Towards a General Framework for Systems Analysis of Inefficiencies Along the Pharmacological Treatment Chain

Abstract: In order for a medication treatment to be considered successful, several roles and functions along the pharmacological treatment chain must function and cooperate effectively. The chain can most easily be described as five transitions; diagnosis, prescription treatment, dispensing, drug use and finally results and follow-up. Unfortunately, there are many problems and inefficiencies in the pharmacological drug chain. Unfortunately, those who study medication errors and their solutions have focused on individual parts of the pharmacological treatment system. However, for this reason, this study aims to develop a general framework for system analysis of inefficiencies along the pharmacological treatment chain. Due to the size of the problem, this project focused on medication adherence. Adherence can be defined as to what extent the patient follows the medication treatment plan. Adherence has many known problems and difficulties, among other things, it has major financial consequences. It can also be difficult to measure compliance, and there is no recognized perfect method. A system dynamic model is a theoretical image of a real system or object, which is a model used to understand the nonlinear behavior of complex systems. These models are useful when considering interventions and their effects when there are complex relationships. The project started with a literature study, and then went into data collection. Here, a search design and refinements were designed to find relevant articles. Once the articles were selected, the data was compiled from the articles and the analysis began. Here, factors and effects on adherence were identified as well as other interesting information from the articles. When the information was compiled and analyzed, the system dynamic model was created. The model was then sent via email to experts in the field to validation and revise the model. During the data collection, 23 relevant articles were found, compiled into 38 factors associated with compliance. In addition to these factors, 8 were excluded because they were too disease-specific or too ambiguous in their effect of adherence. The various articles studied many different chronic diseases, but hypertension was the most common. How adherence was measured in the articles also varied greatly, however, some form of self-report or questionnaire was most common method used. Three out of seven experts responded to the sent-out model and provided valuable comments. Although these are not sufficient to validate the model, their views showed that a validation can be designed in this way. The model would have to be sent to a larger set of experts and stakeholders, but because these experts are recognized in their fields, it gave weight to the results even though they were few reviewers. With the support of the literature and the experts’ statement, it was concluded that this model provides a good foundation and structure to continue to build upon. In addition, the model has proven to have many key relationships and cornerstones with important and relevant factors. It is also concluded that it is possible to translate the model into quantitative patterns, which is based on the fact that the factor itself can be translated quantitatively. Overall, it is also finally concluded that the model created in this project could be of great use in future projects when working towards a framework for system analysis of inefficiencies along the pharmacological treatment chain.