Qualitative Steps For Safety And Quality In The Healthcare Assignment Sample

Qualitative Steps For Safety And Quality In The Healthcare Assignment

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Introduction Of Qualitative Steps For Safety And Quality In The Healthcare Assignment Sample

Quality in the healthcare system is the most important feature for the safety of patients. The quality of healthcare is defined by the degree by which the health services for populations or individuals increase the desires of likelihood outcomes and the consistent or current knowledge of the profession. The majority of the errors in the medical fields resulted from the fault of the process or system, not by the individuals. The variable and inefficient processes that are changing the case mix of the patients, distinctions in provider experience and education, health insurance, and different other factors give its participations to the complications in healthcare. There are many important factors to consider before choosing a suitable control chart. These factors depend on the type of data that is specified, the length and size required to analyze the data, whether the data is from one or many other locations, production levels, and much more. The methods of "statistical control of the process" determine the change in the data for a better enrichment in the process. The reason here is to go through different graphics. In this report we will use the XMR chart for data analysis as it is very important to determine if there is a "particular and common cause of the anomaly". The reason for choosing this table is that the data provided by the ambulance service is very continuous and fluctuates every month.

Background

A detailed analysis of stats has been made in this report relating to the determined calls by using assessments of telephone for “South Western Ambulance Service NHS Foundation Trust”. A brief analysis is required for Part A, B and C where the data is provided and by using “process behavior chart” the concern of call percentage ended by telephone will be made. The process behavior chart is also known as a control chart. In 1920, Walter Stewart invented these charts. Tese statistical tools analyzed the changing processes in the given data over the interval of time. The main aim of these inventions was to decrease the variations in the process of manufacturing. The process behavior chart is simply a graph which displays the changes in the output of a given process over the interval of time. the degree of variance between each data of a particular data group is indicated by the variance. If the process is in control and stable it produces “common cause variations” which means the process is predictable. When a process shows unexpected variations twitch is resulted from external factors that are unknown resulting in a “special cause of variations”.

Part A

Various data is given by the service of Ambulance for every month begins from Sep. 2014 to Sep. 2015 including the values of call percentage ended by advice of telephone and it is needed for the analysis.

Choice of control chart

There are numerous significant factors which should be considered before choosing an appropriate control chart. These factors depend upon the nature of data that has been given, the needed length and size for the analysis of the data, if the data comes from one or many other different locations, volumes of manufacturing and many more.

For carrying out different data and information, different charts are needed, thus it is very necessary to pick the appropriate control chart. Anyways here in this report for the analysis, we are going to utilize the moving chart (Calzada Vázquez, O., 2019). As the number of emergency calls which get response from the service keeps fluctuating every month, this moving chart will be beneficial for this change in every month and a detailed analysis can be made here. However the control chart of Swehart has numerous benefits as the nature of it is really sensitive because of the limits of control both upper and lower, middle lines and giving alert for any future variations timely.

The methods of “Statistical Process Control” determine the change in data for its better enhancement in the process. After that, this control process utilizes the control chart for brief evaluation of deterring any special and common cause changes (Fadahunsi, et al. 2019). The motive here is to go through different charts. In this report, we are going to utilize the XMR chart for data analysis as this is highly essential to determine if there is any “special and common cause of variation”. The reason for selecting this chart is that the data given by the ambulance service is very continuous and keeps fluctuating every month. Also every point of data has a separate column and these are needed to be measured separately. Hence, in such cases XMR chart is utmost important.

Characteristics of XMR chart

When it comes to qualitative operations, the XMR chart works really well for determining unexpected variations in the process. However, its characteristics contain a moving range of average for determining different variation, lower and upper limits relating to the call percentage ended by advice of telephones and it is needed for the analysis (Gomersall, et al.  2017). As the number of emergency calls which get response from the services of ambulance keeps fluctuating every month, this moving chart will be beneficial for this change in every month and a detailed analysis can be made here.

Limitations

There are lots of limitations too in this chart which have to be considered. 

According to Lorimer, B., 2020, It is recommended to do some preliminary work for rarity or seasonal variations of events with different data before using XmR chart. There are different other charts that could be accurate for successes-between-failures data or time-between-events daata.

Statistical Process Control stated that for all kinds of data use of the XmR charts are not reliable all the time. The community of quality improvement is not generally recommended XmR charts for its issue regarding accuracy.

According to Seoh, et al. 2021, Inflated or wide control limits produced by the signals or data noise has to be taken into consideration. And the connection of the signals has to be understood.

(Source: self created)

The process of forming an XMR chart starts with putting the data in Excel. At first the date should be mentioned in the first column and its data which is the call percentage ended by the telephone in the next column. Here in Part A, data of sep-14 to sep-15 is given. In the above chart it is shown how the call percentage keeps changing every month. In sep-14, the call percentage was 11.90% and in next month it becomes 10.50%. In Nov-15, the percentage reached 12 and that of next month it again decreased to 10.80%. However, when these data are collected it is required to determine the average of it. To determine the average, the call percentage needs to be summed up all together and the output should be shown in the next column as average (Schuh, et al. 2021). The basic formula for determining here the average is AVERAGE (B2:B14).

MR Value

When the average is calculated, the next method is to find out the MR value in a separate column. The MR value can be very simple which always provides the distinction between two time periods data (Wheeler, D.J., 2021). As an example, the MR value for sep-14 to oct-14 is 1.40%. It is generated by subtracting call percentage of oct-14 which is 10.50 from the call percentage of sep-14 which is 11.90. The MR value for the next two month was 12% and 10.80%.

UCL (+ 3 sigma)

The UCL (+ 3 sigma) is essential in the XMR chart as it is its main characteristic. Lower and upper limits relating to the call percentage ended by advice of telephones is needed for the analysis as the number of emergency calls that get response from the services of ambulance keeps fluctuating every month. The formula of UCL is (C2+3*D2). By using this formula, the UCL for the last four months of 2014 calculated 11.46, 15.63, 7.01 and 15.07. The value of UCL for jan-15 to Apr-15 was 14.54, 7.26, 9.42 and 9.38. The value of UCL for may-15 to Sep-15 was 12.16, 13.37, 12.83, 10.55 and 14.10

LCL (+ 3 sigma)

After the UCL, LCL needs to be carried out in the chart. The formula is quite the same too in this process. The formula of LCL is (C2-3*D2). By using this formula, the LCL for the last four months of 2014 calculated0.11, 0.07, 0.16 and 0.07. The value of LCL for jan-15 to Apr-15 was 0.085, 0.16, 0.142 and 0.141. The value of LCL for may-15 to Sep-15 was 0.10, 0.09, 0.092, 0.11 and 0.063. These variations from the above chart are now needed to be analyzed and it will further be determined that if any changes has been occurred due to it.

The criteria required to determine any special cause of the variation

At first it is required to see the guidelines to convey to the chart whether any variation has formed or not. Some important operations are needed to go through the shifting process, special causes and monthly changes. For determining shifts this control process utilizes the control chart for brief evaluation of deterring any special and common cause changes. The motive here is to go through different charts. In this report, we are going to utilize the XMR chart for data analysis as this is highly essential to determine if there is any “special and common cause of variation” (Vassli, et al. 2018). The reason for selecting this chart is that the data given by the ambulance service is very continuous and keeps fluctuating every month.

Part B

Again in this new part, new criteria is required to be made. According to the new rule of protocol data has been gathered from the next five months i.e from oct-15 to mar-15 of DOD. in order to find out that call percentage has fixed or not, a detail further chart has been made below which continues from the base of the first chart and includes the current 5 months added already.

Choice of control chart

Now as more data has been collected, the analysis we have made before now needs to be more descriptive due to this new change in it. For the overall study and analysis, the time is now increased to fix the call percentage. The changes occurred in control chart of call percentage ended by advices of telephone is given below:

In the above chart, it is shown how the values of Average, MR Value, UCL and LCL changes when a change occurs in their value. Here in Part B, data of Oct-15 to Mar-16 is given. In the above chart it is shown how the call percentage keeps changing every month. In Oct-15, the call percentage was 10.70% and next month it becomes 11.02%. In Nov-15, the percentage diminished to 10.10% and that of next month it again increased to 10.40%. However, when these data are collected it is required to determine the average as we did on the first chart (Wheeler, D.J., 2021). To determine the average, the call percentage needs to be summed up all together and the output should be shown in the next column as average. The basic formula for determining here the average is AVERAGE(B2:B14).

MR Value

AFter calculating the average it is again required to determine the Mr value in a separate column. The MR value is not a complex process and gives the distinction between two time periods data. Here, the MR value for Oct-15 is 0 and that of Nov-15 is 0.60%. The MR value of Dec-15 to Mar-16 is -0.30%, -0.20%, -0.60% and -1.60%. It is generated by subtracting the call percentage of Oct-15 which is 10.70 from the call percentage of Mar-16 which is 12.80. The MR value for the next two month was 10.40% and 10.60%.

UCL (+ 3 sigma)

The formula of UCL is (C2+3*D2). By using this formula, the UCL for the given 6 months of 2015 and 2016 calculated 10.96, 12.82, 10.35, 10.93, 10.20 and 8.00. Here the UCL has brought several changes in the overall process of variation (prc repository.org, 2021).

LCL (+ 3 sigma)

After the UCL, LCL needs to be carried out in this chart too. The formula of LCL is (C2-3*D2). By using this formula, the LCL for the given 6 months of 2015 and 2016 calculated 0.10, 0.09, 0.12, 0.121, 0.138 and 0.176. These variations from the above chart are now needed to be analyzed and it will further be determined if any changes have occurred due to it.

Part C

Again, in order to go through the call fixed by the advice and responses of telephone, a very new protocol has been released. This Protocol continues from the time period of the previous chart Apr-16 to Sep-16.

Choice of control chart

In the above two charts, we have been talking about the call percentage but now in this chart it will be required to see whether the calls are fixed appropriately by telephone or not (Johnson, et al. 2020). Relating to this new protocol a new and fresh XMR chart has been made below:

In the above chart, it is shown that 6220 calls have been resolved in Apt-16 and in May-16 7600 have been resolved. In jun-16, 7105 calls and in jul-16 8348 calls have been fixed. Here in Part C, data of Apr-16 to Sep-16 is given. In the above chart it is shown how the resolved call percentage keeps changing every month (web.archive.org, 2021).

MR value

The MR value for Apr-16 to Sep-16 calculated as 0, -1380, 495, -1243, 474 and 1040.

UCL (+ 3 sigma)

The formula of UCL is (C2+3*D2). By using this formula, the UCL for the given 6 months of 2016 calculated 7330, 3412, 9025, 3956, 8776 and 9954.

LCL (- 3 sigma)

The formula of LCL is (C2-3*D2). By using this formula, the LCL for the given 6 months of 2016 calculated 7330, 11692, 6055, 11414, 5932 and 3714.

Conclusion

From the above discussed report it is concluded that there are new changes made after each part. In part A, lots of data were given by the service of Ambulance for every month begins from Sep. 2014 to Sep. 2015 including the values of call percentage ended by advice of telephone and it is needed for the analysis. Then after in Part B, a new protocol was released according to which data has been gathered from the next five months i.e from oct-15 to mar-15 of DOD. in order to find out if the call percentage has been fixed or not. In part C,for going through the call fixed by the advice and responses of the telephone, a very new protocol has been released. This Protocol continues from the time period of the previous chart Apr-16 to Sep-16.

References

Journals

Calzada Vázquez, O., 2019. Application of Lean Manufacturing and Lean Six Sigma Tools to Improve Back to Back Activities in the Compression Process. Manufacturing Competitiveness;.

Fadahunsi, K.P., Akinlua, J.T., O’Connor, S., Wark, P.A., Gallagher, J., Carroll, C., Majeed, A. and O’Donoghue, J., 2019. Protocol for a systematic review and qualitative synthesis of information quality frameworks in eHealth. BMJ open, 9(3), p.e024722.

Gomersall, J.S., Gibson, O., Dwyer, J., O'Donnell, K., Stephenson, M., Carter, D., Canuto, K., Munn, Z., Aromataris, E. and Brown, A., 2017. What Indigenous Australian clients value about primary health care: a systematic review of qualitative evidence. Australian and New Zealand journal of public health, 41(4), pp.417-423.

Harvey, G., Gifford, W., Cummings, G., Kelly, J., Kislov, R., Kitson, A., Pettersson, L., Wallin, L., Wilson, P. and Ehrenberg, A., 2019. Mobilising evidence to improve nursing practice: A qualitative study of leadership roles and processes in four countries. International Journal of Nursing Studies, 90, pp.21-30.

Johnson, J.L., Adkins, D. and Chauvin, S., 2020. A review of the quality indicators of rigor in qualitative research. American Journal of Pharmaceutical Education, 84(1).

Lorimer, B., 2020. The Improvement of Shewhart-Stable Time Series Processes by Applying Jensen-Shannon Complexity Measures to Characterize Emergent Structure.

Moore, J. and Mello, M.M., 2017. Improving reconciliation following medical injury: a qualitative study of responses to patient safety incidents in New Zealand. BMJ quality & safety, 26(10), pp.788-798.

Schuh, G., Gützlaff, A., Hast, K. and Quarder, A.O., 2021. Performance Measurement in Global Production Networks to Identify Knowledge Transfer Needs Using Statistical Process Control. Journal of Production Systems and Logistics 1 (2021).

Seoh, Y.K., Wong, V.H. and Sirdari, M.Z., 2021. A study on the application of control chart in healthcare. In ITM Web of Conferences (Vol. 36). EDP Sciences.

Vassli, L.T. and Farshchian, B.A., 2018. Acceptance of health-related ICT among elderly people living in the community: A systematic review of qualitative evidence. International Journal of Human–Computer Interaction, 34(2), pp.99-116.

Wheeler, D.J., A History of the Chart for Individual Values The ultimate in homogeneous subgroups.

Wheeler, D.J., We All Have to Use Less Than Perfect Data And process behavior charts work better than you know with imperfect data.

Online Articles

A study on the application of control chart in healthcare, available at: https://web.archive.org/web/20210227131125id_/https://www.itm-conferences.org/articles/itmconf/pdf/2021/01/itmconf_icmsa2021_01001.pdf [accessed from: 1/10/21]

Application of Lean Manufacturing and Lean Six Sigma Tools to Improve Back to Back Activities in the Compression Process, available at: http://prcrepository.org/xmlui/bitstream/handle/20.500.12475/313/SP-19_Articulo%20Final_Omar%20Calzada.pdf?sequence=1&isAllowed=y [ accessed from: 1/10/21]