A simulation framework for mapping risks in clinical processes: the case of in-patient transfers

Bow-Tie Diagrams for Risk Management in Anaesthesia

Bow-tie analysis is a risk analysis and management tool that has been readily adopted into routine practice in many high reliability industries such as engineering, aviation and emergency services. However, it has received little exposure so far in healthcare. Nevertheless, its simplicity, versatility, and pictorial display may have benefits for the analysis of a range of healthcare risks, including complex and multiple risks and their interactions. Bow-tie diagrams are a combination of a fault tree and an event tree, which when combined take the shape of a bow tie. Central to bow-tie methodology is the concept of an undesired or ‘Top Event’, which occurs if a hazard progresses past all prevention controls. Top Events may also occasionally occur idiosyncratically. Irrespective of the cause of a Top Event, mitigation and recovery controls may influence the outcome. Hence the relationship of hazard to outcome can be viewed in one diagram along with possible causal sequences or accident trajectories. Potential uses for bow-tie diagrams in anaesthesia risk management include improved understanding of anaesthesia hazards and risks, pre-emptive identification of absent or inadequate hazard controls, investigation of clinical incidents, teaching anaesthesia risk management, and demonstrating risk management strategies to third parties when required.

A Systematic Literature Review of Agents Applied in Healthcare

Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions. A review of the most recent literature (2009-2014) of applications of agents in healthcare is discussed, and two classifications considering the main goal of the health systems as well as the main actors involved have been investigated. This review shows that the number of published works exhibits a growing interest of researchers in this field in a wide range of applications.

Applying operations research to optimize a novel population management system for cancer screening

OBJECTIVE

To optimize a new visit-independent, population-based cancer screening system (TopCare) by using operations research techniques to simulate changes in patient outreach staffing levels (delegates, navigators), modifications to user workflow within the information technology (IT) system, and changes in cancer screening recommendations.

MATERIALS AND METHODS

TopCare was modeled as a multiserver, multiphase queueing system. Simulation experiments implemented the queueing network model following a next-event time-advance mechanism, in which systematic adjustments were made to staffing levels, IT workflow settings, and cancer screening frequency in order to assess their impact on overdue screenings per patient.

RESULTS

TopCare reduced the average number of overdue screenings per patient from 1.17 at inception to 0.86 during simulation to 0.23 at steady state. Increases in the workforce improved the effectiveness of TopCare. In particular, increasing the delegate or navigator staff level by one person improved screening completion rates by 1.3% or 12.2%, respectively. In contrast, changes in the amount of time a patient entry stays on delegate and navigator lists had little impact on overdue screenings. Finally, lengthening the screening interval increased efficiency within TopCare by decreasing overdue screenings at the patient level, resulting in a smaller number of overdue patients needing delegates for screening and a higher fraction of screenings completed by delegates.

CONCLUSIONS

Simulating the impact of changes in staffing, system parameters, and clinical inputs on the effectiveness and efficiency of care can inform the allocation of limited resources in population management.

Discovery of clinical pathway patterns from event logs using probabilistic topic models

Discovery of clinical pathway (CP) patterns has experienced increased attention over the years due to its importance for revealing the structure, semantics and dynamics of CPs, and to its usefulness for providing clinicians with explicit knowledge which can be directly used to guide treatment activities of individual patients. Generally, discovery of CP patterns is a challenging task as treatment behaviors in CPs often have a large variability depending on factors such as time, location and patient individual. Based on the assumption that CP patterns can be derived from clinical event logs which usually record various treatment activities in CP executions, this study proposes a novel approach to CP pattern discovery by modeling CPs using mixtures of an extension to the Latent Dirichlet Allocation family that jointly models various treatment activities and their occurring time stamps in CPs. Clinical case studies are performed to evaluate the proposed approach via real-world data sets recording typical treatment behaviors in patient careflow. The obtained results demonstrate the suitability of the proposed approach for CP pattern discovery, and indicate the promise in research efforts related to CP analysis and optimization.

Creating an oversight infrastructure for electronic health record-related patient safety hazards

Electronic health records (EHRs) have potential quality and safety benefits. However, reports of EHR-related safety hazards are now emerging. The Office of the National Coordinator for Health Information Technology recently sponsored an Institute of Medicine committee to evaluate how health information technology use affects patient safety. In this article, we propose the creation of a national EHR oversight program to provide dedicated surveillance of EHR-related safety hazards and to promote learning from identified errors, close calls, and adverse events. The program calls for data gathering, investigation/analysis, and regulatory components. The first 2 functions will depend on institution-level EHR safety committees that will investigate all known EHR-related adverse events and near-misses and report them nationally using standardized methods. These committees should also perform routine safety self-assessments to proactively identify new risks. Nationally, we propose the long-term creation of a centralized, nonpartisan board with an appropriate legal and regulatory infrastructure to ensure the safety of EHRs. We discuss the rationale of the proposed oversight program and its potential organizational components and functions. These include mechanisms for robust data collection and analyses of all safety concerns using multiple methods that extend beyond reporting, multidisciplinary investigation of selected high-risk safety events, and enhanced coordination with other national agencies to facilitate broad dissemination of hazards information. Implementation of this proposed infrastructure can facilitate identification of EHR-related adverse events and errors and potentially create a safer and more effective EHR-based health care delivery system.