According to QHG, (2014), the VLAD was introduced with an aim to monitor the quality of services provided. Furthermore, the methodology provides an easy way to understand a graphical overview of medical outcomes over a period and draft the cumulative difference between the expected result and the actual consequences (Søvik, 2014). Also, the data which is used to develop the VLAD often gotten from the corporately patient data that was submitted. The purpose of this article is to interpret the VLAD graph and analyzing it and identifying how the pyramid of investigation contributes to the changes experienced on the VLAD graph (Gan, 2017). Furthermore, the article describes the strategies that should get put in place to monitor stroke management and understand the patient experience feedback.
The VLAD graph got drafted when the cumulative sum gets plotted against the patients’ consecutive numbers; thus the chart will not get affected by the changes in the number of admissions per period unit. Moreover, a stable performance may produce a linear VLAD graph; therefore, the horizontal linear VLAD graph denotes stable performance (Neuburger, 2017). It is often identical to that of the chosen reference model, and in from the information in our case, the horizontal line represents the case number of stroke In-Hospital Mortality VLAD. Furthermore, a point of downward deflection represents a decline in performance while an upward deflection in the graph indicates an improvement of performance. Also, when the VLAD graph rises subsequently in a linear manner, then the performance is stable though better than the model of reference. Therefore, continuous improvement in performance creates an upward-curving graph while a constant downward-curving chart indicates a decrease in the performance. Also, the chart is presented to differentiate the number of actual deaths and expected deaths over a period and uses the cumulative sum test.
According to ACSQHC (2014), the central black line acts as a representation of the deaths in the medical facility thus it is used as an estimate of the statistical lives lost or gained below or above the expected rate for the patients that got treated for the conditions monitored in the medical facility. Furthermore, the red and blue lines that are below and above the central black line gets termed control limits. Therefore, the central black line crosses either of the control limits thus indicating the level of variation between the medical facility and the average of the state. Therefore, there are often three possibilities of VLAD graph trend, for instance, the central black line remains broadly level thus staying within the lower and upper control limits. It indicates that the medical facility’s mortality rate is similar to the national mortality rate. Secondly, the central black line may move down and touch the lower control limit which indicates that the medical facility has a higher mortality rate compared to the national average mortality rate. From the information in the VLAD graph, the central black line touches the lower red control limit line which indicates that the mortality in the hospital is higher than that of the state. Thirdly, the central black line may move up touching the upper control limit which indicates that the medical facility has a lower mortality rate compared to the national average mortality rate (ACSQHC, 2014). From the information in the graph, the central black line touches the upper blue control limit which indicates that the mortality in the hospital is lower compared to that of the state. Furthermore, by reviewing the chart of a patient associated with the downward trend of the VLAD graph may help in identifying the factors that contributed to the increase in deaths. Also, the contributing factors to the rise in the mortality rate in a medical facility may be due to incorrect documentation or coding issues. Hence, the review may help in identifying resources and professional problems that require to get changed to address the increase in the mortality rate of the medical facility.
From the information in the VLAD graph, the vertical line of the chart (-10 to 20) acts as an estimate of the statistical lives gained while the horizontal line on the VLAD graph (324 to 639) indicates the cases of the number of Stroke In-Hospital Mortality VLAD that got reported. Furthermore, there are indented lines that appear from the horizontal line to the top of the graph which acts as the period that is from 09/11/2011 to 16/07/2014. There is the central black line which serves as a representation on the estimate of the statistical lives lost or gained below or above the expected rate for the patients that got treated for the conditions monitored in the medical facility. Moreover, from the VLAD graph, the central black line touches the blue upper control limit (369 to 414) which is within the period 09/11/2011 to 17/05/2012. Furthermore, the estimated lives gained are about five from where the central black line touches the blue upper control limit. Hence, this indicates that the mortality rate in the medical facility was lower compared to that of the state which shows that there was professionalism in healthcare services that led to saving of lives. However, the central black line touches the red lower control limit which indicates that the mortality rate in the hospital was higher compared to that of the state. It is an indication that there was poor professionalism in delivering health care services which led to the increase in the mortality rate in the hospital. It got experienced at a period that is beyond 16/07/2014 mean that lives got lost.
According to the VGHI (2012), data is the place for the Director of the Acute Stroke Unit to begin the investigation because some of the factors contributing to the change in the VLAD graph may be due to change in coder or change in the coding standards. According to Johnson, (2016), the action that can be put in place to investigate this change is by accessing the data because there are always information systems that collect essential activity data. However, this can be problematic because most IT issues are either local or national governance information issues. Furthermore, accessing the skills may help in investigating the changes experienced on the VLAD graph because some of the techniques require a reasonable competence level of manipulating large datasets and understanding the statistics.
It should also get noted that patient case mix may act as a contributing factor to this change in the VLAD graph because the central risk adjustment often only allows data that gets contained within the VAED (VGHI, 2012). According to Rogers (2004), another action that can be put to investigate this change may be by the use of the risk-adjusted method which helps in controlling the mixed case in sequential monitoring in health outcomes. However, the disadvantages and advantages of different forms of unadjusted charts have an equal effect on their risk-adjusted counterparts (Novick, 2006).
In this category, some of the factors that may have contributed to the change may include bed availability or when the patients in a particular group get cared for in a different cite in the hospital. The actions that can be put in place to investigate these changes understand whether the hospital operates differently from others in the region or country. Moreover, it is essential if the hospital knows if it has different care pathways, especially for the end-of-life care. Also, other structural differences such as not having weekend discharges can form an excellent base for investigating the changes. Furthermore, an action that can be put in place to understand the admission process of patients into the medical facility and ensure that there is a well-resourced emergency department and the medical healthcare system that is dependable.
Some of the contributing factors in this category may include new policies and protocols that may have affected the normal operations of the medical facility. Moreover, the implementation of new care pathways may result in an improved outcome that promotes the saving of lives (VGHI, 2012). The actions that can be used to investigate this issues is by understanding the appropriate healthcare services that get used when administering care to all patients. Therefore, when health professionals understand the intensive clinical assessment process, this will help in managing the conditions of the patients.
In this category, the contributing factors the changes on the VLAD graph may be due to the care that the clinical staff offers the patients. Therefore, a change in the clinical team or the techniques that they used to administer may cause the difference in the performance of the medical facility just as indicated in the VLAD graph. The actions that would be put in place to investigate this changes understands how the experienced team of stroke coordinators, consultants, and nursing staff helped in monitoring each that got treated in the facility (ACSQHC, 2014). It will assist in reviewing all the clinical care that got provided as required by the clinical guidelines regarding treatment and medication of the patients.
It is essential that patients who suffer from stroke get admitted in the medical facility, and they get treated in the stroke unit which will have a multidisciplinary team who are well equipped to deal with stroke-related cases. Also, all the patients who get admitted to the medical facility should get directly transferred to the stroke unit which will save time and ease congestion that may occur in the emergency department. Moreover, if patients who suffer from stroke are present in the non-stroke units in the hospital, there should be transfer protocols developed and used in helping transfer arrangements to the stroke unit (Stroke Foundation, 2010).
According to the Stroke Foundation (2010), this is important because the relevant staff in the multidisciplinary team will help in providing specific and fully equipped training for the caregivers before the stroke patient get discharged from the facility. It will assist in controlling and managing the treated patient and help them recover fully because of the sophisticated physical handling techniques (Party, 2012). Moreover, other stroke-related cases will be manageable because the caregivers get trained and understand how they should handle the stroke survivor.
According to the Stroke Foundation (2010), the health professionals should be encouraged to set goals for the stroke patient care. It is essential that the purposes get prescribed, precise and challenging such that it will motivate the stroke patient to have a definite strive for accomplishing the goals and targets sets for their wellbeing. It is essential that the goals are recorded, reviewed and updated frequently.
It will ensure that all the stroke patients receive early and active rehabilitation from a dedicated stroke team thus the health system should contain comprehensive services which link to the rehabilitation care. Furthermore, the stroke patients can get transferred to the stroke rehabilitation unit if inpatient rehabilitation still gets required. Moreover, all stroke patients who have not yet received the palliative care they should get assessed by the specialist rehabilitation team before getting discharged from the hospital.
According to Silva (2013), patient experience can get considered when the information indeed defines what needs to be measured. Moreover, three factors help in regulating the patient feedback on the stroke rehabilitated patient which are the focus of attention, feedback scheduling, and feedback content. The attentional focus may help in the recovery during a repetitive training pointing intervention which means if the goal of the patient was rehabilitation then the patient may benefit from the knowledge of Performance (Willems, 2015). As for the feedback scheduling, it provides reduced feedback regarding stroke patients which enhances learning thus, influencing education in stroke patients.
Conclusion
The article gives a detailed understanding of the VLAD graph and how it gets interpreted and analyzed. Furthermore, there is the use of the pyramid of investigation that has helped to identify the possible contributors to the changes experienced on the VLAD graph. These factors include the change in coding of the data which may have caused the change and the implementation of new policies that changed the structure of resources in the facility. Moreover, the article identifies the strategies that may be put in place to monitor stroke management and help in reducing the high mortality that got experienced.
References
Australian Commission on Safety and Quality in Health Care (ACSQHC). (2014). Using Hospital Mortality Indicators to Improve Patient Care: A Guide for Boards and Chief Executives. Retrieved from: https://www.safetyandquality.gov.au/…/2014/…/Using-hospital-mortality-indicators-to…
Gan, F. F., Tang, X., Zhu, Y., & Lim, P. W. (2017). Monitoring the quality of cardiac surgery based on three or more surgical outcomes using a new variable life-adjusted display. International Journal for Quality in Health Care, 29(3), 427-432.
Johnson, S.C, & Bardsley, M. (2016). Monitoring Change in Health Care through Statistical Process Control Methods. Retrieved from: https://www.nuffieldtrust.org.uk/files/2017-01/spc-for-monitoring-change-in-health-care-web-final.pdf
Neuburger, J., Walker, K., Sherlaw-Johnson, C., van der Meulen, J., & Cromwell, D. A. (2017). Comparison of control charts for monitoring clinical performance using binary data. BMJ Qual Saf, bmjqs-2016.
Novick, R. J., Fox, S. A., Stitt, L. W., Forbes, T. L., & Steiner, S. (2006). Direct comparison of risk-adjusted and non–risk-adjusted CUSUM analyses of coronary artery bypass surgery outcomes. The Journal of thoracic and ca
Party, I. S. W. (2012). National clinical guideline for stroke. Retrieved from: https://bsnr.org.uk/wp-content/uploads/2014/05/national-clinical-guidelines-for-stroke-fourth-edition.pdf
Queensland Health Government (QHG). (2014). Variable Life Adjustment Display. Retrieved from: https://www.health.qld.gov.au/psu/vlad
Rogers, C. A., Reeves, B. C., Caputo, M., Ganesh, J. S., Bonser, R. S., & Angelini, G. D. (2004). Control chart methods for monitoring cardiac surgical performance and their interpretation. The Journal of Thoracic and Cardiovascular Surgery, 128(6), 811-819.
Silva, D.D, (2013). No. 18 Measuring Patient Experience. Retrieved from: https://www.health.org.uk/sites/health/files/MeasuringPatientExperience.pdf
Søvik, S., Skaga, N. O., Hanoa, R., & Eken, T. (2014). Sudden survival improvement in critical neurotrauma: An exploratory analysis using a stratified statistical process control technique. Injury, 45(11), 1722-1730.
Stroke Foundation, (2010). Clinical Guidelines for Stroke Management: A Quick Guide for Nursing. Retrieved from: file:///C:/Users/My/Downloads/NSF_Concise-Guidelines_Nursing_2011.pdf
Victorian Government Health Information (VGHI). (2012). How to Use Patient safety Indicators. Retrieved from: https://www.health.vic.gov.au/psi/auspsi/how-to-use-the-auspsi
Willems, L., Tetteroo, D., & Markopoulos, P. (2015). Towards Guidelines for the Design of Patient Feedback in Stroke Rehabilitation Technology. In HEALTHINF (pp. 60-68).
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