Introduction: Studies have shown Emergency Department (ED) crowding contributes to reduced quality of patient care, delays in starting treatments, and increased number of patients leaving without being seen. This anal...Introduction: Studies have shown Emergency Department (ED) crowding contributes to reduced quality of patient care, delays in starting treatments, and increased number of patients leaving without being seen. This analysis shows how to theoretically and optimally align staffing to demand. Methods: The ED value stream was identified and mapped. Patients were stratified into three resource-driven care flow cells based on the severity indices. Time observations were conducted for each of the key care team members and the manual cycle times and service rate were calculated and stratified by severity indices. Using X32 Healthcare’s Online Staffing Optimization (OSO) tool, staffing inefficiencies were identified and an optimal schedule was created for each provider group. Results: Lower Severity Indices (higher acuity patient) led to longer times for providers, nurses, patient care assistants, and clerks. The patient length of stay varied from under one hour to over five hours. The flow of patients varied considerably over the 24 hours’ period but was similar by day of the week. Using flow data, we showed that we needed more nurses, more care team members during peak times of patient flow. Eight hour shifts would allow better flexibility. We showed that the additional salary hours added to the budget would be made up for by increased revenue recognized by decreasing the number of patients who leave without being seen. Conclusion: If implemented, these changes will improve ED flow by using lean tools and principles, ultimately leading to timeliness of care, reduced waits, and improved patient experience.展开更多
OBJECTIVE: The ambulatory clinic was an important departmental problem. Providers hated working there and patients complained about the wait times there. It seemed there were equal numbers of patients and provider com...OBJECTIVE: The ambulatory clinic was an important departmental problem. Providers hated working there and patients complained about the wait times there. It seemed there were equal numbers of patients and provider complaints. In the spirit of solving the problem, data was gathered, a LEAN intervention was planned, and data was collected. METHODS: We defined the service families in the clinic as registration, vital signs, provider or ultrasound visit, nursing visit, and registration for the return visit. We walked the Gemba engaging all the staff in the process. Many observations pointed to long waits between and among the five stations. In order to study the current state, time data was collected by attaching a sheet of paper to a folder that the patient would carry themselves to all the clinical steps. On the sheet of paper each station logged the time that patient appeared and the time the patient left their sight. Data was gathered each day and every day from October 2016 to the summer of 2017. The data was analyzed. Leadership met and identified value and waste in the process. A Kaizen event was scheduled after the first set of measurements engaging all the staff. After the data was thoroughly analyzed and digested, brainstorming occurred. Together we determined our future state. We created a vision and strategic goals to reach our future state. RESULTS: The data pre-Kaizen event showed that the process of arrival to leaving took 124 minutes. We discovered that not every patient passed through each station. We learned the patients were on time or early for their visit most of the time. The providers were late most of the time by 1 - 1.5 hours. We learned how long each station took from the patient’s point of view. There were no statistically significant differences between ultrasound and provider visits;there were no statistically significant differences between midwife and physician visits. Each day of the week was similar. The arrival rate was higher in the morning because of the template. After the event, the total time in clinic did not change however the variability in time between and among each station decreased in variance. We informed the staff of these findings so that they could take responsibility for their part in the process. The atmosphere in clinic changed dramatically and the complaints from both providers and patients stopped. CONCLUSION: LEAN management was used to improve the clinic. It yielded important results, got the staff engaged in the process, and provided a way for the patients to see the efforts made by staff to improve.展开更多
文摘Introduction: Studies have shown Emergency Department (ED) crowding contributes to reduced quality of patient care, delays in starting treatments, and increased number of patients leaving without being seen. This analysis shows how to theoretically and optimally align staffing to demand. Methods: The ED value stream was identified and mapped. Patients were stratified into three resource-driven care flow cells based on the severity indices. Time observations were conducted for each of the key care team members and the manual cycle times and service rate were calculated and stratified by severity indices. Using X32 Healthcare’s Online Staffing Optimization (OSO) tool, staffing inefficiencies were identified and an optimal schedule was created for each provider group. Results: Lower Severity Indices (higher acuity patient) led to longer times for providers, nurses, patient care assistants, and clerks. The patient length of stay varied from under one hour to over five hours. The flow of patients varied considerably over the 24 hours’ period but was similar by day of the week. Using flow data, we showed that we needed more nurses, more care team members during peak times of patient flow. Eight hour shifts would allow better flexibility. We showed that the additional salary hours added to the budget would be made up for by increased revenue recognized by decreasing the number of patients who leave without being seen. Conclusion: If implemented, these changes will improve ED flow by using lean tools and principles, ultimately leading to timeliness of care, reduced waits, and improved patient experience.
文摘OBJECTIVE: The ambulatory clinic was an important departmental problem. Providers hated working there and patients complained about the wait times there. It seemed there were equal numbers of patients and provider complaints. In the spirit of solving the problem, data was gathered, a LEAN intervention was planned, and data was collected. METHODS: We defined the service families in the clinic as registration, vital signs, provider or ultrasound visit, nursing visit, and registration for the return visit. We walked the Gemba engaging all the staff in the process. Many observations pointed to long waits between and among the five stations. In order to study the current state, time data was collected by attaching a sheet of paper to a folder that the patient would carry themselves to all the clinical steps. On the sheet of paper each station logged the time that patient appeared and the time the patient left their sight. Data was gathered each day and every day from October 2016 to the summer of 2017. The data was analyzed. Leadership met and identified value and waste in the process. A Kaizen event was scheduled after the first set of measurements engaging all the staff. After the data was thoroughly analyzed and digested, brainstorming occurred. Together we determined our future state. We created a vision and strategic goals to reach our future state. RESULTS: The data pre-Kaizen event showed that the process of arrival to leaving took 124 minutes. We discovered that not every patient passed through each station. We learned the patients were on time or early for their visit most of the time. The providers were late most of the time by 1 - 1.5 hours. We learned how long each station took from the patient’s point of view. There were no statistically significant differences between ultrasound and provider visits;there were no statistically significant differences between midwife and physician visits. Each day of the week was similar. The arrival rate was higher in the morning because of the template. After the event, the total time in clinic did not change however the variability in time between and among each station decreased in variance. We informed the staff of these findings so that they could take responsibility for their part in the process. The atmosphere in clinic changed dramatically and the complaints from both providers and patients stopped. CONCLUSION: LEAN management was used to improve the clinic. It yielded important results, got the staff engaged in the process, and provided a way for the patients to see the efforts made by staff to improve.
