North Shore University hospital in Manhasset NY is part of the North Shore Long Island Jewish health system. The third largest non-sectarian health system in the US. The management noticed significant delays in post anesthesia care unit, and the emergency department. It appeared that the hospital is always short of stuff, or beds available to accept addition patients, so that the pre-planned start times were often seriously delayed. Surgeon productivity was low, customer satisfaction was bottom low.
After studying the problem, the management realized that the staff was incorrectly using the bed tracking system (BTS), the electronic system that indicates the status of each bed. It caused considerable delay in notifying the registered nurse (RN) who was responsible for patient admission of a ready bed. The bed turn around time (time between the previous patient) discharged to the time a new patient was assigned to the bed) was long, the RN would not be able to admit new patients even when there was actually available beds. This delay impacted the patient flow throughout the hospital.
To clearly define and analyze this delay issue, a sig sigma project team was formed. They started with developing a process map that described in detail the steps of the patient admission/discharge process. The admissions RN's were identified as the primary customer of the process and they were surveys to establish process targets. These "voice of customer" responses helped establish a target bed turn around time of 120 minutes, with a upper limit of 150 minutes. It would be classified as a defect whenever there was turn around time longer than 150 minutes. The project team measured the process by having a team member on the surgical unit monitor the process for one week, which yielded data on 195 patients. Based on this data, the team found 130 defects, representing a DPMO of 672,725 (1 sigma quality level). The average turn around time was 226 minutes. This process was apparently unacceptable.
Then the team developed a cause and effect diagram to help identify all the variables that affect the turn around time. After a series of statistical analysis, they found there was NO statistical difference in the process based on the day of the week or shift. With thorough investigation the team communication failure and tech failure were the two major causes of long turn around time. The team realized that there was absolutely no communication between admission RN's and other patient care team members. They completely depend on BTS to transmit the bed utilization information. But some staff members lacked proficiency in the use of BTS. From time to time, some patient care RN's forgot to enter patient discharge information into the BTS on a timely manner, or entered the information in wrong ways.
Several solutions were developed by the project team to resolve those problems, including staff training and the use of improved documentation about discharged patients, laminated bedside cards, and reformatted beepers for RN's to accelerate the process. These approaches were first introduced in a limited number of divisions. After one month, the project team collected data from these testing divisions and found that the turn around time was reduced from a mean of 226 minutes to 90 minutes, which resulted in ameticof 2.3 sigma.
Finally, the project team used control charts and other statistical process control methods to further reduce the turn around time to 69 minutes. They presented the results to the management, which standardized the process that the team suggested into established policies. These policies were applied to all units in the hospital. For the purpose of continuously improving turn around time management, from time to time, sampled data were collected from various units and compared with the original with process standards established by the project team. Both physician and customer satisfaction has been dramatically improved.
1. What is the quality problem North Shore Hospital was facing in the case? What was the CTQ?
2. What did the project team do during the DEFINE stage for their six sigma initiative?
3. What did they measure in the Measurement stage?
4. What contributing factors (causes) that had been identified to affect the CTQ?
5. What improvements had been made in the IMPROVE process? What were the results of those improvements?
6. What did they do in the CONTROL stage