Methodologies

Quality Improvement in a Tertiary Healthcare Setting Your Name Subject and Section Professor’s Name November 2, 2024 In my internship at a tertiary healthcare facility, I saw different operational issues surrounding patients' waiting time in the patient department and the everyday cruelty that customers and providers encounter. This was an excellent platform to practice a Quality Improvement (QI) project focusing on the duration that patients wait for the physician and thus the overall satisfaction. We focused on selecting problem-solving processes applying the plan-do-check-act (PDCA) cycle and Lean Six Sigma to outline the causes of problems, implement the appropriate changes, and assess the results over time. This paper focuses on the methods used before and after the intervention, measures obtained, statistical data treatment, and how data tools can foster QI. Methodologies in Practice: Applying Lean Six Sigma and PDCA The application of the QI procedure started with excessive patient wait, which is one of the vital areas where improvement is needed. Using the PDCA cycle, a widely known and successful process improvement strategy, we divided our strategy into planning the scope of the issue, testing solutions, evaluating the effects, and implementing successful interventions (Foundation of the American College of Healthcare Executives, 2022). Another aspect of our operations that benefited from Lean Six Sigma concepts involved the DMAIC organizational structure for dealing with waste and nonvalue-added work. After exploring historical data in the Plan phase, the authors noted that patient wait times were approximately 45 minutes, with increased wait experienced during rush hours. Our target was to bring this average below 30 minutes—a target based on internal norms and patient feedback as obtained from various surveys. Taking into account these primary sources of preliminary systematic observations, the noted first root causes of delay are as follows: As indicated under the Data collection phase, several interventions were undertaken in the Do phase of the cycle. First, we changed the scheduling pattern to take patient arrival in each hour to help reduce overcrowding. We also included the telehealth features for the initial evaluation; patients can start pre-procedure steps online. Also, check-in kiosks eliminated the need for most special registration services provided by the front staff and cut the registration time by 10%. The Check phase was concerned with evaluating the consequences of these changes. We obtained fresh data a two-month implementation period later, and the average wait time was further reduced to 36, which, while still below the initial sample, did not reach our desired goal. This data feeds into the conclusion of the last Act phase, where further strategies are developed. We wanted to expand the options for telehealth assessment and increase the availability of self-check-in kiosks, building these new changes into our current patient flow. Statistical Approaches in Quality Improvement It was mandatory to assess pre and post-intervention data about the subject in order to evaluate our intervention. Before the intervention, the mean wait time was found to be at least 45 minutes with a standard deviation of 12 minutes, suggesting excellent variability in the wait time that patients received. Regression analyses were used to determine that the changes in wait times before and after the intervention were statistically significant: 45 to 36 minutes. More importantly, most of the time, the method known as analytic statistics was used to identify the variation caused by special causes compared to typical causes. Special causes under this category could be ...