Statistical process control is a tool with multiple applications, and it can easily be adapted to various environments. When it comes to healthcare, itâ€™s one of the best ways to ensure that change is controlled precisely, and that the primary focus remains on improving the condition of each patient. Healthcare as a whole is a hectic field that always requires a fast response to changing situations, and statistical process control is one of the best ways to get the upper hand and ensure that youâ€™re in control of everything.
It can sometimes be extremely challenging to ensure that everything is under your control in a healthcare environment, but thankfully, applying statistical process control methods can significantly simplify that and allow you to get a good overview of the current situation.
Dealing with Varying Measurements
One of the biggest hurdles to overcome in controlling change in a healthcare environment is the variance youâ€™ll get from different measurements. This can sometimes be very hard to avoid, especially when those measurements are taken under completely different circumstances, which can severely impact their final values.
Large variations have to be accounted for properly, and you must ensure that the facility also has a good outlook for the future in terms of implementing those changes in its standard workflow. If something keeps getting measured with large differences in its values, it may be time to discuss your measurement practices with your specialists, or perhaps look into the way the measurements are being taken.
Sometimes it can be a matter of a different operator, in which case statistical process control methods can help you weed out those inconsistencies and focus on the main points of importance. In general, youâ€™ll find that applying some statistical analysis to your work in a healthcare facility can result in great improvements in the stability of the facility in the long run.
This is actually a point thatâ€™s heavily discussed in some cases when dealing with statistical process control methods, and you should familiarize yourself with the way operator differences can impact the results of various measurement processes.
Reconciling Different Reports
Sometimes youâ€™ll get a different report on the same situation from multiple different sources, all without anyone having made a mistake. This is normal in the work of most hospitals, especially when investigating a more complex condition which requires the patient to go through multiple different physicians. Youâ€™ll want to make sure that you have some statistical methods in place to minimize the difference in the final conclusions.
In case the reports are completely different and there is no obvious way to reconcile them, youâ€™ll be forced to rely on statistical analysis to figure out where the truth lies. In this case, having a lot of access to historic data and a good way to filter through it adequately can play a major role in improving your workflow.
While statistical process control methods can be great for minimizing those differences and eliminating the most harmful ones, you must still have a solid underlying foundation for collecting the data that will be used in those processes in the first place. Otherwise, you canâ€™t rely on its validity, and therefore you canâ€™t be sure that your statistical analysis is producing anything that makes sense in your current situation.
Keep in mind that in some cases, those variations can be the result of an improper diagnosis, or the incorrect use of certain tools, or something else along those lines. Catching those problems early on is important, so you must make sure that you have systems in place for that. In case you have to change something in the way the place is being run, you should know where to start with those changes.
There is a lot to gain from using statistical process control methods in a healthcare environment, and minimizing the harmful effects of variance on your daily work is going to be one of the biggest benefits on the table. The sooner you implement some adequate measures in your workflow, the better equipped youâ€™ll be to catch problems in the long run, and the better results youâ€™re going to see in terms of patient well-being and satisfaction.