Development of an emergency data set: a worthwhile challenge

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department.

Toward vocabulary control for chief complaint

The chief complaint (CC) is the data element that documents the patient's reason for visiting the emergency department (ED). The need for a CC vocabulary has been acknowledged at national meetings and in multiple publications, but to our knowledge no groups have specifically focused on the requirements and development plans for a CC vocabulary. The national consensus meeting "Towards Vocabulary Control for Chief Complaint" was convened to identify the potential uses for ED CC and to develop the framework for CC vocabulary control. The 10-point consensus recommendations for action were 1) begin to develop a controlled vocabulary for CC, 2) obtain funding, 3) establish an infrastructure, 4) work with standards organizations, 5) address CC vocabulary characteristics for all user communities, 6) create a collection of CC for research, 7) identify the best candidate vocabulary for ED CCs, 8) conduct vocabulary validation studies, 9) establish beta test sites, and 10) plan publicity and marketing for the vocabulary.

Development of a nursing data set for school nursing

School nurses need to clearly identify how they promote the health and educational achievement of children. School nurses contribute to student health by providing health assessment and nursing interventions, advocating for healthy living, and contributing to prevention of illness and disease management. A Nursing Data Set for School Nursing can identify those data elements that are needed to prove that school nurses have a positive effect on children, families, and the community. The purpose of this project was to develop a Nursing Data Set for School Nursing that would describe and validate school nursing practice. Building on the Nursing Minimum Data Set developed by Werley, Devine, and Zorn, a Nursing Data Set for School Nursing was developed with guidance from experts in the field of school nursing. A Nursing Data Set for School Nursing has the potential to assist school nurses in documentation and validation of their nursing practice. It can validate the complexity of the role of the school nurse, the resources needed, and the effect school nurses have on improving the health and educational outcomes of students.

Using nurses’ natural language entries to build a concept-oriented terminology for patients’ chief complaints in the emergency department

Information about the chief complaint (CC), also known as the patient's reason for seeking emergency care, is critical for patient prioritization for treatment and determination of patient flow through the emergency department (ED). Triage nurses document the CC at the start of the ED visit, and the data are increasingly available in electronic form. Despite the clinical and operational significance of the CC to the ED, there is no standard CC terminology. We propose the construction of concept-oriented nursing terminologies from the actual language used by experts. We use text analysis to extract CC concepts from triage nurses' natural language entries. Our methodology for building the nursing terminology utilizes natural language processing techniques and the Unified Medical Language System.