VLAD as a Monitoring Tool for Various Clinical Indicators - Coursework Example

Analysis of VLAD Data Introduction Variable life adjustment display (VLAD) isa methodology introduced to Queensland Health to facilitate monitoring of the quality of health care services provided. VLAD provides an easy to understand graphs that represented clinical outcomes over a particular period. VLAD plots the cumulative variances between the speculated and the actual results. The data that are mostly used in the construction of VLAD come to the patient data submitted by the corporates. The data is getting a monthly update and in that respect, VLAD allows for the real-time evaluation of the problems or enhancement in the performance (Braithwaite, Westbrook & Iedema, 2005). VLAD serves as a monitoring tool for various clinical indicators. It displays the trends over a given period within a given hospital in regards to a particular subject of concern. It then enables accumulation, analysis and comparison of individual hospitals data with that of the state mean/average. VLAD offers a very simplified presentation of the data and facilitates prompt look at the data. VLAD exhibit a definite set of characteristics. It offers an investigation of a clinical indicator. The research into the clinical indicators is in regards to a stated outcome. Besides, VLAD acts against a specific cut-off for any increase or decrease. It is conducted for a given period in a particular hospital taking into consideration of each patient (Kirk et al., 2007). Interpretation of VLAD Graphs VLAD is a graph of cumulative summation of variations in the speculated and the observed outcomes. These plots include the upper and lower limits. The limits project whether the performance is better or worse than the expectation. The signaling occurs at the interception of lines, that is, the interception of the VLAD Line and either the red or the blue line. The intersection of the VLAD curves and the included limits acts as a signal that the healthcare services of the hospital is out of control. It indicates the level of the hospital performance relative to the national average. Appropriate signals are used in the determination of system performance relative to the expectations of such regimes. The kinds and degree of investigation rely upon the type of signaling engaged in the analysis of the system performance (Braithwaite, Westbrook & Iedema, 2005) VLAD is clinically appealing and is most likely to be a useful tool for checking any conditions that may be out of control in the Victorian hospitals. VLAD presents as a control chart that can possibly be implemented on the basis of quality control. There is the ultimate distinction between any common variations and special instances of variations in any system. The special variation is a distinct form of change in which the changes occurs as a product of the incorrect implementation of the otherwise healthy and normal system. The common cause of differences entails the variations due to the normal daily expected variability in nature which presents with the day-to-day implementation of any system. Apart from measuring the out of control states, the implementation of the VLAD also reaffirms that the existing system used in the health care are in due control. The VLAD technology offers an ever increasing potential that can ultimately introduce and establish opportunities for accurate measurement of various healthcare in the Victorian health care centers. The method production of an interpretable result is of principal concern. The result due to the VLAD method of analysis should not in any way give a false positive result. The review of all result is a requirement to ensure accuracy, quality and interpretability of the results before they are ushered out for use (Braithwaite, Westbrook & Iedema, 2005). The most crucial aspect of the validity of the VLAD is the due approximation of the expected outcomes. The overestimation of the outcome expectation leads to the high frequency of signaling that the performance of the system is better than it is. Besides, increasing the rate for worse than performance expectations results due to the underestimation of the system outcome. The VLAD intersection with the limits top the signals out-of-control states is ideal techniques in aiding the quality control assessment in the hospitals. They give a relatively non-complicated visual representation that potentiates the engagement of various clinicians and other administrators in the hospital system. Particular signals are engaged in the determination and assessment of the system performance below or above the expectations. Different levels of interpretation and review of the results ensure a complete quality assessment of the health care system quality. Assignment 3: Analysis of VLAD data (Stroke In-Hospital Mortality VLAD, Jul 2011 - Nov 2014) and action plan development The VLAD above represents the Stoke in-hospital mortality for a given hospital within the periods from June 2011 to November 2014 in a particular hospital. The X-axis represents the number of cases of the stroke mortality. In addition, the y-axis is a scale that runs from positive to negative values. The lines facing between any plots is an indication of a positive outcome. The lines facing the negative values (facing down) on the other hand presents an adverse result, that is, mortality due to stroke in the hospitals. The flat regions of the plots is a representation of no of no particular variations in the in-hospital and other cases of stroke mortality. In the determination of each of the points used in the plotting the given curves, the probability basis is employed (Kirk et al., 2007). In each case, the flagging represents the limits in the plots. The flagging is points where the VLAD charts intersect with the red or the blue line in the above graph. The Flagging does not occur as a consequence of a single outcome. The flagging occurs as a result cumulative effects of either good or poor outcomes. The flagging effect is a direct representation of the cumulative and repetitive errors. As a result of flagging, several mistakes that results in poor system performance can be identified and corrected in due time. Any breach of the positive limit presents the opportunity for exploration and determination of the factors that can further increase the positive outcome of the stroke in-hospital mortality (Kirk et al., 2007). In the above VLAD, there are two flagging. The first is the flagging at the upper limit as well as the flagging at the lower limit. The flagging at the upper limits is a presentation of a case where there was an increase in the positive outcome of the stroke in-hospital mortality. Besides, the flagging at the lower limits presents the accumulation of poor outcomes in regards to the stroke in-hospital mortality. The outcome of the system, as a result, is becoming poor and poor with time. The system should, therefore, be evaluated for better performance (Langley et al., 2009). Description of the Issues Evident in analyzing VLAD graph above From the graph, there is an objective conclusion that there is an increase in the stroke in hospital mortality between the years 2011 and 2014. In 2011, the number of death due to the stroke in the in-hospital set up was relatively lower. In contrast, in 2014, there was a significant increase in such mortality in the hospitals due to stroke. The visual representation of such changes deserves critical analysis in determining a more accurate contributing factors to such trends. Evidently, the system is deteriorating with time, and there is a need to worry about the quality of such health service. The initial number of the stroke in-hospital mortality is 324 cases in June 2011 and increases significantly to 639 cases in 2014 according to the figure above. The rise in the reported number of cases leads alongside to the poor outcomes of the system quality (Langley et al., 2009). Question 3. Question A: The underlying issues The problems evident in the graph have several underlying issues. The most critical issues revolve around the estimation of the lower and the upper limits of the plots in the graph. Overestimation of the limits can always lead to a false positive or a false negative result from the chart. In this regards, the underestimation of the lower limits especially towards the end of 2014 results to the poor outcome of the system. Noteworthy as well, the poor estimation of the upper limits of the plots results to the false positive results (Weiss, & Amaral, 2010). Since each plot is dependent upon the probability considerations, another critical issue is the utilization of wrong probability backgrounds in the determination of various schemes. The plots may, therefore, give an incorrect visual representation of poor results that might not necessarily be the case. Other underlying issues may include the use of poor and less effective medication in the management of stroke and stroke-related complications in the hospital set-up. Such medication may result to increase in the mortality rates of the in- hospital stroke patients. The timing of the medication and poor patient monitoring in the hospital set up can also contribute significantly to the increase reduction in the quality of stroke management in the hospital (Weiss & Amaral, 2010). Question B: Actions for adequate analysis of the problems in Question A. above Several measures can facilitate the investigation of the problems that lead to the poor system performance in stroke management. First, the monitoring of stroke therapy and drug administration can lead to an overwhelming positive outcome and low mortality. Due to the low therapeutic window of most drugs used in the stroke management, close monitoring of such drug administration is of great significance in addressing poor stroke management (Doyle et al., 2013). Also, feasible estimation of the plotting of the upper and the lower limits increases the accuracy of the presentation of the system efficiency. The correct estimation of such points will not lead to unnecessary flagging that may result in a false negative or positive results. The red and the blue limits dictate the level of accuracy of such plots. Thirdly, the plotting of individual entries should entail precise probability to result in an actual representation of the data representation. The individual plots cumulatively lead to positive or negative flagging that affect the overall visual representation of the raw data gathered from the different hospitals (Doyle et al., 2013). Question C: Strategies of Monitoring Stroke Management within the Hospital The monitoring of the stroke management takes diverse approaches within the hospital set up. The health professionals assigned stroke patients should fill the treatment forms which indicates any development of worse or better outcomes. The outcomes should explicitly include any symptoms due to the drug therapies and such kinds of details. Besides, use of various modern medical equipment, for instance, in checking the blood pressure and development of relevant prognosis aid in the monitoring of stroke patients in the hospital. As a result, there are low mortality rates due to the stroke in the hospital (Porter, 2009). Monitoring of stroke management may also entail taking of X-ray and CT-scan to evaluate the extent of damage to the nervous system. Further evaluations of the areas alongside therapy indicate the progress made in the course of treatment. It suggests the need for improving medication and other factors in ensuring effective stroke management (Porter, 2009 Response to Quality manager Recommendations on patient experience Consideration of patient experience feedback as part of the investigation forms a reliable and accurate source of data on the hospital stroke management. Such a recommendation by the quality manager can lead to an enhancement in the quality of the health care, tailor-made to suit the patient demands. The feedbacks from the customer may also present an idea of the real-time change in trends on the perception of the stroke management. It is, therefore, for important to take into consideration the patients experience feedback. References Braithwaite, J., Westbrook, J., & Iedema, R. (2005). Restructuring as gratification. Journal of the Royal Society of Medicine, 98(12), 542-544. Doyle, C., Howe, C., Woodcock, T., Myron, R., Phekoo, K., McNicholas, C., ... & Bell, D. (2013). Making change last: applying the NHS Institute for innovation and improvement sustainability model to healthcare improvement. Implement Science, 8(1), 127. Kirk, S., Parker, D., Claridge, T., Esmail, A., & Marshall, M. (2007). Patient safety culture in primary care: developing a theoretical framework for practical use. Quality and Safety in Health Care, 16(4), 313-320. Langley, G. J., Moen, R., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009). The improvement guide: a practical approach to enhancing organizational performance. London: John Wiley & Sons. Porter, M. E. (2009). A strategy for health care reform toward a value-based system. New England Journal of Medicine, 361(2), 109-112. Weiss, C. H., & Amaral, L. A. (2010). Moving the science of quality improvement in critical care medicine forward. American journal of respiratory and critical care medicine, 182(12), 1461-1462. Read More