Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial.

HIGHLIGHTS: This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.

Inverse optimization on hierarchical networks: an application to breast cancer clinical pathways.

Clinical pathways are standardized processes that outline the steps required for managing a specific disease. However, patient pathways often deviate from clinical pathways. Measuring the concordance of patient pathways to clinical pathways is important for health system monitoring and informing quality improvement initiatives. In this paper, we develop an inverse optimization-based approach to measuring pathway concordance in breast cancer, a complex disease. We capture this complexity in a hierarchical network that models the patient's journey through the health system. A novel inverse shortest path model is formulated and solved on this hierarchical network to estimate arc costs, which are used to form a concordance metric to measure the distance between patient pathways and shortest paths (i.e., clinical pathways). Using real breast cancer patient data from Ontario, Canada, we demonstrate that our concordance metric has a statistically significant association with survival for all breast cancer patient subgroups. We also use it to quantify the extent of patient pathway discordances across all subgroups, finding that patients undertaking additional clinical activities constitute the primary driver of discordance in the population.

A new decision making model for diagnosing acute appendicitis among non-pregnant women and its comparison with alvarado scoring system.

Background: Although acute appendicitis is a common problem, it remains a difficult diagnosis to establish, particularly among females of reproductive age. The present study was conducted to devise a new decision making model for diagnosing acute appendicitis in non-pregnant women. Methods: The present study was a retrospective study consisting of women who had undergone an appendectomy between 2007 and 2015 at the emergency department of Imam Hossein Medical Center, Tehran, Iran. The inclusion criteria were being a female, presenting with abdominal pain, being a suspected case of acute appendicitis, and undergoing an emergency appendectomy. A classification and regression tree (CART) analysis was performed to partition exam and laboratory data obtained from these patients into homogeneous groups in order to develop a prediction rule for appendicitis diagnosis. Results: The study population included 433 non pregnant women who underwent emergency operations with a preliminary diagnosis of acute appendicis. Out of these patients, 295 patients (68.1%) were appendicitis positive based on the pathology exam results, while 138 patients had a normal appendix, indicating a negative appendectomy rate of 31.8%. The final devised CART model included hemoglobin level, PMN count, age, and history of abdominal incision and yielded a sensitivity of 82.7% and specificity of 55.8%, which were better than Alvarado prediction results for the Asian population. Conclusion: We have devised a simple and cost effective prediction model for predicting the outcome among non-pregnant women undergoing emergency appendectomy operation with good sensitivity and specificity compared to the Alvarado model.