Using Electronic Health Records in Preventing Healthcare Acquired Injuries.

Introduction

Among the majority of US citizens, the health risk factor lies in the complications arising from their pre-operative health and physical strength to withstand surgery-related challenges. However, there is a window for avoiding negative post-surgery outcomes in this pre-operative period through accurate measurement and identification of the patients who need inter-operative care patterns; to offset the underlying risks. Using Electronic Health Records in Preventing Healthcare Acquired Injuries.

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Pre-operative examination of surgical risks necessitates collectingvast amounts of clinical data, with the most common method being the physical status classification. This method’s major disadvantage is that it requires data not available in the Electronic Health Records (EHRs),which necessitates the nurses to input data based on their subjective assessment. Thus, there is a need to develop an algorithm that will be universally applicable for all surgical operations while being compatible with any platform containing EHR data(Bihorac et al., 2019). Using Electronic Health Records in Preventing Healthcare Acquired Injuries.

Using Electronic Health Records in Preventing Healthcare Acquired Injuries

Fulfillment of the assessment task requires that the algorithmbe automated and have a machine learning feature. These features will enable it to be applied in real-time clinical workflows for automated surgical risk assessment platforms. Additionally, the algorithm will account for the patient’s specific physical traits, provide a consistent prediction of these characteristics, and give precise and sensitive interpretations; with a near-instantaneous reporting timeline. The other advantage includes expanded use of the EHR for real-time assessments; as a higher capacity and relatively low-cost data processing platform. Besides, this intervention will positively impact the overall healthcare outcomes, with the most benefit being improved risk prediction (Classen et al., 2018). Using Electronic Health Records in Preventing Healthcare Acquired Injuries.

Despite the usefulness of the real-time EHR service, it has some limitations. One of them is that the machine learning process depends on a specific definition of features, depending on an institutional training dataset(Bihorac et al., 2019). Thus, if the physicians allocated during the training period ignore some possible surgical oncomes and parameters, the system will not work as expected. Therefore, this project is entirely dependent on the nurses’ experience and commitment to details. If this challenge is avoided, the project will be a success. Using Electronic Health Records in Preventing Healthcare Acquired Injuries.

References List

Bihorac, A., Ozrazgat-Baslanti, T., Ebadi, A., Motaei, A., Madkour, M., Pardalos, P. M., … &Momcilovic, P. (2019). MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Annals of Surgery, 269(4), 652.

Classen, D., Li, M., Miller, S., & Ladner, D. (2018). An electronic health record-based real-time analytics program for patient safety surveillance and improvement. Health Affairs, 37(11), 1805-1812.

Using Electronic Health Records in Preventing Healthcare Acquired Injuries.