Data Entities Description
The EHRs implementation leads to collection of vast quantities of data; both structured and unstructured, that providers and facilities need to use to make decisions. Structure data comprises of patient demographics, medications, allergy and vitals, and family history. Structured information pulled in the EHR is easy to evaluate and complete identification of patients at risk for SSIs. Unstructured data comprises of information that does not have a definite model or structural framework. These include medical notes, faxed laboratory results, x-ray images and even patient phone calls (Assale et al., 2019). The information helps clinicians to figure out and obtain more accurate information about a patient’s overall risk for SSIs.
The development of this database will entail pulling data from multiple sources; either as structured or unstructured. However, the database will have mainly structured data as it involves the use of medications before the surgical procedure. The database will have check boxed for past and family medical history, patient age, and patient weight that will be document in pounds. The system will have a drop down for gender with options like male, female and non-binary. Upon the completion of the surgical procedure, the information would be pulled into the database together with the documentation in the operating room done by the anesthesiologist. These would include procedure start and finish time, and time they administered preoperative antibiotic.
The operating surgeon will assess demographics and surgical information for accuracy. The anesthesiologist will assess and validate data on aspects like gender, weight, and body mass index (BMI for accuracy. The implication is that the database will store all associated data that is needed by the facility to make critical decisions to address the issue of SSIs. The system is designed for recording basic details for any facility to reduce SSIs. The current system in most facilities has details about patient ID, name, and address. However, this database will store all information in a structured manner so that the user can easily navigate it based on the system’s requirements.
Conclusion
Through the use of structured data as mentioned in the assignment, facilities can identify with enhanced accuracy, the susceptibility of patients to SSIs. Further, based on this information, they can prophetically medicate them before they develop surgical site infections. SSIs are a significant cause of morbidity, mortality and are associated with not only increased length of stay but also increased rates of readmissions, high costs, and poor patient outcomes. The implication is that there is need to implement practices that will lower the incidences of associated complications and enhance patient safety, quality, and clinical outcomes.
References
Assale, M., Dui, L. G., Cina, A., Seveso, A., & Cabitza, F. (2019). The Revival of the Notes
Field: Leveraging the Unstructured Content in Electronic Health Records. Frontiers in
medicine, 6, 66. https://doi.org/10.3389/fmed.2019.00066
Coccolini, F., Improta, M., Cicuttin, E., Catena, F., Sartelli, M., Bova, R., … & Chiarugi, M.
(2021). Surgical site infection prevention and management in immunocompromised patients: a systematic review of the literature. World Journal of Emergency Surgery, 16(1), 1-13. https://doi.org/10.1186/s13017-021-00375-y
De Simone, B., Sartelli, M., Coccolini, F., Ball, C. G., Brambillasca, P., Chiarugi, M., … &
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