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Originally published octubre 16, 2007
Walk down the halls of any hospital, clinic, long-term care facility or home-health agency and you are likely to see a large number of run charts decorating the walls. These charts, as I am sure you are aware, are used to measure an almost endless array of clinical, service, administrative and financial indicators. The data feeding these charts may come from many sources, but the goal is always the same: to focus management attention in order to improve performance across time.
Run charting and business intelligence seem to have been made for each other. Not only can you use business intelligence data to power up your run charts, but you can also use the insights gained using run charts to power up your business intelligence.
Throughout this article, I will be using a simple efficiency service variable to illustrate the power of merging run charts with business intelligence. The variable is called time to first clinical entry. Picture a visit to the doctor. You register at the front desk and sit down in the waiting room. You get the call to come into examining room #3, where you wait a bit longer. The nurse comes in and takes your vitals. He or she signs into the system and records your blood pressure, pulse, temperature and so forth.
From the moment your record was brought up at the front desk until the moment your vital information was entered is called time to first clinical entry. As you can see, a lot of steps were involved, including giving your information at the front desk, the time you spent in the waiting room, the time it took to get to the examining room, the exam room wait time plus, of course, the time it took to get your vitals.
Why is this time measure important? It is important because you came in for clinical service, not for administrative recordkeeping or to wait around. Until the nurse entered your vital information, everything else is overhead. And there are a huge number of potential improvements in each of these steps that can affect your health, your satisfaction and your compliance with the advice you eventually get from the doctor.
This is a simple measure that can be done easily with an integrated patient care system (i.e., time of first clinical input minus time of first administrative input). Simple yes, but for a number of reasons, it is a highly effective measure.
First, a quick description of run charts. Run charts have been in use for nearly a century, so there is a great deal of information available on the Web about how and why to use them. In the references listed at the end of this article, I have included some sources that are pretty good. Figure 1 is a picture of a basic run chart.
Figure 1: Basic Run Chart
For our purposes, we need to focus on eight run chart fundamentals:
Figure 2: The Effect of Disruption
Run charts are usually borne of quality improvement efforts such as lean events, resulting in a high degree of ownership by those tracking and posting the measurements. But they can also be high maintenance tools to keep up to date using ad hoc reports and manual tracking. Plus, they are often not integrated with other data sources or with other areas of the organization, which limits their effect on the enterprise. Traditional run charting efforts might be best described as pilot projects.
The first applications of run charts in most organizations cover small, defined data points in a single subject area. In addition, every clinic is likely to be measuring different variables, or measuring similar variables using different criteria and business rules. For instance, one clinic might measure the time to first clinical entry from the beginning of the registration process while another might measure it from the end of the registration process. This is fine for early efforts, but limits the value of improvement efforts across the organization.
In order to get this value, tapping into your business intelligence data repositories would be a wise move. Doing so would give your run charts a boost by allowing you to drill into formal hierarchy. For instance, begin by evaluating performance at the corporate level, then drill down by clinic group (region, district) and then to the individual clinic. Plus, if you have data at the department level, you can drill to it. Furthermore, with data at the specific person level (PCP, PA, NP, RN), you can measure performance and standardize practice.
With data at the individual visit level, you can drill across various subsets that don't follow strict hierarchical lines. Some examples include looking at:
Slicing and dicing our data this way leads to earlier identification and intervention of special causes that are not part of the normal functioning of a stable process. In addition, they provide us with ideas to focus our improvement efforts (i.e., deliberate disruptions).
The real power from using business intelligence data as a feeder for run charts comes from measuring variables that are tracked by different sources. One example is combining revenue data with service metrics, which can improve performance on revenue per hour. With run charts, the entire organization can see improvement over time (or not) and can see how their improvement efforts contribute to the overall good of the organization. In short, the effect of new programs and different methods becomes powerfully visible.
Business intelligence data is very useful in supporting statistical analysis, giving you the ability to slice, dice, sort and sum your analysis. But the real power of merging run charts with business intelligence comes from using the insights gained from the charts in your other business performance analysis applications.
Let's say you analyze your run charts and find these situations:
If business intelligence is using the collective knowledge of your organization and of your environment to find and exploit opportunities, then it is imperative that you record your history of special causes and disruptions that are highlighted by your run charts.
Merge your business intelligence with your statistical analysis. Both will be significantly improved by this merger. Then, create deliberate disruptions. Using the data you already own gives you the ability to find, test, judge and even predict the outcomes of the disruptions you introduce into the system. Your organization, your stakeholders, your staff and, of course, your patients will be better for it.
Thanks for reading!
Schmidt S, Kiemele M, Berdine R, Knowledge Based Management: Unleashing the Power of Quality Improvement, Colorado Springs: Air Academy Press, 2005.
Deming, W E, Out of the Crisis, The MIT Press, 2000.
NIST/SEMATECH e-Handbook of Statistical Methods, September 30, 2007.
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