The History Of Laboratory Turn Around Time Nursing Essay

INTRODUCTION

Healthcare improvement or quality improvement is very significant for the sustenance of the health services of a clinic or a hospital or any healthcare service provider. Just like any endeavor to change for development or enhancement of any services, such healthcare improvement must be with reliable and valid indicators if changes done are more effective. In other words, these improvements bring more benefits. As such, there is a need for rigorous and scientific monitoring

However, there could be some problems with the monitoring of the performance or in the measurement of the beneficial effects of changes such as the intricacy or complexity of in the analysis of data due to the presence of natural variation because as expected, repeated measurements could bring different values. The example of Benneyan et. Al (2003) showed that repeated measures of the blood pressure of a patient could vary because of the rise and fall of the patients biological mechanism, service process variations, and errors in the use of the measuring device or even by the measurement process itself.

The good news is these problems are solved with the advent of Statistical process control (SPC) with its control chart hold together precise time series analysis methods with graphical presentation of data that makes the formulation of insights into the data very fast and above all can be understood by anyone even the lay administrators who decide and evaluate the implications and effectiveness of healthcare improvement endeavors.

Flash sterilization rate

A debate on the reason behind the increasing rate with the use of flash sterilization in hospital surgical room started upon the arrival of the new group of orthopaedic surgeons in the hospital that was thought to cause in such variation in rates. The different opinions and questions were answered through the data analysis at FS rate which is the number of FS per 100 surgeries over time. The assigned committee’s analyst used the U chart which is according to the Poisson distribution in order to find out the difference between the hospital’s baseline rate and the rate after the arrival of the new surgeons. The control chart showed that the arrival or presence of the new surgeons in the hospitals illustrated an increase in the FS rate mean of 50 per 100 surgeries. Thus, the analyst suggested that the shift in the process performance was due to the arrival of the new surgeons. However, it should be noted that the findings simply showed that there was a high possibility that the change in the FS rate could be due to the process of handling surgical instruments and could also just coincide with the arrival of the new surgeons. Therefore, there is a need for investigation on this matter.

Summary:

Due to the arrival of a new group of orthopaedic surgeons in the hospital, use of flash sterilization of the surgical room created variation in rates. The assigned committee’s analyst used U chart according to the Poisson distribution in order to find out difference between hospital’s baseline rate and rate after the arrival of new surgeons. Control chart illustrated an increase in FS rate mean of 50 per 100 surgeries. So this matter needs investigation as findings simply showed a high possibility that a change in FS rate could be due to a process of handling surgical instruments and could also just coincide with the arrival of new surgeons.

Laboratory turn around time (TAT)

There was a complaint from clinicians in the A&E department on the turnaround time (TAT) that is being used in getting data for the complete blood counts. Clinicians were whining that the TAT was out of control and its malfunctioning was getting worse. In order to find out behind this change, the assigned committee used the X-bar and S types of control chart wherein the X-bar showed the mean TAT for the three orders each day and the bottom chart (S) exhibited the SD for the same three orders. The analysis of the committee illustrated when the routine complete blood count was done during the day shift, it .Took place for about 45 minutes with a mean SD of about 21 minutes. They found-out then that the process is performing consistently and therefore the process was in a state of statistical control. It should be noted also that a common cause of deviation does not mean having acceptable results. In this case, appropriate improvement strategies should be done to lower the mean TAT and reduce the variation

Summary:

Clinicians complained about Turnaround time (TAT) which is used in getting data for complete blood counts in A & E department as its functioning was deteriorating. To find out this change, the assigned committee used X-bar and S types of control chart. The X-bar showed the mean TAT for the three orders each day and bottom chart (S) exhibited the SD for same three orders. The inquiry committee explored that this process was arithmetically as when the routine complete blood count was done during the day shift, it .Took place for about 45 minutes with a mean SD of about 21 minutes. To lower mean TAT and reduce variation, development tactics should be done.

Surgical site infections

An interdisciplinary team wanted to decrease the postoperative surgical site infection (SSI) rate in some surgical procedures. The team experimented and used the g type of control chart in their presentation and analysis of data. The g chart was illustrated the relationship between the number of surgeries and the occurrences of infection. The team was expecting that the postoperative wound cleaning protocol would help to reduce surgical site infection but the control chart showed that there was no change or such procedure has no effect on the decrease in infection rate. Finding this, the team suggested the use of shaving preparation techniques for preparing the surgical site prior to surgery. Amazingly, the researchers found-out through the control chart that this procedure resulted to change or improve the reduction of the SSI rate from about 2.1% to 0.9%.

Summary:

To decrease the postoperative surgical site infection (SSI) rate in some surgical procedures, an interdisciplinary team experimented and used the g type of control chart in their presentation and analysis of data. The g chart illustrated affiliation between the number of surgeries and the incidences of infection. The control chart showed that postoperative wound cleaning protocol would not help to reduce surgical site infection and this procedure has no effect on the decrease in infection rate. The team recommended that for preparing the surgical site before surgery, shaving preparation techniques must be used. Through control chart, this procedure resulted in improvement and reduction of the SSI rate from about 2.1% to 0.9%.

Appointment access satisfaction

A survey was done to the patients’ appointment access satisfaction specifically on the delay, telephone satisfaction, in office waiting times, able to see provider of choice. The aim of such survey was to find out the present satisfaction level of the patients on the appointment access satisfaction and from such survey appropriate suggestions for improvement would be formulated and implemented. The researchers got the percentage of the patient’s responses to the question of how satisfied they were with the delay to get an appointment with their primary care provider. Then the team plotted the percentages in a p control chart which is (based on the binomial distribution. After getting the preliminary responses from the patients, the team formatted the strategies to improve such practices such as decreasing the number of appointment types, making telephone scripts easier, and offering appointments with the practice nurse when doctors are not around for certain minor conditions. Indeed, the control chart showed distinguished development in the appointment access satisfaction.

Summary:

A survey was conducted to find a present gratification level of the patients on the appointment access satisfaction and how improvement would be formulated and implemented through suggestions. The researchers got the percentage of the patient’s contentment through comebacks to the query about the delay to get an appointment with their primary care provider. Then the team plotted the percentages in a p control chart which is based on the binomial distribution. After getting the maiden responses the team formatted stratagems to mend some practices i.e. decreasing number of appointment types, making telephone scripts easier and offering appointments with the practice nurse when doctors are not around for certain minor conditions. As a result, the control chart showed eminent progress in the appointment access fulfillment

Infectious waste monitoring

Many hospitals do not a standardized ways of disposing their infectious waste which is necessary. The problem was the team does not know how much infectious wastes they have in their hospital. The first step they did was to establish a baseline and they used an appropriate control chart, the XmR chart which was based on the normal distribution. They found out that during the baseline period there was just a little over 7 lb (3.2 kg) infectious wastes in their hospital. In other words, the process was stable and showed only ordinary cause variation. Thus, the team created some appropriate intervention improvement strategy. They first operationally defined infectious wastes and organized educational campaign awareness in order for the people is able to identify the different infectious wastes in their hospital. And the next time around, the team found out that the process has shifted to a new and more acceptable level of performance and that the new mean of the daily production of infectious waste was a little more than 4 lb (1.8 kg) per day. Indeed, the control chart helped the team in examining the impact of their efforts. This time, there was a noticeable shift in the process going in the correct direction.

Summary:

The difficulty was that the team does not know how much transmittable wastes they have in their hospital. They used an appropriate control chart as a first step as a starting point by using, the XmR chart based on the normal distribution. They found out that during the starting point there was just a little over 7 lb (3.2 kg) infectious wastes in their hospital. In other words, the process was constant and displayed only ordinary cause variation. Thus, the team created some appropriate intercession tactic. In order to make people identify the different infectious wastes in their hospital, they first operationally defined infectious wastes and organized educational campaign. In the next phase, the team found out that the method has shifted to a more conventional level of performance and new mean of daily production of infectious waste was a little more than 4 lb (1.8 kg) per day. The control chart showed a noticeable shift in the process going in the accurate direction.

Conclusion:

The paper of J. C. Benneyan, R. C. Lloyd, and P. E. Plsek entitled Statistical Process Control as a Tool for Research and Healthcare improvement discussed Statistical process control (SPC). Through the different examples, this study showed the power, user friendliness and significance of control chart for researchers to know how effective or beneficial the changes into the health care improvement endeavours are. The use of this tool is very beneficial to the process improvement managers and practitioners as well as to researchers as it helps in the usage of the objective data and how this can be done or the process of statistical thinking itself before making suitable decisions based on the findings and analysis of data.