Don discussed why the ACA will persist despite attempts to repeal.
https://www.nytimes.com/2017/04/01/opinion/sunday/obamacare-can-survive-trump.html?_r=0
Provide examples and discussion for applying Shewhart's and Deming's ideas for understanding variation as described in the article:
http://www.apiweb.org/images/PDFs/understanding-variation26-years-later.pdf
Many organizations are involved in improvement activities aimed at cancer screening. There are known good ideas to improve screening percentages (use of CIS reminders, huddles, max-packing, etc.). However, there is another source of ideas for improvement that may already be in the organization, but hidden.
Consider these data for breast cancer screening for 60 providers in an organization:
| Provider | Percent Screened |
Provider | Percent Screened |
Provider | Percent Screened |
| Provider1 | 58.1 | Provider21 | 53.3 | Provider41 | 55.0 |
| Provider2 | 42.5 | Provider22 | 50.0 | Provider42 | 54.5 |
| Provider3 | 28.0 | Provider23 | 34.5 | Provider43 | 30.6 |
| Provider4 | 41.9 | Provider24 | 74.9 | Provider44 | 42.9 |
| Provider5 | 32.3 | Provider25 | 45.2 | Provider45 | 24.3 |
| Provider6 | 57.6 | Provider26 | 43.7 | Provider46 | 53.6 |
| Provider7 | 64.5 | Provider27 | 50.0 | Provider47 | 41.7 |
| Provider8 | 37.3 | Provider28 | 25.8 | Provider48 | 100.0 |
| Provider9 | 41.7 | Provider29 | 55.4 | Provider49 | 42.9 |
| Provider10 | 61.0 | Provider30 | 0.0 | Provider50 | 43.3 |
| Provider11 | 50.0 | Provider31 | 62.1 | Provider51 | 48.6 |
| Provider12 | 77.3 | Provider32 | 43.2 | Provider52 | 36.8 |
| Provider13 | 48.6 | Provider33 | 43.8 | Provider53 | 30.0 |
| Provider14 | 56.0 | Provider34 | 42.4 | Provider54 | 46.6 |
| Provider15 | 32.4 | Provider35 | 58.2 | Provider55 | 51.1 |
| Provider16 | 51.0 | Provider36 | 40.0 | Provider56 | 29.2 |
| Provider17 | 43.0 | Provider37 | 54.3 | Provider57 | 36.4 |
| Provider18 | 54.4 | Provider38 | 41.8 | Provider58 | 43.4 |
| Provider19 | 38.4 | Provider39 | 43.1 | Provider59 | 45.3 |
| Provider20 | 63.0 | Provider40 | 50.0 | Provider60 | 45.9 |
Of course, the data vary. But is there variation is these data that might be useful for guiding improvement efforts? When one thinks about variation within the concepts from Shewhart about common causes of variation and assignable causes of variation, and apply that kind of thinking to our provider data, the following questions arise:
Are all the providers part of the same system? Are some of the providers outside the system? If so, what is different about the way screening is done with the providers who are outside the system.
How can we answer those questions? Shewhart gave us the operational definition of "outside the system" with the control chart methodology that he developed.
A run chart alone does not tell us who is in the system and who is outside (see first figure)
By using P-chart calculations, control limits can be calculated (this requires the raw data of count of patients who are up-to-date with their screening and the total number of patients eligible for screening within the providers panel). The figure below shows a control chart for these data.
Note that there are several provider outside the system (outside of the control limits). Note that the provider with the highest percent of patients screened is inside the system (100%). To help see these differences, we can highlight the data points that indicate a provider who is outside the system (see the figure below);
Red dots show the providers who are outside the system high and blue dot show the providers who are outside the system low.
What can you do with this analysis? Find out why these 7 providers are outside the system. What are they (their care teams) doing differently? The answers to this question will often result in effective ideas for changing the system.
The method described above for looking at variation among providers needs a cautionary note here at the end. These data are a snapshot in time. To look at variation among providers over time, the best tool that I have found is small multiples. Look for another posting on analyzing variation among providers over time where I will post some examples of small multiples.
Even Better Way to View to View Data by Provider (added May 18, 2017)
The funnel plot is a method takes advantage of the idea that the data being analyzed do not have a time order. So, when there is not an inherent order for a set of data, the investigator can order the data in a way that makes it easier to see who is in the system and who is out.
Below is a Shewhart chart of breast cancer screening percentages for 144 providers in a large health system. They are ordered by the size of each provider's panel of women who should be screened for breast cancer, based on age. The providers with the largest panel are on the right-most end of the horizontal axis and those with the smallest panels are at the left-most end of the horizontal axis. Note that most of the providers are "inside the system", meaning that their percentages fall within the Shewhart upper and lower limits. However, there are clearly some of the providers are outside the system high and some of the providers outside the system low.
Adding color-coded circles makes it clear which providers we should talk to about why they are outside the system high (blue in chart below). Knowledge about how to do a better job of screening could be learned from this group of providers and then tested and adapted for the rest of the providers.
Knowledge about why the providers in the red circles are not able to screen at the "system" rate would likely also prove very useful for improving screening rate.