Application of the Failure Mode and Effects Analysis (FMEA) to identify vulnerabilities and opportunities for improvement prior to implementing a computerized prescription order entry (CPOE) system in a university hospital oncology clinic

Introduction Prior to implementing a new computerized prescription order entry (CPOE) application, the potential risks associated with this system were assessed and compared to those of paper-based prescriptions. The goal of this study is to identify the vulnerabilities of the CPOE process in order to adapt its design and prevent these potential risks. Methods and materials Failure mode and effects analysis (FMEA) was used as a prospective risk-management technique to evaluate the chemotherapy medication process in a university hospital oncology clinic. A multidisciplinary team assessed the process and compared the critical steps of a newly developed CPOE application versus paper-based prescriptions. The potential severity, occurrence and detectability were assessed prior to the implementation of the CPOE application in the clinical setting. Results The FMEA led to the identification of 24 process steps that could theoretically be vulnerable, therefore called failure modes. These failure modes were grouped into four categories of potential risk factors: prescription writing, patient scheduling, treatment dispensing and patient follow-up. Criticality scores were calculated and compared for both strategies. Three failure modes were prioritized and led to modification of the CPOE design. Overall, the CPOE pathway showed a potential risk reduction of 51% compared to paper-based prescriptions. Conclusion FMEA was found to be a useful approach to identify potential risks in the chemotherapy medication process using either CPOE or paper-based prescriptions. The e-prescription mode was estimated to result in less risk than the traditional paper mode.

Service improvement in a nurse-led clinic for extracorporeal photopheresis

The skin tumour unit at one London hospital has been providing extracorporeal photopheresis (ECP) treatment since 2012, using an online fully integrated system. This report summarises 9 years of continuous process improvement, which has enabled the team to meet the growing demand for ECP treatments for cutaneous T-cell lymphoma (CTCL) and graft-versus-host disease (GvHD) patients. The unit formed a partnership with a lean-management company to go through the process of evaluation of capacity constraints, design layout and patient scheduling. Methods: Increased capacity year-on-year and over the 9-year period was calculated based on data collected from records. The authors reviewed the nurse staffing resources allocated for ECP treatments over the same period, and financial value created by the continuous improvement (additional number of treatments multiplied by the national tariff for ECP treatments). Results: In 2012 the average number of ECP treatments per nurse per week was 11. With the implementation of the new planning tool, and improved working practices, the average number of treatments per nurse per week has more than doubled to 23. Nurse staffing was maintained at 4 nurses per shift to deliver ECP treatments. The unit recorded additional revenue of approximately £3.2 million in 2020 compared with 2012. Conclusions: The team has successfully increased the capacity of the service to deliver treatments without incurring any additional nursing costs, resulting in more patients with CTCL and GvHD being able to access ECP treatment and a cost benefit for the Trust. The service continues in its mission to grow and provide a superior patient experience.

Integrated Multiresource Capacity Planning and Multitype Patient Scheduling

The joint optimization problem of multiresource capacity planning and multitype patient scheduling under uncertain demands and random capacity consumption poses a significant computational challenge. The common practice in solving this problem is to first identify capacity levels and then determine patient scheduling decisions separately, which typically leads to suboptimal decisions that often result in ineffective outcomes of care. In order to overcome these inefficiencies, in this paper, we propose a novel two-stage stochastic optimization model that integrates these two decisions, which can lower costs by exploring the coupling relationship between patient scheduling and capacity configuration. The patient scheduling problem is modeled as a Markov decision process. We first analyze the properties for the multitype patient case under specific assumptions and then establish structural properties of the optimal scheduling policy for the one-type patient case. Based on these findings, we propose optimal solution algorithms to solve the joint optimization problem for this special case. Because it is intractable to solve the original two-stage problem for a general multitype system with large state space, we propose a heuristic policy and a two-stage stochastic mixed-integer programming model solved by the Benders decomposition algorithm, which is further improved by combining an approximate linear program and the look-ahead strategy. To illustrate the efficiency of our approaches and draw managerial insights, we apply our solutions to a data set from the day surgery center of a large public hospital in Shanghai, China. The results show that the joint optimization of capacity planning and patient scheduling could significantly improve the performance. Furthermore, our model can be applied to a rolling-horizon framework to optimize dynamic patient scheduling decisions. Through extensive numerical analyses, we demonstrate that our approaches yield good performances, as measured by the gap against an upper bound, and that these approaches outperform several benchmark policies. Summary of Contribution: First, this paper investigates the joint optimization problem of multiresource capacity planning and multitype patient scheduling under uncertain demands and random capacity consumption, which poses a significant computational challenge. It belongs to the scope of computing and operations research. Second, this paper formulates a mathematical model, establishes optimality properties, proposes solution algorithms, and performs extensive numerical experiments using real-world data. This work includes aspects of dynamic stochastic control, computing algorithms, and experiments. Moreover, this paper is motivated by a practical problem (joint management of capacity planning and patient scheduling in the day surgery center) in our cooperative hospital, which is also key to numerous other applications, for example, the make-to-order manufacturing systems and computing facility systems. By using the optimality properties, solution algorithms, and management insights derived in this paper, the practitioners can be equipped with a decision support tool for efficient and effective operation decisions.

Impact of COVID-19 on Breast Imaging Practice Operations and Recovery Efforts: A North American Study

Objective To assess the impact of the COVID-19 pandemic on breast imaging facilities’ operations and recovery efforts across North America. Methods A survey on breast imaging facilities’ operations and strategies for recovery during the COVID-19 pandemic was distributed to the membership of the Society of Breast Imaging and National Consortium of Breast Centers from June 4, 2020, to July 14, 2020. A descriptive summary of responses was performed. Comparisons were made between demographic variables of respondents and questions of interest using a Pearson chi-square test. Results There were 473 survey respondents (response rate of 13%). The majority of respondents (70%; 332/473) reported 80%–100% breast imaging volume reduction, with 94% (447/473) reporting postponement of screening mammography. The majority of respondents (97%; 457/473) continued to perform biopsies. There were regional differences in safety measures taken for staff (P = 0.004), with practices in the West more likely reporting no changes in the work environment compared to other regions. The most common changes to patients’ experience included spacing out of furniture in waiting rooms (94%; 445/473), limiting visitors (91%; 430/473), and spacing out appointments (83%). A significantly higher proportion of practices in the Northeast (95%; 104/109) initiated patient scheduling changes compared to other regions (P = 0.004). Conclusion COVID-19 had an acute impact on breast imaging facilities. Although common national operational patterns emerged, geographic variability was notable in particular in recovery efforts. These findings may inform future best practices for delivering breast imaging care amid the ongoing and geographically shifting COVID-19 pandemic.

Big data simulations for capacity improvement in a general ophthalmology clinic

Purpose Long total waiting times (TWT) experienced by patients during a clinic visit have a significant adverse effect on patient’s satisfaction. Our aim was to use big data simulations of a patient scheduling calendar and its effect on TWT in a general ophthalmology clinic. Based on the simulation, we implemented changes to the calendar and verified their effect on TWT in clinical practice. Design and methods For this retrospective simulation study, we generated a discrete event simulation (DES) model based on clinical timepoints of 4.401 visits to our clinic. All data points were exported from our clinical warehouse for further processing. If not available from the electronic health record, manual time measurements of the process were used. Various patient scheduling models were simulated and evaluated based on their reduction of TWT. The most promising model was implemented into clinical practice in 2017. Results During validation of our simulation model, we achieved a high agreement of mean TWT between the real data (229 ± 100 min) and the corresponding simulated data (225 ± 112 min). This indicates a high quality of the simulation model. Following the simulations, a patient scheduling calendar was introduced, which, compared with the old calendar, provided block intervals and extended time windows for patients. The simulated TWT of this model was 153 min. After implementation in clinical practice, TWT per patient in our general ophthalmology clinic has been reduced from 229 ± 100 to 183 ± 89 min. Conclusion By implementing a big data simulation model, we have achieved a cost-neutral reduction of the mean TWT by 21%. Big data simulation enables users to evaluate variations to an existing system before implementation into clinical practice. Various models for improving patient flow or reducing capacity loads can be evaluated cost-effectively.