MemberFebruary 6, 2015 at 5:24 pm
Why there is no foolproof method?
Thanks for the heads up on this post, Shirin. The reason we have not been able to do a better job of figuring out who will get a hernia, whether a hernia will cause a problem within someone’s lifetime and what is the best treatment option for each patient (lap vs open, mesh vs no mesh, etc.) is because those are complex biologic issues/problems and until now, in healthcare, we have only been using reductionist scientific methods to understand our biologic world. In reductionist science, we try to prove or disprove a hypothesis. This is a fallacy in attempting to understand a complex system- there is no one right answer. In complex systems science, the scientific method does not attempt to prove or disprove, but to improve outcomes over time through a better and better understanding of whatever process is being measured. The tools used include continuous quality improvement principles (actually should be termed continuous value improvement because costs have to be measured as well) and non-linear statistical analysis (like factor analysis, which produces weighted correlations). Ultimately, it is the same thing as the information science that is being applied in other industries, but I think is desperately needed in healthcare. The process of improvement involves collecting data that matters to the outcomes of a definable patient process (in this case, inguinal hernia) and to collect outcome measures that define value. Then as data accumulates, the non-linear analyses can lead to predictive analytics that get better and better at predicting: who will get a hernia?, whose hernia will become incarcerated?, who will do better (or worse) with a mesh (or non-mesh) repair? We have started to do this and it will be the future of our healthcare system- just very messy at first and we still have a lot of people in healthcare firmly rooted (and benefiting from) the reductionist principles for research and for the design of our organizations. I hope this helps- thanks again.