Project #123:
PINCER change detection

Medication related harm describes any error related to prescribing that may cause harm to a patient (e.g., prescribing a medicine at the wrong dose or not carrying out a necessary blood test before prescribing a medicine). Some important indicators of medication related harm have been designed as part of the PINCER programme, which helps pharmacists find instances of “potentially hazardous prescribing” in the electronic health records of their practices, so that clinically important errors can be reduced.

We are interested in understanding whether these PINCER hazardous prescribing indicators were affected by the disruption of primary care services due to COVID-19 and to what extent those rates of medication related harm have recovered since primary care services were restored. Once we have an overall understanding of how rates have changed, we would like to explore the data in more detail by looking at the rates for each practice over time and applying data science methodologies to identify any short- and long-term patterns of change. We will be asking questions like: are there any practices that did not experience any COVID-19 disruption in their PINCER indicators? Were some practices able to recover faster than others? Should we find any practices that correspond to these patterns, we will explore whether these practices have any characteristics in common. This is important as it may help us identify best practice and describe the most effective and efficient response to primary care disruption in the future.

We do consider this project a “proof of concept”, in which we demonstrate the utility of our approach. The intention in the longer term is to use the same analysis pipeline across all clinical areas to identify variation in change over time and expose factors associated with positive change over time.