Improve Data Quality Research Articles

Artifact Management for Cerebral Near-Infrared Spectroscopy Signals: A Systematic Scoping Review.

Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust "gold-standard" method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.

EEG might be better left alone, but ERPs must be attended to: Optimizing the late positive potential preprocessing pipeline

The late positive potential (LPP) is an ERP component commonly used to study emotional processes and has been proposed as a neuroaffective biomarker for research and clinical uses. These applications, however, require standardized procedures for elicitation and ERP data processing.We evaluated the impact of different EEG preprocessing steps on the LPP's data quality and statistical power. Using a diverse sample of 158 adults, we implemented a multiverse analytical approach to compare preprocessing pipelines that progressively incorporated more steps: artifact detection and rejection, bad channel interpolation, and bad segment deletion. We assessed each pipeline's effectiveness by computing the standardized measurement error (SME) and conducting simulated experiments to estimate statistical power in detecting significant LPP differences between emotional and neutral images.Our findings highlighted that artifact rejection is crucial for enhancing data quality and statistical power. Voltage thresholds to reject trials contaminated by artifacts significantly affected SME and statistical power. Once artifact detection was optimized, further steps provided minor improvements in data quality and statistical power. Importantly, different preprocessing pipelines yielded similar outcomes.These results underscore the robustness of the LPP's affective modulation to preprocessing choices and the critical role of effective artifact management. By refining and standardizing preprocessing procedures, the LPP can become a reliable neuroaffective biomarker, supporting personalized clinical interventions for affective disorders.

Differences in changes of data completeness after the implementation of an electronic medical record in three surgical departments of a German hospital–a longitudinal comparative document analysis

PurposeThe European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record’s adoption process.MethodsPaper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type.ResultsA total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences.ConclusionThe study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones.

Single pulse data collection with an X-ray chopper at in situ room temperature Laue crystallography beamline BL03HB

in situ room temperature Laue crystallography has gained increasing attention. This method is suitable for time-resolved Laue crystallography experiments to get crystal structure with only several single-frame data collections. To improve the diffraction efficiency of protein crystals at room temperature, a simple X-ray chopper combined with a fast shutter has been successfully developed and tested at in situ room temperature Laue crystallography beamline BL03HB of the Shanghai Synchrotron Radiation Facility (SSRF). This chopper is built with two aluminum blades. There are two windows on both blades, as well as an internal lead block. The width of windows for transmission of beam can be flexibly adjusted by a steel block coated with lead. The chopping frequency is controlled independently. The device allows chopping frequencies ranging from 0 to 10 Hz, and exposure times down to 100 μs can be obtained for our Laue diffraction experiment with the combination of a fast shutter. Moreover, a single pulse data collection strategy has been developed at BL03HB. The performance of our single pulse data collection method with the designed chopper for the improvement of data quality has been analyzed. Results show that the quality of diffraction data obtained from the single pulse data collection method is significantly improved, and the developed chopper is very suitable for room temperature Laue crystallography experiments at the synchrotron beamline with super bending magnet as light source.

Barriers and enablers to secondary use of multi-regional linked administrative health data for three distinct user groups: academic researchers, health-system knowledge users, and private sector researchers

Objective and ApproachWe analyzed barriers and enablers to accessing pan-Canadian health sector data and data analytic services for three distinct user groups: 1) academic researchers, 2) health-system knowledge users (KUs), and 3) private sector researchers. Specifically, we a) conducted a legislative and policy scan regarding use of health administrative data for research, quality improvement, and health system management and b) systematically consulted with data centre directors and staff, privacy and legal experts, knowledge users (KUs), private sector entities, and university and government officials. ResultsKUs and private sector organizations face unique barriers because structures and regulations related to data access and use are set at multiple university and government institutions and are primarily designed for academic research. Additionally, data repositories often have specific policy/legislative barriers to working with KUs and/or private sector researchers. However, all groups face common issues including variations in regional regulations and policies making harmonized and streamlined processes challenging; lengthy data access and ethics review processes; and regional variations in data availability. Enablers included diverse expertise and initiatives across Canada such as: initiatives to streamline ethics reviews for multi-regional research, expand data holdings, increase standardization and harmonization, and implement systems for federated analyses. ConclusionsAccess to health system data and analytics in Canada varies by user group and across regions. Although each user group faces unique challenges, they also all have many barriers and enablers in common. ImplicationsImproving health system data access for one use group has the potential to benefit the others.

Pregnant People-Infant Linkage for Surveillance

Objectives and ApproachAs maternal mortality and morbidity continue to progress, better data is needed to comprehensively understand the problem, especially in underserved populations who are differentially impacted. Surveillance in safety-net populations is complicated by care fragmentation and interoperability challenges between primary care and hospital settings. . AllianceChicago (AC), a health center-controlled network, supports a common data infrastructure across a set of Federally Qualified Health Centers (FQHCs) in Chicago that is further linked to a distributed query infrastructure connecting Chicago’s major health systems known as CAPriCORN. Leveraging electronic health record (EHR) data collected by the AC and CAPriCORN consortium, the objective of this surveillance effort was to connect infant data with maternal data while protecting privacy. We developed a deterministic matching algorithm that utilized multiple approaches and technologies for Pregnant People-Infant (PPI) pair identification. The linkage process involved a five-step method in which each step is recorded within the linkage database and the confidence interval is calculated. ResultsPreliminary analyses are ongoing. Through each step of the linkage process, the confidence interval increased with the increase of matching nodes, highlighting its accumulative accuracy. The result demonstrates the feasibility of linking maternal and baby healthcare characteristics using a range of clinical and demographic variables captured in pseudonymized hospital and ambulatory data. Conclusions and ImplicationsOur approach improves the capacity of EHR data for surveillance while protecting patient privacy and preserving data confidentiality. Triangulating outcomes recorded in different settings can help improve data quality for research and surveillance.

Determining the minimum data set of geriatric assessment at the Iran primary health care referral system: shifting from fragmentation to integration care for older people

BackgroundGeriatric assessment (GA) is a multidimensional process that disrupts the primary health care (PHC) referral system. Accessing consistent data is central to the provision of integrated geriatric care across multiple healthcare settings. However, due to poor-quality data and documentation of GA, developing an agreed minimum data set (MDS) is required. Therefore, this study aimed to develop a GA-MDS in the PHC referral system to improve data quality, data exchange, and continuum of care to address the multifaceted necessities of older people.MethodsIn our study, the items to be included within GA-MDS were determined in a three-stepwise process. First, an exploratory literature search was done to determine the related items. Then, we used a two-round Delphi survey to obtain an agreement view on items to be contained within GA-MDS. Finally, the validity of the GA-MDS content was evaluated.ResultsSixty specialists from different health geriatric care disciplines scored data items. After, the Delphi phase from the 230 selected items, 35 items were removed by calculating the content validity index (CVI), content validity ratio (CVR), and other statistical measures. Finally, GA-MDS was prepared with 195 items and four sections including administrative data, clinical, physiological, and psychological assessments.ConclusionsThe development of GA-MDS can serve as a platform to inform the geriatric referral system, standardize the GA process, and streamline their referral to specialized levels of care. We hope GA-MDS supports clinicians, researchers, and policymakers by providing aggregated data to inform medical practice and enhance patient-centered outcomes.

Developing time-resolved x-ray diffraction diagnostics at the National Ignition Facility (invited).

As part of a program to measure phase transition timescales in materials under dynamic compression, we have designed new x-ray imaging diagnostics to record multiple x-ray diffraction measurements during a single laser-driven experiment. Our design places several ns-gated hybrid CMOS (hCMOS) sensors within a few cm of a laser-driven target. The sensors must be protected from an extremely harsh environment, including debris, electromagnetic pulses, and unconverted laser light. Another key challenge is reducing the x-ray background relative to the faint diffraction signal. Building on the success of our predecessor (Target Diffraction In Situ), we implemented a staged approach to platform development. First, we built a demonstration diagnostic (Gated Diffraction Development Diagnostic) with two hCMOS sensors to confirm we could adequately protect them from the harsh environment and also acquire acceptable diffraction data. This allowed the team to quickly assess the risks and address the most significant challenges. We also collected scientifically useful data during development. Leveraging what we learned, we recently developed a much more ambitious instrument (Flexible Imaging Diffraction Diagnostic for Laser Experiments) that can field up to eight hCMOS sensors in a flexible geometry and participate in back-to-back shots at the National Ignition Facility (NIF). The design also allows for future iterations, such as faster hCMOS sensors and an embedded x-ray streak camera. The enhanced capabilities of the new instrument required a much more complex design, and the unexpected issues encountered on the first few shots at NIF remind us that complexity has consequences. Our progress in addressing these challenges is described herein, as is our current focus on improving data quality by reducing x-ray background and quantifying the uncertainties of our diffraction measurements.

Equity in Pediatric Hospital-Based Safety and Quality Improvement

There are well-documented inequities in the quality of care and health outcomes of minoritized youth. Patient safety and quality improvement (QI) work with an equity focus has been identified as an important strategy to remedy these existing inequities. In this article, we will present evidence of inequities in pediatric hospital-based care, describe root causes with a focus on structural racism, highlight existing frameworks for applying equity principles to patient safety and QI, and provide best practices and recommendations on evaluating patient safety and QI data towards advancing equity in pediatric hospital-based care.

Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban Environments

Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban Environments

Trust as moral currency: Perspectives of health researchers in sub-Saharan Africa on strategies to promote equitable data sharing.

Groundbreaking data-sharing techniques and quick access to stored research data from the African continent are highly beneficial to create diverse unbiased datasets to inform digital health technologies and artificial intelligence in healthcare. Yet health researchers in sub-Saharan Africa (SSA) experience individual and collective challenges that render them cautious and even hesitant to share data despite acknowledging the public health benefits of sharing. This qualitative study reports on the perspectives of health researchers regarding strategies to mitigate these challenges. In-depth interviews were conducted via Microsoft Teams with 16 researchers from 16 different countries across SSA between July 2022 and April 2023. Purposive and snowball sampling techniques were used to invite participants via email. Recorded interviews were transcribed, cleaned, coded and managed through Atlas.ti.22. Thematic Analysis was used to analyse the data. Three recurrent themes and several subthemes emerged around strategies to improve governance of data sharing. The main themes identified were (1) Strategies for change at a policy level: guideline development, (2) Strengthening data governance to improve data quality and (3) Reciprocity: towards equitable data sharing. Building trust is central to the promotion of data sharing amongst researchers on the African continent and with global partners. This can be achieved by enhancing research integrity and strengthening micro and macro level governance. Substantial resources are required from funders and governments to enhance data governance practices, to improve data literacy and to enhance data quality. High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. Engagement with multiple stakeholders including researchers and research communities is necessary to establish an equitable data sharing approach based on reciprocity and mutual benefit.

Data Analysis and Congestion Prediction Model in Intelligent Transportation Systems

The relentless march of urbanization has led to a surge in population density and a dramatic increase in the number of motor vehicles, with traffic congestion emerging as a significant barrier to urban development. Intelligent Transportation Systems (ITS) utilize advanced information technology to monitor and analyze traffic flow in real time, playing a pivotal role in alleviating urban traffic pressures and enhancing road usage efficiency. This paper aims to propose a data analysis method based on machine learning algorithms for real-time traffic monitoring and congestion prediction, thereby providing scientific decision support for urban traffic management. The research encompasses methods of traffic data collection, including data on vehicle location, speed, and flow; preprocessing and feature extraction techniques for traffic data to improve data quality and extract features useful for congestion prediction; and the application of various machine learning algorithms to establish a traffic congestion prediction model. Additionally, this paper assesses and optimizes the model and tests it with actual traffic data to verify its effectiveness and practicality. The research findings indicate that the proposed machine learning-based method can effectively predict traffic congestion, providing a powerful tool for traffic management departments, aiding in preemptive measures to reduce traffic delays and improve the travel experience for citizens. This study not only enriches the field of intelligent transportation systems research but also provides theoretical foundations and technical support for urban traffic management practices.

Bibliometric Analysis of Factors Affecting Performance and Completeness of Electronic Medical Records (EMR) in Primary Health Services

ABSTRACT Electronic Medical Records (EMR) completeness and accuracy are fundamental to effective patient care, clinical decision-making, and overall healthcare management. In recent years, there has been increasing attention to improving EMR documentation by healthcare professionals to improve data quality and patient care outcomes. This bibliometric study aimed to explore the trends, research focus, and gaps in the literature regarding EMR completeness from 2019 to 2024. The study used Publish or Perish software to collect data from Google Scholar with specific keywords such as Performance, Factors, Electronic Medical Record (EMR), EMR Completeness, Primary Healthcare, Filling, Documentation, and Medical Record Accuracy. The analysis included the 30 highest ranked journals. In addition, VOS Viewer was used for network visualization, overlay, and density to analyze thematic linkages and identify potential research gaps. Bibliometric analysis showed that Indonesia, the United States, and Ethiopia were the main contributors in EMR-related publications. Frequent themes such as data completeness, quality, and accuracy, emphasize their crucial role in the EMR research field. These findings emphasize the importance of continuously improving EMR data completeness and accuracy, which are critical for reliable clinical decision making and effective healthcare delivery. Future research should focus on developing methodologies to improve data quality and integration of EMR systems with other health information systems. In addition, exploration of policy and regulatory implications related to EMR completeness may provide valuable insights for healthcare practice. Keywords: EMR Completeness, Data Quality, Bibliometric Analysis, Health Documentation, VOS Viewer.

The use of artificial intelligence in the management of investment portfolios

The ever-increasing complexity of financial markets, characterized by high market volatility, economic uncertainty, and the massive influx of various data pose a significant challenge in investment portfolio management. Traditional methods can fall short in addressing these challenges, requiring too much from an individual managing investment portfolios. The ability of AI to process a tremendous number of large datasets and to detect patterns offers a significant advantage for a manager, providing for more informed and timely investment decisions. The purpose of this annex is to encapsulate findings of the primary paper on the use of AI in investment portfolio management, highlighting the benefits and challenges, and also identifying areas for improvement and further research. The paper explores the possibility of using AI to enhance the effectiveness of an investment portfolio manager, through the improvement of analysis’ quality, help in data collection and processing, recognition of complex patterns, and faster decision-making. This could help optimize investment strategies through risk reduction, improved forecasting, and faster adaptation to market fluctuations. In addition, the paper aims to identify potential risks associated with AI integration, for example quality of data, algorithm transparency, and cybersecurity issues. Literature analysis demonstrated that application of AI can increase the efficiency of investment portfolio management. This is possible because of AI systems' ability to conduct advanced data search and analysis, providing for better decision-making. The use of AI algorithms allows investment portfolio managers to identify market trends more easily, optimize asset allocations, as well as minimize market volatility risks. А good example is BlackRock's Aladdin expert system, which provides comprehensive data insights, providing a competitive advantage. However, the paper also demonstrates, that implementation of AI systems is associated with several challenges, particularly - quality of provided data and opacity of AI algorithms, which can raise doubts regarding their effectiveness. Moreover, cybersecurity remains a significant concern. Overall, the use of AI presents opportunities for investment portfolio managers to improve results by providing competitive advantages. However, certain challenges need to be overcome. AI holds great promise for investment portfolio management, and future studies need to focus on AI integration, cybersecurity, and improvement of data quality.

Impact of multiple factors on the use of an UAV-mounted infrared thermography method for detection of debonding in facade tiles

Anomalies detection of adhesive tiling systems on the building facade plays a crucial role in structural healthy of buildings. Among defects, the debonding is a major potential threat due to its invisibility and universality. In recent years, there has been a growing trend in utilizing Unmanned Aerial Vehicles (UAVs) equipped with infrared lenses to acquire image data of facade tiles. Numerous previous studies mainly concentrate on the improvement of detection accuracy, and fewer studies investigated the physical range and lighting conditions when capturing infrared photographs using UAVs. This study based on experiments analyses the effects of three operating conditions factors, namely angle, distance, and solar illumination, on the detection effectiveness of tiles debonding by means of calculating the F2 score. There are some reasonable proposals for improving data quality and planning the flight route when using visual methods to detect the debonding defect of ceramic tiles on the building facade.

Estimating Adverse Events Associated With Herbal Medicines Using Pharmacovigilance Databases: Systematic Review and Meta-Analysis.

Herbal medicines (HMs) are extensively used by consumers/patients worldwide. However, their safety profiles are often poorly reported and characterized. Previous studies have documented adverse events (AEs) associated with HMs, such as hepatotoxicity, renal failure, and allergic reactions. However, the prevalence rate of AEs related to HMs has been reported to be low. To date, no systematic review and meta-analysis has comprehensively analyzed the AEs of HMs using published data acquired from pharmacovigilance (PV) databases. This study aimed to (1) estimate the reporting rate of the AEs of HMs using PV databases and (2) assess the detailed data provided in AE reports. In this systematic review and meta-analysis, MEDLINE/PubMed, SCOPUS, EMBASE, and CINAHL were systematically searched for relevant studies (until December 2023). The DerSimonian-Laird random effects model was used for pooling the data, and subgroup analyses, the meta-regression model, and sensitivity analysis were used to explore the source of heterogeneity. Crombie's checklist was used to evaluate the risk of bias (ROB) of the included studies. In total, 26 studies met the eligibility criteria. The reporting rate of the AEs of HMs ranged considerably, from 0.03% to 29.84%, with a median overall pooled estimate of 1.42% (IQR 1.12%-1.72%). Subgroup analyses combined with the meta-regression model revealed that the reporting rate of the AEs of HMs was associated with the source of the reporter (P=.01). None of the included studies provided full details of suspected herbal products, only the main ingredients were disclosed, and other potentially harmful components were not listed. This systematic review and meta-analysis highlighted risks related to HMs, with a wide range of reporting rates, depending on the source of the reporter. Continuous efforts are necessary to standardize consumer reporting systems in terms of the reporting form, education, and follow-up strategy to improve data quality assurance, aiming to enhance the reliability and utility of PV data for monitoring the safety of HMs. Achieving effective monitoring and reporting of these AEs necessitates collaborative efforts from diverse stakeholders, including patients/consumers, manufacturers, physicians, complementary practitioners, sellers/distributors, and health authorities. PROSPERO (Prospective International Register of Systematic Reviews) CRD42021276492.

Coping mechanisms and strategies adopted to improve the quality and timeliness of immunization data among lower-level private-for-profit service providers in Kampala Capital City, Uganda.

Lower-level urban private-for-profit health service providers are actively engaged in the delivery of immunization services. However, not much is known about their everyday endeavours to improve data quality and ensure the submitted data meets the quality and timeliness requirements as per established guidelines. The objective of this paper was to examine the coping mechanisms and strategies adopted to improve the quality and timeliness of immunization data among lower-level private-for-profit service providers in Kampala Capital City, Uganda. A qualitative study design was adopted with in-depth interviews (n = 17) and key informant interviews (n = 8) completed among frontline health workers, district health managers and immunization implementing partners. Analysis followed a thematic approach with coding conducted using Atlas. ti, a qualitative data management software. Overall, coping mechanisms and strategies adopted to improve the data quality among lower-level urban private-for-profit immunization service providers included; Experiential attachment for practical skills acquisition in data management, data quality peer-to-peer learning among private-for-profit immunization service providers, registration using cohort system for easy tracking of records during subsequent visits, separation of visiting service user records from regular attendants, service delivery modifications such as reward for better performance, engagement of Village Health Teams (VHTs) in outreaches and data completion, and data quality checks through review of monitoring charts to identify gaps in data quality. Within the urban context, the delivery of immunization services by lower-level private-for-profit services faces data quality challenges some of which stem from the implementation context. Different coping strategies focusing on frontline health worker skills, enhanced experience sharing through peer-to-peer learning, modifications to registration and routine review of monitoring charts. However, these efforts were often faced with chronic barriers such as the high staff turnover calling for dedicated efforts to optimize the available implementation opportunities like guidelines mandating the public health facilities to supervise the lower-level private immunization service providers within their catchment areas to respond to the identified gaps.

Clinical Data Warehousing: A Scoping Review

INTRODUCTION: A clinical data warehouse (CDW) is a powerfulresource that supports clinical decision-making and secondary data use byintegrating and presenting heterogeneous data sources. Despite considerableeffort within healthcare organizations (HCOs) to develop CDWs, scientific literaturesurrounding clinical data warehousing methods is limited.OBJECTIVES: The scoping review aims to characterize thecurrent state of CDW methods within HCOs, to identify extant evidence forpractice recommendations, and ultimately to advance the design, implementation,and use of CDWs. METHODS: The review encompasses CDW articles publishedfrom 2011 through 2021 identified through a systematic PubMed search. Articleabstracts were systematically screened by two authors. Full-text articles werereviewed and abstracted independently by two authors with discrepanciesresolved through consensus.   RESULTS: 137 articles, from 55 journals and 3conference proceedings, were categorized and analyzed.  Areas for increased CDW focus include dataintegration of increased data types and sources; extract-transform-load (ETL)optimization; data quality improvement processes; semantic data representation;support tools/documentation and data literacy efforts for staff and end-users;data governance; business model/financial support for CDWs including staffing. CONCLUSION:  Thestudy indicates the topics that have been significantly developed and theaspects needing additional focus and reporting in CDW between existing generaldata management best practices and recently articulated requirements forresearch data. Also, more multi-site and multi-aspect studies are needed tofoster maturity at CDWs.

Using Focus Groups to Evaluate a Self-Assessment Approach to Coproduction Value Creation in an International Community of Practice: Phase 2 of the CO-VALUE Study.

Coproduction learning health system models clearly define the use of clinical and patient-reported data for system learning and quality improvement, but less is known about how to document formative learning about coproduction value creation over the course of a quality improvement initiative. The authors aimed to 1) assess the feasibility, utility, and acceptability of novel self-assessment tools for coproduction value creation and 2) identify domains of coproduction value creation. The authors conducted 4 focus groups with quality improvement teams from 4 health systems in the United States and Sweden between June 2021 and September 2023. A single analyst coded transcripts and proposed themes, with investigator triangulation validating results. Participants found the self-assessment tools acceptable and useful. The improvement passport was seen as more feasible for routine use than the full self-assessment guide. Peer learning within the community of practice, diverse multidisciplinary improvement teams, and leadership support facilitated teams' work. Domains of coproduction value creation included communication, self-efficacy, interconnectedness, direct and indirect costs of health care utilization, health professional experience, and access to the right care. Peer learning and camaraderie within the community of practice maintained momentum among participating teams during a challenging time of limited resources and mounting responsibilities in health care settings, suggesting enhanced resiliency through approaching difficult tasks in community. The authors identified themes of coproduction value creation and drivers of engagement. Future research will draw on the measurement domains established in this study to inform the development of measures of coproduction value creation. Those measures could then be incorporated into the data-rich environments of coproduction learning health systems to enhance focus on value from service user and professional perspectives.

Closing the information gap between the field and scientific literature for improved disease management- with a focus on rice and bacterial blight.

A path to sustainably reduce world hunger, food insecurity, and malnutrition is to close the crop yield gap, particularly, losses due to pathogens. Breeding resistant crops is key to achieving this goal, an effort requiring collaboration among stakeholders, scientists, breeders, farmers and policymakers. During a disease outbreak, epidemiologists survey the occurrence of a disease after which pathologists investigate mechanisms to stop an infection. Policymakers then implement strategies with farmers and breeders to overcome the outbreak. Information flow from the field to the lab and back to the field involves several processing hubs that require different information inputs. Failure to communicate the necessary information results in the transfer of meaningless data. Here, we discuss gaps in information acquisition and transfer between the field and laboratory. Using rice bacterial blight disease as an example, we discuss pathogen biology and disease resistance to point out the importance of reporting pathogen strains that caused an outbreak to optimize the deployment of resistant crop varieties. We examine differences between infection in the field and assays performed in the laboratory to draw awareness of possible misinformation concerning plant resistance or susceptibility. We discuss key data considered useful for reporting disease outbreaks, sampling bias, and suggestions for improving data quality. We also touch on the knowledge gap in the state-of-the-art literature regarding disease dispersal and transmission. We use a recent case study to exemplify the gaps mentioned. We conclude by highlighting potential actions that may contribute to food security and to closing of the yield gap.