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438 results on '"Harrison, James"'

3. Evolution of radiation profiles in a strongly baffled divertor on MAST Upgrade

Author

Federici, Fabio, Reinke, Matthew L., Lipschultz, Bruce, Lovell, Jack J., Verhaegh, Kevin, Cowley, Cyd, Kryjak, Mike, Ryan, Peter, Thornton, Andrew J., Harrison, James R., Peterson, Byron J., Lomanowski, Bartosz, Lore, Jeremy D., and Damizia, Yacopo

Subjects
Physics - Plasma Physics
Abstract

Plasma detachment involves interactions of the plasma with impurities and neutral particles, leading to significant losses of plasma power, momentum, and particles. Accurate mapping of plasma emissivity in the divertor and X-point region is essential for assessing the relationship between particle flux and radiative detachment. The recently validated InfraRed Video Bolometer (IRVB) diagnostic, in MAST-U enables this mapping with higher spatial resolution than more established methods like resistive bolometers. In previous preliminary work, the evolution of radiative detachment was characterised in L-mode (power entering the scrape-off layer, PSOL ~0.4MW). With a conventional divertor the inner leg consistently detached ahead of the outer leg, and radiative detachment preceded particle flux detachment. This work presents results also from the third MAST-U experimental campaign, fuelled from the low field side instead of the high field side, including Ohmic and beam heated L-mode shots (with a power exiting the core up to PSOL ~1-1.5MW). The radiation peak moves upstream from the target at lower upstream densities than the ion target flux roll-over (typically considered the detachment onset), while the inner leg detaches before the outer one. The movement of the radiation is in partial agreement with the expectations from the DLS model, predicting a sudden shift from the target to the X-point. The energy confinement is found to be related to detachment, but there seems to be some margin between the radiation on the inner leg reaching the X-point and confinement being affected, a beneficial characteristic if it could be extrapolated to future reactors. For increasing PSOL the particle flux roll over is almost unaffected, while radiative detachment occurs at higher density in both legs, but much higher on the outer, suggesting an uneven distribution of the power exiting the core., Comment: Submitted to Nuclear Materials and Energy

Published
2024

7. Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments

Author

Nayak, Siddharth, Orozco, Adelmo Morrison, Have, Marina Ten, Thirumalai, Vittal, Zhang, Jackson, Chen, Darren, Kapoor, Aditya, Robinson, Eric, Gopalakrishnan, Karthik, Harrison, James, Ichter, Brian, Mahajan, Anuj, and Balakrishnan, Hamsa

Subjects
Computer Science - Robotics, Computer Science - Multiagent Systems
Abstract

The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tasks with minimal tuning, acting as a compressed knowledge base. However, LMs in their standard form face challenges with long-horizon tasks, particularly in partially observable multi-agent settings. We propose an LM-based Long-Horizon Planner for Multi-Agent Robotics (LLaMAR), a cognitive architecture for planning that achieves state-of-the-art results in long-horizon tasks within partially observable environments. LLaMAR employs a plan-act-correct-verify framework, allowing self-correction from action execution feedback without relying on oracles or simulators. Additionally, we present MAP-THOR, a comprehensive test suite encompassing household tasks of varying complexity within the AI2-THOR environment. Experiments show that LLaMAR achieves a 30% higher success rate compared to other state-of-the-art LM-based multi-agent planners., Comment: 27 pages, 4 figures, 5 tables

Published
2024

9. Use of Muscle Relaxants After Surgery in Traditional Medicare Part D Enrollees.

Author

Bongiovanni, Tasce, Gan, Siqi, Finlayson, Emily, Ross, Joseph, Harrison, James, Boscardin, John, and Steinman, Michael

Subjects
Humans, Aged, Medicare Part D, Male, United States, Female, Pain, Postoperative, Aged, 80 and over, Retrospective Studies, Practice Patterns, Physicians, Drug Prescriptions
Abstract

BACKGROUND: Surgeons have come under increased scrutiny for postoperative pain management, particularly for opioid prescribing. To decrease opioid use but still provide pain control, nonopioid medications such as muscle relaxants are being used, which can be harmful in older adults. However, the prevalence of muscle relaxant prescribing, trends in use over time, and risk of prolonged use are unknown. STUDY DESIGN: Using a 20% representative Medicare sample, we conducted a retrospective analysis of muscle relaxant prescribing to patients ≥ 65 years of age. We merged patient data from Medicare Carrier, MedPAR, and Outpatient Files with Medicare Part D for the years 2013-2018. A total of 14 surgical procedures were included to represent a wide range of anatomic regions and specialties. RESULTS: The study cohort included 543,929 patients. Of the cohort, 8111 (1.5%) received a new muscle relaxant prescription at discharge. Spine procedures accounted for 12% of all procedures but 56% of postoperative prescribing. Overall, the rate of prescribing increased over the time period (1.4-2.0%, p < 0.001), with increases in prescribing primarily in the spine (7-9.6%, p < 0.0001) and orthopedic procedure groups (0.9-1.4%, p < 0.0001). Of patients discharged with a new muscle relaxant prescription, 10.7% had prolonged use. CONCLUSIONS: The use of muscle relaxants in the postoperative period for older adults is low, but increasing over time, especially in ortho and spine procedures. While pain control after surgery is crucial, surgeons should carefully consider the risks of muscle relaxant use, especially for older adults who are at higher risk for medication-related problems.

Published
2024

10. Variational Bayesian Last Layers

Author

Harrison, James, Willes, John, and Snoek, Jasper

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

We introduce a deterministic variational formulation for training Bayesian last layer neural networks. This yields a sampling-free, single-pass model and loss that effectively improves uncertainty estimation. Our variational Bayesian last layer (VBLL) can be trained and evaluated with only quadratic complexity in last layer width, and is thus (nearly) computationally free to add to standard architectures. We experimentally investigate VBLLs, and show that they improve predictive accuracy, calibration, and out of distribution detection over baselines across both regression and classification. Finally, we investigate combining VBLL layers with variational Bayesian feature learning, yielding a lower variance collapsed variational inference method for Bayesian neural networks., Comment: International Conference on Learning Representations (ICLR) 2024

Published
2024

11. Two-dimensional inference of divertor plasma characteristics: advancements to a multi-instrument Bayesian analysis system

Author

Greenhouse, Daniel, Bowman, Chris, Lipschultz, Bruce, Verhaegh, Kevin, Harrison, James, and Fil, Alexandre

Subjects
Physics - Plasma Physics
Abstract

An integrated data analysis system based on Bayesian inference has been developed for application to data from multiple diagnostics over the two-dimensional cross-section of tokamak divertors. Tests of the divertor multi-instrument Bayesian analysis system (D-MIBAS) on a synthetic data set (including realistic experimental uncertainties) generated from SOLPS-ITER predictions of the MAST-U divertor have been performed. The resulting inference was within 6\%, 5\% and 30\% median absolute percentage error of the SOLPS-predicted electron temperature, electron density and neutral atomic hydrogen density, respectively, across a two-dimensional poloidal cross-section of the MAST-U Super-X outer divertor. To accommodate molecular contributions to Balmer emission, an advanced emission model has been developed which is shown to be crucial for inference accuracy. Our D-MIBAS system utilises a mesh aligned to poloidal magnetic flux-surfaces, throughout the divertor, with plasma parameters assigned to each mesh vertex and collectively considered in the inference. This allowed comprehensive forward models to multiple diagnostics and the inclusion of expected physics. This is shown to be important for inference precision when including molecular contributions to Balmer emission. These developments pave the way for accurate two-dimensional electron temperature, electron density and neutral atomic hydrogen density inferences for MAST-U divertor experimental data for the first time., Comment: 22 pages, 8 figures

Published
2024

12. Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts

Author

Enders, Tobias, Harrison, James, and Schiffer, Maximilian

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract

We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the operations research domain. In this context, risk-sensitive algorithms promise to learn robust policies. While this field is of general interest to the reinforcement learning community, most studies up-to-date focus on theoretical results rather than real-world performance. With this work, we aim to bridge this gap by formally deriving a novel risk-sensitive deep reinforcement learning algorithm while providing numerical evidence for its efficacy. Specifically, we introduce discrete Soft Actor-Critic for the entropic risk measure by deriving a version of the Bellman equation for the respective Q-values. We establish a corresponding policy improvement result and infer a practical algorithm. We introduce an environment that represents typical contextual multi-stage stochastic combinatorial optimization problems and perform numerical experiments to empirically validate our algorithm's robustness against realistic distribution shifts, without compromising performance on the training distribution. We show that our algorithm is superior to risk-neutral Soft Actor-Critic as well as to two benchmark approaches for robust deep reinforcement learning. Thereby, we provide the first structured analysis on the robustness of reinforcement learning under distribution shifts in the realm of contextual multi-stage stochastic combinatorial optimization problems., Comment: 11 pages, 8 figures

Published
2024

13. Universal Neural Functionals

Author

Zhou, Allan, Finn, Chelsea, and Harrison, James

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

A challenging problem in many modern machine learning tasks is to process weight-space features, i.e., to transform or extract information from the weights and gradients of a neural network. Recent works have developed promising weight-space models that are equivariant to the permutation symmetries of simple feedforward networks. However, they are not applicable to general architectures, since the permutation symmetries of a weight space can be complicated by recurrence or residual connections. This work proposes an algorithm that automatically constructs permutation equivariant models, which we refer to as universal neural functionals (UNFs), for any weight space. Among other applications, we demonstrate how UNFs can be substituted into existing learned optimizer designs, and find promising improvements over prior methods when optimizing small image classifiers and language models. Our results suggest that learned optimizers can benefit from considering the (symmetry) structure of the weight space they optimize. We open-source our library for constructing UNFs at https://github.com/AllanYangZhou/universal_neural_functional.

Published
2024

14. Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Author

Singh, Avi, Co-Reyes, John D., Agarwal, Rishabh, Anand, Ankesh, Patil, Piyush, Garcia, Xavier, Liu, Peter J., Harrison, James, Lee, Jaehoon, Xu, Kelvin, Parisi, Aaron, Kumar, Abhishek, Alemi, Alex, Rizkowsky, Alex, Nova, Azade, Adlam, Ben, Bohnet, Bernd, Elsayed, Gamaleldin, Sedghi, Hanie, Mordatch, Igor, Simpson, Isabelle, Gur, Izzeddin, Snoek, Jasper, Pennington, Jeffrey, Hron, Jiri, Kenealy, Kathleen, Swersky, Kevin, Mahajan, Kshiteej, Culp, Laura, Xiao, Lechao, Bileschi, Maxwell L., Constant, Noah, Novak, Roman, Liu, Rosanne, Warkentin, Tris, Qian, Yundi, Bansal, Yamini, Dyer, Ethan, Neyshabur, Behnam, Sohl-Dickstein, Jascha, and Fiedel, Noah

Subjects
Computer Science - Machine Learning
Abstract

Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST$^{EM}$, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times. Testing on advanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find that ReST$^{EM}$ scales favorably with model size and significantly surpasses fine-tuning only on human data. Overall, our findings suggest self-training with feedback can substantially reduce dependence on human-generated data., Comment: Accepted to TMLR. Camera-ready version. First three authors contributed equally

Published
2023

15. Development and evaluation of a concise nurse-driven non-pharmacological delirium reduction workflow for hospitalized patients: An interrupted time series study.

Author

Rathfon, Megan, Binford, Sasha, Miranda, Jennifer, Oreper, Sandra, Holt, Brian, Rogers, Stephanie, and Harrison, James

Subjects
Delirium, Hospitals, Intervention studies, Nurses, Quality improvement, Humans, Delirium, Interrupted Time Series Analysis, Pandemics, Workflow, Intensive Care Units
Abstract

We created a concise nurse-driven delirium reduction workflow with the aim of reducing delirium rates and length of stay for hospitalized adults. Our nurse-driven workflow included five evidence-based daytime sunrise interventions (patient room lights on, blinds up, mobilization/out-of-bed, water within patients reach and patient awake) and five nighttime turndown interventions (patient room lights off, blinds down, television off, noise reduction and pre-set bedtime). Interventions were also chosen because fidelity could be quickly monitored twice daily without patient interruption from outside the room. To evaluate the workflow, we used an interrupted time series study design between 06/01/17 and 05/30/22 to determine if the workflow significantly reduced the units delirium rate and average length of stay. Our workflow is feasible to implement and monitor and initially significantly reduced delirium rates but not length of stay. However, the reduction in delirium rates were not sustained following the emergence of the COVID-19 pandemic.

Published
2024

16. Achieving diagnostic excellence through prevention and teamwork (ADEPT) study protocol: A multicenter, prospective quality and safety program to improve diagnostic processes in medical inpatients.

Author

Schnipper, Jeffrey, Raffel, Katie, Keniston, Angela, Burden, Marisha, Glasheen, Jeffrey, Ranji, Sumant, Hubbard, Colin, Kantor, Molly, Adler-Milstein, Julia, John Boscardin, W, Harrison, James, Dalal, Anuj, Lee, Tiffany, Auerbach, Andrew, and Barish, Peter

Subjects
Humans, Inpatients, Prospective Studies, Hospitals, Hospitalization, Diagnostic Errors, Multicenter Studies as Topic
Abstract

BACKGROUND: Few hospitals have built surveillance for diagnostic errors into usual care or used comparative quantitative and qualitative data to understand their diagnostic processes and implement interventions designed to reduce these errors. OBJECTIVES: To build surveillance for diagnostic errors into usual care, benchmark diagnostic performance across sites, pilot test interventions, and evaluate the programs impact on diagnostic error rates. METHODS AND ANALYSIS: Achieving diagnostic excellence through prevention and teamwork (ADEPT) is a multicenter, real-world quality and safety program utilizing interrupted time-series techniques to evaluate outcomes. Study subjects will be a randomly sampled population of medical patients hospitalized at 16 US hospitals who died, were transferred to intensive care, or had a rapid response during the hospitalization. Surveillance for diagnostic errors will occur on 10 events per month per site using a previously established two-person adjudication process. Concurrent reviews of patients who had a qualifying event in the previous week will allow for surveys of clinicians to better understand contributors to diagnostic error, or conversely, examples of diagnostic excellence, which cannot be gleaned from medical record review alone. With guidance from national experts in quality and safety, sites will report and benchmark diagnostic error rates, share lessons regarding underlying causes, and design, implement, and pilot test interventions using both Safety I and Safety II approaches aimed at patients, providers, and health systems. Safety II approaches will focus on cases where diagnostic error did not occur, applying theories of how people and systems are able to succeed under varying conditions. The primary outcome will be the number of diagnostic errors per patient, using segmented multivariable regression to evaluate change in y-intercept and change in slope after initiation of the program. ETHICS AND DISSEMINATION: The study has been approved by the University of California, San Francisco Institutional Review Board (IRB), which is serving as the single IRB. Intervention toolkits and study findings will be disseminated through partners including Vizient, The Joint Commission, and Press-Ganey, and through national meetings, scientific journals, and publications aimed at the general public.

Published
2023

17. Graph Reinforcement Learning for Network Control via Bi-Level Optimization

Author

Gammelli, Daniele, Harrison, James, Yang, Kaidi, Pavone, Marco, Rodrigues, Filipe, and Pereira, Francisco C.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework., Comment: 9 pages, 4 figures

Published
2023

18. Impact of standardized, language-concordant hospital discharge instructions on postdischarge medication questions.

Author

Khoong, Elaine, Sherwin, Elizabeth, Harrison, James, Wheeler, Margaret, Shah, Sachin, Mourad, Michelle, and Khanna, Raman

Subjects
Humans, Patient Discharge, Aftercare, Language, Comprehension, Hospitals
Abstract

Written instructions improve patient comprehension of discharge instructions but are often provided only in English even for patients with a non-English language preference (NELP). We implemented standardized written discharge instructions in English, Spanish, and Chinese for hospital medicine patients at an urban academic medical center. Using an interrupted time series analysis, we assessed the impact on medication-related postdischarge questions for patients with English, Spanish, or Chinese language preferences. Of 4013 patients, ∼15% had NELP. Preintervention, Chinese-preferring patients had a 5.6 percentage point higher probability of questions (adjusted odds ratio [aOR] = 1.55, 95% confidence interval [CI]: 1.08, 2.21) compared to English-preferring patients; Spanish-preferring and English-preferring patients had similar rates of questions. Postintervention, English-preferring and Spanish-preferring patients had no significant change; Chinese-preferring patients had a significant 10.9 percentage point decrease in the probability of questions (aOR = 0.38, 95% CI: 0.21, 0.69) thereby closing the disparity. Language-concordant written discharge instructions may reduce disparities in medication-related postdischarge questions for patients with NELP.

Published
2023

20. Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies

Author

Li, Oscar, Harrison, James, Sohl-Dickstein, Jascha, Smith, Virginia, and Metz, Luke

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient estimation methods when their loss functions exhibit extreme local sensitivtiy, discontinuity, or blackbox characteristics. In such scenarios, online evolution strategies methods are a more capable alternative, while being more parallelizable than vanilla evolution strategies (ES) by interleaving partial unrolls and gradient updates. In this work, we propose a general class of unbiased online evolution strategies methods. We analytically and empirically characterize the variance of this class of gradient estimators and identify the one with the least variance, which we term Noise-Reuse Evolution Strategies (NRES). Experimentally, we show NRES results in faster convergence than existing AD and ES methods in terms of wall-clock time and number of unroll steps across a variety of applications, including learning dynamical systems, meta-training learned optimizers, and reinforcement learning., Comment: NeurIPS 2023. 41 pages. Code available at https://github.com/OscarcarLi/Noise-Reuse-Evolution-Strategies

Published
2023

21. The role of plasma-atom and molecule interactions on power \& particle balance during detachment on the MAST Upgrade Super-X divertor

Author

Verhaegh, Kevin, Lipschultz, Bruce, Harrison, James, Federici, Fabio, Moulton, David, Lonigro, Nicola, Kobussen, Stijn, O'Mullane, Martin, Osborne, Nick, Ryan, Peter, Wijkamp, Tijs, Kool, Bob, Rose, Effy, Theiler, Christian, and Thornton, Andrew

Subjects
Physics - Plasma Physics
Abstract

This paper shows first quantitative analysis of the detachment processes in the MAST Upgrade Super-X divertor (SXD). We identify an unprecedented impact of plasma-molecular interactions involving molecular ions (likely $D_2^+$), resulting in strong ion sinks (Molecular Activated Recombination - MAR), leading to a reduction of ion target flux. The MAR ion sinks exceed the divertor ion sources before electron-ion recombination (EIR) starts to occur, suggesting that significant ionisation occurs outside of the divertor chamber. In the EIR region, $T_e \ll 0.2$ eV is observed and MAR remains significant in these deep detached phases. The total ion sink strength demonstrates the capability for particle (ion) exhaust in the Super-X Configuration. Molecular Activated Dissociation (MAD) is the dominant volumetric neutral atom creation process can lead to an electron cooling of 20\% of $P_{SOL}$. The measured total radiative power losses \emph{in the divertor chamber} are consistent with inferred hydrogenic radiative power losses. This suggests that intrinsic divertor impurity radiation, despite the carbon walls, is minor in the divertor chamber. This contrasts previous TCV results, which may be associated with enhanced plasma-neutral interactions and reduced chemical erosion in the detached, tightly baffled SXD. The above observations have also been observed in higher heat flux (narrower SOL width) type I ELMy H-mode discharges. This provides evidence that the characterisation in this paper may be general.

Published
2023
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22. Patient Perceptions of e-Visits: Qualitative Study of Older Adults to Inform Health System Implementation.

Author

Judson, Timothy J, Subash, Meera, Harrison, James D, Yeager, Jan, Williams, Aimée M, Grouse, Carrie K, and Byron, Maria

Subjects
attitude, digital health tool, e-consult, e-visit, eHealth, messaging, patient portal, patient portal message, perception, qualitative, remote care, remote consult, remote visit, telehealth, telemedicine, vulnerable, Health Services, Clinical Research, Management of diseases and conditions, 7.1 Individual care needs, Good Health and Well Being, e -visit, e -consult, remote, visit
Abstract

BackgroundElectronic visits (e-visits) are billable, asynchronous patient-initiated messages that require at least five minutes of medical decision-making by a provider. Unequal use of patient portal tools like e-visits by certain patient populations may worsen health disparities. To date, no study has attempted to qualitatively assess perceptions of e-visits in older adults.ObjectiveIn this qualitative study, we aimed to understand patient perceptions of e-visits, including their perceived utility, barriers to use, and care implications, with a focus on vulnerable patient groups.MethodsWe conducted a qualitative study using in-depth structured individual interviews with patients from diverse backgrounds to assess their knowledge and perceptions surrounding e-visits as compared with unbilled portal messages and other visit types. We used content analysis to analyze interview data.ResultsWe conducted 20 interviews, all in adults older than 65 years. We identified 4 overarching coding categories or themes. First, participants were generally accepting of the concept of e-visits and willing to try them. Second, nearly two-thirds of the participants voiced a preference for synchronous communication. Third, participants had specific concerns about the name "e-visit" and when to choose this type of visit in the patient portal. Fourth, some participants indicated discomfort using or accessing technology for e-visits. Financial barriers to the use of e-visits was not a common theme.ConclusionsOur findings suggest that older adults are generally accepting of the concept of e-visits, but uptake may be limited due to their preference for synchronous communication. We identified several opportunities to improve e-visit implementation.

Published
2023

23. Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

Author

Enders, Tobias, Harrison, James, Pavone, Marco, and Schiffer, Maximilian

Subjects
Computer Science - Machine Learning, Computer Science - Multiagent Systems, Electrical Engineering and Systems Science - Systems and Control
Abstract

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy. Thereby, we factorize the operator's otherwise intractable action space, but still obtain a globally coordinated decision. Experiments based on real-world taxi data show that our method outperforms state of the art benchmarks with respect to performance, stability, and computational tractability., Comment: 20 pages, 7 figures, extended version of paper accepted at the 5th Learning for Dynamics & Control Conference (L4DC 2023)

Published
2022

24. General-Purpose In-Context Learning by Meta-Learning Transformers

Author

Kirsch, Louis, Harrison, James, Sohl-Dickstein, Jascha, and Metz, Luke

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and promises to unlock greater capabilities with less manual effort. One particularly ambitious goal of meta-learning is to train general-purpose in-context learning algorithms from scratch, using only black-box models with minimal inductive bias. Such a model takes in training data, and produces test-set predictions across a wide range of problems, without any explicit definition of an inference model, training loss, or optimization algorithm. In this paper we show that Transformers and other black-box models can be meta-trained to act as general-purpose in-context learners. We characterize transitions between algorithms that generalize, algorithms that memorize, and algorithms that fail to meta-train at all, induced by changes in model size, number of tasks, and meta-optimization. We further show that the capabilities of meta-trained algorithms are bottlenecked by the accessible state size (memory) determining the next prediction, unlike standard models which are thought to be bottlenecked by parameter count. Finally, we propose practical interventions such as biasing the training distribution that improve the meta-training and meta-generalization of general-purpose in-context learning algorithms., Comment: Published at the NeurIPS 2022 Workshop on Meta-Learning. Full version currently under review

Published
2022

25. Adaptive Robust Model Predictive Control via Uncertainty Cancellation

Author

Sinha, Rohan, Harrison, James, Richards, Spencer M., and Pavone, Marco

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning, Computer Science - Robotics
Abstract

We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component. Such systems commonly model the nonlinear effects of an unknown environment on a nominal system. We optimize over a class of nonlinear feedback policies inspired by certainty equivalent "estimate-and-cancel" control laws pioneered in classical adaptive control to achieve significant performance improvements in the presence of uncertainties of large magnitude, a setting in which existing learning-based predictive control algorithms often struggle to guarantee safety. In contrast to previous work in robust adaptive MPC, our approach allows us to take advantage of structure (i.e., the numerical predictions) in the a priori unknown dynamics learned online through function approximation. Our approach also extends typical nonlinear adaptive control methods to systems with state and input constraints even when we cannot directly cancel the additive uncertain function from the dynamics. We apply contemporary statistical estimation techniques to certify the system's safety through persistent constraint satisfaction with high probability. Moreover, we propose using Bayesian meta-learning algorithms that learn calibrated model priors to help satisfy the assumptions of the control design in challenging settings. Finally, we show in simulation that our method can accommodate more significant unknown dynamics terms than existing methods and that the use of Bayesian meta-learning allows us to adapt to the test environments more rapidly., Comment: Under review for the IEEE Transaction on Automatic Control, special issue on learning and control. arXiv admin note: text overlap with arXiv:2104.08261

Published
2022

26. VeLO: Training Versatile Learned Optimizers by Scaling Up

Author

Metz, Luke, Harrison, James, Freeman, C. Daniel, Merchant, Amil, Beyer, Lucas, Bradbury, James, Agrawal, Naman, Poole, Ben, Mordatch, Igor, Roberts, Adam, and Sohl-Dickstein, Jascha

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the same scaling approach behind the success of deep learning to learn versatile optimizers. We train an optimizer for deep learning which is itself a small neural network that ingests gradients and outputs parameter updates. Meta-trained with approximately four thousand TPU-months of compute on a wide variety of optimization tasks, our optimizer not only exhibits compelling performance, but optimizes in interesting and unexpected ways. It requires no hyperparameter tuning, instead automatically adapting to the specifics of the problem being optimized. We open source our learned optimizer, meta-training code, the associated train and test data, and an extensive optimizer benchmark suite with baselines at velo-code.github.io.

Published
2022

27. Inpatient Understanding of Their Care Team and Receipt of Mixed Messages: a Two-Site Cross-Sectional Study

Author

Atkinson, Mariam Krikorian, Wazir, Mohammed, Barkoudah, Ebrahim, Khalil, Hassan, Mani, Sampathkumar, Harrison, James D, Yao-Cohen, Erin, Weiss, Rachel, To, C, Bambury, Elizabeth A, Cimino, Jenica, Mora, Rosa, Maru, Johsias, Curatola, Nicole, Juergens, Nathan, and Schnipper, Jeffrey L

Subjects
Behavioral and Social Science, Clinical Research, Health Services, Aging, Patient Safety, 7.3 Management and decision making, Health and social care services research, 8.1 Organisation and delivery of services, Management of diseases and conditions, Generic health relevance, Good Health and Well Being, patient understanding, mixed messages, care team, length of stay, inpatient care, Clinical Sciences, General & Internal Medicine
Abstract

BackgroundPatient understanding of their care, supported by physician involvement and consistent communication, is key to positive health outcomes. However, patient and care team characteristics can hinder this understanding.ObjectiveWe aimed to assess inpatients' understanding of their care and their perceived receipt of mixed messages, as well as the associated patient, care team, and hospitalization characteristics.DesignWe administered a 30-item survey to inpatients between February 2020 and November 2021 and incorporated other hospitalization data from patients' health records.ParticipantsRandomly selected inpatients at two urban academic hospitals in the USA who were (1) admitted to general medicine services and (2) on or past the third day of their hospitalization.Main measuresOutcome measures include (1) knowledge of main doctor and (2) frequency of mixed messages. Potential predictors included mean notes per day, number of consultants involved in the patient's care, number of unit transfers, number of attending physicians, length of stay, age, sex, insurance type, and primary race.Key resultsA total of 172 patients participated in our survey. Most patients were unaware of their main doctor, an issue related to more daily interactions with care team members. Twenty-three percent of patients reported receiving mixed messages at least sometimes, most often between doctors on the primary team and consulting doctors. However, the likelihood of receiving mixed messages decreased with more daily interactions with care team members.ConclusionsPatients were often unaware of their main doctor, and almost a quarter perceived receiving mixed messages about their care. Future research should examine patients' understanding of different aspects of their care, and the nature of interactions that might improve clarity around who's in charge while simultaneously reducing the receipt of mixed messages.

Published
2023

28. Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning

Author

Ivanovic, Boris, Harrison, James, and Pavone, Marco

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e.g., in fleets of self-driving vehicles, which are beginning to commercially operate in major cities across the world. Despite their advancements, however, the vast majority of prediction systems are specialized to a set of well-explored geographic regions or operational design domains, complicating deployment to additional cities, countries, or continents. Towards this end, we present a novel method for efficiently adapting behavior prediction models to new environments. Our approach leverages recent advances in meta-learning, specifically Bayesian regression, to augment existing behavior prediction models with an adaptive layer that enables efficient domain transfer via offline fine-tuning, online adaptation, or both. Experiments across multiple real-world datasets demonstrate that our method can efficiently adapt to a variety of unseen environments., Comment: 12 pages, 13 figures, 2 tables. ICRA 2023

Published
2022

29. A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases

Author

Harrison, James, Metz, Luke, and Sohl-Dickstein, Jascha

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

Learned optimizers -- neural networks that are trained to act as optimizers -- have the potential to dramatically accelerate training of machine learning models. However, even when meta-trained across thousands of tasks at huge computational expense, blackbox learned optimizers often struggle with stability and generalization when applied to tasks unlike those in their meta-training set. In this paper, we use tools from dynamical systems to investigate the inductive biases and stability properties of optimization algorithms, and apply the resulting insights to designing inductive biases for blackbox optimizers. Our investigation begins with a noisy quadratic model, where we characterize conditions in which optimization is stable, in terms of eigenvalues of the training dynamics. We then introduce simple modifications to a learned optimizer's architecture and meta-training procedure which lead to improved stability, and improve the optimizer's inductive bias. We apply the resulting learned optimizer to a variety of neural network training tasks, where it outperforms the current state of the art learned optimizer -- at matched optimizer computational overhead -- with regard to optimization performance and meta-training speed, and is capable of generalization to tasks far different from those it was meta-trained on., Comment: NeurIPS 2022

Published
2022

30. Label-free imaging for drug delivery across biophysical barriers

Author

Harrison, James Samuel Peter and Mahajan, Sumeet

Abstract

Every year countless drug products fail to make it through the discovery and approval process required for commercialisation and widespread patient use. This failure is often due to issues with poor efficacy and side effects. Drug delivery systems based upon nanotechnology are currently at the forefront of drug discovery research and boast the ability to greatly improve treatment capabilities. However, a general lack of understanding of drug delivery and distribution at the cellular and subcellular levels, is ultimately making the widespread use of these systems an uphill battle. Current 'go to' techniques for in vitro and in vivo quantification primarily revolve around using fluorescent labels and/or processes which cause sample destruction. Both of these are less than ideal. Raman spectroscopy is a non-invasive and label-free technique that can provide molecular information on a wide variety of sample types, ranging from drug formulations to cell and tissue samples. When coupled with microscopy, Raman is able to generate vibrationally, and often chemically, specific images of samples. This thesis examines the use of microscopies utilising the process of Raman scattering, to gain insight into drug delivery systems. A novel methodology is presented using Raman spectral unmixing achieved through multivariate curve resolution alternating least squares analysis to quantify non-ionic surfactant vesicle formulations in terms of drug loading and vesicle component concentration. The method presented was able to accurately quantify a series of calibration samples before providing invaluable insight into vesicle component concentrations. Drug loading calculated using the technique showed similar results to those seen with fluorimetry. Investigation into the intracellular response of cell cultures treated with vesicular drug delivery systems was also completed. Analysis using microscopies based upon the multiphoton techniques of coherent anti-Stokes Raman scattering and two-photon excited fluorescence allowed for complementary label-free and label-based imaging. A cellular response was observed upon treatment. The source of this response was investigated further, quantified and a dose-response relationship identified. Extent of drug delivery from vesicles was also quantified and compared to that of delivery from drug free in solution. The research outlined in this thesis demonstrates the excellent versatility and quantitative power of Raman spectroscopy to further elucidate the role drug delivery systems can play in improving therapeutic treatments.

Published
2023

31. Prolonged use of newly prescribed gabapentin after surgery.

Author

Gan, Siqi, Finlayson, Emily, Ross, Joseph, Boscardin, John, Steinman, Michael, Bongiovanni, Tasce, and Harrison, James

Subjects
deprescribing, older adults (3-5), pain control, postoperative care, Female, Aged, Humans, United States, Male, Gabapentin, Analgesics, Opioid, Practice Patterns, Physicians, Medicare, Opioid-Related Disorders, Pain, Postoperative, Retrospective Studies
Abstract

BACKGROUND: Surgeons have made substantial efforts to decrease postoperative opioid prescribing, largely because it can lead to prolonged use. These efforts include adoption of non-opioid pain medication including gabapentin. Like opioids, gabapentin use may be prolonged, increasing the risk of altered mental status and even overdose and death when taken concurrently with opioids. However, little is known about postoperative prolonged use of gabapentin in older adults. METHODS: We merged a 20% sample of Medicare Carrier, MedPAR and Outpatient Files with Part D for 2013-2018. We included patients >65 years old without prior gabapentinoid use who underwent common non-cataract surgical procedures. We defined new postoperative gabapentin as fills for 7 days before surgery until 7 days after discharge. We excluded patients whose discharge disposition was hospice or death. The primary outcome was prolonged use of gabapentin, defined as a fill>90 days after discharge. To identify risk factors for prolonged use, we constructed logistic regression models, adjusted for procedure and patient characteristics, length of stay, disposition location, and care complexity. RESULTS: Overall, 17,970 patients (3% of all eligible patients) had a new prescription for gabapentin after surgery. Of these, the mean age was 73 years old and 62% were female. The most common procedures were total knee (45%) and total hip (21%) replacements. Prolonged use occurred in 22%. Those with prolonged use were more likely to be women (64% vs. 61%), be non-White (14% vs. 12%), have concurrent prolonged opioid use (44% vs. 18%), and have undergone emergency surgery (8% vs. 4%). On multivariable analysis, being female, having a higher Charlson comorbidity score, having an opioid prescription at discharge and at >90 days and having a higher care complexity were associated with prolonged use of gabapentin. CONCLUSIONS: More than one-fifth of older adults prescribed gabapentin postoperatively filled a prescription >90 days after discharge, especially among patients with more comorbidities and concurrent prolonged opioid use, increasing the risk of adverse drug events and polypharmacy.

Published
2022

34. Spectroscopic investigations of detachment on the MAST Upgrade Super-X divertor

Author

Verhaegh, Kevin, Lipschultz, Bruce, Harrison, James, Osborne, Nick, Williams, Aelwyn, Ryan, Peter, Clark, James, Federici, Fabio, Kool, Bob, Wijkamp, Tijs, Fil, Alexandre, Moulton, David, Myatra, Omkar, Thornton, Andrew, Bosman, Thomas, Cunningham, Geof, Duval, Basil, Henderson, Stuart, Scannell, Rory, and team, the MAST Upgrade

Subjects
Physics - Plasma Physics
Abstract

We present the first analysis of the atomic and molecular processes at play during detachment in the MAST-U Super-X divertor using divertor spectroscopy data. Our analysis indicates detachment in the MAST-U Super-X divertor can be separated into four sequential phases: First, the ionisation region detaches from the target at detachment onset leaving a region of increased molecular densities downstream. The plasma interacts with these molecules, resulting in molecular ions ($D_2^+$ and/or $D_2^- \rightarrow D + D^-$) that further react with the plasma leading to Molecular Activated Recombination and Dissociation (MAR and MAD), which results in excited atoms and significant Balmer line emission. Second, the MAR region detaches from the target leaving a sub-eV temperature region downstream. Third, an onset of strong emission from electron-ion recombination (EIR) ensues. Finally, the electron density decays near the target, resulting in a density front moving upstream. The analysis in this paper indicates that plasma-molecule interactions have a larger impact than previously reported and play a critical role in the intensity and interpretation of hydrogen atomic line emission characteristics on MAST-U. Furthermore, we find that the Fulcher band emission profile in the divertor can be used as a proxy for the ionisation region and may also be employed as a plasma temperature diagnostic for improving the separation of hydrogenic emission arising from electron-impact excitation and that from plasma-molecular interactions. We provide evidences for the presence of low electron temperatures ($<0.5$ eV) during detachment phases III-IV based on quantitative spectroscopy analysis, a Boltzmann relation of the high-n Balmer line transitions together with an analysis of the brightness of high-n Balmer lines.

Published
2022
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35. Practical tradeoffs between memory, compute, and performance in learned optimizers

Author

Metz, Luke, Freeman, C. Daniel, Harrison, James, Maheswaranathan, Niru, and Sohl-Dickstein, Jascha

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

Optimization plays a costly and crucial role in developing machine learning systems. In learned optimizers, the few hyperparameters of commonly used hand-designed optimizers, e.g. Adam or SGD, are replaced with flexible parametric functions. The parameters of these functions are then optimized so that the resulting learned optimizer minimizes a target loss on a chosen class of models. Learned optimizers can both reduce the number of required training steps and improve the final test loss. However, they can be expensive to train, and once trained can be expensive to use due to computational and memory overhead for the optimizer itself. In this work, we identify and quantify the design features governing the memory, compute, and performance trade-offs for many learned and hand-designed optimizers. We further leverage our analysis to construct a learned optimizer that is both faster and more memory efficient than previous work. Our model and training code are open source.

Published
2022

36. Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand

Author

Gammelli, Daniele, Yang, Kaidi, Harrison, James, Rodrigues, Filipe, Pereira, Francisco C., and Pavone, Marco

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs. By centrally controlling a fleet of self-driving vehicles, these systems provide mobility service to customers and are currently starting to be deployed in a number of cities around the world. Current learning-based approaches for controlling AMoD systems are limited to the single-city scenario, whereby the service operator is allowed to take an unlimited amount of operational decisions within the same transportation system. However, real-world system operators can hardly afford to fully re-train AMoD controllers for every city they operate in, as this could result in a high number of poor-quality decisions during training, making the single-city strategy a potentially impractical solution. To address these limitations, we propose to formalize the multi-city AMoD problem through the lens of meta-reinforcement learning (meta-RL) and devise an actor-critic algorithm based on recurrent graph neural networks. In our approach, AMoD controllers are explicitly trained such that a small amount of experience within a new city will produce good system performance. Empirically, we show how control policies learned through meta-RL are able to achieve near-optimal performance on unseen cities by learning rapidly adaptable policies, thus making them more robust not only to novel environments, but also to distribution shifts common in real-world operations, such as special events, unexpected congestion, and dynamic pricing schemes., Comment: 11 pages, 4 figures

Published
2022

37. Automated telephone follow-up programs after hospital discharge: Do older adults engage with these programs?

Author

Auerbach, Andrew, Shah, Sachin, Oreper, Sandra, Wheeler, Margaret, Fang, Margaret, Sudore, Rebecca, and Harrison, James

Subjects
health services delivery, telemedicine, transitional care, Aged, Aged, 80 and over, Humans, Aftercare, Continuity of Patient Care, Follow-Up Studies, Hospitals, Patient Discharge, Telephone
Abstract

BACKGROUND: Health systems have developed automated telephone call programs to screen and triage patients post-hospital discharge issues and concerns. The aims of our study were to determine whether and how older adults engage with automated post-hospital discharge telephone programs and to describe the prevalence of patient-reported post-discharge issues. METHODS: We identified all telephone calls made by an urban academic medical center as part of a post-hospital discharge program between May 1, 2018 and April 30, 2019. The program used automated telephone outreach to patients or their caregivers that included 11 distinct steps 3 days post-discharge. All adults discharged home from the hospital, were included, and we categorized patients into ≤64 years, 65-84 years, and ≥85 years age groups. We then compared call reach rate, completeness of 11-step calls and patient-reported issues between age groups. RESULTS: Eighteen thousand and seventy six patients were included. More patients 65-84 years old were reached compared to patients ≤64 years old (84.3% vs. 78.9%, AME 5.52%; 95%CI: 3.58%-7.45%). Completion rates of automated calls for those ≥85 years old were also high. Patients ≥85 years old were more likely to have questions about their follow-up plans and need assistance scheduling appointments compared to those ≤64 years old (19.0% vs. 11.9%, AME 7.0% (95%CI: 2.7%-11.3%). CONCLUSION: Post-hospital automated telephone calls are feasible and effective at reaching older adults. Future work should focus on improving discharge communication to ensure older adults are aware of their follow-up plan and appointments.

Published
2022

38. On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation

Author

Lew, Thomas, Sharma, Apoorva, Harrison, James, Schmerling, Edward, and Pavone, Marco

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Robotics
Abstract

We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation. Specifically, we discuss the approximation that replaces a model with fixed but uncertain parameters (a source of epistemic uncertainty) with a model subject to external disturbances modeled as a Brownian motion (corresponding to aleatoric uncertainty).

Published
2021

39. Factors Associated With Coronary Angiography Performed Within 6 Months of Randomization to the Conservative Strategy in the ISCHEMIA Trial

Author

Pracoń, Radosław, Spertus, John A., Broderick, Samuel, Bangalore, Sripal, Rockhold, Frank W., Ruzyllo, Witold, Demchenko, Elena, Nageh, Thuraia, Grossman, Gabriel Blacher, Mavromatis, Kreton, Manjunath, Cholenahally N., Smanio, Paola E.P., Stone, Gregg W., Mancini, G.B. John, Boden, William E., Newman, Jonathan D., Reynolds, Harmony R., Hochman, Judith S., Maron, David J., Doan, John, Linefsky, Jason, Lee, Raven, Patel, Risha, Miller, Todd, Yang Cho, So, Milbrandt, Susan, Shelstad, Dawn, Banerjee, Subhash, Kamath, Preeti, Tejani, Ishita, Cobos, Stanley E., Quiles, Kirsten J., Dwyer, Raven R., Donnino, Robert M., Espinosa, Dalisa, Phillips, Lawrence M., Saric, Muhamed, Abdul-Nour, Khaled, Schley, Allison, Golden, Heather, Stone, Peter H., Osseni, Hermine, Wiyarand, Charlene, Douglass, Peter, Pomeroy, Hayley, Craft, Alexandra, Harvey, Bethany, Jang, James J., Anaya, Olivia, Yee, Gennie, Goold, Phoebe, Weitz, Steven, Giovannone, Steven, Pritchard, Lori, Arnold, Suzanne, Gans, Rosann, Henry O’Keefe, Jr, James, Kennedy, Paul, Shapiro, Michael D., Ganesan, Shobana, Schlichting, David, Naher, Aynun, El-Hajjar, Mohammad, Sidhu, Mandeep S., Fein, Steven A., Stewart, Wendy L., Torosoff, Mikhail T., Salmi, Kristin M., Lyubarova, Radmila, Mookherjee, Sulagna, Drzymalski, Krzysztof, McFalls, Edward O., Garcia, Santiago A., Bertog, Stefan C., Johnson, Debra K., Siddiqui, Rizwan A., Herrmann, Rebekah R., Ishani, Areef, Hansen, Ronnell A., Georges Khouri, Michel, Arges, Kristine, LeFevre, Melissa, Tomfohr, Jennifer, Goldberg, Jonathan L., Ann Byrne, Kimberly, Zappernick, Taissa, Goldweit, Richard, Canada, Sallie, Kakade, Meghana, Mieses, Patricia, Cobos, Stanley E., Dwyer, Raven R., Cohen, Ronny A., Espinosa, Dalisa, Mirrer, Brooks, Quiles, Kirsten J., Navarro, Victor, Rantinella, Magdalena, Rodriguez, Jessica, Mancilla, Olivia, Winchester, David E., Stinson, Susan, Kronenberg, Marvin, Weyand, Terry, Rogal, Philip, Crook, Sherron C., McFarren, Christopher, Heitner, John F., Ho, Jean, Khan, Saadat, Mohamed, Mahmoud, Dauber, Ira M., Soltau, Mary R., Rose, Delsa K., Wimmer, Rebecca J., Siegel, Kathy E., Derbyshire, Susan, Cannan, Charles, Dixon, Michelle, Leonard, Gerald, Sudarshan, Sriram, Heard, Ciarra, Gabriel, Viviana, Desire, Sukie, Mehta, Puja K., McDaniel, Michael, Rashid, Fauzia, Lerakis, Stamatios, Asier, Senait, Quyyumi, Arshed, Patel, Keyur, Wenger, Nanette K., Hedgepeth, Chester M., Gillis, Jennifer, Hurlburt, Heather, Manocchia, Megan, Rosen, Alan, Moore, Susan, Congdon, Elizabeth, Sahul, Zakir, Brandt, Gail, Marchelletta, Nora, Wippler, Kristina, Booth, David, Taul, Yvonne, Leung, Steve, Isaacs, Jennifer, Abdel-Latif, Ahmed, Bulkley, Viktoria, Reda, Hassan, Rodgers, Caroline, Ziada, Khaled, Setty, Sampoornima, Halverson, Kimberly E., Roraff, Christine, Thorsen, Jonean, Barua, Rajat S., Ojajuni, Amarachi, Olurinde, Oni, Surineni, Kamalakar, Hage, Fadi, Valaiyapathi, Badhma, Caldeira, Christiano, Davies, James E., Leesar, Massoud, Heo, Jaekyeong, Iskandrian, Amy, Al Solaiman, Firas, Singh, Satinder, Dajani, Khaled, Kartje, Carol M., El-Hajjar, Mohammad, Mesropian, Paul Der, Sacco, Joseph, Rawlins, Michele, McCandless, Brian, Thomson, Jennifer, Orgera, Marisa, Sidhu, Mandeep S., Colleen Rogge, Mary, Arif, Imran, Bunke, Julie, Kerr, Hanan, Unterbrink, Kendra, Fannon, Jacqueline, Burman, Cynthia, Trejo, Jorge F., Dubin, Marcia F., Fletcher, Gerald, Lane, Gary E., Neeson, Lynn M., Parikh, Pragnesh P., Pollak, Peter M., Shapiro, Brian P., Landolfo, Kevin, Gemignani, Anthony, Beaudry, Sarah, O’Rourke, Daniel, Meadows, Judith L., Tirado, Stephanie A., Halliday, Janet, Julian, Pamela, Call, Jason T., Lane, Stephanie M., Stanford, Jennifer L., Hannan, Joseph, Bojar, Robert, Arsenault, Patricia, Kumar, Deepti, Sigel, Pamela, Mukai, John, Martin, Edward T., Brooks, Miriam, Vorobiof, Gabriel, Douangvila, Ladda, Gevorgyan, Rubine, Moorman, Alec, Ranjbaran, Fatima, Smith, Bryn, Ohmart, Carly, Kinlay, Scott, Hamburger, Robert J., Rocco, Thomas P., Ly, Samantha, Bhatt, Deepak L., Quinn, Margot C., Croce, Kevin, Temiyasathit, Sara, Quin, Jacquelyn A, Do, Jacquelyn, Anumpa, Jati, Tobin, Desiree, Zenati, Marco, Faxon, David P, Rayos, Glenn, Langdon, Jennifer, Werner Bayer, Marcia, Seedhom, Ashraf, O’Malley, Amanda, Sullenberger, Lance, Orvis, Erin, Kumkumian, Gregory, Murphy, Mandy, Greenberg, Ann, Iraola, Margaret, Sedlis, Steven P., Maranan, Leandro C., Donnino, Robert M., Lorin, Jeffrey, Tamis-Holland, Jacqueline E., Malinay, Ammy, Kornberg, Robert, Leber, Robert, Saba, Souheil, Edillo, Candice P., Lee, Michael W., Small, Delano R., Nona, Wassim, Alexander, Patrick B., Rehman, Iram, Badami, Umesh, Ostrander, Ann, Wasmiller, Stephanie, Marzo, Kevin, Drewes, Wendy, Patel, Dipti, Robbins, Inga H., Levite, Howard A., White, Jackie M, Shetty, Sanjay, Hallam, Alison, Patel, Mayuri, Hamroff, Glenn S., Spooner, Benjamin J, Hollenweger, Linda M, Little, Raymond W., Little, Holly, Zimbelman, Brandi D., Little, Tiffany, Lui, Charles Y., Eskelson, Nona A, Smith, Brigham R., Vezina, Daniel P., Khor, Lillian L., Abraham, Josephine D., Bull, David A., McKellar, Stephen H., Booth, David, Taul, Yvonne, Kotter, John, Rodgers, Caroline, Abdel-Latif, Ahmed, Isaacs, Jennifer, Bulkley, Viktoria, Hu, Bob, Kaneshiro, Renee, Labovitz, Arthur J., Berlowitz, Michael, Kirby, Bonnie J., Rogal, Philip, Tran, Nhi N., McFarren, Christopher, Jahrsdorfer, Catherine, Matar, Fadi, Caldeira, Christiano, Rodriguez, Fatima, Yunis, Reem, Schnittger, Ingela, Patro, Jhina, Fearon, William F., Deedwania, Prakash, Vega, Antonia, Reddy, Kiran, Sweeny, Joseph, Bloise-Adames, Hugo, Jimenez, Santa, Saint Vrestil, Nicole, Bhandari, Reyna, Spizzieri, Christopher, Schade, Danielle, Yost, Roxanne, Hochberg, Claudia P, Beardsley, Paula, Fine, Denise, Salerno, William D., Tancredi, Jana, Arakelian, Patricia, Mathus, Susan, O’Neill, Deborah, Wyman, Ray, Burkhardt, Joy, Hosino, Suellen, Lubyanaya, Oksana A., Salas, Jose D., Zarka, Amer, Aguirre, Maria, Shah, Anil V., Dhawan, Manu, Parra, Diana, Tran, Tri, Haldis, Thomas, Weick, Catherine, Fowler-Lehman, Katie, Spitzer, Natalie, Riedberger, Casey, Weick, Catherine, Kohn, Jeffrey A., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Girotra, Saket, Drum, Carrie, Miller-Cox, Kimberly, Ollinger, Amy, Almousalli, Omar, Capasso-Gulve, Elizabeth, Melanie Loehr, Alaine, Mosley, Marlowe, Krishnam, Mayil S., Heydari, Shirin, Milliken, Jeffrey C., Lundeen, Andrea M., Patel, Pranav M., Karanjah, Edgar, Seto, Arnold H., Marfori, Wanda C., Harley, Kevin T., Hernandez-Rangel, Eduardo, Gibson, Michael A., Singh, Pam, Allen, Byron J., Coram, Rita, Marie Webb, Anne, Fridell, Ellie, Wilson, Heidi, Thomas, Sabu, Kim, Angela, Schwartz, Ronald G, Wilmot, Patrick, Chen, Wei, El Shahawy, Mahfouz, Stevens, Ramona, Stafford, James, Black, Loriane, Abernethy, William B., Hull, Amber B., Lim, Olivia J., Tucker, Helen C., Putnam, Natasha C., Hall, Linda L., Cauthren, Tia, Tucker, Trish, Zurick, Andrew, Horton, Hollie, Orga, Jan, Meyer, Thomas M., White, Joyce R., Morford, Ronald G., Baumann, Cynthia, Rutkin, Bruce, Seeratan, Vidya, Bokhari, Sabahat, Jimenez, Magnolia, Sokol, Seth I., Schultz, Cidney, Meisner, Jay, Russo, Jeanne, Hamzeh, Ihab, Misra, Arunima, Huda, Zohra, Wall, Matthew, Boan, Araceli, Lenges De Rosen, Veronica, Alam, Mahboob, Turner, Michael C., Hinton, Christine R, Mulhearn, Thomas J., Good, Arnold P., Archer, Beth A., Dionne, Julia S., Allardyce, Cheryl A., Sikora, Lindsey N., Czerniak, Jennifer H., Mull, Jennifer A., Ferguson, Elizabeth, Laube, Frances, Shammas, Nicolas W., Shammas, Gail A, Christensen, Lori, Park, Holly, Chilton, Robert, Hecht, Joan, Nguyen, Patricia K., Vo, Davis, Hirsch, James, Jezior, Matthew, Bindeman, Jody, Salkind, Sara, Espinosa, Dalisa, Desimone, Lori-Ann, Gordon, Paul C., Felix-Stern, Lina, Crain, Thomas, Gomes, Jassira, Gordon, Catherine, Stenberg, Robert, Mann, Aimee, McCreary, Theresa, Pedalino, Ronald P., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Wiesel, Joseph, Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Juang, George J., Gopaul, Candace, Hultberg, Karen, Huk, Tauqir, Hussain, Afshan, Al-Amoodi, Mohammed, Zambrano, Yesenia, Medina Rodriguez, Sarah, Milner, Trudie, Wohns, David, Mulder, Abbey, Van Oosterhout, Stacie, Lader, Ellis W., Meyer, Martha, Mumma, Michael, Clapp, Nancy L., Barrentine, Heather, Dharmarajan, Lekshmi, Jose, Jenne M., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Manchery, Jenne, McGarvey Jr, Joseph F.X., McKinney, Vera, Schwarz, Linda, Downes, Thomas R., Kaczkowski, Scott M., Luckasen, Gary J., Jaskowiak, Adam J., Klitch, Joel, Cheong, Benjamin, Dees, Debra, Potluri, Srinivasa, Vasquez, Precilia, Mastouri, Ronald A., Breall, Jeffery A., Hannemann, Elise L., Revtyak, George E., Mae Foltz, Judy, Bazeley, Jonathan W., Li, Dayuan, DeRosa, Emily, Jorgenson, Beth, Riestenberg-Smith, Joyce, Giedd, Kenneth, Old, Wayne, Bariciano, Rebecca, Burt, Francis, Sokhon, Kozhaya, Waldron, Jessica, Mayon, Michelle, Gopal, Deepika, Valeti, Uma S., Ann Peichel, Gretchen, Kobashigawa, Jon, Starks, Brandy, Garcia, Lucilla, Thottam, Maria, Bhargava, Balram, Anand, Anjali, Chakanalil Govindan, Sajeev, Raj, Janitha, Gopalan Nair, Rajesh, Ravindran, Reshma, Rajalekshmi, VS, Nataraj, Nandita, Moorthy, Nagaraja, Nayak, Soundarya, Mylarappa, Mahevamma, Narayanappa, Suryaprakash, Pandit, Neeraj, Bajaj, Sheromani, Kumar Nath, Ranjit, Yadav, Vandana, Mishra, Girish, Dwivedi, S.K., Tewari, Roma, Narain, V.S., Mishra, Meenakshi, Chandra, Sharad, Patel, Shivali, Singh, Suman, Wander, Gurpreet S., Tandon, Rohit, Ralhan, Sarju, Kaur, Baljeet, Aslam, Naved, Gupta, Sonika, Goyal, Abhishek, Bhargava, Balram, Suvarna, Chandini, Karthikeyan, G., Ramakrishnan, S., Seth, Sandeep, Yadav, Rakesh, Singh, Sandeep, Roy, Ambuj, Parakh, Neeraj, Kumar Verma, Sunil, Narang, Rajiv, Mishra, Sundeep, Naik, Nitish, Sharma, Gautam, Kumar Choudhary, Shiv, Patel, Chetan, Gulati, Gurpreet, Sharma, Sanjeev, Bahl, V K, Mathew, Anoop, Mannekkattukudy Kurian, Binoy, Punnoose, Eapen, Avdhoot Gadkari, Milind, Rupesh Karwa, Sheetal, Gadage, Siddharth, Kolhe, Suvarna, Umesh Pillay, Tapan, Satheesh, Santhosh, Vindhya, R. J., Jain, Peeyush, Seth, Ashok, Singh Meharwal, Zile, Mathur, Atul, Verma, Atul, Kaul, Upendra, Bhatia, Mona, Sachdeva, Ankush, Indira Devi, Thounaojam, Jungla, Nungshi, Christopher, Johann, Manjula Rani, K., Menon, Rajeev, Sowjanya Reddy, M., Kumar, Nirmal, Preethi, K., Oomman, Abraham, sidh, Rinu R, Mao, Robert, Ramakrishnan, T., Solomon, Hilda, Francis, Rajesh, Naik, Sudhir, Vamshi, Priya P., Parveen Khan, Sajeeda, Christopher, Johann, Preethi, Kotiboinna, Kumar, Nirmal, Grant, Purvez, Hande, Shweta, Sonawane, Poonam, Kachru, Ranjan, Dubey, Abhishek, Rawat, Kavita, Kumar, Ajit, Ganapathi, Sanjay, K, Jayakumar, CP, Vineeth, Sivadasanpillai, Harikrishnan, Chacko, Manas, Sasidharan, Bijulal, Babu, Suresh, TR, Kapilamoorthy, Christopher, Johann, Reddy, Sowjanya, Polamuri, Praneeth, Rani, Manjula, Kaul, Upendra, Arambam, Priyadarshani, Singh, Bebek, Senior, Roxy, Fox, Keith AA, Young, Grace M., Carruthers, Kathryn, Senior, Roxy, Elghamaz, Ahmed, Gurunathan, Sothinathan, Karogiannis, Nikolaos, Young, Grace M., Shah, Benoy N, Kinsey, Christopher, Trimlett, Richard HJ, Kavalakkat, Raisa, Rubens, Michael B, Evans, Jo, Nicol, Edward D, Hassan, Ikraam, Mittal, Tarun K, Hampson, Reinette, Andreas Gamma, Reto, Williams, Sarah, Holland, Kim, Swan, Karen, de Belder, Mark A, Atkinson, Bev, Thambyrajah, Jeet, Kunhunny, Swapna, Davies, John R, Lindsay, Steven J., Atkinson, Craig, Kurian, John, Krannila, Carita, Jamil, Haqeel, Vinod, Manitha, Raheem, Osama, Hoye, Angela, Chaytor, Lisa, Cox, Leanne, Morrow, Julie, Rowe, Kay, Donnelly, Patrick, Kelly, Stephanie, Valecka, Bernardas, Regan, Susan, Turnbull, Dawn, Chauhan, Anoop, Fleming, Catherine, Ghosh, Arijit, Gratrix, Karen, Preston, Stephen, Barr, Craig, Cartwright, Anne, Alfakih, Khaled, Knighton, Abigail, Byrne, Jonathan, Martin, Katherine, Webb, Ian, Henriksen, Peter, Flint, Laura, Harrison, James, OKane, Peter, Lakeman, Nicki, Ljubez, Anja, de Silva, Ramesh, Conway, Dwayne S. G., Wright, Judith, Exley, Donna, Sirker, Alexander A, Andiapen, Mervyn, Richards, Amy J., Hoole, Stephen P, Wong, Lisa, Witherow, Fraser N., Munro, Melanie J., Johnston, Nicola, Harbinson, Mark, McEvoy, Michelle, Walsh, Simon, Brown, Caroline, Douglas, Hanna, Luckie, Matthew, Charles, Thabitha, Kolakaluri, Laurel, Phillips, Hannah, Sobolewska, Jolanta, Morby, Louise, Hallett, Karen, Corbett, Carolyn, Winstanley, Lynne, Jeetley, Paramjit, Smit, Angelique, Patel, Niket, Kotecha, Tushar, Travill, Christopher, Gent, Susan, Karimullah, Iqbal, Hussain, Nafisa, Al-Bustami, Mahmud, Braganza, Denise, Haines, Fiona, Taaffe, Joanne, Henderson, Robert, Burton, Jane, Pointon, Kate, Colton, Maria, Naik, Surendra, King, Rachel, Mathew, Thomas, Brown, Ammani, Docherty, Andrew, Berry, Colin, McCloy, Lisa, Collison, Damien, Robb, Kate, Roditi, Giles, Paterson, Craig, Crawford, Wenda, Kelly, Joanne, McGregor, Lorraine, Moriarty, Andrew J, Mackin, Anne, Glover, Jason D., Knight, Janet P, Pradhan, Jiwan, Mikhail, Ghada, Bose, Tuhina, Francis, Darrel P., Dzavik, Vladimir, Goodman, Shaun, Gosselin, Gilbert, Gosselin, Gilbert, Proietti, Anna, Brousseau, Myriam, Corfias, Magalie, Blaise, Patricia, Harvey, Luc, Diaz, Ariel, Rheault, Philippe, Barrero, Miguel, Gagné, Carl-Éric, Alarie, Patricia, Pépin-Dubois, Yanek, Arcand, Linda, Costa, Ricardo, Roy, Isabelle, Tung Sia, Ying, Montpetit, Estelle, Lemay, Catherine, Gisbert, Alejandro, Gervais, Pierre, Rheault, Alain, Drouin, Katia, Carl Phaneuf, Denis, Bergeron, Christine, Gosselin, Gilbert, Shelley, Christine, Masson, Christine, Garg, Pallav, Carr, Sandy, Bone, Catherine, Chow, Benjamin J.W., Moga, Ermina, Hessian, Renee C., Kourzenkova, Janetta, Beanlands, Rob S., Walter, Olga, Davies, Richard F., Bainey, Kevin R., Hogg, Norma, Welsh, Suzanne, Cheema, Asim N., Bagai, Akshay, Wald, Ron, Goodman, Shaun, Kushniriuk, Khrystyna, Joseph Graham, John, Hussain, Mohammed, Peterson, Mark, Bello, Olugbenga, Chow, Chi-Ming, Abramson, Beth, Nazir Cheema, Asim, Syed, Ishba, Hussain, Mohammed, Kushniriuk, Khrystyna, Cha, James, Otis, Judy, Otis, Rebecca, Howarth, Andrew G, Seib, Michelle M, Rivest, Sandra M, Sandonato, Rosa, Wong, Graham, Chow, Jackie, Starovoytov, Andrew, Uchida, Naomi, Meadows, Ngaire, Uxa, Amar, Asif, Nadia, Tavares, Suzana, Galiwango, Paul, Bozek, Bev, Kassam, Saleem, Shier, Maria, Mukherjee, Ashok, Larmand, Lori-Ann, Ricci, A. Joseph, Janmohamed, Amir, Hart, Brenda, Lam, Andy, Marucci, Jane, Tai, Sharon, Mehta, Shamir, Brons, Sonya, Beck, Chris, Wong, Glenda, Etherington, Krystal, Arumairajah, Thippeekaa, Udell, Jacob, Aprile, Maria, Karlsson, Sara, Webber, Susan, Généreux, Philippe, Mercure, Chantale, Hameed, Adnan, Aedy, Nancy, Daba, Ledjalem, Farquharson, Fran, Siddiqui, Anam, Carlos Carvalho, Antonio, Lopes, Renato D., Hueb, Whady, Emy Takiuti, Myrthes, Cury Rezende, Paulo, Eustáquio Ribeiro Silva, Expedito, Ciappina Hueb, Alexandre, Pizzol Caetano, Leonardo, Schaan de Quadros, Alexandre, Abdala Karam Kalil, Renato, Peixoto Deiro, Aline, Luiz da Costa Vieira, José, Manica Muller, Alice, Antonieta Pereira de Moraes, Maria, Píccaro de Oliveira, Pedro, Maria Ascoli, Bruna, Bridi, Leonardo, Zottis Poletti, Sílvia, Savaris, Simone, Vitola, João V, Cerci, Rodrigo J, Zier, Sandra S., Farias, Fabio R, Veiga Jr, Vilmar, Fernandes, Miguel M, Antonio Marin-Neto, José, Schmidt, André, de Oliveira Lima Filho, Moysés, Franca da Cunha, Diego, Mendes Oliveira, Ricardo, Reynaldo Abbud Chierice, João, Polanczyk, Carísi A., Rucatti, Guilherme G, Furtado, Mariana V., Igansi, Fernanda, Smidt, Luis F., Haeffner, Mauren P, Carlos Carvalho, Antonio, Almeida, Viviane, Pucci, Gustavo, Sanchez de Souza, Gabriela, Lyra, Flavio, Rabelo Alves Junior, Alvaro, Almeida, Mayana, dos Santos, Viviane, Dracoulakis, Marianna D. A., Oliveira, Natalia S, Lima, Rodolfo G. S. D, Figueiredo, Estevao, Edilena Paulino Azevedo, Bruna, Ricardo Caramori, Paulo, Bizzaro Santos, Marco, Germann, Amanda, Gomes, Vitor, Homem, Rosa, Magedanz, Ellen, Tumelero, Rogerio, Laimer, Rosane, Tognon, Alexandre, Dall’Orto, Frederico, Mesquita, Claudio T., Santos, Roberta P, Colafranseschi, Alexandre S., Oliveira, Amarino C., Carvalho, Luiz A., Palazzo, Isabella C., Sousa, Andre S., Eustáquio Ribeiro da Silva, Expedito, Gabriel Melo de Barros e Silva, Pedro, Yumi Okada, Mariana, de Pádua Silva Baptista, Luciana, Paula Batista, Ana, Jamus Rodrigues, Marcelo, Nogueira Rabaça, Aline, Valério Coimbra de Resende, Marcos, Francisco Saraiva, Jose, Miranda Trama, Larissa, Silva, Talita, Thais de Souza Ormundo, Camila, Vicente, Carla, Costantini, Costantino, Pinheiro, Caroline, Komar, Daniele, Szwed, Hanna, Demkow, Marcin, Kepka, Cezary, Teresinska, Anna, Walesiak, Olga, Kryczka, Karolina, Malinowska, Katarzyna, Henzel, Jan, Solecki, Mateusz, Kaczmarska, Edyta, Mazurek, Tomasz, Maksym, Jakub, Wojtera, Karolina, Fojt, Anna, Szczerba, Ewa, Drozdz, Jaroslaw, Czarniak, Bartosz, Frach, Malgorzata, Szymczyk, Konrad, Niedzwiecka, Iwona, Sobczak, Sebastian, Ciurus, Tomasz, Jakubowski, Piotr, Misztal-Teodorczyk, Magdalena, Teodorczyk, Dawid, Swiderek, Marta, Fratczak, Aleksandra, Wojtala, Ewelina, Szkopiak, Marcin, Lebioda, Patrycja, Wlodarczyk, Michal, Plachcinska, Anna, Kusmierek, Jacek, Miller, Magdalena, Marciniak, Halina, Wojtczak-Soska, Karolina, Łuczak, Katarzyna, Tarchalski, Tomasz, Cichocka-Radwan, Anna, Szwed, Hanna, Karwowski, Jaroslaw, Anna Szulczyk, Grazyna, Witkowski, Adam, Kukuła, Krzysztof, Celińska-Spodar, Małgorzta, Zalewska, Joanna, Gajos, Grzegorz, Bury, Krzysztof, Pruszczyk, Piotr, Łabyk, Andrzej, Roik, Marek, Szramowska, Agnieszka, Zdończyk, Olga, Łoboz-Grudzień, Krystyna, Jaroch, Joanna, Sokalski, Leszek, Brzezińska, Barbara, Lesiak, Maciej, Łanocha, Magdalena, Reczuch, Krzysztof W., Kolodziej, Adam, Kalarus, Zbigniew, Swiatkowski, Andrzej, Szulik, Mariola, Musial, Wlodzimierz J., Marcinkiewicz-Siemion, Marta, Bockeria, Olga, Bockeria, Leo, Bockeria, Olga, Petrosyan, Karen, Kudzoeva, Zalina, Trifonova, Tatiana, Aripova, Nodira, Chernyavskiy, Alexander M., Naryshkin, Ivan A., Kretov, Evgeniy I., Kuleshova, Alena, Grazhdankin, Igor O., Malaev, Dastan, Bershtein, Leonid L., Sayganov, Sergey A., Subbotina, Irina, Kuzmina-Krutetskaya, Anastasia M., Gumerova, Victoria, Zbyshevskaya, Elizaveta V., Katamadze, Nana O., Nikolaeva, Olga B., Kozlov, Pavel S., Kozulin, Vikentiy Y., Lubinskaya, Ekaterina I., Luis Lopez-Sendon, Jose, Castro, Almudena, Lopez-Sendon, Jose, Fernández-Figares, Virginia, Castro, Almudena, Refoyo Salicio, Elena, Guzman, Gabriela, Galeote, Gabriel, Valbuena, Silvia, Peteiro, Jesús, Dolores Martínez-Ruíz, María, Pérez-Fernández, Ruth, Blanco-Calvo, Moisés, Cuenca-Castillo, José J, Alonso-Álvarez, Encarnación, Flores-Ríos, Xacobe, García-González, Paula, Prada-Delgado, Óscar, Barge-Caballero, Gonzalo, Ramon Gonzalez Juanatey, Jose, Seijas Amigo, Jose, Souto Bayarri, Miguel, Pubull Nuñez, Virginia, Ocaranza Sanchez, Raymundo, Cid Alvarez, Belen, Peña Gil, Carlos, Martinez Monzonis, Amparo, Sionis, Alessandro, Fernández Martínez, Ana, Vila Perales, Montserrat, Maria Padró, Josep, Serra Peñaranda, Antonio, García Picart, Joan, Ginel Iglesias, Antonino, Garcia-Moll Marimon, Xavier, Pons Lladó, Guillem, Carreras Costa, Francesc, Miro, Vicente, Igual, Begoña, Diez, Jose L, Calvillo, Pilar, Ortuño, F. Marin, Valdés Chávarri, M., Quintana Giner, M., Tello Montolliu, A., Romero Aniorte, A.I., Pinar Bermudez, E., Rivera Caravaca, JM., De La Morena, G., Gracida Blancas, Montserrat, Cañavate, Olga, Guerrero, Sonia, Riera, Silvia, Enrique Castillo Luena, Jose, Enrique Castillo Luena, Jose, Lasala, Maria, Fernandez-Aviles, Francisco, Lorenzo, Maria, Sobrino, Olga, Vazquez, Alexandra, Jiang, Lixin, Chen, Jiyan, Dong, Haojian, He, Peiyu, Xia, Chunli, Yang, Junqing, Zhong, Qi, Wu, Yongjian, Tian, Yanmeng, Li, Dongze, Ma, Yitong, Li, Xiaomei, Yang, Yining, Ma, Xiang, Yu, Zixiang, Zhao, Qian, Ji, Zheng, Li, Chunguang, Zhang, Lei, Zhao, Yu, Zhu, Bolin, Yang, Xinchun, Chen, Mulei, Chi, Hongjie, Wang, Yang, Zhang, Jing, Lin, Wenhua, Jing, Rui, Liu, Jingjing, Zeng, Hesong, Zhou, Qiang, Xu, Chang, Li, Zhuxi, Li, Junhua, Xiong, Luyang, Fu, Xin, Gao, Dan, Jiang, Dengke, Leng, Ran, Wang, Xutong, Yuan, Qianqian, Zhang, Lili, Yang, Bin, Bai, Ziliang, Li, Jianhua, Qi, Jie, Wang, Fei, Wang, Haitao, Yang, Bin, Yue, Zhou, Zhang, Zhulin, Wang, Songtao, Dong, Yumei, Mao, Jiajia, Zhang, Bin, Cheng, Gong, Li, Xiuhong, Yao, Xiaowei, Zhong, Nier, Zhou, Ning, Zhao, Yulan, Huang, Yaping, Zhou, Panpan, Fang, Xuehua, Su, Wei, Zeng, Qiutang, Kunwu, Yu, Peng, Yudong, Su, Xin, Su, Xi, Wang, Chen, Zhao, Yunhai, Li, Qingxian, Geng, Yaming, Wang, Yanfu, Nie, Shao-ping, Fan, Jing-yao, Feng, Si-ting, Wang, Xiao, Yan, Yan, Zhang, Hui-min, Yu, Qin, Chi, Lingping, Liu, Fang, Wang, Jian’an, Chen, Han, Jiang, Jun, Li, Huajun, Wang, Jian’an, Han, Yechen, Xu, Lihong, Zhang, Shuyang, Liu, Zhenyu, Liu, Zhenyu, Chen, Gang, Hu, Rongrong, Maggioni, Aldo P., Piero Perna, Gian, Pietrucci, Francesca, Marini, Marco, Gabrielli, Gabriele, Provasoli, Stefano, Di Donato, Anna, Verna, Edoardo, Monti, Lorenzo, Nardi, Barbara, Di Chiara, Antonio, Pezzetta, Francesca, Mortara, Andrea, Casali, Valentina, Galvani, Marcello, Attanasio, Chiara, Ottani, Filippo, Sicuro, Marco, Leone, Gianpiero, Pisano, Francesco, Bare, Cristina, Calabro, Paolo, Fimiani, Fabio, Formisano, Tiziana, Tarantini, Giuseppe, Barioli, Alberto, Cucchini, Umberto, Ramani, Federica, Luigi Andres, Anto, Racca, Emanuela, Rolfo, Fabrizio, Goletto, Cecilia, Briguori, Carlo, De Micco, Francesca, Amati, Roberto, Di Marco, Stefano, Vergoni, William, Tricoli, Martina, Russo, Aldo, Villella, Massimo, Fanelli, Raffaele, Douglas White, Harvey, Alsweiler, Caroline, Poh, Kian-Keong, Chai, Ping, Lau, Titus, Loh, Joshua P., Tay, Edgar L., Teoh, Kristine, Tan, Sik-Yin V, Teo, Lynette L., Sia, Winnie C, Ong, Ching-Ching, Leong, Audrey W, Wong, Raymond C., Loh, Poay-Huan, Kofidis, Theodoros, Xian Chan, Wan, Hui Chan, Koo, Foo, David, Hai Yan, Li, Loh Kwok Kong, Jason, Min Er, Ching, Haider Jafary, Fahim, Chua, Terrance, Ismail, Nasrul, Tun Kyaw, Min, Yip, Deborah, Doerr, Rolf, Doerr, Rolf, Stumpf, Juergen, Grahl, Dorit, Matschke, Klaus, Guenther, Franziska, Simonis, Gregor, Bonin, Kerstin, Kadalie, Clemens T., Sechtem, Udo, Wenzelburger, Ina, Ong, Peter, Gruensfelder, Susanne, Christian Schulze, P., Goebel, Bjoern, Lenk, Karsten, Nickenig, Georg, Sinning, Jan-Malte, Weber, Marcel, Werner, Nikos, Marthe Lang, Irene, Huber, Kurt, Schuchlenz, Herwig, Steinmaurer, Gudrun, Weikl, Stefan, Marthe Lang, Irene, Winter, Max-Paul, Andric, Tijana, Huber, Kurt, Tscharre, Maximilian, Jakl-Kotauschek, Gabriele, Wegmayr, Claudia, Jäger, Bernhard, Egger, Florian, Keltai, Matyas, Vertes, Andras, Sebo, Judit, Davidovits, Zoltan, Matics, Laszlone, Varga, Albert, Ágoston, Gergely, Fontos, Geza, Dekany, Gabor, Merkely, Bela, Bartykowszki, Andrea, Maurovich-Horvat, Pal, Kerecsen, Gabor, Jakal, Agnes, Hinic, Sasa, Djokic, Jelena, Zdravkovic, Marija, Mudrenovic, Vladan, Crnokrak, Bogdan, Beleslin, Branko D., Boskovic, Nikola N., Djordjevic-Dikic, Ana D., Petrovic, Marija T., Giga, Vojislav L., Dobric, Milan R., Stepanovic, Jelena J., Markovic, Zeljko Z., Mladenovic, Ana S., Cemerlic-Adjic, Nada, Velicki, Lazar, Kamenica, Sremska, Pupic, Ljiljana, Davidović, Goran, Simović, Stefan M., Vučić, Rada, Dekleva, Milica Nikola, Martinovic, Miroslav Stevo, Stevanovic, Gordana, Stankovic, Goran, Dobric, Milan, Apostolovic, Svetlana, Martinovic, Sonja Salinger, Stanojevic, Dragana, Escobedo, Jorge, Jesús-Pérez, Ramon de, Juarez, Benito, Baleón-Espinosa, Rubén, Campos-Santaolalla, Arturo S, Durán-Cortés, Elihú, Flores-Palacios, José M, García-Rincón, Andrés, Jiménez-Santos, Moisés, Peñafiel, Joaquín V, Ortega-Ramírez, José A, Valdespino-Estrada, Aquiles, Rosas, Erick Alexánderson, Canales Brassetti, María Fernanda, Vences Anaya, Diego Adrián, García, María Pérez, Carvajal Juarez, Isabel Estela, Rovalo, Magdalena Madero, Morales Rodríguez, Erick Donato, Selvanayagam, Joseph B., Rankin, Jamie, Murphy, Deirdre, Selvanayagam, Joseph B., Lee, Sau, Joseph, Majo X., Thomas, Prince, Thambar, Suku T., Chaplin, Melissa D, Boer, Stephanie C, Beltrame, John F., Stansborough, Jeanette K., Black, Marilyn, Hillis, Graham S., Bonner, Michelle M., Ireland, Kim F., Venn-Edmonds, Clare, Steg, Philippe-Gabriel, Abergel, Helene, Juliard, Jean-Michel, Thobois, Corine, Pasteur, C.H. Louis, Thuaire, Christophe, Tachot, Emilie, Dutoiu, Téodora, Laure, Christophe, Vassaliere, Christel, Steg, Philippe Gabriel, Abergel, Helene, Juliard, Jean-Michel, Fuentes, Axelle, Slama, Michel S., Eliahou, Ludivine, Cedex, Clamart, El Mahmoud, Rami, Dubourg, Olivier, Michaud, Pierre, Nicollet, Eric, Hadjih, Sarah, Cedex, Corbeil-Essonnes, Goube, Pascal, Brito, Patricia, Barone-Rochette, Gilles, Barone-Rochette, Gilles, Furber, Alain, Cornet, Charles, Bière, Loïc, Rautureau, Jeremy, Juceviciene, Agne, Kalibataite-Rutkauskiene, Irma, Keinaite, Laura, Laucevicius, Aleksandras, Laukyte, Monika, Celutkiene, Jelena, Mikolaitiene, Gelmina, Smigelskaite, Akvile, Tamasauskiene, Ilona, Urboniene, Agne, Kedhi, Elvin, Klinieken, Isala, Timmer, Jorik, Bouwhuis, Ilse, Hermanides, Rik, Nijmeijer, Lia, Kaplan, Eliza, Riezebos, Robert K., Samadi, Pouneh, Schoep Jeannette, J. M., Dongen, Elise van, Janzen, Elisabeth M., Niehe, Sander R., Suryapranata, Harry, Ahoud, Sandra, Vugt, Stijn van, Ramos, Ruben, Santa Marta, Hospital de, Cacela, Duarte, Santana, Ana, Fiarresga, Antonio, Sousa, Lidia, Marques, Hugo, Patricio, Lino, Selas, Mafalda, Bernanrdes, Luis, Silva, Filipa, Rio, Pedro, Freixo, Cláudia, Carvalho, Ramiro, Ferreira, Rui, Silva, Tiago, Rodrigues, Ines, Modas, Pedro, Portugal, Guilherme, Fragata, Jose, Pinto, Fausto J., Cabrita, Inês Zimbarra, Menezes, Miguel Nobre, Rocha, Andreia, Lopes, Guilhermina Cantinho, Figueiras, Francisca Patuleia, Almeida, Ana Gomes, Coelho, Andreia, CanVas Silva, Pedro, Capinha, Marta, Nobre, Angelo, Caetano, Maria Inês, Francisco, Ana Rita, Silva, Susana, Ferreira, Nuno, de Gaia, Vila Nova, Lopes, Ricardo L., Diaz, Rafael, Guzman, Luis, Tinnirello, Veronica, Figal, Julio César, Nicolás Mungo, Matías, Buenos Aires, Ciudad Autonoma de, Méndiz, Oscar, Cortés, Claudia, Favaloro, Roberto René, Alvarez, Carlos, Garcia, Marina, Blanca, Bahia, Courtis, Javier, Godoy, Valeria, Zeballos, Gabriela, Schiavi, Lilia, Actis, Maria Victoria, Rubio, Mariano, Scaro, Graciela, White, Harvey Douglas, Alsweiler, Caroline, Devlin, Gerard Patrick, Low, Liz, Fisher, Raewyn, Scales, Jayne, Abercrombie, Kirsty, Stewart, Ralph Alan Huston, Howell, Leah, White, Harvey Douglas, Patten, Cathrine, Benatar, Jocelyne, Kedev, Sasko, Mitevska, Irena Peovska, Kostovska, Elizabeta Srbinovska, Pejkov, Hristo, Held, Claes, Held, Claes, Eggers, Kai, Frostfelt, Gunnar, Björklund, Christina, Johnston, Nina, Andreasson, Maria, Olsowka, Maciej, Essermark, Marie, Åkerblom, Axel, Soveri, Inga, Aspberg, Johannes, Persson, Liselotte, Beyar, Rafael, Sharir, Tali, Nikolsky, Eugenia, Sharir, Tali, Harel, Or, Elian, Dan, Kerner, Arthur, Bentzvi, Margalit, Massalha, Samia, Helmer, Ludmila, Kohsaka, Shun, Fukuda, Keiichi, Ueda, Ikuko, Kohsaka, Shun, Fujita, Jun, Yasuda, Satoshi, Furukawa, Akemi, Hirase, Kanae, Nagai, Toshiyuki, Otsuka, Fumiyuki, Nishimura, Shigeyuki, Nakano, Shintaro, de Werf, Frans Van, Goetschalckx, Kaatje, Goetschalckx, Kaatje, Robesyn, Valerie, de Werf, Frans Van, Claes, Kathleen, White, Harvey Douglas, Alsweiler, Caroline, Hung, Chung-Lieh, Yang, Yi-Hsuan, Yun, Chun-Ho, Hou, Charles Jia-Yin, Kuo, Jen-Yuan, Yeh, Hung-I, Hung, Ta-Chuan, Li, Jiun-Yi, Chien, Chen-Yen, Tsai, Cheng-Ting, Liu, Chun-Chieh, Yu, Fa-Chang, Lin, Yueh-Hung, Lan, Wei-Ren, Yen, Chih-Hsuan, Tsai, Jui-Peng, Sung, Kuo-Tzu, Ntsekhe, Mpiko, Pandie, Shaheen, Philander (Nee Talliard), Constance, Viljoen, Charle A, Mtana, Noloyiso, De Andrade, Marianne, Maggioni, Aldo P., Moccetti, Tiziano, Anesini, Adriana, Rossi, M.Grazia, Maspoli, Simona, Mombelli, Manuela, Abdelhamid, Magdy, Talaat, Ahmed, Adel, Ahmed, Kamal, Ahmed, Mahrous, Hossam, Kaffas, Sameh El, Fishawy, Hussien El, Pop, Calin, Claudia, Matei, Popescu, Bogdan A., Ginghina, Carmen, Rosca, Monica, Deleanu, Dan, Beladan, Carmen C., Iliescu, Vlad A., Al-Mallah, Mouaz H., Zahrani, Sarah, Aljzeeri, Ahmed, Najm, Hani, Alghamdi, Ali, Mogrovejo Ramos, Walter Enrique, Monsalve Davila, Marco Antonio, White, Harvey Douglas, Alsweiler, Caroline, Kuanprasert, Srun, Mai, Chiang, Prommintikul, Arintaya, Nawarawong, Weerachai, Khwakhong, Supatchara, Woragidpoonpol, Surin, Chaiyasri, Anong, Tepsuwan, Thitipong, Mekara, Warangkana, Taksaudom, Noppon, Kulthawong, Supap, Rimsukcharoenchai, Chataroon, Amaritakomol, Anong, Euathrongchit, Juntima, Wannasopha, Yutthaphan, Yamwong, Sukit, Panpunuan, Pachara, Sritara, Piyamitr, Aramcharoen, Suthara, Meemuk, Krissada, White, Harvey Douglas, Alsweiler, Caroline, Khairuddin, Ahmad, Mokhtar, Noor Syamira, Hadi, Hafidz Abd, Basri, Nor Asiah, Yahaya, Shaiful Azmi, Yusnida, Irni, Hashim, Humayrah, Harrington, Robert, Williams, David, Alexander, Karen P., Berger, Jeffrey, Harrington, Robert, Mark, Daniel, O’Brien, Sean M., Rosenberg, Yves, Shaw, Leslee J., Ballantyne, Christie, Berman, Daniel, Beyar, Rafael, Bhargava, Balram, Buller, Chris, (Tony) Carvalho*, Antonio, Chaitman, Bernard R., Diaz, Rafael, Doerr, Rolf, Dzavik, Vladimir, Goodman, Shaun, Gosselin, Gilbert, Hachamovitch, Rory, Hamm, Christian, Held, Claes, Helm, Malte, Huber, Kurt, Jiang, Lixin, Keltai, Matyas, Kohsaka, Shun, Lang, Irene, Lopes, Renato, Lopez-Sendon, Jose, Maggioni, Aldo, Bairey Merz, C. Noel, Min, James, Peterson, Eric, Picard, Michael H., Selvanayagam, Joseph, Senior, Roxy, Sharir, Tali, Steg, Gabriel, Szwed, Hanna, de Werf, Frans Van, Weintraub, William, White, Harvey, Williams, David, Ballantyne, Christie, Calfas*, Karen, Chaitman, Bernard R., Champagne, Mary Ann, Davidson, Michael, Fleg, Jerome, McCullough, Peter A., Stone, Peter, Menasche, Philippe, Davidson*, Michael, Fremes, Stephen, Guyton, Robert, Mack, Michael, Mohr, Fred, Rao, Anupama, Sabik, Joe, Shapira, Oz, Taggart, David, Tatoulis, James, Williams, David, Blankenship, Jim, Brener, Sorin, Buller, Chris, Colombo, Antonio, Bruyne, Bernard de, Généreux, Philippe, Harrington, Robert, Kereiakes, Dean, Lefevre, Thierry, Moses, Jeffrey, Chaitman, Bernard R., Alexander, Karen P., Mahaffey, Ken, White, Harvey, Chaitman, Bernard R., Cruz-Flores, Salvador, Danchin, Nicholas, Feen, Eli, Garcia, Mario J., Hauptman, Paul, Laddu, Abhay A., Passamani, Eugene, Pina, Ileana L., Simoons, Maarten, Skali, Hicham, Thygesen, Kristian, Waters, David, Alexander, Karen P., Endsley, Patricia, Esposito, Gerard, Kanters, Jeffrey, Pownall, John, Stournaras, Dimitrios, Shaw, Leslee J., Berman, Daniel, Friedrich, Matthias, Hachamovitch, Rory, Kwong, Raymond, Min, James, Oliver, Dana, Picard, Michael H., Harrell, Frank, Blume, Jeffrey, Lee, Kerry, O’Brien, Sean M., Berger, Jeffrey, Held, Claes, Kullo, Iftikhar, McManus, Bruce, Newby, Kristin, Mark, Daniel, Cohen, David, Weintraub, William, Merz, C. Noel Bairey, Bugiardini, Raffaele, Celutkiene, Jelena, Escobedo, Jorge, Hoye, Angela, Lyubarova, Radmila, Mattina, Deirdre, Peteiro, Jesus, Alexander, Karen P., Berger, Jeffrey, Harrington, Robert, O’Brien, Sean M., Rosenberg, Yves, Mark, Daniel, Mark, Daniel, Shaw, Leslee J., Berman, Dan, Chaitman, Bernard R., Fleg, Jerome, Kwong, Raymond, Picard, Michael H., Senior, Roxy, Min, James, Leipsic, Jonathan, Ali, Ziad, Williams, David, Fleg, Jerome, Berger, Jeffrey, Chaitman, Bernard R., Alexander, Karen P., Alexander, Karen P., Fleg, Jerome, Mathew, Roy, O’Brien, Sean M., Sidhu, Mandeep, Friedman, Lawrence, Anderson, Jeffrey, Berg, Jessica, DeMets, David, Gibson, C. Michael, Lamas, Gervasio, Deming, Nicole, Himmelfarb, Jonathan, Ouyang, Pamela, Woodard, Pamela, Harrell, Frank, Nwosu, Samuel, Rosenberg, Yves, Fleg, Jerome, Kirby, Ruth, Jeffries, Neal, Berger, Jeffrey, Sidhu, Mandeep, Denaro*, Jean E., Mavromichalis, Stephanie, Chan, Kevin, Cobb, Gia, Contreras, Aira, Cukali, Diana, Ferket, Stephanie, Gabriel, Andre, Hansen, Antonietta, Roberts, Arline, Chang, Michelle, Islam, Sharder, Wayser, Graceanne, Yakubov, Solomon, Yee, Michelle, Callison, Caroline, Hogan, Isabelle, Qelaj, Albertina, Pirro, Charlotte, Loo, Kerrie Van, Wisniewski, Brianna, Gilsenan, Margaret, Lang, Bevin, Mohamed, Samaa, Esquenazi-Karonika, Shari, Mathews, Patenne, Naumova, Anna, Lyo, Jihyun, Setang, Vincent, Xavier, Mark, O’Brien, Sean M., Alexander, Karen P., Mark, Daniel B., Anstrom, Kevin, Baloch, Khaula, Blount, Janet, Cowper, Patricia, Davidson-Ray, Linda, Drew, Laura, Harding, Tina, Knight, J David, Liu, Diane Minshall, O’Neal, Betsy, Redick, Thomas, Jones, Philip, Nugent, Karen, Wang, Grace Jingyan, Shaw, Leslee J., Phillips, Lawrence, Goyal, Abhinav, Hetrick, Holly, Oliver, Dana, Berman, Daniel, Hayes, Sean W., Friedman, John D., Gerlach, R. James, Hyun, Mark, Miranda-Peats, Romalisa, Slomka, Piotr, Thomson, Louise, Kwong, Raymond Y., Friedrich, Matthias, Mongeon, Francois Pierre, Michael, Steven, Picard, Michael H., Hung, Judy, Scherrer-Crosbie, Marielle, Zeng, Xin, Chaitman, Bernard R., Eckstein, Jane, Guruge, Bandula, Streif, Mary, Ali, Ziad, Genereux, Philippe, Alfonso, Maria A., Corral, Maria P., Garcia, Javier J., Horst, Jennifer, Jankovic, Ivana, Konigstein, Maayan, Lustre, Mitchel B., Peralta, Yolayfi, Sanchez, Raquel, Min, James, Arsanjani, Reza, Budoff, Matthew, Elmore, Kimberly, Gomez, Millie, Hague, Cameron, Hindoyan, Niree, Leipsic, Jonathan, Nakanishi, Rine, Srichai-Parsia, M. Barbara, Yeoh, Eunice, Youn, Tricia, Maggioni, Aldo P., Bianchini, Francesca, Ceseri, Martina, Lorimer, Andrea, Magnoni, Marco, Orso, Francesco, Sarti, Laura, Tricoli, Martinia, Carvalho, Antonio, Lopes, Renato, Barbosa, Lilian Mazza, Duarte, Tauane Bello, Soares, Tamara Colaiácovo, Aveiro Morata, Julia de, Carvalho, Pedro, Carvalho Maffei, Natalia de, Egydio, Flávia, Kawakami, Anelise, Oliveira, Janaina, Piloto, Elissa Restelli, Pozzibon, Jaqueline, Goodman, Shaun, Camara, Diane, Mowafy, Neamat, Spindler, Caroline, Jiang, Lixin, Dai, Hao, Feng, Fang, Li, Jia, Li, Li, Liu, Jiamin, Xie, Qiulan, Zhang, Haibo, Zhang, Jianxin, Zhang, Lihua, Zhang, Liping, Zhang, Ning, Zhong, Hui, Diaz, Rafael, Escobar, Claudia, Martin, Maria Eugenia, Pascual, Andrea, Lopez-Sendon, José, Moraga, Paloma, Hernandez, Victoria, Castro, Almudena, Posada, Maria, Fernandez, Sara, Villanueva, José Luis Narro, Selgas, Rafael, Steg, Gabriel, Abergel, Helene, Juliard, Jean Michel, White, Harvey, Alsweiler, Caroline, de Werf, Frans Van, Claes, Kathleen, Goetschalckx, Kaatje, Luyten, Ann, Robesyn, Valerie, Selvanayagam, Joseph B., Murphy, Deirdre, Ahmed, Asker, Bhatt, Richa, Chadha, Nitika, Kumar, Vijay, Lubna, Sadath, Naik, Pushpa, Pandey, Shruti, Ramasamy, Karthik, Saleem, Mohammed, Sharma, Pratiksha, and Siddaram, Hemalata

Published
2024
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42. Using Participatory Design to Engage Physicians in the Development of a Provider-Level Performance Dashboard and Feedback System

Author

Patel, Sajan, Pierce, Logan, Jones, Maggie, Lai, Andrew, Cai, Michelle, Sharpe, Bradley A, and Harrison, James D

Subjects
Health Services and Systems, Health Sciences, Clinical Research, Behavioral and Social Science, Generic health relevance, Benchmarking, Feedback, Hospitalists, Humans, Quality Improvement, Surveys and Questionnaires, Public Health and Health Services, General & Internal Medicine, Epidemiology, Health services and systems
Abstract

Problem definitionPerformance feedback, in which clinicians are given data on select metrics, is widely used in the context of quality improvement. However, there is a lack of practical guidance describing the process of developing performance feedback systems.Initial approachThis study took place at the University of California, San Francisco (UCSF) with hospitalist physicians. Participatory design methodology was used to develop a performance dashboard and feedback system. Twenty hospitalist physicians participated in a series of six design sessions and two surveys. Each design session and survey systematically addressed key components of the feedback system, including design, metric selection, data delivery, and incentives. The Capability Opportunity Motivation and Behavior (COM-B) model was then used to identify behavior change interventions to facilitate engagement with the dashboard during a pilot implementation.Key insights, lessons learnedIn regard to performance improvement, physicians preferred collaboration over competition and internal motivation over external incentives. Physicians preferred that the dashboard be used as a tool to aid in clinical practice improvement and not punitively by leadership. Metrics that were clinical or patient-centered were perceived as more meaningful and more likely to motivate behavior change.Next stepsThe performance dashboard has been introduced to the entire hospitalist group, and evaluation of implementation continues by monitoring engagement and physician attitudes. This will be followed by targeted feedback interventions to attempt to improve performance.

Published
2022

46. Lessons learned from academic medical centers’ response to the COVID-19 pandemic in partnership with the Navajo Nation

Author

Bongiovanni, Tasce, Shamasunder, Sriram, Brown, William, Carpenter, Cristina Rivera, Pantell, Matthew, Ghali, Bassem, and Harrison, James D

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Clinical Research, Generic health relevance, Good Health and Well Being, Academic Medical Centers, COVID-19, Humans, Pandemics, General Science & Technology
Abstract

IntroductionStructural forces that drive health inequalities are magnified in crises. This was especially true during the COVID-19 pandemic, and minority communities were particularly affected. The University of California San Francisco and Health, Equity, Action, Leadership Initiative jointly sent volunteer teams of nurses and doctors to work in the Navajo Nation during the COVID-19 pandemic. This presented an opportunity to explore how academic medical centers (AMCs) could effectively partner with vulnerable communities to provide support during healthcare crises. Therefore, the aims of this study were to describe volunteers' perspectives of academic-community partnerships by exploring their personal, professional and societal insights and lessons learned based on their time in the Navajo Nation during COVID-19.MethodsWe recruited key informants using purposeful sampling of physicians and nurses who volunteered to go to the Navajo Nation during the spring 2020 COVID-19 surge, as well as hospital administrators and leaders involved in organizing the COVID-19 efforts. We used in-depth qualitative interviews to explore key informants' experiences pre-departure, during their stay, and after their return, as well as perspectives of the partnership between an AMC and the Navajo Nation. We used thematic analysis to systematically identify, analyze and report patterns (themes) within the data.ResultsIn total, 37 clinicians and hospital administrators were interviewed including 14 physicians, 16 nurses, and 7 health system leaders. Overall, we found 4 main themes each with several subthemes that defined the partnership between the AMC and the Navajo Nation. Mission and values incorporated civic duty, community engagement, leadership commitment and employee dedication. Solidarity, trust and humility encompassed pre-existing trust, workforce sustainability, humility and erasure of 'savior narratives.' Coordination included logistical coordination, flexibility, selectivity of who and what traveled to the response and coordination around media response. Workforce preparation and support encompassed understanding of historical context and providing healthcare in limited settings, dangers of inadequate preparation and the need for emotional support.ConclusionThis study provides guidelines which AMCs might use to develop and improve partnerships they have or would like to develop with vulnerable communities. These guidelines may even be broadly applied to partnerships outside of a pandemic response. Importantly, such partnerships need to be built with trust and with an eye towards sustainability and long-term relationships as opposed to 'medical missions'.

Published
2022

47. Addressing the challenges of conducting community-engaged research during COVID-19: Rapid development and evaluation of a COVID-19 Research Patient and Community Advisory Board (PCAB)

Author

Harrison, James D, Palmer, Nynikka RA, Cabrera, Abby, Fleisher, Paula, Wong, Erica, LeSarre, Monique, Grumbach, Kevin, Banta, Jim, Tealer, Lisa, Reynolds, Andrew, Wassmann, Arianna, Rose, Teri, and Nguyen, Tung

Subjects
Health Services and Systems, Health Sciences, Behavioral and Social Science, Bioengineering, Generic health relevance, Good Health and Well Being, COVID-19, advisory committees, stakeholder engagement, healthcare disparities, translational science
Abstract

IntroductionWe created a COVID-19 Research Patient and Community Advisory Board (PCAB) to provide patient and community input into clinical and translational research studies. The purpose of this article is to describe the PCAB creation, implementation, and evaluation.MethodsWe identified PCAB members who had participated in previous stakeholder engaged activities at our institution and invited their participation. We created a systematic consultation process where researchers could submit plain language research summaries and questions for the PCAB. A facilitated 1-hour virtual consultation was then held where PCAB members provided feedback. We assessed satisfaction of PCAB members and researchers who received consultations using surveys. We also reviewed video recordings of PCAB consultations and reflections from team meetings to identify key lessons learned.ResultsTwenty-seven PCAB members took part in 23 consultation sessions. Twenty-two completed an evaluation survey (81% response rate). Most members agreed or strongly agreed their opinions were valued (86%), it was a productive use of time (86%) and were satisfied (86%). Nineteen researchers completed an evaluation survey (83% response rate). Researchers reported positive experiences of working with the PCAB. Additional insights include limited funding in COVID-19 research for equitable community engagement, deficiencies in researcher communication skills, and a lack of cultural humility incorporated into study activities.ConclusionsPCAB members provided recommendations that maximized the patient-centeredness and health equity focus of COVID-19 research. The detailed description of the process of developing, implementing, and evaluating our PCAB can be used as a template for others wishing to replicate this engagement model.

Published
2022

48. The three-part model for coding causes and mechanisms of healthcare-related adverse events

Author

Southern, Danielle A, Harrison, James E, Romano, Patrick S, Le Pogam, Marie-Annick, Pincus, Harold A, and Ghali, William A

Subjects
Health Services and Systems, Health Sciences, Neurosciences, Delivery of Health Care, Health Facilities, Humans, International Classification of Diseases, Adverse events, Quality and safety, ICD11, Information Systems, Clinical Sciences, Medical Informatics, Health services and systems
Abstract

ICD-11 provides a promising new way to capture healthcare-related harm or injury. In this paper, we elaborate on the framework for describing healthcare-related events where there is a presumed causal link between an event and underlying healthcare-related factors. The three-part model for describing healthcare-related harm or injury in ICD-11 consists of (1) a healthcare-related activity that is the cause of injury or other harm (selected from Chapter 23 of ICD-11); (2) a mode or mechanism of injury or harm, related to the underlying cause (also from Chapter 23 of ICD-11); and (3) the harmful consequences of the event to the patient, selected from any of Chapters 1 through 22 of ICD-11 (most importantly, the injury or harm experienced by the patient). Concepts from these three elements are linked/clustered through postcoordination to reflect the three-part model in a single coded expression. ICD-11 contains many novel features, and the three-part model described here for healthcare-related adverse events is a notable example.

Published
2021

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