Patient Flow: Reducing Delay in Healthcare Delivery

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

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Machine learning for medical ultrasound: status, methods, and future opportunities.

The aims of this chapter are: (1) To provide a comprehensive view of the stages of the evolution of simulation. (2) To emphasize the phenomenal developments in many aspects of simulation which made it an important and even a vital infrastructure for many disciplines. (3) To underline the fact that the transition from “model-based” paradigm to “simulation-based” paradigm may be beneficial for many disciplines. In Sect. 1.2, references for a systematic collection and a critical review of about 100 definitions of simulation as well as a comprehensive and integrative definition of simulation are given. In Sect. 1.3, the reasons simulation is used are clarified. These reasons make simulation very useful for many disciplines. In Sect. 1.4, nine aspects of the evolution of simulation are clarified including simulation-based disciplines. In Sect. 1.5, many disciplines for which simulation-based paradigm would make them much more powerful and efficient are elaborated. The authors affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions or viewpoints expressed by the author. Approved for public release: Case: SM_001 T. Ören (&) University of Ottawa, Ottawa, Canada e-mail: oren@eecs.uOttawa.ca S. Mittal The MITRE Corporation, Virginia, USA e-mail: smittal@mitre.org U. Durak German Aerospace Center (DLR), Braunschweig, Germany e-mail: umut.durak@dlr.de © Springer International Publishing AG 2017 S. Mittal et al. (eds.), Guide to Simulation-Based Disciplines, Simulation Foundations, Methods and Applications, DOI 10.1007/978-3-319-61264-5_1 3

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Guide to Simulation-Based Disciplines

Comparison of hemodialysis arteriovenous fistula blood flow rates measured by Doppler ultrasound and phase-contrast magnetic resonance imaging.

Objective:We describe health care simulation, designed primarily for training, and provide examples of how human factors experts can collaborate with health care professionals and simulationists—experts in the design and implementation of simulation—to use contemporary simulation to improve health care delivery.Background:The need—and the opportunity—to apply human factors expertise in efforts to achieve improved health outcomes has never been greater. Health care is a complex adaptive system, and simulation is an effective and flexible tool that can be used by human factors experts to better understand and improve individual, team, and system performance within health care.Method:Expert opinion is presented, based on a panel delivered during the 2014 Human Factors and Ergonomics Society Health Care Symposium.Results:Diverse simulators, physically or virtually representing humans or human organs, and simulation applications in education, research, and systems analysis that may be of use to human factors e...

https://journals.sagepub.com/doi/pdf/10.1177/0018720816650781

Leveraging Health Care Simulation Technology for Human Factors Research Closing the Gap Between Lab and Bedside

Purpose: To use feed-forward active contours (snakes) to track and measure brachial artery vasomotion on ultrasound images recorded in both transverse and longitudinal views; and to compare the algorithm's performance in each view. Methods: Longitudinal and transverse view ultrasound image sequences of 45 brachial arteries were segmented by feed-forward active contour (FFAC). The segmented regions were used to measure vasomotion artery diameter, cross-sectional area, and distention both as peak-to-peak diameter and as area. ECG waveforms were also simultaneously extracted frame-by-frame by thresholding a running finite-difference image between consecutive images. The arterial and ECG waveforms were compared as they traced each phase of the cardiac cycle. Results: FFAC successfully segmented arteries in longitudinal and transverse views in all 45 cases. The automated analysis took significantly less time than manual tracing, but produced superior, well-behaved arterial waveforms. Automated arterial measurements also had lower interobserver variability as measured by correlation, difference in mean values, and coefficient of variation. Although FFAC successfully segmented both the longitudinal and transverse images, transverse measurements were less variable. The cross-sectional area computed from the longitudinal images was 27% lower than the area measured from transverse images, possibly due to the compression of the artery along the image depthmore » by transducer pressure. Conclusions: FFAC is a robust and sensitive vasomotion segmentation algorithm in both transverse and longitudinal views. Transverse imaging may offer advantages over longitudinal imaging: transverse measurements are more consistent, possibly because the method is less sensitive to variations in transducer pressure during imaging.« less

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Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound

Background Maternal–fetal medicine is a rapidly growing field requiring collaboration from many subspecialties. We provide an evidence-based estimate of capacity needs for our clinic, as well as demonstrate how simulation can aid in capacity planning in similar environments. Methods A Discrete Event Simulation of the Center for Fetal Diagnosis and Treatment and Special Delivery Unit at The Children's Hospital of Philadelphia was designed and validated. This model was then used to determine the time until demand overwhelms inpatient bed availability under increasing capacity. Findings No significant deviation was found between historical inpatient censuses and simulated censuses for the validation phase (p = 0.889). Prospectively increasing capacity was found to delay time to balk (the inability of the center to provide bed space for a patient in need of admission). With current capacity, the model predicts mean time to balk of 276 days. Adding three beds delays mean time to first balk to 762 days; an additional six beds to 1,335 days. Conclusion Providing sufficient access is a patient safety issue, and good planning is crucial for targeting infrastructure investments appropriately. Computer-simulated analysis can provide an evidence base for both medical and administrative decision making in a complex clinical environment.

Capacity planning for maternal-fetal medicine using discrete event simulation.

The object of this study was to utilize a novel feed-forward active contour (FFAC) algorithm to find a reproducible technique for analysis of brachial artery reactivity. Flow-mediated dilation (FMD) is an important marker of vascular endothelial function but has not been adopted for widespread clinical use given its technical limitations, including inter-observer variability and differences in technique across clinical sites. We developed a novel FFAC algorithm with the goal of validating a more reliable standard. Forty-six healthy volunteers underwent FMD measurement according to the standard technique. Ultrasound videos lasting 5-10 seconds each were obtained pre-cuff inflation and at minutes 1 through 5 post-cuff deflation in longitudinal and transverse views. Automated segmentation using the FFAC algorithm with initial boundary definition from three different observers was used to analyze the images to measure diameter/cross-sectional area over the cardiac cycle. The %FMD was calculated for average, minimum, and maximum diameters/areas. Using the FFAC algorithm, the population-specific coefficient of variation (CV) at end-diastole was 3.24% for transverse compared to 9.96% for longitudinal measurements; the subject-specific CV was 15.03% compared to 57.41%, respectively. For longitudinal measurements made via the conventional method, the population-specific CV was 4.77% and subject-specific CV was 117.79%. The intraclass correlation coefficient (ICC) for transverse measurements was 0.97 (95% CI: 0.95-0.98) compared to 0.90 (95% CI: 0.84-0.94) for longitudinal measurements with FFAC and 0.72 (95% CI: 0.51-0.84) for conventional measurements. In conclusion, transverse views using the novel FFAC method provide less inter-observer variability than traditional longitudinal views. Improved reproducibility may allow adoption of FMD testing in a clinical setting. The FFAC algorithm is a robust technique that should be evaluated further for its ability to replace the more limited conventional technique for measurement of FMD.

https://journals.sagepub.com/doi/pdf/10.1177/1358863X16634194

Courtney B. Reamer

Papers

Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound

Capacity planning for maternal-fetal medicine using discrete event simulation.

Feed-forward active contour analysis for improved brachial artery reactivity testing