A Practical Guide for Building Collaborations Between Clinical Researchers and Engineers: Lessons Learned From a Multidisciplinary Patient Safety Project.

OBJECTIVES: Engineering and operations research have much to contribute to improve patient safety, especially within complex, highly regulated, and constantly evolving hospital environments. Despite new technologies, clinical checklists, and alarm systems, basic challenges persist that impact patient safety, such as how to improve communication between healthcare providers to prevent hospital-acquired complications. Because these collaborations are often new territory for both clinical researchers and engineers, the aim of the study was to prepare research teams that are embarking on similar collaborations regarding common challenges and training needs to anticipate while developing multidisciplinary teams. METHODS: Using a specific patient safety project as a case study, we share lessons learned and research training tools developed in our experience from recent multidisciplinary collaborations between clinical and engineering teams, which included many nonclinical undergraduate and graduate students. RESULTS: We developed a practical guide to describe anticipated challenges and solutions to consider for developing successful partnerships between engineering and clinical researchers. To address the extensive clinical, regulatory, data collection, and laboratory education needed for orienting multidisciplinary team members to join research projects, we also developed and shared a checklist for project managers as well as the training materials as adaptable resources to facilitate other teams' initiation into these types of collaborations. These resources are appropriate and tailorable for orienting both clinical and nonclinical team members, including faculty and staff as well as undergraduate and graduate students. CONCLUSIONS: We shared a practical guide to prepare teams for new multidisciplinary collaborations between clinicians and engineers.

Predicting Patient Treatment Deferrals at an Outpatient Chemotherapy Infusion Center: A Statistical Approach.

PURPOSE: Patients scheduled for outpatient infusion sometimes may be deferred for treatment after arriving for their appointment. This can be the result of a secondary illness, not meeting required bloodwork counts, or other medical complications. The ability to generate high-quality predictions of patient deferrals can be highly valuable in managing clinical operations, such as scheduling patients, determining which drugs to make before patients arrive, and establishing the proper staffing for a given day. METHODS: In collaboration with the University of Michigan Comprehensive Cancer Center, we have developed a predictive model that uses patient-specific data to estimate the probability that a patient will defer or not show for treatment on a given day. This model incorporates demographic, treatment protocol, and prior appointment history data. We tested a wide range of predictive models including logistic regression, tree-based methods, neural networks, and various ensemble models. We then compared the performance of these models, evaluating both their prediction error and their complexity level. RESULTS: We have tested multiple classification models to determine which would best determine whether a patient will defer or not show for treatment on a given day. We found that a Bayesian additive regression tree model performs best with the University of Michigan Comprehensive Cancer Center data on the basis of out-of-sample area under the curve, Brier score, and F1 score. We emphasize that similar statistical procedures must be taken to reach a final model in alternative settings. CONCLUSION: This article introduces the existence and selection process of a wide variety of statistical models for predicting patient deferrals for a specific clinical environment. With proper implementation, these models will enable clinicians and clinical managers to achieve the in-practice benefits of deferral predictions.

Predicting emergency department volume using forecasting methods to create a "surge response" for noncrisis events.

OBJECTIVES: This study investigated whether emergency department (ED) variables could be used in mathematical models to predict a future surge in ED volume based on recent levels of use of physician capacity. The models may be used to guide decisions related to on-call staffing in non-crisis-related surges of patient volume. METHODS: A retrospective analysis was conducted using information spanning July 2009 through June 2010 from a large urban teaching hospital with a Level I trauma center. A comparison of significance was used to assess the impact of multiple patient-specific variables on the state of the ED. Physician capacity was modeled based on historical physician treatment capacity and productivity. Binary logistic regression analysis was used to determine the probability that the available physician capacity would be sufficient to treat all patients forecasted to arrive in the next time period. The prediction horizons used were 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 12 hours. Five consecutive months of patient data from July 2010 through November 2010, similar to the data used to generate the models, was used to validate the models. Positive predictive values, Type I and Type II errors, and real-time accuracy in predicting noncrisis surge events were used to evaluate the forecast accuracy of the models. RESULTS: The ratio of new patients requiring treatment over total physician capacity (termed the care utilization ratio [CUR]) was deemed a robust predictor of the state of the ED (with a CUR greater than 1 indicating that the physician capacity would not be sufficient to treat all patients forecasted to arrive). Prediction intervals of 30 minutes, 8 hours, and 12 hours performed best of all models analyzed, with deviances of 1.000, 0.951, and 0.864, respectively. A 95% significance was used to validate the models against the July 2010 through November 2010 data set. Positive predictive values ranged from 0.738 to 0.872, true positives ranged from 74% to 94%, and true negatives ranged from 70% to 90% depending on the threshold used to determine the state of the ED with the 30-minute prediction model. CONCLUSIONS: The CUR is a new and robust indicator of an ED system's performance. The study was able to model the tradeoff of longer time to response versus shorter but more accurate predictions, by investigating different prediction intervals. Current practice would have been improved by using the proposed models and would have identified the surge in patient volume earlier on noncrisis days.