Phd student, School of Nursing, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil, and Radiology Service Charge Nurse, Hospital de Clínicas de Porto Alegre.
Full Professor, School of Nursing, Graduate Program in Nursing, School of Nursing, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil.
J Nurs Scholarsh. 2021 May;53(3):315-322. doi: 10.1111/jnu.12648. Epub 2021 Mar 18.
To describe the application of a big data science framework to develop a pain information model and to discuss the potential for its use in predictive modeling.
This is an application of a cross-industry standard process for a data mining adapted framework (the Applied Healthcare Data Science Framework) to build an information model on pain management and its potential for predictive modeling. Data were derived from electronic health records and were composed of approximately 51,000 records of unique adult patients admitted to clinical and surgical units between July 2015 and June 2019.
The application of the Applied Healthcare Data Science Framework steps allowed the development of an information model on pain management, considering pain assessment, interventions, goals, and outcomes. The developed model has the potential to be used for predicting which patients are most likely to be discharged with self-reported pain.
Through the application of the framework, it is possible to support health professionals' decision making on the use of data to improve the effectiveness of pain management.
In the long term, the framework is intended to guide data science methodologies to personalize treatments, reduce costs, and improve health outcomes.
描述大数据科学框架在开发疼痛信息模型中的应用，并讨论其在预测建模中的潜在用途。
这是对数据挖掘适应框架（应用医疗保健数据科学框架）的跨行业标准流程的应用，用于构建疼痛管理信息模型及其在预测建模中的潜在用途。数据来自电子健康记录，由 2015 年 7 月至 2019 年 6 月期间入住临床和外科病房的约 51000 名独特成年患者的记录组成。
应用应用医疗保健数据科学框架的步骤允许开发一个疼痛管理信息模型，考虑疼痛评估、干预、目标和结果。所开发的模型有可能用于预测哪些患者最有可能在自我报告疼痛的情况下出院。
通过应用该框架，可以支持医疗保健专业人员做出使用数据来提高疼痛管理效果的决策。
从长远来看，该框架旨在指导数据科学方法，以实现治疗个性化、降低成本和改善健康结果。
