Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.

OBJECTIVE: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources. METHODS: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation. RESULTS: TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics. DISCUSSION: TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability. CONCLUSION: TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.

Does integration with national registers improve the data completeness of local COVID-19 contact tracing tools? A register-based study in Norway, May 2020 - September 2021.

BACKGROUND: During the COVID-19 response in Norway, many municipalities used the Fiks contact tracing tool (FiksCT) to register positive individuals and follow-up contacts. This tool is based on DHIS2, an open source, web-based platform. In this study we examined if data completeness in FiksCT improved after integration with national registers between May 2020 and September 2021. METHODS: Data from municipalities using FiksCT was extracted from the Norwegian Emergency Preparedness Register for COVID-19 (Beredt C19). We linked FiksCT data to the Norwegian Surveillance System for Communicable Diseases (MSIS), the National Population Register (FREG), and the Norwegian Vaccine Registry (SYSVAK) using unique identification numbers (ID). Completeness for each variable linked with a national register was calculated before and after integration with these registers. RESULTS: Of the 125 municipalities using FiksCT, 87 (69.6%) agreed to share and upload their data to Beredt C19. Data completeness for positive individuals improved after integration with national registers. After integration with FREG, the proportion of missing values decreased from 12.5 to 1.6% for ID, from 4.5 to 0.9% for sex, and from 1.2 to 0.4% for date of birth. Missing values for vaccine type decreased from 63.0 to 15.2% and 39.3-36.7% for first and second dose, respectively. In addition, direct reporting from FiksCT to MSIS increased the proportion of complete records in MSIS (on the selected variables) from 68.6% before to 77.0% after integration. CONCLUSION: The completeness of local contact tracing data can be improved by enabling integration with established national registers. In addition, providing the option to submit local data to the national registers could ease workload and reduce the need to collect duplicate data.

Stakeholder-driven development and implementation of CRICIT: an app to support high-quality data capture and protocol monitoring for outpatient clinical trials with vulnerable populations.

Introduction: Choosing an appropriate electronic data capture system (EDC) is a critical decision for all randomized controlled trials (RCT). In this paper, we document our process for developing and implementing an EDC for a multisite RCT evaluating the efficacy and implementation of an enhanced primary care model for individuals with opioid use disorder who are returning to the community from incarceration. Methods: Informed by the Knowledge-to-Action conceptual framework and user-centered design principles, we used Claris Filemaker software to design and implement CRICIT, a novel EDC that could meet the varied needs of the many stakeholders involved in our study. Results: CRICIT was deployed in May 2021 and has been continuously iterated and adapted since. CRICIT's features include extensive participant tracking capabilities, site-specific adaptability, integrated randomization protocols, and the ability to generate both site-specific and study-wide summary reports. Conclusions: CRICIT is highly customizable, adaptable, and secure. Its implementation has enhanced the quality of the study's data, increased fidelity to a complicated research protocol, and reduced research staff's administrative burden. CRICIT and similar systems have the potential to streamline research activities and contribute to the efficient collection and utilization of clinical research data.

Assessing the efficient use of the lightwave health information management system for health service delivery in Ghana.

BACKGROUND: In achieving the WHO's Universal Health Coverage and the Global Developmental Agenda: Sustainable Development Goal 3 and 9, the Ministry of Health launched a nationwide deployment of the lightwave health information management system (LHIMS) in the Central Region to facilitate health service delivery. This paper assessed the efficient use of the LHIMS among health professionals in the Central Region. METHODS: A non-interventional descriptive cross-sectional study design was employed for this research. The study used stratified and simple random sampling for selecting 1126 study respondents from 10 health facilities that use the LHIMS. The respondents included prescribers, nurses, midwives and auxiliary staff. Descriptive statistics (weighted mean) was computed to determine the average weighted score for all the indicators under efficiency. Also, bivariate (χ2) and multivariate (ordinal logistic regression) analyses were conducted to test the study's hypotheses. RESULTS: Findings revealed that the LHIMS enhanced efficient health service delivery. From the bivariate analysis, external factors; sex, educational qualification, work experience, profession type and computer literacy were associated with the efficient use of the LHIMS. However, training offered prior to the use of the LHIMS, and the duration of training had no association. At the multivariate level, only work experience and computer literacy significantly influenced the efficient use of the LHIMS. CONCLUSION: The implementation of LHIMS has the potential to significantly improve health service delivery. General computing skills should be offered to system users by the Ministry of Health to improve literacy in the use of computers. Active participation in the use of LHIMS by all relevant healthcare professionals should be encouraged.

Medicines postpartum in Sweden and coverage in Janusmed Breastfeeding.

PURPOSE: The purpose of this article is (1) to investigate which medicines are prescribed and dispensed to women the first 6 months postpartum, (2) to identify medicines dispensed postpartum but not recommended during breastfeeding, and (3) to find medicines commonly dispensed postpartum, but not currently included in Janusmed Breastfeeding. METHODS: In this register-based cohort study covering births between January 2017 and August 2019, the Swedish Medical Birth Register (MBR), the Prescribed Drug Register, and Janusmed Breastfeeding were linked to identify medicines dispensed to women during the first 6 months postpartum, and how they are covered and classified in Janusmed Breastfeeding. RESULTS: During the first 6 months postpartum, 66% of women purchased at least one prescription medicine from the pharmacy. The most common medicines were contraceptive agents, analgesics, antibiotics, and glucocorticoids. A third of the 30 most commonly dispensed medicines have no information available about the safety of use in breastfeeding. The most dispensed medicines, where the database advises against use in breastfeeding, included several antitussive agents, a local anaesthetic, and several gestagens. The most commonly dispensed medicines not covered by the Janusmed Breastfeeding were medicines for dry eyes, for assisted reproduction, and HIV. CONCLUSION: Prescribed medicines compatible with breastfeeding are more common during the first 6 months postpartum than medicines not compatible with breastfeeding, but medicines which lack evidence for safety in breastfeeding are still commonly used.

Malmö Breast ImaginG database: objectives and development.

Purpose: We describe the design and implementation of the Malmö Breast ImaginG (M-BIG) database, which will support research projects investigating various aspects of current and future breast cancer screening programs. Specifically, M-BIG will provide clinical data to:1.investigate the effect of breast cancer screening on breast cancer prognosis and mortality;2.develop and validate the use of artificial intelligence and machine learning in breast image interpretation; and3.develop and validate image-based radiological breast cancer risk profiles. Approach: The M-BIG database is intended to include a wide range of digital mammography (DM) and digital breast tomosynthesis (DBT) examinations performed on women at the Mammography Clinic in Malmö, Sweden, from the introduction of DM in 2004 through 2020. Subjects may be included multiple times and for diverse reasons. The image data are linked to extensive clinical, diagnostic, and demographic data from several registries. Results: To date, the database contains a total of 451,054 examinations from 104,791 women. During the inclusion period, 95,258 unique women were screened. A total of 19,968 examinations were performed using DBT, whereas the rest used DM. Conclusions: We describe the design and implementation of the M-BIG database as a representative and accessible medical image database linked to various types of medical data. Work is ongoing to add features and curate the existing data.

IMPACTO-MR: um estudo brasileiro de plataforma nacional para avaliar infecções e multirresistência em unidades de terapia intensiva / IMPACTO-MR: a Brazilian nationwide platform study to assess infections and multidrug resistance in intensive care units

RESUMO Objetivo: Descrever o IMPACTO-MR, um estudo brasileiro de plataforma nacional em unidades de terapia intensiva focado no impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. Métodos: Descrevemos a plataforma IMPACTO-MR, seu desenvolvimento, critérios para seleção das unidades de terapia intensiva, caracterização da coleta de dados, objetivos e projetos de pesquisa futuros a serem realizados na plataforma. Resultados: Os dados principais foram coletados por meio do Epimed Monitor System® e consistiram em dados demográficos, dados de comorbidades, estado funcional, escores clínicos, diagnóstico de internação e diagnósticos secundários, dados laboratoriais, clínicos e microbiológicos e suporte de órgãos durante a internação na unidade de terapia intensiva, entre outros. De outubro de 2019 a dezembro de 2020, 33.983 pacientes de 51 unidades de terapia intensiva foram incluídos no banco de dados principal. Conclusão: A plataforma IMPACTO-MR é um banco de dados clínico brasileiro de unidades de terapia intensiva focado na pesquisa do impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. Essa plataforma fornece dados para o desenvolvimento e pesquisa de unidades de terapia intensiva individuais e ensaios clínicos observacionais e prospectivos multicêntricos.

ABSTRACT Objective: To describe the IMPACTO-MR, a Brazilian nationwide intensive care unit platform study focused on the impact of health care-associated infections due to multidrug-resistant bacteria. Methods: We described the IMPACTO-MR platform, its development, criteria for intensive care unit selection, characterization of core data collection, objectives, and future research projects to be held within the platform. Results: The core data were collected using the Epimed Monitor System® and consisted of demographic data, comorbidity data, functional status, clinical scores, admission diagnosis and secondary diagnoses, laboratory, clinical, and microbiological data, and organ support during intensive care unit stay, among others. From October 2019 to December 2020, 33,983 patients from 51 intensive care units were included in the core database. Conclusion: The IMPACTO-MR platform is a nationwide Brazilian intensive care unit clinical database focused on researching the impact of health care-associated infections due to multidrug-resistant bacteria. This platform provides data for individual intensive care unit development and research and multicenter observational and prospective trials.

Development of a REDCap-based workflow for high-volume relational data analysis on real-time data in a medical department using open source software.

BACKGROUND/AIM: The current availability of large volumes of clinical data has provided medical departments with the opportunity for large-scale analyses, but it has also brought forth the need for an effective strategy of data-storage and data-analysis that is both technically feasible and economically sustainable in the context of limited resources and manpower. Therefore, the aim of this study was to develop a widely-usable data-collection and data-analysis workflow that could be applied in medical departments to perform high-volume relational data analysis on real-time data. METHODS: A sample project, based on a research database on prostate-specific-membrane-antigen/positron-emission-tomography scans performed in prostate cancer patients at our department, was used to develop a new workflow for data-collection and data-analysis. A checklist of requirements for a successful data-collection/analysis strategy, based on shared clinical research experience, was used as reference standard. Software libraries were selected based on widespread availability, reliability, cost, and technical expertise of the research team (REDCap-v11.0.0 for collaborative data-collection, Python-v3.8.5 for data retrieval and SQLite-v3.31.1 for data storage). The primary objective of this study was to develop and implement a workflow to: a) easily store large volumes of structured data into a relational database, b) perform scripted analyses on relational data retrieved in real-time from the database. The secondary objective was to enhance the strategy cost-effectiveness by using open-source/cost-free software libraries. RESULTS: A fully working data strategy was developed and successfully applied to a sample research project. The REDCap platform provided a remote and secure method to collaboratively collect large volumes of standardized relational data, with low technical difficulty and role-based access-control. A Python software was coded to retrieve live data through the REDCap-API and persist them to an SQLite database, preserving data-relationships. The SQL-language enabled complex datasets retrieval, while Python allowed for scripted data computation and analysis. Only cost-free software libraries were used and the sample code was made available through a GitHub repository. CONCLUSIONS: A REDCap-based data-collection and data-analysis workflow, suitable for high-volume relational data-analysis on live data, was developed and successfully implemented using open-source software.

MyCap: a flexible and configurable platform for mobilizing the participant voice.

This paper provides a description of the MyCap data collection platform, utilization metrics, and vignettes associated with use from diverse research institutions. MyCap is a participant-facing mobile application for survey data collection and the automated administration of active tasks (activities performed by participants using mobile device sensors under semi-controlled conditions). Launched in 2018, MyCap is a no-code solution for research teams conducting longitudinal studies, integrates tightly with REDCap and is available at no cost to research teams at academic, nonprofit, or government organizations. MyCap has been deployed at multiple research institutions with application usage logged across 135 countries in 2021. Vignettes demonstrate that MyCap empowered research teams to explore and implement novel methods of information collection and use. MyCap's integration with REDCap provides a comprehensive data collection ecosystem and is best suited for longitudinal studies with frequent requests for information from participants.

Establishing a colorectal cancer research database from routinely collected health data: the process and potential from a pilot study.

OBJECTIVE: Colorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source. METHODS: Clinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically. RESULTS: Three pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed. DISCUSSION: Algorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer. CONCLUSION: The data set has great potential to facilitate research into colorectal cancer.

Development of a customised programme to standardise comorbidity diagnosis codes in a large-scale database.

OBJECTIVES: The transition from ICD-9 to ICD-10 coding creates a data standardisation challenge for large-scale longitudinal research. We sought to develop a programme that automated this standardisation process. METHODS: A programme was developed to standardise ICD-9 and ICD-10 terminology into one system. Code was improved to reduce runtime, and two iterations were tested on a joint ICD-9/ICD-10 database of 15.8 million patients. RESULTS: Both programmes successfully standardised diagnostic terminology in the database. While the original programme updated 100 000 cells in 12.5 hours, the improved programme translated 3.1 million cells in 38 min. DISCUSSION: While both programmes successfully translated ICD-related data into a standardised format, the original programme suffered from excessive runtimes. Code improvement with hash tables and parallelisation exponentially reduced these runtimes. CONCLUSION: Databases with ICD-9 and ICD-10 codes require terminology standardisation for analysis. By sharing our programme's implementation, we hope to assist other researchers in standardising their own databases.

[Evaluation of contingency plans in the care of patients in intensive care units in the COVID-19 pandemic]. / Evaluación de los planes de contingencia en la atención a pacientes en Unidades de Cuidados Intensivos en la pandemia por COVID-19.

BACKGROUND: The expansion of intensive care areas has been one of the most significant measures in this forced adaptation to the evolution of the different phases of the COVID-19 pandemic. The objective is to evaluate the deployment of contingency plans in the care of patients admitted to intensive care units during the period from March 19 to April 20, 2020, in a public hospital, reference for 300,000 inhabitants belonging to the Galician Health Service. MATERIALS AND METHODS: Qualitative research from focus groups, with systematic sampling. Based on the adaptation to the health environment of the 10 measures recommended by Deloitte to face a pandemic, an evaluation was carried out by the quality unit of the Health Area. As outcome indicators, the number of COVID-19 patients, % of patients admitted to hospital, % patients admitted to intensive care units, as well as the number of deaths from COVID-19, were evaluated in seven health areas in the period March 19 to April 20, 2020. RESULTS: The qualitative evaluation identified 2 areas for improvement (communication and risk assessment) of the 10 recommended measures. The Health Area has the lowest relative (2.6%) and absolute (16) rate of deceased patients, as well as the lowest relative (7.9%) and absolute (24) rate of patients admitted to intensive services for COVID-19. The infection rate in professionals has been 4.4%. CONCLUSIONS: The simplicity and identification of areas for improvement added to the low consumption of resources are strengths of this proposal. This self-assessment can be helpful in identifying areas for improvement.

Credit and Recognition for Contributions to Data-Sharing Platforms Among Cohort Holders and Platform Developers in Europe: Interview Study.

BACKGROUND: The European Commission is funding projects that aim to establish data-sharing platforms. These platforms are envisioned to enhance and facilitate the international sharing of cohort data. Nevertheless, broad data sharing may be restricted by the lack of adequate recognition for those who share data. OBJECTIVE: The aim of this study is to describe in depth the concerns about acquiring credit for data sharing within epidemiological research. METHODS: A total of 17 participants linked to European Union-funded data-sharing platforms were recruited for a semistructured interview. Transcripts were analyzed using inductive content analysis. RESULTS: Interviewees argued that data sharing within international projects could challenge authorship guidelines in multiple ways. Some respondents considered that the acquisition of credit for articles with extensive author lists could be problematic in some instances, such as for junior researchers. In addition, universities may be critical of researchers who share data more often than leading research. Some considered that the evaluation system undervalues data generators and specialists. Respondents generally looked favorably upon alternatives to the current evaluation system to potentially ameliorate these issues. CONCLUSIONS: The evaluation system might impede data sharing because it mainly focuses on first and last authorship and undervalues the contributor's work. Further movement of crediting models toward contributorship could potentially address this issue. Appropriate crediting mechanisms that are better aligned with the way science ought to be conducted in the future need to be developed.

The Primary Care Research Landscape and its Relationship with Clinical Practice: A Scientometric Analysis.

INTRODUCTION: It is unclear if research published in primary care journals aligns with the broad spectrum of problems managed in primary care practice. The aim of this study was to analyse publication trends concerning the burden of medical conditions reported in primary care journals, and to compare these findings with the burden of problems seen in clinical practice, in order to identify research gaps. MATERIAL AND METHODS: Scientometric tools were used to analyse 9956 articles of primary care journals indexed in MEDLINE. Through keyword analysis, a relations map was built. Literature review and a primary care database were used to identify active problems and reasons for visiting a family physician. Rankings and frequencies of research output and conditions were compared. RESULTS: Keyword analysis identified five clusters of publication trends: cardiovascular conditions and conditions related with unhealthy lifestyles; mental disorders; infections; oncology and health management. By comparing publications with clinical problems, the fields of orthopaedics, endocrinology/metabolism, gastroenterology/hepatology, dermatology, ophthalmology, and the respiratory system show the biggest gaps. Through the relations map, more concrete potential research topics were identified such as palliative care, chronic pain, insomnia, antibiotic prescribing, burnout, osteoporosis, osteoarthritis, and COVID-19. DISCUSSION: The present study acknowledged potential research and publication gaps in Primary Care journals. The findings might help to direct continuing medical education and help with guideline development. CONCLUSION: The distribution of publications in primary care journals is distinct from the burden of problems faced in clinical practice and reasons for visiting a family physician. The use of scientometric tools to identify publication trends and their comparison with common problems could be a strategy to identify areas with research gaps in primary care.

Introdução: Desconhece-se se a investigação publicada na área dos cuidados de saúde primários está alinhada com os problemas geridos na prática clínica. Pretendemos analisar as tendências de publicação das revistas científicas de cuidados de saúde primários no que diz respeito a problemas médicos e comparar os resultados com a prevalência dos problemas na prática, para encontrar lacunas investigacionais. Material e Métodos: Utilizando ferramentas cienciométricas, analisámos 9956 artigos de revistas de cuidados de saúde primários indexadas à MEDLINE. Através da análise de palavras-chave, construímos um mapa de relações. Identificámos os problemas prevalentes através da revisão da literatura e de uma base de dados dos Cuidados de Saúde Primários. Comparámos as áreas de investigação com problemas ativos e motivos de consulta quanto à frequência e ranking. Resultados: Identificámos cinco grupos de tendências de publicação: doenças cardiovasculares/condições relacionadas com estilos de vida não saudáveis; patologia mental; infeções; oncologia; e gestão em saúde. Comparando publicações com problemas clínicos, as áreas de ortopedia, endocrinologia/metabolismo, gastrenterologia/hepatologia, dermatologia, oftalmologia e sistema respiratório apresentam as maiores lacunas. Através do mapa de relações, encontrámos potenciais áreas de investigação mais concretas, como cuidados paliativos, dor crónica, insónia, burnout, osteoporose, artrose, COVID-19. Discussão: Identificámos lacunas de investigação e de publicação em revistas de Medicina Geral e Familiar. Os resultados podem ajudar a orientar a educação médica contínua e o desenvolvimento de guidelines. Conclusão: A distribuição das publicações em revistas de Medicina Geral e Familiar diverge da proporção dos problemas e motivos de consulta. A identificação de tendências de publicação com ferramentas cienciométricas e a sua comparação com problemas comuns pode ser uma estratégia para reconhecer lacunas de investigação.

Construction and operation of hospital radiation protection management information system / 中华医院管理杂志

The construction and operation of the hospital-wide radiation protection management information system is of great significance for improving the efficiency and quality of radiation protection management. A hospital has built a hospital-wide radiation protection management system, which consisted of four modules: radiation staff management module, protection items management module, site and equipment management module, and self-inspection module. The system adopted B/S mode, carried the lightweight J2EE framework-SpringBoot framework, and used mybatis, which was flexible and practical, as the persistence layer of the system, to store the data completely in the SQLServer database.Java language was used as the system development language, and the front and back-end separation architecture was used. Each of the four modules implemented the management responsibility departments and responsible personnel to carry out the all-round management of radiation protection, and realized the integrated, information-based and standardized management of radiation protection management in the hospital.

Development and clinical application of contrast enhanced ultrasound Liver Imaging Reporting and Data System / 临床肝胆病杂志

Hepatocellular carcinoma (HCC) is a malignant tumor with high incidence in China and the whole world, and early diagnosis and treatment are the key to improving the prognosis of patients. To facilitate the communication and cooperation between doctors of different centers and specialties, American College of Radiology issued the first edition of contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) in 2016 to standardize the technical terms, techniques, interpretation, reporting, and data collection for liver imaging and perform HCC risk stratification for different focal liver lesions. This article reviews the development and clinical application of CEUS LI-RADS and believes that the application of CEUS LI-RADS has a great potential value in the clinical management of focal liver lesions in the population at a high risk of HCC, and the applicable population and indications for CEUS LI-RADS will continue to expand in the near future, so as to provide better service to clinical practice.

Are child health information services a viable source of accurate vaccination data for clinicians working in paediatric emergency departments in England?

Vaccination is a global success story, yet UK coverage remains undertarget for a number of diseases. The paediatric emergency department (PED) offers the potential for opportunistic vaccination interventions. OBJECTIVES: To map the Greater Manchester (GM) Child Health Information System network to see if it was a viable source of vaccination data for clinicians working in the PED as a case study. METHODS: Postprimary care vaccination management systems for GM were visualised using a systems mapping approach, with data obtained from the Office for National Statistics and commissioners in the GM Health and Social Care Partnership. RESULTS: Once vaccination data left primary care, it passed through 1 of 10 local child health information services (CHISs), using an assortment of different information technology systems, after which it shed individual identifiers and was aggregated within national systems. None of the existing GM CHISs were accessible to PED practitioners. CONCLUSION: More work needs to be done to explore possible alternative sources of accurate vaccination data during a PED consultation.

The REDCap Mobile Application: a data collection platform for research in regions or situations with internet scarcity.

OBJECTIVES: To share our approach for designing, developing, and deploying the Research Electronic Data Capture (REDCap) Mobile Application, details about its dissemination and support through the REDCap Consortium, and a set of lessons learned and guidance recommendations for others developing mobile platforms to support research in regions or situations with internet scarcity. MATERIALS AND METHODS: We defined minimum viable product requirements centered around Android and iOS platform availability, data capture specifications and project initiation workflow, study data synchronization, and data security. After launch, we added features based on feedback from end-users and REDCap administrators. We prioritized new features based on expected impact, difficulty, and anticipated long-term cost for sustainability. RESULTS: We chose Apache Cordova, a combined iOS and Android development framework, based on targeted end-user technology expectations, available programmer resources, and the need to provide solutions for resource-limited settings. The REDCap Mobile Application was launched in 2015, has been enabled at over 800 REDCap Consortium partner organizations, and has supported diverse scientific studies around the world. DISCUSSION: Apache Cordova enabled early software releases for both iOS and Android, but required ongoing optimization efforts to improve software responsiveness. Developing a robust and efficient mobile device synchronization architecture was difficult without direct access to global network infrastructures for testing. Research teams in sub-Saharan Africa helped our development team understand and simulate real-world scenarios of intermittent internet connectivity. CONCLUSION: Guidance recommendations based on designing, developing, deploying, and disseminating the REDCap Mobile Application may help other teams looking to develop clinical research informatics applications.

Dental implant quality registries and databases: A systematic review.

BACKGROUND: The importance of dental implant quality register has been well-documented. However, no systematic review conducted on dental implant quality register can be found in the literature. Therefore, the purpose of this study was to study the existed dental implant quality registries to explain the goals, data elements, and reports of dental implant quality registries. MATERIALS AND METHODS: This systematic study was conducted in PubMed, Scopus, Web of Science, and Embase databases. For evaluating dental implant quality registers, all studies in the English language were examined with no time limitation. Case reports, conference abstracts, and letters to the editor were excluded. The analysis of the quality of the studies was done by the STrengthening the Reporting of OBservational studies in Epidemiology checklist. RESULTS: The primary search identified 5565 articles. After eliminating duplicate articles and articles that did not meet the inclusion criteria and reviewing 40 full texts, 11 studies were included in this study. In this review, seven countries as Sweden, the USA, Canada, Germany, Finland, Australia, and South Korea had dental implant quality registers. Furthermore, the goals of dental implant quality registers were classified into the categories of research, epidemiology, administrative, clinical, and surveillance. CONCLUSION: The results of this study provide dentists and other stakeholders useful information on the existed dental implant quality registers and databases worldwide. It also provides a framework of the goals, data elements, and reports of dental implant quality registry. The establishment of dental implant quality register will be beneficial for societies and also allows them to control the complications of dental implants in future.