Information Model on Pain Management: An Analysis of Big Data

Abstract

Purpose To develop an information model to support secondary use of data using electronic health records. Design Retrospective observational data‐driven study with secondary use of data. The sample was composed of structured data from all adults admitted to clinical and surgical inpatient units of a public university hospital. Data between June 2014 and July 2019 were included, totaling approximately 51,000 unique patients. Methods Six systematic steps of the Applied Healthcare Data Science Roadmap were applied. Findings An information model on pain management was developed. Conclusions The data science methodology used allowed the development of information model in pain management, mapping attributes about pain management and to categorize them into assessment and reassessment, goals, interventions, and outcomes. Clinical Relevance Based on the information model developed, it is possible to optimize the electronic health system and improve the quality of patient care delivery in pain management.

Study on Control of Risks Existing in the Construction of Information Management System in Public Hospitals

“…e expert workforce, computer networks, system models, and system information necessary to execute different operations such as information collection, processing, storage, access, and dissemination are all included in information systems. Information systems can be viewed as systems that aim to provide accurate, up-todate information when and where it is needed [5]. According to Dalairi et al [6], information management systems are used to monitor the environment and consider how external elements interact with one another and with government agencies.…”

Nursing Big Data Science

“…Nursing informaticians are building the infrastructure for clinical documentation in electronic health records by standardizing nursing knowledge through information models (Lytle et al 2021) and knowledge graphs (Hussey, Das, Farrell, Ledger, & Spencer, 2021) focused on nurse‐sensitive indicators such as pain management, fall risk, and nurse staffing. They are developing predictive models for disease conditions, such as venous disease (Romero‐Collado et al 2021) and thromboembolism (Park, Kim, Lee, Zheng, & Amin, 2021), and pain (Nomura, Almeida, & Pruinelli, 2021), to improve care and meet mandatory reporting requirements for the Joint Commission and Centers for Medicare and Medicaid’s Value Based Purchasing Quality Program. They are using data analytics to interrogate large, complex health system databases to develop real‐world evidence from real‐world data on chronic diseases.…”