JOHN F. KROS, PHD, is the Vincent K. McMahon Distinguished Professor of Business in the Marketing and Supply Chain Management Departmentin the College of Business at East Carolina University.
DAVID A. ROSENTHAL, PHD, is Professor and Chair of Health Care Management at Baptist Memorial College of Health Sciences.
Statistics and Excel
- Understand how this book differs from other statistics texts
- Understand how knowledge of statistics may be beneficial to health policy or health administration professionals
- Understand the "big picture" with regard to the use of statistics for health policy and administration
- Understand the definitions of the following terms:
- Populations and samples
- Random and nonrandom samples
- Types of random samples
- Variables, independent and dependent
- Identify the five separate statistical tests: chi-square test, the t test, analysis of variance (ANOVA), regression analysis, and Logit
Statistics is a subject that for many people is pure tedium. For others, it is more likely to be anathema. Still others find statistics interesting, even stimulating, but they are usually in the minority in any group.
This book is premised on the recognition that in the health care industry, as indeed among people in any industry or discipline, there are at least these three different views of statistics, and that any statistics class is likely to be made up more of the first two groups than the last one. This book provides an introduction to statistics in health policy and administration that is relevant, useful, challenging, and informative.
1.1 How This Book Differs from Other Statistics Texts
The primary difference between this statistics text and most others is that this text uses Microsoft Excel as the tool for carrying out statistical operations and understanding statistical concepts as they relate to health policy and health administration issues. This is not to say that no other statistics texts use Excel. Levine, Stephan, Szabat (2013) have produced a very useable text, Statistics for Managers Using Microsoft Excel. But their book focuses almost exclusively on non-health-related topics. We agree that the closer the applications of statistics are to students' real-life interests and experiences, the more effective students will be in understanding and using statistics. Consequently, this book focuses its examples entirely on subjects that should be immediately familiar to people in the health care industry.
Excel, which most people know as a spreadsheet program for creating budgets, comparing budgeted and expended amounts, and generally fulfilling accounting needs, is also a very powerful statistical tool. Books that do not use Excel for teaching statistics generally leave the question of how to carry out the actual statistical operations in the hands of the student or the instructor. It is often assumed that relatively simple calculations, such as means, standard deviations, and t tests, will be carried out on paper or with a calculator. For more complicated calculations, the assumption is usually that a dedicated statistical package, such as SAS, SPSS, STATA, or SYSTAT, will be used. There are at least two problems with this approach that we hope to overcome in this book. First, calculations done on paper, or even those done with a calculator, can make even simple statistical operations overly tedious and prone to errors in arithmetic. Second, because dedicated statistical packages are designed for use rather than for teaching, they often obscure the actual process of calculating the statistical results, thereby hindering students' understanding of both how the statistic is calculated and what the statistic means.
In general, this is not true of Excel. It is true that when using this book, a certain amount of time must be devoted to the understanding of how to use Excel as a statistical tool. But once that has been done, Excel makes the process of carrying out the statistical procedures under consideration relatively clear and transparent. The student should end up with a better understanding of what the statistic means, through an understanding of how it is calculated, and not simply come away with the ability to get a result by entering a few commands into a statistical package. This is not to say that Excel cannot be used to eliminate many of the steps needed to get particular statistical results. A number of statistical tests and procedures are available as add-ins to Excel. However, using Excel as a relatively powerful-yet transparent-calculator can lead to a much clearer understanding of what a statistic means and how it may be used.
1.2 Statistical Applications in Health Policy and Health Administration
When teaching statistics to health policy and health administration students, we often encounter the same question: "All these statistics are fine, but how do they apply to anything I am concerned with?" The question not only is a reasonable one, but also points directly to one of the most important and difficult challenges for a statistics teacher, a statistics class, or a statistics text. How can it be demonstrated that these statistics have any real relevance to anything that the average person working in the health care industry ever needs to know or do?
To work toward a better understanding of why and when the knowledge of statistics may be useful to someone working in health policy or health administration, we've selected six examples of situations in which statistical applications can play a role. All six of these examples were inspired by real problems faced by students in statistics classes, and they represent real statistical challenges that students have faced and hoped to solve. In virtually every case, the person who presented the problem recognized it as one that could probably be dealt with using some statistical tool. But also in every case, the solution to the problem was not obvious in the absence of some understanding of statistics. Although these case examples are not likely to resonate with every reader, perhaps they will give many readers a little better insight into why knowledge of statistics can be useful.
Documentation of Medicare Reimbursement Claims
The Pentad Home Health Agency provides home health services in five counties of an eastern state. The agency must be certain that its Medicare reimbursement claims are appropriately and correctly documented in order to ensure that Medicare will process these claims and issue benefits in a timely manner. All physician orders, including medications, home visits for physical therapy, home visits of skilled nursing staff, and any other orders for service, must be correctly documented on a Form CMS-485. Poorly or otherwise inadequately prepared documentation can lead to rejection or delay in processing of the claim for reimbursement by the Centers for Medicare and Medicaid Services (CMS).
Pentad serves about 800 clients in the five-county region. In order to assure themselves that all records are properly documented, the administration runs a chart audit of 1 in 10 charts each quarter. The audit seeks to determine (1) whether all orders indicated in the chart have been carried out and (2) if the orders have been correctly documented in the Form CMS-485. Orders that have not been carried out, or orders incorrectly documented, lead to follow-up training and intervention to address these issues and ensure that the orders and documentation are properly prepared going forward.
Historically, the chart audit has been done by selecting each tenth chart, commencing at the beginning or at the end of the chart list. Typically, the chart audit determines that the majority of charts, usually 85 to 95 percent, have been correctly documented. But there are occasionally areas, such as in skilled nursing care, where the percentage of correct documentation may fall below that level. When this happens, the administration initiates appropriate corrective action.
Sampling, Data Display, and Probability
One of the questions of the audit has been the selection of the sample. Because the list of clients changes relatively slowly, the selection of every tenth chart often results in the same charts being selected for audit from one quarter to the next. That being the case, a different strategy for chart selection is desirable. It has been suggested by statisticians that using a strictly random sample of the charts might be a better way to select them for quarterly review, as this selection would have a lesser likelihood of resulting in a review of the same charts from quarter to quarter. But how does one go about drawing a strictly random sample from any population? Or, for that matter, what does "strictly random" actually mean and why is it important beyond the likelihood that the same files may not be picked from quarter to quarter? These questions are addressed by statistics, specifically the statistics associated with sample selection and data collection.
Another question related to the audit concerns when to initiate corrective action. Suppose a sample of 1 in 10 records is drawn (for 800 clients that would be 80 records) and it is discovered that 20 of the records have been incorrectly documented. Twenty of 80 records incorrectly documented would mean that only 75 percent of the records were correctly documented. This would suggest that an intervention should be initiated to correct the documentation problem. But it was a sample of the 800 records that was examined, not the entire 800. Suppose that the 20 incorrectly documented records were, by the luck of the draw, so to speak, the only incorrectly documented records in the entire 800. That would mean that only 2.5 percent of the cases were incorrectly documented.
If the required...