Reducing failures in daily medical practice: Healthcare failure mode and effect analysis combined with computer simulation

This study proposes a risk analysis approach for complex healthcare processes that combines qualitative and quantitative methods to improve patient safety. We combine Healthcare Failure Mode and Effect Analysis with Computer Simulation (HFMEA-CS), to overcome widely recognised HFMEA drawbacks regarding the reproducibility and validity of the outcomes due to human interpretation, and show the application of this methodology in a complex healthcare setting. HFMEA-CS is applied to analyse drug adherence performance in the surgical admission to discharge process of pheochromocytoma patients. The multidisciplinary team identified and scored the failure modes, and the simulation model supported in prioritisation of failure modes, uncovered dependencies between failure modes, and predicted the impact of measures on system behaviour. The results show that drug adherence, defined as the percentage of required drugs received at the right time, can be significantly improved with 12%, to reach a drug adherence of 99%. We conclude that HFMEA-CS is both a viable and effective risk analysis approach, combining strengths of expert opinion and quantitative analysis, for analysing human-system interactions in socio-technical systems. Practitioner summary: We propose combining Healthcare Failure Mode and Effects Analysis with Computer Simulation (HFMEA-CS) for prospective risk analysis of complex and potentially harmful processes, to prevent critical incidents from occurring. HFMEA-CS combines expert opinions with quantitative analyses, such that the results are more reliable, reproducible, and fitting for complex healthcare settings.

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multi-disciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample average approximation approach. Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital's problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

Multi-disciplinary planning in health care: a review

Multi-disciplinary planning in health care is an emerging research field that applies to many health care areas with similar underlying planning characteristics. We provide a review of the literature and describe cross-relations between different applications. We identify multiple fields to classify the literature upon. These fields relate to the system characteristics, decision characteristics, and applicability. The relevant papers for each of these fields are discussed, which provides a broad and thorough overview of the present research, and guides readers towards identifying the applicable literature for their research based on the characteristics of their problem. Furthermore, we disclose research gaps and present open challenges for further research.

Impact of diagnosis-to-treatment waiting time on survival in esophageal cancer patients - A population-based study in The Netherlands

BACKGROUND

The aim of this study was to determine whether the waiting time from diagnosis to treatment with curative intent for esophageal cancer impacts oncologic outcomes.

PATIENTS AND METHODS

All patients treated by esophagectomy for esophageal carcinoma in 2005-2013 were identified from the Netherlands Cancer Registry. Patients who underwent multimodality treatment and patients treated with surgery only were analyzed separately. Multivariable logistic regression analyses were performed to evaluate the impact of diagnosis-to-treatment waiting time on pT-status, pN-status, and R0 resection rates. Cox regression was applied to estimate the influence of waiting time on overall survival. Analyses were performed with the original scale and in three categorized groups of waiting time (≤5 weeks, 5-8 weeks, and >8 weeks) based on guidelines and previous studies.

RESULTS

Of 3839 patients, 2589 underwent multimodality treatment and 1250 were treated with surgery only. In both groups, pT-status, pN-status, and R0 resection rates were not significantly influenced by waiting time (p-values >0.05). Also, waiting time was not significantly associated with overall survival in the multimodality treatment group (5-8 weeks vs. ≤5 weeks, hazard ratio [HR] 1.12, p = 0.171; and >8 weeks vs. ≤5 weeks, HR 1.21, p = 0.167), nor in the surgery only group (5-8 weeks vs. ≤5 weeks, HR 0.92, p = 0.432; and >8 weeks vs. ≤5 weeks, HR 1.00, p = 0.973).

CONCLUSION

This large population-based cohort study demonstrates that longer waiting time from diagnosis to treatment in patients treated for esophageal cancer with curative intent does not negatively impact pT-status, pN-status, R0 resection rates, and overall survival.

Waiting Time from Diagnosis to Treatment has no Impact on Survival in Patients with Esophageal Cancer

Background

Waiting time from diagnosis to treatment has emerged as an important quality indicator in cancer care. This study was designed to determine the impact of waiting time on long-term outcome of patients with esophageal cancer who are treated with neoadjuvant therapy followed by surgery or primary surgery.

Methods

Patients who underwent esophagectomy for esophageal cancer at the University Medical Center Utrecht between 2003 and 2014 were included. Patients treated with neoadjuvant therapy followed by surgery and treated with primary surgery were separately analyzed. The influence of waiting time on survival was analyzed using Cox proportional hazard analyses. Kaplan–Meier curves for short (<8 weeks) and long (≥8 weeks) waiting times were constructed.

Results

A total of 351 patients were included; 214 received neoadjuvant treatment, and 137 underwent primary surgery. In the neoadjuvant group, the waiting time had no impact on disease-free survival (DFS) [hazard ratio (HR) 0.96, 95 % confidence interval (CI) 0.88–1.04; p = 0.312] or overall survival (OS) (HR 0.96, 95 % CI 0.88–1.05; p = 0.372). Accordingly, no differences were found between neoadjuvantly treated patients with waiting times of <8 and ≥8 weeks in terms of DFS (p = 0.506) and OS (p = 0.693). In the primary surgery group, the waiting time had no impact on DFS (HR 1.03, 95 % CI 0.95–1.12; p = 0.443) or OS (HR 1.06, 95 % CI 0.99–1.13; p = 0.108). Waiting times of <8 weeks versus ≥8 weeks did not result in differences regarding DFS (p = 0.884) or OS (p = 0.374).

Conclusions

In esophageal cancer patients treated with curative intent by either neoadjuvant therapy followed by surgery or primary surgery, waiting time from diagnosis to treatment has no impact on long-term outcome.

Predicting turnaround time reductions of the diagnostic track in the histopathology laboratory using mathematical modelling

BACKGROUND

Pathology departments face a growing volume of more and more complex testing in an era where healthcare costs tend to explode and short turnaround times (TATs) are expected. In contrast, the histopathology workforce tends to shrink, so histopathology employees experience high workload during their shifts. This points to the need for efficient planning of activities in the histopathology laboratory, to ensure an equal division of workload and low TATs, at minimum costs.

METHODS

The histopathology laboratory of a large academic hospital in The Netherlands was analysed using mathematical modelling. Data were collected from the Laboratory Management System to determine laboratory TATs and workload performance during regular working hours. A mixed integer linear programme (MILP) was developed to model the histopathology processes and to measure the expected performance of possible interventions in terms of TATs and spread of workload.

RESULTS

The MILP model predicted that tissue processing at specific moments during the day, combined with earlier starting shifts, can result in up to 25% decrease of TATs, and a more equally spread workload over the day.

CONCLUSIONS

Mathematical modelling can help to optimally organise the workload in the histopathology laboratory by predicting the performance of possible interventions before actual implementation. The interventions that were predicted by the model to have the highest performance have been implemented in the histopathology laboratory of University Medical Center Utrecht. Further research should be executed to collect empirical evidence and evaluate the actual impact on TAT, quality of work and employee stress levels.