Systems approach to assessing and improving local human research Institutional Review Board performance.

ObjectiveTo quantifying the interdependency within the regulatory environment governing human subject research, including Institutional Review Boards (IRBs), federally mandated Medicare coverage analysis and contract negotiations.MethodsOver 8000 IRB, coverage analysis and contract applications initiated between 2013 and 2016 were analyzed using traditional and machine learning analytics for a quality improvement effort to improve the time required to authorize the start of human research studies.ResultsStaffing ratios, study characteristics such as the number of arms, source of funding and number and type of ancillary reviews significantly influenced the timelines. Using key variables, a predictive algorithm identified outliers for a workflow distinct from the standard process. Improved communication between regulatory units, integration of common functions, and education outreach improved the regulatory approval process.ConclusionsUnderstanding and improving the interdependencies between IRB, coverage analysis and contract negotiation offices requires a systems approach and might benefit from predictive analytics

Customizable Adaptive User Interfaces Implementation in Control and Learning Automated Systems as Way of Increasing their Reliability and Efficiency

In modern automated systems users are often facing the information overload problem because of ever increasing volumes of information requiring treatment in short time. Working in these conditions affects the system operator’s work quality and the systems’ reliability. One possible approach to solving the information overload problem is to create personalized interfaces that take into account the user’s information management particularities. System operator’s features, which determine their preferred information representation shape and pace, form the user’s cognitive portrait. Cognitive portrait is built as a result of user interaction with the software diagnostic tools that are based on the cognitive psychology methods. The effect of using personalized user interface in an automated system can be estimated by quantifying how exactly a reduction in user response time to critical events affects the reliability and efficiency of the system. To do this, the formulae in the theory of reliability of complex automated systems are used, showing the dependency between the system reliability and critical event response time

Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions

Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times

Quality improvement: understanding the adoption and diffusion of digital technologies related to surgical performance

Purpose Quantifying the performance level of surgeons with digital virtual reality (VR) simulators can help ensure that quality requirements in healthcare are met. In order to better understand integration amongst quality principles, practices and technologies in the adoption and diffusion of VR simulators, the authors applied a technological innovation system (TIS) framework. The purpose of this study is to understand how the adoption and diffusion of VR surgical simulators in a Swedish healthcare context is influenced by various system factors. Design/methodology/approach In this study, single-case holistic design based on innovation system theory was used to analyse the adoption of digital quality technologies related to surgical performance in Swedish hospitals. The case employs a mixed methods approach triangulating data longitudinally from published documents and expert interviews. Findings Adoption of digital technologies regarding surgical performance is restricted by system factors relating to inconsistent normative and regulatory requirements for quantified performance criteria to judge surgical expertise. Addressing these systems\u27 weaknesses with evidence-based training programmes can have a significant impact on the further development of the innovation system and can ultimately affect healthcare reliability and quality. Originality/value This paper explores quality management (QM) challenges in the context of digital transformation in healthcare. The paper attempts to fill the gap for TIS studies in a healthcare context and highlight the role of innovation function strength along the value chain and in relation to technology cycles to increase the understanding of adoption of digital technologies relating to surgical performance