Uses of Statistical Information


Usesof Statistical Information

Usesof Statistical Information

Useof statistics at the workplace

Theevidence-based practice uses evidence from past recoveries and frommedical books to determine the best approaches in care (Koch &ampWaterstraat, 2010). The implication is that statistics is importantin the recovery room because they determine the kind of medication tobe administered to the patients after surgery based on the chances ofsuccess (Koch &amp Waterstraat, 2010). Therefore, in the recoveryrooms, statistics are used to estimate the chances of success and tocalculate the probability of any outcome. Where the chances ofsuccess are favorable, a particular approach is selected. Patientcharacteristics determine the choice of anesthesia and the choice ofmedication to administer in the recovery room (Koch &ampWaterstraat, 2010). For example, there is an average recovery time ofevery operation and the different types of anesthesia. Where thepatient exceeds the average time, there may be a need forintervention based on the calculated chances of recovery with delayedrecovery time based on the nature of the operation.

Descriptivestatistics example at place of work

Aftercollection of data, it is analyzed and presented in ways that areeasy to understand for all the medical staff. The analysis is presentin medical instructional manuals and from studies conducted by themedical personnel. For example, say of fifty women, 10 selected theC-section delivery. The descriptive statistics would say that 20% ofall women require the C-Section in delivery (Melnyk &amp Overholt,2011). In the recovery room, many examples of descriptive statisticsexist. They provide a brief overview of the situation and theexpected outcomes. From the overview, the probability of everyoutcome is known, and it is easy to monitor the progress ofrecovering patients. For example, say the average time taken for apatient to recover consciousness after cosmetic surgery is thirtyminutes (Melnyk &amp Overholt, 2011). The figure is an expression ofobservations from the past and computation of an average time.Therefore, the descriptive statistic would be that patients wake upafter thirty minutes.

Inferentialstatistics at the place of work

Inferentialstatistics is generalizations. They apply a specific observation onall situations in the hospital and suggest that all patients aresupposed to show the same characteristics. For example, if patientstake thirty minutes to recover conscience after reconstructivesurgery, and more than half the patients who took over 45 minutes towake up had a complication, the inferential statistics would say thatall patients who take more than 45 minutes to recover consciousnesshad a problem (Koch &amp Koch, 2012). At the same time, if patientswith low blood pressure have an increased chance of negative reactionto anesthetics, an inferential statistic would claim that allpatients with low blood pressure react negatively to anesthesia. Theinferential statistics are useful because they reduce the chances oftaking risking interventions by creating the impression that theresult of the intervention is a certainty. Therefore, no calculationsof probability are required in the inferential statistics because thestatistics have already made the conclusion.

Useof data at four levels

Ratio:in ratio, there is an absolute zero. Therefore, all rationmeasurements consider the bare minimum requirement (Israel, 2007).For example, the human body temperature is constant, and variationsare indications of a problem with the patient. At the same time, theblood pressure of patients in the recovery rooms is based on absoluterelations between the first and the second number. Deviationindicates a problem.

Interval:an interval is a distance between any two points. For example,temperature differences are intervals used to make decisions in therecovery room (Israel, 2007). At the same time, differences inpressure are interval measures that are used in the administration ofanesthesia in the recovery room.

Ordinal:ordinal data is used to select the best available candidates for ajob (Israel, 2007). For example, if one of the nurses is an internand the other is a qualified and experienced nurse, the experiencednurse is more likely to be selected for complicated operationsbecause of the manner of risk involved.

Nominal:nominal data is used to assign shifts in the workplace (Israel,2007). For example, provided two nurses have the same level ofexperience in surgeries, assignment in the recovery room is dependenton availability and random selection as opposed to differencesbetween the nurses.

Accurateinterpretation advantage for decision making

Inthe recovery room, monitoring the recovering patient is veryimportant because the slightest deviation may indicate a potentiallylife-threatening problem (Hatfield &amp Tronson, 2009). Statisticaldata is used to determine the correlation between different variablesand the success of successful recoveries. Therefore, since therecovery room is based on evidence-based care, the statisticalvariables are a very important part of the process because theydetermine the chances of success. To improve the decision-makingprocess, more data needs to be supplied to detail the specificrecovery conditions and the results of deviations to determine theapproaches of care taken.


Hatfield,A., &amp Tronson, M. (2009). The complete recovery room book (4thed.). Oxford: Oxford University Press.

Israel,J. (2007). Recovery room care (2nd ed.). Chicago: Year Book Medical.

Koch,G., &amp Waterstraat, F. (2010). Basic allied health statistics andanalysis (2nd ed.). Albany: Delmar/Thomson Learning.

Koch,G., &amp Koch, G. (2012). Instructor`s Manual to accompany Basicallied health statistics and analysis. Clifton Park, NY: ThomsonDelmar Learning.

Melnyk,B., &amp Overholt, E. (2011). Evidence-based practice in nursing &amphealthcare: A guide to best practice (2nd ed.). Philadelphia: WoltersKluwer/Lippincott Williams &amp Wilkins.