Quantifying the Resilience of a Healthcare System: Entropy and Network Science Perspectives

In this study, we consider the human body and the healthcare system as two complex networks and use theories regarding entropy, requisite variety, and network centrality metrics with resilience to assess and quantify the strengths and weaknesses of healthcare systems. Entropy is used to quantify the uncertainty and variety regarding a patient's health state. The extent of the entropy defines the requisite variety a healthcare system should contain to be able to treat a patient safely and correctly. We use network centrality metrics to visualize and quantify the healthcare system as a network and assign the strengths and weaknesses of the network and of individual agents in the network. We apply organization design theories to formulate improvements and explain how a healthcare system should adjust to create a more robust and resilient healthcare system that is able to continuously deal with variations and uncertainties regarding a patient's health, despite possible stressors and disturbances at the healthcare system. In this article, these concepts and theories are explained and applied to a fictive and a real-life example. We conclude that entropy and network science can be used as tools to quantify the resilience of healthcare systems.

Recovery 3 and 12 months after hysterectomy: epidemiology and predictors of chronic pain, physical functioning, and global surgical recovery

Chronic postsurgical pain (CPSP) is 1 important aspect of surgical recovery. To improve perioperative care and postoperative recovery knowledge on predictors of impaired recovery is essential. The aim of this study is to assess predictors and epidemiological data of CPSP, physical functioning (SF-36PF, 0-100), and global surgical recovery (global surgical recovery index, 0-100%) 3 and 12 months after hysterectomy for benign indication.A prospective multicenter cohort study was performed. Sociodemographic, somatic, and psychosocial data were assessed in the week before surgery, postoperatively up to day 4, and at 3- and 12-month follow-up. Generalized linear model (CPSP) and linear-mixed model analyses (SF-36PF and global surgical recovery index) were used. Baseline data of 468 patients were collected, 412 (88%) patients provided data for 3-month evaluation and 376 (80%) patients for 12-month evaluation.After 3 and 12 months, prevalence of CPSP (numeric rating scale ≥ 4, scale 0-10) was 10.2% and 9.0%, respectively, SF-36PF means (SD) were 83.5 (20.0) and 85.9 (20.2), global surgical recovery index 88.1% (15.6) and 93.3% (13.4). Neuropathic pain was reported by 20 (5.0%) patients at 3 months and 14 (3.9%) patients at 12 months. Preoperative pain, surgery-related worries, acute postsurgical pain on day 4, and surgery-related infection were significant predictors of CPSP. Baseline level, participating center, general psychological robustness, indication, acute postsurgical pain, and surgery-related infection were significant predictors of SF-36PF. Predictors of global surgical recovery were baseline expectations, surgery-related worries, American Society of Anesthesiologists classification, type of anesthesia, acute postsurgical pain, and surgery-related infection.Several predictors were identified for CPSP, physical functioning, and global surgical recovery. Some of the identified factors are modifiable and optimization of patients' preoperative pain status and psychological condition as well as reduction of acute postsurgical pain and surgery-related infection may lead to improvement of outcome.