Learning from an equitable, data-informed response to COVID-19: Translating knowledge into future action and preparation

Introduction

The COVID-19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community-level data to create learning systems in support of local public health decision responses. Early in the COVID-19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic.

Methods

In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID-19. The theoretical drivers were explored qualitatively through a series of nine 45-min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID-19 experiences.

Results

Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID-19: real-time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool.

Conclusions

There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID-19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives.

[Artificial Intelligence and employee's health - new challenges]

BACKGROUND

The presence of artificial intelligence (AI) in many areas of social life is becoming widespread. The advantages of AI are being observed in medicine, commerce, automobiles, customer service, agriculture and production in factory settings, among others. Workers first encountered robots in the work environment in the 1960s. Since then, intelligent systems have become much more advanced. The expansion of AI functionality in the work environment exacerbates human health risks. These can be physical (lack of adequate machine control, accidents) or psychological (technostress, fear, automation leading to job exclusion, changes in the labour market, widening social differences).

MATERIAL AND METHODS

The purpose of this article is to identify, based on selected literature, possible applications of AI and the potential benefits and risks for humans.

RESULTS

The main area of interest was the contemporary work environment and the health consequences associated with access to smart technologies. A key research area for us was the relationship between AI and increased worker control.

CONCLUSIONS

In the article, the authors emphasize the importance of relevant EU legislation that guarantees respect for the rights of the employed. The authors put forward the thesis that the new reality with the widespread use of AI, requires an analysis of its impact on the human psycho-social and health situation. Thus, a legal framework defining the scope of monitoring and collection of sensitive data is necessary. Med Pr. 2023;74(3):227-33.

Assessment of the Study Habits of Residents in Physical Medicine and Rehabilitation Programs in Saudi Arabia: A Cross-Sectional Study

Background

Residents in training must employ a variety of study strategies, as they not only participate in academic studies but also interact with patients. This study aimed to evaluate the study practices and factors affecting those practices among Saudi Arabian physical medicine and rehabilitation residents during their residency program.

Methods

In this cross-sectional study, a previously used questionnaire was distributed to Saudi Arabian physiatry residents from July 1 to August 15, 2022, via a social media platform and completed using a Google Forms survey. A Microsoft Excel spreadsheet was used to collect, clean, and import the data before IBM SPSS Statistics for Windows, version 22.0 was utilized for statistical analysis.

Results

The data of 94.91% of respondents were included in the analysis. Individuals who were female, unmarried or divorced, and without children predominated. Only 17.9% (n = 10) of the residents believed that their training program effectively prepared them to pass the board examination, which was the most strongly motivating factor for studying for 85.7% of respondents. Over two-thirds of the residents mentioned that they regularly exercise. Residents who studied more than 11 hours per week had a significantly lower score in the category of factors that negatively affect examination performance (M = 12.33 ± 2.82, F = 2.794, P < 0.05). Females, final-year residents, and Riyadh residents studied more than their counterparts.

Conclusion

Our study is the first to investigate how Saudi physiatrists study, with the finding that current physiatry residents employ a combination of traditional and contemporary learning strategies. This information can help stakeholders to understand current training challenges, improve the quality of training for physiatry residents, and create an ideal learning environment.

Learning Systems versus Future Everyday Domestic Life: A Designer's Interpretation of Social Practice Imaginaries

Smart home technologies with the ability to learn over time promise to adjust their actions to inhabitants’ unique preferences and circumstances. For example, by learning to anticipate their routines. However, these promises show frictions with the reality of everyday life, which is characterized by its complexity and unpredictability. These systems and their design can thus benefit from meaningful ways of eliciting reflections on potential challenges for integrating learning systems into everyday domestic contexts, both for the inhabitants of the home as for the technologies and their designers. For example, is there a risk that inhabitants’ everyday lives will reshape to accommodate the learning system’s preference for predictability and measurability? To this end, in this paper we build a designer’s interpretation on the Social Practice Imaginaries method as developed by Strengers et al. to create a set of diverse, plausible imaginaries for the year 2030. As a basis for these imaginaries, we have selected three social practices in a domestic context: waking up, doing groceries, and heating/cooling the home. For each practice, we create one imaginary in which the inhabitants’ routine is flawlessly supported by the learning system and one that features everyday crises of that routine. The resulting social practice imaginaries are then viewed through the perspective of the inhabitant, the learning system, and the designer. In doing so, we aim to enable designers and design researchers to uncover a diverse and dynamic set of implications the integration of these systems in everyday life pose.

Mandatory reporting legislation in Canada: improving systems for patient safety?

Patient safety is a complex systems issue. In this study, we used a scoping review of peer-reviewed literature and a case study of provincial and territorial legislation in Canada to explore the influence of mandatory reporting legislation on patient safety outcomes in hospital settings. We drew from a conceptual model that examines the components of mandatory reporting legislation that must be in place as a part of a systems governance approach to patient safety and used this model to frame our results. Our results suggest that mandatory reporting legislation across Canada is generally designed to gather information about - rather than respond to and prevent - patient safety incidents. Overall, we found limited evidence of impact of mandatory reporting legislation on patient safety outcomes. Although legislation is one lever among many to improve patient safety outcomes, there are nonetheless several considerations for patient safety legislation to assist in broader system improvement efforts in Canada and elsewhere. Legislative frameworks may be enhanced by strengthening learning systems, accountability mechanisms and patient safety culture.

Recognition of Non-Manual Content in Continuous Japanese Sign Language

The quality of recognition systems for continuous utterances in signed languages could be largely advanced within the last years. However, research efforts often do not address specific linguistic features of signed languages, as e.g., non-manual expressions. In this work, we evaluate the potential of a single video camera-based recognition system with respect to the latter. For this, we introduce a two-stage pipeline based on two-dimensional body joint positions extracted from RGB camera data. The system first separates the data flow of a signed expression into meaningful word segments on the base of a frame-wise binary Random Forest. Next, every segment is transformed into image-like shape and classified with a Convolutional Neural Network. The proposed system is then evaluated on a data set of continuous sentence expressions in Japanese Sign Language with a variation of non-manual expressions. Exploring multiple variations of data representations and network parameters, we are able to distinguish word segments of specific non-manual intonations with 86% accuracy from the underlying body joint movement data. Full sentence predictions achieve a total Word Error Rate of 15.75%. This marks an improvement of 13.22% as compared to ground truth predictions obtained from labeling insensitive towards non-manual content. Consequently, our analysis constitutes an important contribution for a better understanding of mixed manual and non-manual content in signed communication.

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET

In this paper, a modified encoder-decoder structured fully convolutional network (ED-FCN) is proposed to generate the camera-like color image from the light detection and ranging (LiDAR) reflection image. Previously, we showed the possibility to generate a color image from a heterogeneous source using the asymmetric ED-FCN. In addition, modified ED-FCNs, i.e., UNET and selected connection UNET (SC-UNET), have been successfully applied to the biomedical image segmentation and concealed-object detection for military purposes, respectively. In this paper, we apply the SC-UNET to generate a color image from a heterogeneous image. Various connections between encoder and decoder are analyzed. The LiDAR reflection image has only 5.28% valid values, i.e., its data are extremely sparse. The severe sparseness of the reflection image limits the generation performance when the UNET is applied directly to this heterogeneous image generation. In this paper, we present a methodology of network connection in SC-UNET that considers the sparseness of each level in the encoder network and the similarity between the same levels of encoder and decoder networks. The simulation results show that the proposed SC-UNET with the connection between encoder and decoder at two lowest levels yields improvements of 3.87 dB and 0.17 in peak signal-to-noise ratio and structural similarity, respectively, over the conventional asymmetric ED-FCN. The methodology presented in this paper would be a powerful tool for generating data from heterogeneous sources.

Anaesthesia and perioperative incident reporting systems: Opportunities and challenges

Incident Reporting Systems (IRS) continue to be an important influence on improving patient safety. IRS can provide valuable insights into how to prevent patients from being harmed at the organizational level. But inadequate expectations and misuse, for performance assessment, patient safety measurement or research, have hindered the full IRS potential. Health care organizations need to develop effective strategies built on trust and truth telling to improve the impact of IRS. This requires strategies to address the limited resources to analyse the near-misses or adverse events; avoid the punitive drift through maintaining the anonymity and protective legislation; integrating IRS and avoiding its confusion with mandatory adverse event response systems; training data analysts to focus on the system instead of the individual through a balanced simple taxonomy; combine the analyses at the local level, to reinforce effective and personalized feedback, with the potential of a national or supranational learning platform.

Towards a Unified Theory of Learning and Information

In this paper, we introduce the notion of "learning capacity" for algorithms that learn from data, which is analogous to the Shannon channel capacity for communication systems. We show how "learning capacity" bridges the gap between statistical learning theory and information theory, and we will use it to derive generalization bounds for finite hypothesis spaces, differential privacy, and countable domains, among others. Moreover, we prove that under the Axiom of Choice, the existence of an empirical risk minimization (ERM) rule that has a vanishing learning capacity is equivalent to the assertion that the hypothesis space has a finite Vapnik-Chervonenkis (VC) dimension, thus establishing an equivalence relation between two of the most fundamental concepts in statistical learning theory and information theory. In addition, we show how the learning capacity of an algorithm provides important qualitative results, such as on the relation between generalization and algorithmic stability, information leakage, and data processing. Finally, we conclude by listing some open problems and suggesting future directions of research.

Learning Entropy as a Learning-Based Information Concept

Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-scale quantification of unusually large learning efforts of machine learning systems, was introduced as learning entropy (LE). The key finding with LE is that the learning effort of learning systems is quantifiable as a novelty measure for each individually observed data point of otherwise complex dynamic systems, while the model accuracy is not a necessary requirement for novelty detection. This brief paper extends the explanation of LE from the point of an informatics approach towards a cognitive (learning-based) information measure emphasizing the distinction from Shannon’s concept of probabilistic information. Fundamental derivations of learning entropy and of its practical estimations are recalled and further extended. The potentials, limitations, and, thus, the current challenges of LE are discussed.

Advance Care Planning in the Nursing Home Setting: A Practice Improvement Evaluation

This study evaluated a practice improvement initiative conducted over a 6 month period in 15 Canadian nursing homes. Goals of the initiative included: (1) use the Plan-Do-Study-Act (PDSA) model to improve advance care planning (ACP) within the sample of nursing homes; (2) investigate whether improved ACP practice resulted in a change in residents' hospital use and ACP preferences for home-based care; (3) engage participating facilities in regular data collection to inform the initiative and provide a basis for reflection about ACP practice and; (4) foster a team-based participatory care culture. The initiative entailed two cycles of learning sessions followed by implementation of ACP practice improvement projects in the facilities using a PDSA approach by participating clinicians (e.g., physicians, social workers, nurses). Clinicians reported significantly increased confidence in many dimensions of ACP activities. Rates of hospital use and resident preference for home-based care did not change significantly. The initiative established routine data collection of outcomes to inform practice change, and successfully engaged physicians and non-physician clinicians to work together to improve ACP practices. Results suggest recurrent PDSA cycles that engage a 'critical mass' of clinicians may be warranted to reinforce the standardization of ACP in practice.

Resource Selection in Cognitive Networks With Spiking Neural Networks

This paper explores the feasibility of a spiking neural network-based approach to cognitive networking, that is potentially suitable for low-power neuromorphic chips. We discuss the design of a cognitive network controller (CNC), which can dynamically optimize the selection of resources for recurrent network tasks, based on both its assigned objectives and observations of the actual performance achieved by each resource. We present a coding strategy for the action decisions based on the time-to-fire of spikes, a learning algorithm, and a regulation method to keep synapse strengths within an adequate range. To evaluate the proposed method, we apply the CNC to a challenged network environment using simulation. In this scenario, the CNC requires to optimize the average file transfer time over a multichannel space communication link, which is available only for a time window because of orbital dynamics. Compared to conventional methods, we show that the CNC achieves its objective for a broad range of offered loads. We examine the impact of key system factors that include learning and space protocol parameters. The proposed CNC potentially fosters the development of new cognitive networking applications.

Individual differences in the expression of a "general" learning ability in mice

Human performance on diverse tests of intellect are impacted by a "general" regulatory factor that accounts for up to 50% of the variance between individuals on intelligence tests. Neurobiological determinants of general cognitive abilities are essentially unknown, owing in part to the paucity of animal research wherein neurobiological analyses are possible. We report a methodology with which we have assessed individual differences in the general learning abilities of laboratory mice. Abilities of mice on tests of associative fear conditioning, operant avoidance, path integration, discrimination, and spatial navigation were assessed. Tasks were designed so that each made unique sensory, motor, motivational, and information processing demands on the animals. A sample of 56 genetically diverse outbred mice (CD-1) was used to assess individuals' acquisition on each task. Indicative of a common source of variance, positive correlations were found between individuals' performance on all tasks. When tested on multiple test batteries, the overall performance ranks of individuals were found to be highly reliable and were "normally" distributed. Factor analysis of learning performance variables determined that a single factor accounted for 38% of the total variance across animals. Animals' levels of native activity and body weights accounted for little of the variability in learning, although animals' propensity for exploration loaded strongly (and was positively correlated) with learning abilities. These results indicate that diverse learning abilities of laboratory mice are influenced by a common source of variance and, moreover, that the general learning abilities of individual mice can be specified relative to a sample of peers.