Statistics role in healthcare organization

Statistics is a vital tool in many sectors, including healthcare. In healthcare, it entails the collection, analysis, and interpretation of relevant data to inform the decision-making processes of healthcare practitioners. More specifically, Medline Plus (2020) describes health statistics as numbers that describe “an aspect of health” (n.p). Today, the entire spectrum of care has integrated statistical methods in all its activities. While statistics are essential in all sectors of medical care, they are particularly integral in advancing quality, safety, healthcare promotion, and leadership elements of care.

Statistics are fundamental for any healthcare organization if it is to improve the quality of care afforded to patients. According to Bartkowiak and Finnegan (2014) assert that statistical analysis in a healthcare setting helps nurse managers and other providers to determine trends in patient outcomes. Consequently, it then becomes possible to have a basis with which they can benchmark the effectiveness of their services relative to international, national, state, or industry standards. Principally, data analysis serves as a quality control mechanism. Through statistics, it is possible to develop tools to gauge the level of standards within a healthcare facility. One such tool is the Statistical Process Control (SPC). According to Hagan (2018), SPC provides a systematic way of visualizing a process. Accordingly, one can detect any significant variations from the process, an outcome that is necessary for evaluating the quality process. Thus, Hagan (2018) asserts that it is possible to monitor vital healthcare indicators, such as patient falls and Hospital Acquired Infections (HAI). By providing measurement metrics, statistics help to offer an overview of a system’s process; hence, it provides the foundation for gauging potential areas of improvement. Therefore, it is evident that statistical measures are a critical quality control tool in healthcare.

The second integral application of statistics in healthcare is the maintenance and improvement of patient safety. Patient safety is a primary consideration in assessing the quality of care in an institution. Some of the common patient safety measures include patient falls with injuries, Surgical Site Infections (SSIs), Central Line-Associated Blood Stream Infections (CLABSI), and Ventilator-Associated Pneumonia (VAP). To reduce the occurrence of these safety issues, the stakeholders need to measure the level of their occurrence. The rationale for this view is that it is impossible to intervene in situations where one does not know the true magnitude of a problem. Therefore, statistical measures like the mean, mode, and percentiles are essential in this regard. Once a safety issue (and its extent) has been identified, an appropriate intervention must be applied. However, even the response must be tested for its suitability in addressing a particular safety concern. In modern healthcare, the focus has shifted towards assessing the efficacy of proposed solutions using evidence-based research. Research methodologies like true-experimentation are essential for evidence-based studies (Boulesteix, Wilson, & Hapfelmeier, 2017) — such methodologies are an outcome of statistics.

Additionally, once an appropriate safety intervention has been identified, it necessary to evaluate its effectiveness. Consequently, statistical tools like forecasting and inferential methods are vital in predicting the anticipated success of the solutions. Therefore, statistics are not only necessary for improving patient safety but also for decision-making.

The third major function of statistics in medical care is health promotion. This concept entails the processes of empowering individuals, through education and advocacy, to take charge of their healthcare outcomes. One of the primary uses of data in health promotion is the determination of the possibility of disease risk. It is vital to prioritize spreading the awareness of those conditions with a high risk of occurrence. Nevertheless, it is impossible to gauge the level of risk without collecting relevant data like the rate of prevalence. Secondly, collection and analysis of information are vital for examining the section of the population that is the most vulnerable to a condition. For instance, the Center for Disease Control and Prevention’s (CDC) Adolescent and School Health Surveys help in determining risk behaviors that face the youth in the country (CDC, 2020) — surveys are one of the data collection methods in statistics.

Leadership is an important element in healthcare. Its central role entails decision-making. It is not possible to make effective decisions without having a proper assessment of situations in medical care. Accordingly, statistical analysis helps healthcare managers to appreciate the circumstances they face, and in the process, it enables them to make appropriate decisions. Other integral elements in a medical institution include financial integrity. Adequate data collection on aspects like costs, revenues, and profit margins, is essential if a hospital is to remain a going concern. Therefore, leaders need to establish proper methods of collecting financial information if they are to make progressive decisions. Some of these methods include revenue management systems. Human resource management is another critical consideration for healthcare managers. To deliver a high quality of care, an institution’s nurses and providers need a conducive working environment. To gauge the conduciveness of the working environment, a leader must collect data using means like job satisfaction surveys. Consequently, they are then able to make informed resolutions on how to bridge gaps in care provision.

In conclusion, statistics is an indispensable part of healthcare. It provides management with a framework to guide decision-making. Accordingly, integral elements of a capable healthcare institution like leadership, safety, health promotion, and leadership must rely on data. Therefore, healthcare managers need to entrench opportunities for statistical applications.

References

Bartkowiak, B. A., & Finnegan, B. J. (2004). Health Statistics. Clinical Medicine & Research, 2(3), 189–190. doi:10.3121/cmr.2.3.189

Boulesteix, A.-L., Wilson, R., & Hapfelmeier, A. (2017). Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies. BMC Medical Research Methodology, 17(1), 138. doi:10.1186/s12874-017-0417-2

Centers for Disease Control and Prevention [CDC] (2020). CDC Adolescent and School Health Surveys. Retrieved from https://www.cdc.gov/features/dsyouthmonitoring/index.html

Hagan, J. (2018). The Use of Statistics in Healthcare Quality Improvement. JOJ Nursing and Healthcare, 8(5), 1-3.

Medline Plus (2020). Health Statistics. Retrieved from https://medlineplus.gov/healthstatistics.html