STATISTICS FOR HEALTH CARE PROFESSIONALS 3
Statisticsfor Health Care Professionals
Statisticsfor Health Care Professionals
Whenit comes to healthcare, one can include many things in a dashboard.For instance in order to know how well patients are treated in ahealthcare facility one important metric that I would recommend isthe rate of return of patients. Although it might seem that the rateof return may indicate that patients are not treated well this doesnot have to be the case. Thus in order to know for sure the benchmarkI would recommend is a 10% rate of return for patients with a similarconcern as the previous one. This is important because it is easy toknow when there is a problem. For instance, a bigger rate of returnof a patient with dissimilar issues indicates that the patient washappy and contented with previous service.
Surgerytimes are varied although allocated time may be almost the samealthough it is clear that no surgery can take place in zero minutes.In fifty operations, the time is so varied, however what is clear isfifty percent of the surgeries took less than hundred and thirtyminutes while the other fifty percent took more than that. This showsdata that is upper ending that is called skewed to the right (Provostet al., 2011, p. 55). This is a more likely a problem because in dataanalysis it violates the assumptions underlying parametric tests(Munro, 2005, p. 64). What ought to be done is to transform the datato a near normal distribution center data set? It is true the visualof where the average is versus where the number is telling because itshows skewness that needs to be handled well to gauge the rightoutcome.
Inevery data collected increasing, the respondents always provide aclearer picture of the issue. The customer satisfaction report willhelp the twenty per cent, however it will not be appropriate to usewhen you are to pinpoint actually how to improve the eighty percentwho are doing well.
Inthe issuance of drugs, avoiding type 1 error over type 2 error ismore prudent because type 1 error effect is more visible. A politicalpolicy that adheres to avoiding type 1 error, in fact, impedes theprogress of marketing new drugs (Heimann, 2010, p. 64). The visibleof type 1 errors makes more open to attacks from interest groups,which politicians like to be therefore many regulations are formed inthe name of managing the industry although they have an adverseeffect on the industry. The matter here is that in type 2-errorvisibility is not there and eventually, when it occurs there are manyvariables that attributing the error to a person is not likely. It isa serious repercussion in the situation of blood or organ donation tomake any type of error because the consequences probably will not bereversible so gauging type 1 over type 2 is not applicable in ablanket manner to every situation.
Thethree tests that are used commonly in inferential statistics testingare known as a general linear model, which includes Analysis ofvariance (ANOVA), t-test, and Chi-square test. The difference betweenthe three tests is in t-test is utilized to determine if the averagescores between two groups are statistically different. The ANOVA testis used to test differences among many groups while Chi-square testis used to test the significance of frequencies and nominal data(Boslaugh, 2012, p. 65). A t-test is only used in two groups whereasthe other two go beyond that.
Thetwo tests in that scenario are excellent but perhaps it would be eyeopening to apply ANOVA too to compare if you get the same result asthe t-test because t-test is only used on the null hypothesis. Thisall points to running every test to be confident in the result towork on because all tests, in the long run, prove a vital aspect thatis crucial to progress.
Boslaugh,S. (2012). Statisticsin a Nutshell.USA. "O`Reilly Media, Inc.
Heimann,C.FL. (2010). AcceptableRisks: Politics, Policy, and Risky Technologies. USA.
Universityof Michigan Press.
Munro,B.H. (2005). StatisticalMethods for Health Care Research, Volume 1. Philadelphia.
LippincottWilliams & Wilkins.
Provost,L.P. & Murray S. (2011). TheHealth Care Data Guide: Learning from Data for
Improvement.San Francisco. John Wiley & Sons.