnutrition risk screening of patients is critical for optimal care and comprises part of the National Australian Hospital Standards. Identification of malnutrition also ensures reimbursement for hospitals to adequately treat these high-risk patients. However, timely, accurate screening, assessment and coding of malnutrition remains suboptimal. The present study aimed to investigate manual and digital interventions to overcome barriers to malnutrition identification for improvements in the hospital setting.
Retrospective reporting on malnutrition identification processes was conducted through two stages: (1) manual auditing intervention and (2) development of a digital solution - the electronic malnutrition management solution (eMS). Repeated process audits were completed at approximately 6-monthly intervals through both stages between 2016 and 2019 and the results were analysed. In Stage 2, time investment and staff adoption of the digital solution were measured.
Overall, the combined effect of both regular auditing and use of the eMS resulted in statistically significant improvements across all six key measures: patients identified (97%-100%; p < 0.001), screened (68%-95%; p < 0.001), screened within 24 h (51%-89%; p < 0.001), assessed (72%-95%; p < 0.001), assessed within 24 h (66%-93%; p < 0.001) and coded (81%-100%; p = 0.017). The eMS demonstrated a reduction in screening time by over 60% with user adoption 100%. Data analytics enabled automated, real-time auditing with a 95% reduction in time taken to audit.
A single digital solution for management of malnutrition and automation of auditing demonstrated significant improvements where manual or combinations of manual and electronic systems continue to fall short.
对患者进行常规营养不良风险筛查对于优化护理至关重要，是澳大利亚国家医院标准的一部分。识别营养不良也能确保医院获得报销，以便为这些高危患者提供充分治疗。然而，营养不良的及时、准确筛查、评估和编码仍未达到最佳状态。本研究旨在调查手动和数字干预措施，以克服营养不良识别方面的障碍，改善医院环境。
通过两个阶段对营养不良识别过程进行回顾性报告：（1）手动审核干预和（2）开发数字解决方案——电子营养不良管理解决方案（eMS）。在2016年至2019年期间，通过这两个阶段，每隔约6个月完成一次重复的过程审核，并对结果进行分析。在第二阶段，测量了数字解决方案的时间投入和工作人员采用情况。
总体而言，定期审核和使用eMS的综合效果在所有六项关键指标上均产生了具有统计学意义的改善：识别出的患者（97%-100%；p<0.001）、接受筛查的患者（68%-95%；p<0.001）、在24小时内接受筛查的患者（51%-89%；p<0.001）、接受评估的患者（72%-95%；p<0.001）、在24小时内接受评估的患者（66%-93%；p<0.001）以及编码的患者（81%-100%；p=0.017）。eMS显示筛查时间减少了60%以上，用户采用率为100%。数据分析实现了自动化实时审核，审核时间减少了95%。
用于营养不良管理和审核自动化的单一数字解决方案在手动或手动与电子系统组合仍存在不足的方面显示出显著改善。
