Postoperative Respiratory Abnormalities

Overview

The study aims to determine how historical cases of respiratory abnormalities are documented by clinicians in the electronic health records (EHR) of Memorial Hermann Healthcare System (MHHS) inpatient facilities. The knowledge gained from this study will support the design of modern data-driven surveillance approach to continuously collect, monitor and timely recognize postoperative respiratory abnormalities using electronic healthcare recorded data.

* Currently available studies are not clear about avoidable risk factors as actionable tools to reduce patient deterioration triggered by respiratory complications. The lack of this crucial knowledge leads to errors in further cases, and errors in medical documentation leads to limited learning from errors and potentially preventable harm to patients. * The respiratory measurement is an early indicator of disease, yet many clinicians underestimate its importance and hospitals report a poor level of respiratory rate recordings. As respiratory abnormalities are early markers of patient deterioration, it is hoped that improved and continued data collection and monitoring will have an impact on the nature and timeliness of the response to critical illness. Data concordance plays a major role in documentation quality, especially for data-mining and knowledge extraction analysis, therefore it is essential to address the reliability of 'respiratory abnormalities' labelled data within the Electronic Health Record (EHR) system. * It is hypothesized that an exploratory analysis of historical medical records by using an advanced algorithm could reveal novel and improved knowledge about the nature of Respiratory Abnormalities. However, the quality, computability, reliability, accuracy and completeness of the data are questionable. * It's also hypothesized that efficacious and preventive intervention can reduce the increased burden of illness followed by respiratory abnormalities, reduce the enormous number of treatable incidences and be cost-effective when delivered in the real-life clinical environment.

Conditions