Data Infrastructure for a Poisoning Registry with Designing Data Elements and a Minimum Data Set

Background: The prevalence of poisoning is on the rise in Iran. A poisoning registry is a key source of information about poisoning patterns used for decision-making and healthcare provision, and a minimum dataset (MDS) is a prerequisite for developing a registry. Objectives: This study aimed to design a MDS for a poisoning registry. Methods: This applied study was conducted in 2021. A poisoning MDS was developed with a four-stage process: (1) conducting a systematic review of the Web of Science, Scopus, PubMed, and EMBASE, (2) examining poisoning-related websites and online forms, (3) classification of data elements in separate meetings with three toxicology specialists, and (4) validating data elements using the two-stage Delphi technique. A researcher-made checklist was employed for this purpose. The content validity of the checklist was examined based on the opinions of five health information management and medical informatics experts with respect to the topic of the study. Its test-retest reliability was also confirmed with the recruitment of 25 experts (r = 0.8). Results: Overall, 368 data elements were identified from the articles and forms, of which 358 were confirmed via the two-stage Delphi technique and classified into administrative (n = 88) and clinical data elements (n = 270). Conclusions: The creation of a poisoning registry requires identifying the information needs of healthcare centers, and an integrated and comprehensive framework should be developed to meet these needs. To this end, a MDS contains the essential data elements that form a framework for integrated and standard data collection.

Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation

Background Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations. Methods Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings. Results By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis. Conclusion While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.

Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality

PURPOSE The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND METHODS Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). RESULTS Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. CONCLUSION Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.

Big Data Analytics and IoT in Smart City Applications

Big data analytics is a sophisticated approach for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business.

Improving Assessment of Disease Severity and Strategies for Monitoring Progression in Degenerative Cervical Myelopathy [AO Spine RECODE DCM Research Priority Number 4]

Study design Narrative Review. Objective To (i) discuss why assessment and monitoring of disease progression is critical in Degenerative cervical myelopathy (DCM); (ii) outline the important features of an ideal assessment tool and (iii) discuss current and novel strategies for detecting subtle deterioration in DCM. Methods Literature review Results Degenerative cervical myelopathy is an overarching term used to describe progressive injury to the cervical spinal cord by age-related changes of the spinal axis. Based on a study by Smith et al (2020), the prevalence of DCM is approximately 2.3% and is expected to rise as the global population ages. Given the global impact of this disease, it is essential to address important knowledge gaps and prioritize areas for future investigation. As part of the AO Spine RECODE-DCM (Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy) project, a priority setting partnership was initiated to increase research efficiency by identifying the top ten research priorities for DCM. One of the top ten priorities for future DCM research was: What assessment tools can be used to evaluate functional impairment, disability and quality of life in people with DCM? What instruments, tools or methods can be used or developed to monitor people with DCM for disease progression or improvement either before or after surgical treatment? Conclusions With the increasing prevalence of DCM, effective surveillance of this population will require both the implementation of a monitoring framework as well as the development of new assessment tools.

Association of Positive MRI Findings and Clinical Outcomes in Sub-acute and Chronic Phases of Pediatric Mild Traumatic Brain Injury

ObjectiveThis study aims to examine the rates of incidental findings (IF) and radiologic common data elements (rCDE), and to explore how these magnetic resonance imaging (MRI) findings contribute to a broad assessment of clinical outcomes (symptoms, cognitive and behavioral functioning, and quality of life) in the sub-acute (SA: ∼1 week), early chronic (EC: ∼4 months), and late chronic (LC: ∼1 year) phases of pediatric mild traumatic brain injuries (pmTBI).BackgroundIt is unclear whether MRI findings have clinical implications following injury.Design/MethodsTwo hundred thirty-three pmTBI patients and 168 HC aged 8–18 completed an MRI scan and a comprehensive clinical assessment at SA visit, with a subset completing the clinical assessment at EC (182 pmTBI; 158 HC) and LC (143 pmTBI; 141 pmTBI) visits. All MRI findings were noted by board-certified neuroradiologists and coded based on published criteria for rCDE by 2 independent researchers, who were blinded to diagnosis group. A series of 2 × 3 (group [pmTBI vs HC] × MRI findings [IF vs rCDE vs normal]) generalized linear model was conducted for outcomes at each visit. Possible and probable rCDE were pooled for the latter analyses.ResultsOne hundred sixty-four participants (40.9%) showed positive MRI findings (113 IF, 43 possible rCDE, 8 probable rCDE). As expected, probable rCDE was exclusively observed in pmTBI patients (Fisher's exact one-sided = 0.012), however the incidence of IF and possible rCDE was similar between groups (χ2 = 2.969; p's = 0.085). While group effects were observed on several outcome measures, no interaction of Group × MRI findings survived the correction for multiple comparisons (p's > 0.01). An MRI findings effect (p < 0.001) was observed on child-rated Children's Behavior Questionnaire at SA visit (normal > IF and rCDE; p's = 0.009). However, this effect was no longer significant at EC and LC (p's = 0.439).ConclusionsOverall, the current results do not suggest that MRI findings have clinical implications or interacts with pmTBI to worsen outcomes.

Virtual Data Repository Stimulates Data Sharing in a Consortium

Research data may have substantial impact beyond the original study objectives. The Collaborating Consortium of Cohorts Producing NIDA Opportunities (C3PNO) facilitates the combination of data and access to specimens from nine NIDA-funded cohorts in a virtual data repository (VDR). Unique challenges were addressed to create the VDR. An initial set of common data elements was agreed upon, selected based on their importance for a wide range of research proposals. Data were mapped to a common set of values. Bioethics consultations resulted in the development of various controls and procedures to protect against inadvertent disclosure of personally identifiable information. Standard operating procedures govern the evaluation of proposed concepts, and specimen and data use agreements ensure proper data handling and storage. Data from eight cohorts have been loaded into a relational database with tables capturing substance use, available specimens, and other participant data. A total of 6,177 participants were seen at a study visit within the past six months and are considered under active follow-up for C3PNO cohort participation as of the third data transfer, which occurred in January 2020. A total of 70,391 biospecimens of various types are available for these participants to test approved scientific hypotheses. Sociodemographic and clinical data accompany these samples. The VDR is a web-based interactive, searchable database available in the public domain, accessed at www.c3pno.org. The VDR are available to inform both consortium and external investigators interested in submitting concept sheets to address novel scientific questions to address high priority research on HIV/AIDS in the context of substance use. Keywords: common data elements, data repository Abbreviations: National Institute on Drug Abuse (NIDA), Collaborating Consortium of Cohorts Producing NIDA Opportunities (C3PNO), human immunodeficiency virus (HIV), acquired immunodeficiency syndrome (AIDS), injecting drug users (IDU), virtual data repository (VDR) Correspondence: [email protected]*

Gathering Global Perspectives to Establish the Research Priorities and Minimum Data Sets for Degenerative Cervical Myelopathy: Sampling Strategy of the First Round Consensus Surveys of AO Spine RECODE-DCM

Study Design Survey. Introduction AO Spine Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy (RECODE-DCM) is an international initiative that aims to accelerate knowledge discovery and improve outcomes by developing a consensus framework for research. This includes defining the top research priorities, an index term and a minimum data set (core outcome set and core data elements set – core outcome set (COS)/core data elements (CDE)). Objective To describe how perspectives were gathered and report the detailed sampling characteristics. Methods A two-stage, electronic survey was used to gather and seek initial consensus. Perspectives were sought from spinal surgeons, other healthcare professionals and people with degenerative cervical myelopathy (DCM). Participants were allocated to one of two parallel streams: (1) priority setting or (2) minimum dataset. An email campaign was developed to advertise the survey to relevant global stakeholder individuals and organisations. People with DCM were recruited using the international DCM charity Myelopathy.org and its social media channels. A network of global partners was recruited to act as project ambassadors. Data from Google Analytics, MailChimp and Calibrum helped optimise survey dissemination. Results Survey engagement was high amongst the three stakeholder groups: 208 people with DCM, 389 spinal surgeons and 157 other healthcare professionals. Individuals from 76 different countries participated; the United States, United Kingdom and Canada were the most common countries of participants. Conclusion AO Spine RECODE-DCM recruited a diverse and sufficient number of participants for an international PSP and COS/CDE process. Whilst PSP and COS/CDE have been undertaken in other fields, to our knowledge, this is the first time they have been combined in one process.

Neurotrauma Surveillance in National Registries of Low- and Middle-Income Countries: A Scoping Review and Comparative Analysis of Data Dictionaries

Background: Injury is a major global health problem, causing >5,800,000 deaths annually and widespread disability largely attributable to neurotrauma. 89% of trauma deaths occur in low- and middle-income countries (LMICs), however data on neurotrauma epidemiology in LMICs is lacking. In order to support neurotrauma surveillance efforts, we present a review and analysis of data dictionaries from national registries in LMICs. Methods: We performed a scoping review to identify existing national trauma registries for all LMICs. Inclusion/exclusion criteria included articles published since 1991 describing national registry neurotrauma data capture methods in LMICs. Data sources included PubMed and Google Scholar using the terms "trauma/neurotrauma registry" and country name. Resulting registries were analyzed for neurotrauma-specific data dictionaries. These findings were augmented by data from direct contact of neurotrauma organizations, health ministries, and key informants from a convenience sample. These data were then compared to the WHO minimum dataset for injury (MDI) from the international registry for trauma and emergency care. Results: We identified 15 LMICs with 16 total national trauma registries tracking neurotrauma-specific data elements. Among these, Cameroon had the highest concordance with the MDI, followed by Colombia, Iran, Myanmar and Thailand. The MDI elements least often found in the data dictionaries included helmet use, and alcohol level. Data dictionaries differed significantly among LMICs. Common elements included Glasgow Coma Score, mechanism of injury, anatomical site of injury and injury severity scores. Limitations included low response rate in direct contact methods. Conclusion: Significant heterogeneity was observed between the neurotrauma data dictionaries, as well as a spectrum of concordance or discordance with the MDI. Findings offer a contextually relevant menu of possible neurotrauma data elements that LMICs can consider tracking nationally to enhance neurotrauma surveillance and care systems. Standardization of nationwide neurotrauma data collection can facilitate international comparisons and bidirectional learning among health care governments.