Curing the Queue

Curing the Queue

Speaker: Hajo van Bockel

Title: Improving patient care in (L)UMC: Improving logistics and introducing decision support in surgery

Abstract: Developments in health care, both worldwide and in The Netherlands, aim at a) improving quality of care (e.g. less complications, newer and better treatments, safer care, greater patient satisfaction), b) better cost-effectiveness and c) improving the attractiveness for health care professionals, anticipating the problem of increased need for personnel in the near future. To cope with these problems, several approaches and techniques in clinical medicine are applied like: development of clinical paths, lean working, six sigma etc. Challenges are even more complex in University Medical Centers (UMC’s), with more variability in complex referred patients and a larger number of professionals involved in the multidisciplinary approach of diagnosis and therapy. At the same time research and education in an UMC demands more time as well. A recent report on the organization of health care from the RVZ, “Medisch-specialistische zorg in 20/20, advices on concentration and deconcentration of health care including an important role for the UMC’s. All these developments require an even greater need to streamline complex care processes in the UMC’s. The project “Benchmarking OR”, an initiative of the OR organizations of the eight UMC’s since 2004, is an example of an attempt to help streamlining OR organization to improve care and cost-effectiveness.

At the LUMC Dept of Surgery, two strategies have been applied focusing on a) quality and b) efficiency. First, logistics are improved e.g. of acute care patients, clinical paths are introduced and processes are being redesigned like admission and outpatient clinics. An example: logistics regarding admission of patients requiring acute surgical care have been greatly improved resulting e.g. in a reduction of admission out of the region. This was successful by a cooperation of the three regional hospitals involved which agreed to admit patients using an algorithm. Some of the non-technical, human, factors complicating this process will be discussed. Second, medical decision support was introduced. Although medical decisions should preferably be based on “evidence”, evidence is often not available for most of the decisions that should be made with and for individual patients. In cooperation with the Dept of Medical Decision at the LUMC, decision models were developed for coaching and advising patients on the most optimal diagnosis and therapy in complex situations. At the same time it provided surgeons with insight in complex decision making in uncertainty. Finally, it was helpful to assist in handling the complex processes of weighting quality of care and cost-effectiveness. With Monte Carlo Markov modeling, optimal strategies for follow-up and treatment of patients with vascular disease were calculated and compared. In the presentation the results some of the problems of acceptance of the results by both patients and surgeons will be discussed.