scholarly journals Application of the research electronic data capture (REDCap) system in a low- and middle income country– experiences, lessons, and challenges

2021 ◽  
Author(s):  
O. Odukoya ◽  
D. Nenrot ◽  
H. Adelabu ◽  
N. Katam ◽  
E. Christian ◽  
...  
Keyword(s):  
Research Data ◽  
Data Capture ◽  
Sub Saharan Africa ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Middle Income ◽  
Internet Connectivity ◽  
Trained Personnel ◽  
Private Network ◽  
Low And Middle Income

AbstractThe challenges of reliably collecting, storing, organizing, and analyzing research data are critical in low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa where several healthcare and biomedical research organizations have limited data infrastructure. The Research Electronic Data Capture (REDCap) System has been widely used by many institutions and hospitals in the USA for data collection, entry, and management and could help solve this problem. This study reports on the experiences, challenges, and lessons learned from establishing and applying REDCap for a large US-Nigeria research partnership that includes two sites in Nigeria, (the College of Medicine of the University of Lagos (CMUL) and Jos University Teaching Hospital (JUTH)) and Northwestern University (NU) in Chicago, Illinois in the United States. The largest challenges to this implementation were significant technical obstacles: the lack of REDCap-trained personnel, transient electrical power supply, and slow/ intermittent internet connectivity. However, asynchronous communication and on-site hands-on collaboration between the Nigerian sites and NU led to the successful installation and configuration of REDCap to meet the needs of the Nigerian sites. An example of one lesson learned is the use of Virtual Private Network (VPN) as a solution to poor internet connectivity at one of the sites, and its adoption is underway at the other. Virtual Private Servers (VPS) or shared online hosting were also evaluated and offer alternative solutions. Installing and using REDCap in LMIC institutions for research data management is feasible; however, planning for trained personnel and addressing electrical and internet infrastructural requirements are essential to optimize its use. Building this fundamental research capacity within LMICs across Africa could substantially enhance the potential for more cross-institutional and cross-country collaboration in future research endeavors.

A Permissioned Blockchain Network for Security and Sharing of De-identified Tuberculosis Research Data in Brazil

2021 ◽  
Author(s):  
Vinícius Costa Lima ◽  
Filipe Andrade Bernardi ◽  
Domingos Alves ◽  
Afrânio Lineu Kritski ◽  
Rafael Mello Galliez ◽  
...  
Keyword(s):  
Research Data ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Security Issues ◽  
Use Of Data ◽  
Governance Model ◽  
Model Research ◽  
Health Studies ◽  
Evidence Based Decision Making

Abstract Background Tuberculosis (TB) is an infectious disease and is among the top 10 causes of death in the world, and Brazil is part of the top 30 high TB burden countries. Data collection is an essential practice in health studies, and the adoption of electronic data capture (EDC) systems can positively increase the experience of data acquisition and analysis. Also, data-sharing capabilities are crucial to the construction of efficient and effective evidence-based decision-making tools for managerial and operational actions in TB services. Data must be held secure and traceable, as well as available and understandable, for authorized parties. Objectives In this sense, this work aims to propose a blockchain-based approach to build a reusable, decentralized, and de-identified dataset of TB research data, while increasing transparency, accountability, availability, and integrity of raw data collected in EDC systems. Methods After identifying challenges and gaps, a solution was proposed to tackle them, considering its relevance for TB studies. Data security issues are being addressed by a blockchain network and a lightweight and practical governance model. Research Electronic Data Capture (REDCap) and KoBoToolbox are used as EDC systems in TB research. Mechanisms to de-identify data and aggregate semantics to data are also available. Results A permissioned blockchain network was built using Kaleido platform. An integration engine integrates the EDC systems with the blockchain network, performing de-identification and aggregating meaning to data. A governance model addresses operational and legal issues for the proper use of data. Finally, a management system facilitates the handling of necessary metadata, and additional applications are available to explore the blockchain and export data. Conclusions Research data are an important asset not only for the research where it was generated, but also to underpin studies replication and support further investigations. The proposed solution allows the delivery of de-identified databases built in real time by storing data in transactions of a permissioned network, including semantic annotations, as data are being collected in TB research. The governance model promotes the correct use of the solution.

scholarly journals De-novo FAIRification via an Electronic Data Capture system by automated transformation of filled electronic Case Report Forms into machine-readable data

2021 ◽  
Author(s):  
Martijn G. Kersloot ◽  
Annika Jacobsen ◽  
Karlijn H.J. Groenen ◽  
Bruna dos Santos Vieira ◽  
Rajaram Kaliyaperumal ◽  
...  
Keyword(s):  
Case Report ◽  
De Novo ◽  
Research Data ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Research Project ◽  
Novel Method ◽  
Data Point ◽  
Machine Readable

Introduction Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post-hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, data is often collected and stored via electronic Case Report Forms (eCRFs) in Electronic Data Capture (EDC) systems. By implementing a de-novo FAIRification process in such a system, the reusability and, thus, scalability of FAIRification across research projects can be greatly improved. In this study, we developed and implemented a novel method for de-novo FAIRification via an EDC system. We evaluated our method by applying it to the Registry of Vascular Anomalies (VASCA). Methods Our EDC and research project independent method ensures that eCRF data entered into an EDC system can be transformed into machine-readable, FAIR data using a semantic data model (a canonical representation of the data, based on ontology concepts and semantic web standards) and mappings from the model to questions on the eCRF. The FAIRified data are stored in a triple store and can, together with associated metadata, be accessed and queried through a FAIR Data Point. The method was implemented in Castor EDC, an EDC system, through a data transformation application. The FAIRness of the output of the method, the FAIRified data and metadata, was evaluated using the FAIR Evaluation Services. Results We successfully applied our FAIRification method to the VASCA registry. Data entered on eCRFs is automatically transformed into machine-readable data and can be accessed and queried using SPARQL queries in the FAIR Data Point. Twenty-one FAIR Evaluator tests pass and one test regarding the metadata persistence policy fails, since this policy is not in place yet. Conclusion In this study, we developed a novel method for de-novo FAIRification via an EDC system. Its application in the VASCA registry and the automated FAIR evaluation show that the method can be used to make clinical research data FAIR when they are entered in an eCRF without any intervention from data management and data entry personnel. Due to the generic approach and developed tooling, we believe that our method can be used in other registries and clinical trials as well.

scholarly journals 238. Novel Way to Evaluate Antibiotic Use Appropriateness: Moving Towards the “Never Event” Classification by Electronic Algorithm

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S119-S120
Author(s):  
Twisha S Patel ◽  
Lindsay A Petty ◽  
Jiajun Liu ◽  
Marc H Scheetz ◽  
Nicholas Mercuro ◽  
...  
Keyword(s):  
Urine Culture ◽  
Asymptomatic Bacteriuria ◽  
Antibiotic Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
False Positivity ◽  
Never Event ◽  
Manual Review

Abstract Background Antibiotic use is commonly tracked electronically by antimicrobial stewardship programs (ASPs). Traditionally, evaluating the appropriateness of antibiotic use requires time- and labor-intensive manual review of each drug order. A drug-specific “appropriateness” algorithm applied electronically would improve the efficiency of ASPs. We thus created an antibiotic “never event” (NE) algorithm to evaluate vancomycin use, and sought to determine the performance characteristics of the electronic data capture strategy. Methods An antibiotic NE algorithm was developed to characterize vancomycin use (Figure) at a large academic institution (1/2016–8/2019). Patients were electronically classified according to the NE algorithm using data abstracted from their electronic health record. Type 1 NEs, defined as continued use of vancomycin after a vancomycin non-susceptible pathogen was identified, were the focus of this analysis. Type 1 NEs identified by automated data capture were reviewed manually for accuracy by either an infectious diseases (ID) physician or an ID pharmacist. The positive predictive value (PPV) of the electronic data capture was determined. Antibiotic Never Event (NE) Algorithm to Characterize Vancomycin Use Results A total of 38,774 unique cases of vancomycin use were available for screening. Of these, 0.6% (n=225) had a vancomycin non-susceptible pathogen identified, and 12.4% (28/225) were classified as a Type 1 NE by automated data capture. All 28 cases included vancomycin-resistant Enterococcus spp (VRE). Upon manual review, 11 cases were determined to be true positives resulting in a PPV of 39.3%. Reasons for the 17 false positives are given in Table 1. Asymptomatic bacteriuria (ASB) due to VRE in scenarios where vancomycin was being appropriately used to treat a concomitant vancomycin-susceptible infection was the most common reason for false positivity, accounting for 64.7% of false positive cases. After removing urine culture source (n=15) from the algorithm, PPV improved to 53.8%. Conclusion An automated vancomycin NE algorithm identified 28 Type 1 NEs with a PPV of 39%. ASB was the most common cause of false positivity and removing urine culture as a source from the algorithm improved PPV. Future directions include evaluating Type 2 NEs (Figure) and prospective, real-time application of the algorithm. Disclosures Marc H. Scheetz, PharmD, MSc, Merck and Co. (Grant/Research Support)

scholarly journals Are low and middle-income countries prioritising high-value healthcare interventions?

2020 ◽  
Vol 5 (2) ◽  
pp. e001850
Author(s):  
Ashley A Leech ◽  
David D Kim ◽  
Joshua T Cohen ◽  
Peter J Neumann
Keyword(s):  
Cost Effectiveness ◽  
Cost Saving ◽  
National Health ◽  
Sub Saharan Africa ◽  
Health Plans ◽  
Middle Income ◽  
Middle Income Countries ◽  
Health Priority ◽  
Sub Saharan ◽  
Low And Middle Income

IntroductionSince resources are finite, investing in services that produce the highest health gain ‘return on investment’ is critical. We assessed the extent to which low and middle-income countries (LMIC) have included cost-saving interventions in their national strategic health plans.MethodsWe used the Tufts Medical Center Global Health Cost-Effectiveness Analysis Registry, an open-source database of English-language cost-per-disability-adjusted life year (DALY) studies, to identify analyses published in the last 10 years (2008–2017) of cost-saving health interventions in LMICs. To assess whether countries prioritised cost-saving interventions within their latest national health strategic plans, we identified 10 countries, all in sub-Saharan Africa, with the highest measures on the global burden of disease scale and reviewed their national health priority plans.ResultsWe identified 392 studies (63%) targeting LMICs that reported 3315 cost-per-DALY ratios, of which 207 ratios (6%) represented interventions reported to be cost saving. Over half (53%) of these targeted sub-Saharan Africa. For the 10 countries we investigated in sub-Saharan Africa, 58% (79/137) of cost-saving interventions correspond with priorities identified in country plans. Alignment ranged from 95% (21/22 prioritised cost-saving ratios) in South Africa to 17% (2/12 prioritised cost-saving ratios) in Cameroon. Human papillomavirus vaccination was a noted priority in 70% (7/10) of national health prioritisation plans, while 40% (4/10) of countries explicitly included prenatal serological screening for syphilis. HIV prevention and treatment were stated priorities in most country health plans, whereas 40% (2/5) of countries principally outlined efforts for lymphatic filariasis. From our sample of 45 unique interventions, 36% of interventions (16/45) included costs associated directly with the implementation of the intervention.ConclusionOur findings indicate substantial variation across country and disease area in incorporating economic evidence into national health priority plans in a sample of sub-Saharan African countries. To make health economic data more salient, the authors of cost-effectiveness analyses must do more to reflect implementation costs and other factors that could limit healthcare delivery.

scholarly journals Understanding cause of stillbirth: a prospective observational multi-country study from sub-Saharan Africa

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Mamuda Aminu ◽  
Sarah Bar-Zeev ◽  
Sarah White ◽  
Matthews Mathai ◽  
Nynke van den Broek
Keyword(s):  
Sierra Leone ◽  
Cause Of Death ◽  
Expert Panel ◽  
Healthcare Providers ◽  
Sub Saharan Africa ◽  
Primary Data ◽  
Middle Income ◽  
Computer Based ◽  
Sub Saharan ◽  
Low And Middle Income

Abstract Background Every year, an estimated 2.6 million stillbirths occur worldwide, with up to 98% occurring in low- and middle-income countries (LMIC). There is a paucity of primary data on cause of stillbirth from LMIC, and particularly from sub-Saharan Africa to inform effective interventions. This study aimed to identify the cause of stillbirths in low- and middle-income settings and compare methods of assessment. Methods This was a prospective, observational study in 12 hospitals in Kenya, Malawi, Sierra Leone and Zimbabwe. Stillbirths (28 weeks or more) were reviewed to assign the cause of death by healthcare providers, an expert panel and by using computer-based algorithms. Agreement between the three methods was compared using Kappa (κ) analysis. Cause of stillbirth and level of agreement between the methods used to assign cause of death. Results One thousand five hundred sixty-three stillbirths were studied. The stillbirth rate (per 1000 births) was 20.3 in Malawi, 34.7 in Zimbabwe, 38.8 in Kenya and 118.1 in Sierra Leone. Half (50.7%) of all stillbirths occurred during the intrapartum period. Cause of death (range) overall varied by method of assessment and included: asphyxia (18.5–37.4%), placental disorders (8.4–15.1%), maternal hypertensive disorders (5.1–13.6%), infections (4.3–9.0%), cord problems (3.3–6.5%), and ruptured uterus due to obstructed labour (2.6–6.1%). Cause of stillbirth was unknown in 17.9–26.0% of cases. Moderate agreement was observed for cause of stillbirth as assigned by the expert panel and by hospital-based healthcare providers who conducted perinatal death review (κ = 0.69; p < 0.0005). There was only minimal agreement between expert panel review or healthcare provider review and computer-based algorithms (κ = 0.34; 0.31 respectively p < 0.0005). Conclusions For the majority of stillbirths, an underlying likely cause of death could be determined despite limited diagnostic capacity. In these settings, more diagnostic information is, however, needed to establish a more specific cause of death for the majority of stillbirths. Existing computer-based algorithms used to assign cause of death require revision.

How Did a Multi-Institutional Trial Show Feasibility of Electronic Data Capture in Older Patients With Cancer? Results From a Multi-Institutional Qualitative Study (Alliance A171902)

2021 ◽  
pp. 442-449
Author(s):  
Nichole A. Martin ◽  
Elizabeth S. Harlos ◽  
Kathryn D. Cook ◽  
Jennifer M. O'Connor ◽  
Andrew Dodge ◽  
...  
Keyword(s):  
Older Patients ◽  
Electronic Devices ◽  
Data Entry ◽  
Data Capture ◽  
Data Availability ◽  
Electronic Data Capture ◽  
Wireless Internet ◽  
Electronic Data ◽  
Patients With Cancer ◽  
Patient Reported

PURPOSE New technology might pose problems for older patients with cancer. This study sought to understand how a trial in older patients with cancer (Alliance A171603) was successful in capturing electronic patient-reported data. METHODS Study personnel were invited via e-mail to participate in semistructured phone interviews, which were audio-recorded and qualitatively analyzed. RESULTS Twenty-four study personnel from the 10 sites were interviewed; three themes emerged. The first was that successful patient-reported electronic data capture shifted work toward patients and toward study personnel at the beginning of the study. One interviewee explained, “I mean it kind of lost all advantages…by being extremely laborious.” Study personnel described how they ensured electronic devices were charged, wireless internet access was up and running, and login codes were available. The second theme was related to the first and dealt with data filtering. Study personnel described high involvement in data gathering; for example, one interviewee described, “I answered on the iPad, whatever they said. They didn't even want to use it at all.” A third theme dealt with advantages of electronic data entry, such as prompt data availability at study completion. Surprisingly, some remarks described how electronic devices brought people together, “Some of the patients, you know, it just gave them a chance to kinda talk about, you know, what was going on.” CONCLUSION High rates of capture of patient-reported electronic data were viewed favorably but occurred in exchange for increased effort from patients and study personnel and in exchange for data that were not always patient-reported in the strictest sense.

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