Using Statistics in Healthcare

     Statistics defined in the most basic way, is data that is collected and analyzed to form a conclusion.  Using statistics to improve healthcare is not a new idea. Almost every research project or study done that yields new information uses statistics in some form.  For example, if I wanted to test job satisfaction for nurses in a random hospital, I could take a sample of 25 nurses, and have them rate their job satisfaction on a scale of 1-10.  

Here is what the statistics would look like:

     In this case, the most important number is the Mean, or the average of all the numbers.  This shows that the nurses are a little more than half satisfied with their jobs. These numbers can be used to improve working conditions among the nurses to improve their job satisfaction.

     According to the National Institutes of Health (NIH) U.S. National Library of Medicine uses for statistics include:

Health statistics serve diverse needs:

• Individuals use health statistics to understand their own risks, conditions, and health status in a broader context.
• Researchers and physicians use them to study the causes and solutions of health problems.
• Health resource planners use them to understand the allocation of health resources.
• Policy makers and the public use them to monitor progress.
• Legislators consult them when enacting laws, conducting program oversight, and considering appropriations.

Successful users:

• Consult wide array of sources
• Understand their limitations.
• Focus on how data were collected and
• Assess their statistical properties.

This means that they not only search for indicators but also examine:

• The universe covered, and
• The factors that add statistical uncertainty.

According to a study done in 2007, "Statistical process control is a versatile tool which can help diverse stakeholders to manage change in healthcare and improve patients' health.

Quality improvement (QI) practices represent a leading approach to the essential, and often challenging, task of managing organisational change.¹ Statistical process control (SPC) is, in turn, a key approach to QI. SPC was developed in the 1920s by the physicist Walter Shewhart to improve industrial manufacturing. It migrated to healthcare, first in laboratory settings and then into direct patient care applications, along with other approaches to QI. Before we report on our systematic review of the literature on how SPC has been applied to QI in healthcare, there is a need to define SPC and its role in QI.

'Statistical process control (SPC) is a philosophy, a strategy, and a set of methods for ongoing improvement of systems, processes, and outcomes. The SPC approach is based on learning through data and has its foundation in the theory of variation (understanding common and special causes). The SPC strategy incorporates the concepts of an analytic study, process thinking, prevention, stratification, stability, capability, and prediction. SPC incorporates measurement, data collection methods, and planned experimentation. Graphical methods, such as Shewhart charts (more commonly called ‘control charts'), run charts, frequency plots, histograms, Pareto analysis, scatter diagrams, and flow diagrams are the primary tools used in SPC.' (Thor, J., 2007)

      No matter how healthcare organizations use statistics, they will always be helpful to improve quality of patient care, knowledge of studies and research, and quality control.

 
References:


NIH.gov (2013) Finding and Using Health Statistics retrieved January 27, 2014 from
     http://www.nlm.nih.gov/nichsr/usestats/

Thor, J., et. al.  (2007) Application of statistical process control in healthcare improvement:

    systematic review Qual Saf Health Care 2007;16:387–399. doi: 10.1136/qshc.2006.022194

     retrieved January 27, 2014 from

     http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2464970/#!po=1.78571