Application of Statistics in Healthcare
Statistics is an essential unit in healthcare. It is a branch of mathematics that focuses on the study of random events. Unlike factual sciences in which knowledge is obtained through experimentation and observation, statistics is a formal science that focuses on abstract structures and knowledge is acquired through logic reasoning (Ocana-Riola, 2016). Due to its ability to provide intelligence on the random structure of health phenomena, statistics is an essential tool for research, planning, and decision-making. Technological advancement has engendered sophisticated statistical models which are applied in a range of disciplines including healthcare. Statistical application in healthcare is significant for the improvement of quality, patient safety, leadership, and in health promotion.
Statistical data is obtained through various means. First, it is important to note that statistical data can be big: include an analysis of massive amounts of information to illustrate health trends for entire populations or small: information from a minor patient population (Wills, 2014). Questionnaire surveys and patient reports are commonly used to collect data in epidemiologic research. Standardized questionnaires are self or interviewer-administered in person, through mail, or on phone (Saczynski et al., 2014). This tool gathers information on socio-demographic characteristics, medical history, use of medication, and lifestyle practices among others. Participants’ reports are analyzed using standardized, approved instruments. Proxy respondents are also used to collect data in healthcare. Although the use of proxy respondents can undermine the validity or accuracy of data, the method is useful in cases where controls in a study are incompetent or dead (Saczynski et al., 2014). Medical records are important sources of health information, which can be used independently or complementary to other instruments of data collection. For instance, Electronic Health Records (EHRs) collect and store patient information, which, according to Wills, can be converted to clinical summaries to be used in small data analytics (2014). Biological materials like hair, urine, and saliva are obtained to determine metabolic, genomic, and proteomic status of individuals to better understand their response to disease or treatment.
The data obtained is applied in healthcare to achieve various outcomes including quality improvement. Quality is the ability of a healthcare product or service to achieve the desired results (The Victorian Quality Council, 2008). Healthcare systems are complex, thus, creating change to improve quality can be a taxing process. Policy makers are required to have the knowledge of what is happening in the delivery of healthcare, the influencing factors, and what changes can be made to improve quality. Evidence-based information is a crucial for decision-making. Data is collected and analyzed to provide intelligence on factors like causes, effects, or risk factors of diseases and help in developing appropriate improvements (The Victorian Quality Council, 2008). Statistical data enables the identification of problems and create improvement by identifying opportunities. Improvement is achieved by tackling the right problem, implementing the appropriate strategies, and demonstrating the desired outcome and ensuring quality sustainability. Therefore, medical professionals with statistical literacy are better positioned to deliver excellent service in the ever-changing medical field (Aggarwal, 2018).
Safety is another area emphasized by statistical application in healthcare. Patient safety is threatened by factors like hospital acquired infections (HAIs), wrong diagnosis, and wrong treatment. In the new data-driven era, tools of integrated data are used to achieve patient safety. Analytics tools are used to enhance integrated clinical and operational data, machine learning, and predictive analysis (Sacnyski et al., 2014). Safety tools, which are embedded with automated triggers, identify risk and alert frontline caregivers in cases of potential harm. Subsequently, the predictive analytics determines the interventions of preventing or reducing the particular harm. Besides data integration, statistics is applied in product development. Information on the performance of new technologies and treatment is collected through clinical trials. Thus, pharmaceutical companies gain knowledge on the benefits and risks of their products before releasing to the market. This process ensures that only safe products are distributed.
Statistics application in healthcare is also significant in health promotion and effective leadership. Health promotion may target entire communities or at-risk populations, helping them to increase control over their health and the related factors (Aggarwal, 2018). Statistical data like obesity, tobacco use, mental health, and physical inactivity provides important information about health trends and guides policymakers on the required solutions and the target population. Statistical analysis also improves leadership by enhancing management functions like resource allocation. The data helps to determine which products and services to produce and which population to allocate the resources. Leaders consider costs of lost opportunities when making such decisions, which are important in the management of scarce medical resources, thus, better leadership.
The application of statistics in healthcare is crucial in enhancing quality, safety, leadership, and health promotion. Data achieved through surveys, medical records, and other methods is analyzed and used to make decisions on important issues in healthcare. Statistical literacy is, therefore, is a basic requirement in healthcare. Medical professionals should be able to read and interpret statistical data in order to make accurate conclusions on issues like causes, risks, and treatment of diseases. Furthermore, the professionals are required to keep abreast of disease and treatment developments from statistical data to assess their reliability and applicability.
Aggarwal, R. (2018, Oct-Dec). Statistical literacy for healthcare professionals: why is it important?. Annals of Cardiac Anesthesia, 21(4), 349-350. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206780/
Ocana-Riola, R. (2016, Oct). The use of statistics in health sciences: Situation analysis and perspective. Statistics in Biosciences, 8(2), 204-219. Retrieved from https://www.researchgate.net/publication/289585992_The_Use_of_Statistics_in_Health_Sciences_Situation_Analysis_and_Perspective
Saczynski, J.S., McManus, D. & Goldberg, R.J. (2013, Nov). AM J Med, 126(11). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3827694/
The Victorian Quality Council. (2008, June). A guide to using data for healthcare quality improvement. The Victorian Quality Council. Retrieved from https://www.aci.health.nsw.gov.au/__data/assets/pdf_file/0006/273336/vqc-guide-to-using-data.pdf
Wills, M.J. (2014, July-Aug). Decisions through data: analytics in healthcare. Journal of Healthcare Management, 59(4), 254-262. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/25154123