Application of Integrated Emergency Care Model Based on Failure Modes and Effects Analysis in Patients With Ischemic Stroke

Effect of Emergency Green Channel Optimization on Ischemic Stroke Patients: Impact on Emergency Response Time, Effectiveness, Anxiety, and Acute Stress

Objective

The aim was to assess the effectiveness of an optimized emergency green channel in the treatment of acute ischemic stroke (AIS) patients and its effect on emergency response time, effectiveness, anxiety, and acute stress.

Methods

A retrospective analysis was conducted on 349 AIS patients treated with intravenous thrombolysis from January 2019 to May 2022. The patients were divided into those who received optimized emergency green channel treatment (155) and those who did not (194). Propensity score matching (PSM) was used to balance the admission pathways, living conditions, insurance methods, and residential locations of the 2 patient groups. The key metrics comprised the times from onset to admission, admission to computed tomography (CT), CT to thrombolysis, admission to thrombolysis (door-to-needle time (DNT)), National Institute of Health Stroke Scale (NIHSS) scores at various intervals post thrombolysis, heart rate, blood pressure, and scores in Self-Rating Anxiety Scale (SAS) and the Stanford Acute Stress Reaction Questionnaire (SASRQ).

Results

Post PSM, 118 patients were analyzed (54 control and 64 observation). The observation group showed significantly lower time results than the control group, which included the following: the time from onset to admission (t = 31.428, P < .001), door-to-imaging time (t = 27.317, P < .001), imaging-to-needle time (t = 20.951, P < .001, and DNT (t = 25.954, P < .001). Significant differences were observed in 24 hour-post thrombolysis NIHSS scores, heart rate, blood pressure, SAS, and SASRQ scores (P < .05) but not in NIHSS scores at 7 and 30 days.

Conclusion

The optimized emergency green channel process effectively reduced the treatment time for ischemic stroke patients, improved rescue efficiency, and positively influenced the psychological stress levels of patients post treatment.

Application of Failure Mode and Effects Analysis to Improve the Quality of the Front Page of Electronic Medical Records in China: Cross-Sectional Data Mapping Analysis

BACKGROUND

The completeness and accuracy of the front pages of electronic medical records (EMRs) are crucial for evaluating hospital performance and for health insurance payments to inpatients. However, the quality of the first page of EMRs in China's medical system is not satisfactory, which can be partly attributed to deficiencies in the EMR system. Failure mode and effects analysis (FMEA) is a proactive risk management tool that can be used to investigate the potential failure modes in an EMR system and analyze the possible consequences.

OBJECTIVE

The purpose of this study was to preemptively identify the potential failures of the EMR system in China and their causes and effects in order to prevent such failures from recurring. Further, we aimed to implement corresponding improvements to minimize system failure modes.

METHODS

From January 1, 2020, to May 31, 2022, 10 experts, including clinicians, engineers, administrators, and medical record coders, in Zhejiang People's Hospital conducted FMEA to improve the quality of the front page of the EMR. The completeness and accuracy of the front page and the risk priority numbers were compared before and after the implementation of specific improvement measures.

RESULTS

We identified 2 main processes and 6 subprocesses for improving the EMR system. We found that there were 13 potential failure modes, including data messaging errors, data completion errors, incomplete quality control, and coding errors. A questionnaire survey administered to random physicians and coders showed 7 major causes for these failure modes. Therefore, we established quality control rules for medical records and embedded them in the system. We also integrated the medical insurance system and the front page of the EMR on the same interface and established a set of intelligent front pages in the EMR management system. Further, we revamped the quality management systems such as communicating with physicians regularly and conducting special training seminars. The overall accuracy and integrity rate of the front page (P<.001) of the EMR increased significantly after implementation of the improvement measures, while the risk priority number decreased.

CONCLUSIONS

In this study, we were able to identify the potential failure modes in the front page of the EMR system by using the FMEA method and implement corresponding improvement measures in order to minimize recurring errors in the health care services in China.