Uses of Statistical Information
Darryl Lowery
Statistical Applications/HCS438
February 28, 2012
Julieanne Hessler, RN MSN, MBA

Introduction Statistics are used in every phase in the delivery of health care. This is particularly true as it relates to the cost of providing health care services (Eaton, 2006). At Mercy Medical Center, not unlike any other health care facility, the use of statistics is pervasive throughout the organization. First and foremost Mercy uses statistics to develop and maintain its financial imperatives (Minnis, 2008). Simply stated if actual cost of providing health care services exceeds the revenue generated the organization will have difficulty keeping its doors open. This paper will discuss examples of descriptive and inferential statistics in use at Mercy Medical Center. Also discussed will be how data at nominal, ordinal, interval, and ratio levels of measurement are used within the organization. Finally, the advantages of accurate interpretation of statistical data and improved decision making within the organization will be discussed.
Descriptive Statistics An example of a descriptive statistic used at Mercy Medical Center is time spent by the Emergency Department on yellow alert status. Yellow alert is defined by the Maryland Institute for Emergency Medical Services Systems (2012) as ambulance diversion from a designated emergency department that is unable to effectively manage additional patient volume at that time. Several years ago it became an organizational imperative to minimize and indeed eliminate barriers for patients accessing health care services provided by Mercy Medical Center. Yellow alert creates a barrier to accessing health services available at Mercy Medical Center. This barrier is perceived as potential loss in revenue and the subsequent ability of the organization to meet financial imperatives mentioned earlier. The organization developed what is called a pre-yellow alert status to help minimize the need to actually use yellow alert status.
Inferential Statistics On an annual basis Mercy Medical Center’s Division of Nursing conducts an educational needs assessment for staff nurses. The objective of the assessment is to gather information on areas the nursing staff would like to have educational activities directed toward during the coming year. The assessment seeks to identify areas in technology, collaborative practice, quality and patient safety that would be helpful from the nursing staff’s perspective to have additional education. The assessment is in the form of a survey that questions staff regarding specific issues related to these topics. The assessment was able to identify several areas to address and activities were developed to address each.
Levels of Measurement Mercy Medical Center uses all four levels of measurement. There are numerous examples of nominal, ordinal, interval, and ratio measurements used everyday at the medical center. The following discussion will identify an example of each level of measurement and how it is used at Mercy Medical Center. An example of nominal level of measurement, which categories data, is identifying type of bed requirement for admitted post-operative patients. These patients are categorized as needing intensive care, intermediate care or a surgical unit bed. This data is subsequently used to determine staffing levels and other resource allocation needs. The medical center uses ordinal level measurement when determining patient satisfaction. Ordinal level of measurement places data in rank order. The results of the medical center’s patient satisfaction survey results are ranked from very unsatisfied, mostly unsatisfied, satisfied and very satisfied. The medical center further investigates reasons given by patients for each response to formulate appropriate action plans. Interval levels of measurements are used by the medical center when trending patient blood pressure readings over time. Blood pressure readings are reported in specific units. It is important to compare blood pressure readings prior to therapeutic intervention with results post intervention as a means of providing appropriate care. An example of ratio level of measurement at Mercy would be the number of patients admitted to the medical center from the Emergency Department during a given period. Emergency Department admissions are another area that determines appropriate resource allocation. The zero point in this example would be no admissions from the Emergency Department and as a consequence the need to allocate very few resources. On the other hand, the expectation that several patient will be admitted from the Emergency Department within a given time period would require appropriate adjustment in needed resources.
Advantages of Accurate Interpretation of Statistical Data The opportunity to improve decision making within the medical center is enhanced by accurate interpretation of statistical data. Although there are numerous examples of opportunities within the medical center to improve decision making with statistical data, one opportunity that significantly effects the organization’s competitive advantage will be discussed. As mentioned earlier, the medical center routinely conducts patient satisfaction surveys. In addition, regulatory agencies routinely publish information regarding various areas of medical center performance such as infection rates for example. Patients often times have a choice as to which health care facility they would prefer (deGroot, 2011). In order to make the best decisions possible regarding issues that effect patient satisfaction and infection rates, statistical data must be interpreted accurately. Inaccurate interpretation of data could lead to possible misallocation of resources and thus hamper the medical center’s ability to improve patient satisfaction and lower infection rates.
Conclusion
Statistics play a vital role in not only how Mercy Medical Center responds to its business environment but also how it responds to the needs of its patients, staff, and visitors. As health care continues to be a major issue in our society a thorough understanding of how statistical data is useful is fundamental.