Improving the emergency services using quality improvement project and Donabedian model in a quaternary teaching hospital in South India.

INTRODUCTION: The primary goal of quality improvement is to enhance patient outcomes, particularly in the emergency department (ED). Timely and effective care is crucial in these situations. By comprehending the challenges, evaluating current performance and implementing quality improvement projects, areas in need of enhancement can be pinpointed and addressed, resulting in better outcomes. METHODOLOGY: This interventional study explores the implementation of quality improvement in the ED of a quaternary care teaching hospital in South India. It follows the Plan-Do-Check-Act (PDCA) cycle guided by the Donabedian model. Descriptive statistics were employed to measure changes in outcomes before and after implementation. To improve processes, Donabedian principles were applied, and a performance audit was conducted based on patient feedback and stakeholder input. Various ED indicators were measured. To address identified issues, formal root cause analysis was performed, leading to the generation of PDCA rapid change cycles. These cycles were implemented over 6 months, with two cycles executed, followed by postimplementation evaluation. RESULTS: Post implementation, improvements were observed in several aspects of ED operations. These included reduced ED average length of stay, decreased time to analgesia, shorter cross-consultation time, faster transfer time from ED and improved investigation turnaround time (TAT). Additionally, there was a reduction in revisits to ED within 72 hours and a decrease in patients who left without being seen. These positive changes demonstrate the effectiveness of the quality improvement intervention using the PDCA cycle. CONCLUSION: A comprehensive understanding of patient profile in the ED and factors influencing care is essential for the hospital to ensure sufficient resources and skilled emergency medicine physicians are available 24/7. By enhancing services in the ED, reducing patient waiting times and improving TAT, the overall efficiency of services can be improved. This leads to provision of timely quality care to patients and ultimately improves their outcomes.

Effect of Educational Intervention on Knowledge and Level of Adherence among Hemodialysis Patients: A Randomized Controlled Trial.

Purpose: The purpose of the study was to assess the impact of an educational intervention on the level of knowledge and adherence to the treatment regimen among hemodialysis (HD) patients as well as to describe the association between these variables. Methods: In this randomized controlled trial, 160 HD patients at an HD centre of a 2030-bed tertiary teaching hospital in Southern India were randomly assigned into intervention (N = 80, received education and a booklet) and control (N = 80, received standard care) groups. Knowledge and adherence were measured preintervention and postintervention using a validated questionnaire for knowledge and the ESRD-AQ (End-Stage Renal Disease Questionnaire) for the level of adherence. The statistical analysis of the data was performed with the help of the Statistical Program SPSS version 19.0. The statistical significance level was set at 0.05. Results: The increase in knowledge on disease management, fluid adherence, and dietary adherence in the intervention group was significantly higher compared to the control group. There was no significant correlation between knowledge and adherence. Adherence improved for all the domains, i.e., dialysis attendance, episodes of shortening, adherence to medication, fluid restriction, and dietary restriction. Adherence to fluid and dietary restriction was statistically significant. This trail is registered with https://clinicaltrials.gov/ct2/show/CTRI/2018/05/014166.

Application of Predictive Modelling to Improve the Discharge Process in Hospitals.

OBJECTIVE: To find out the factors influencing discharge process turnaround time (TAT) and to accurately predict the discharge process TAT. METHODS: The discharge process of cardiology department inpatients in a tertiary care hospital was mapped over a month. The likely factors influencing discharge TAT were tested for significance by ANOVA. Multiple linear regression (MLR) was used to predict the TAT. The sample was divided into testing and training sets for regression. A model was generated using the training set and compared with the testing set for accuracy. RESULTS: After a process map was plotted, the significant factors influencing the TAT were identified to be the treating doctor, and pending evaluations on the day of discharge. The MLR model was developed with Python libraries based on the two factors identified. The model predicted the discharge TAT with a 69% R2 value and 32.4 minutes (standard error) on the testing set and a 77.3% R2 value and 26.7 minutes (standard error) on the overall sample. CONCLUSION: This study was an initiation to find out factors influencing discharge TAT and how those factors can be used to predict discharge in the hospital of interest. The study was validated and predicted the TAT with 77% accuracy after the significant factors that affect the discharge process were identified.