Online lecture.
We have been able to win over the Danish doctor Jacob Anhøj for this online seminar. He has been involved in statistical process control for over 20 years and can show us many new perspectives. He says, for example:
“We can’t control a person’s health in the same way as we can control the diameter of a screw or the concentration of a chemical solution. We have to take this into account when controlling the process.”
We therefore look forward to new impulses and a stimulating discussion and attach the “Abstract” written by Mr. Anhøj, which we consider worth reading. We also recommend the references listed below.
Note:
This presentation will be held in English.
Questions can also be asked in German.
+++ This presentation was originally scheduled for September 16. Due to technical problems, we have postponed it to October 7. ++++
Rethinking statistical process control for healthcare improvement
Statistical process control (SPC) was originally developed as a tool to monitor and control the quality of goods produced in the manufacturing industry. Today SPC has found its way into service industries including healthcare. While the thinking behind SPC is the same regardless of the domain, the practical application of SPC varies somewhat. First, healthcare is all about people, which which we, contrary to what we like to believe, have very little control over. We cannot simply adjust the health of a person in the same way that we can adjust the diameter of a bolt or the concentration of a chemical solution. For this reason, SPC in healthcare is more about processes (what we do) than about outcomes (what happens). Also, because of the huge knowing-doing gap, SPC in healthcare is much more about improving processes that controlling them. Second, healthcare workers and managers are generally not trained in SPC, and very few know how to construct and interpret a Shewhart control chart. Perhaps for this reason run charts have become popular. A run chart is a time series chart without control limits and with the median as the centre line. It is easy to construct using only pen and paper. Runs analysis comprise test for non-random variation in the distribution of data points around the median and makes no assumptions regarding the underlying theoretical distribution of data. Run charts are generally considered less useful than control charts. In reality, runs analysis, applied sensibly, is better at detecting minor persistent shifts in data over time than the traditional Shewhart control chart. This talk will introduce the concept of runs analysis and its application and suggest a way to reconcile run and control charts to get the best of both worlds. References Jacob Anhøj, Anne Vingaard Olesen (2014). Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non-Random Variation in Health Care Processes (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113825) Jacob Anhøj (2015). Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121349) Jacob Anhøj, Anne-Marie Blok Hellesøe (2017). The problem with red, amber, green: the need to avoid distraction by random variation in organisational performance measures (https://qualitysafety.bmj.com/content/26/1/81) Jacob Anhøj, Tore Wentzel-Larsen (2018). Sense and sensibility: on the diagnostic value of control chart rules for detection of shifts in time series data (https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-018-0564-0) Jacob Anhøj, Tore Wentzel-Larsen (2020). Smooth operator: Modifying the Anhøj rules to improve runs analysis in statistical process control (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233920) Jacob Anhøj (2022). Quality Improvement Charts - An implementation of statistical process control charts for R (https://anhoej.github.io/qicharts2/articles/qicharts2.html)
