DieTryin: An R package for data collection, automated data entry, and post-processing of network-structured economic games, social networks, and other roster-based dyadic data.

Researchers studying social networks and inter-personal sentiments in bounded or small-scale communities face a trade-off between the use of roster-based and free-recall/name-generator-based survey tools. Roster-based methods scale poorly with sample size, and can more easily lead to respondent fatigue; however, they generally yield higher quality data that are less susceptible to recall bias and that require less post-processing. Name-generator-based methods, in contrast, scale well with sample size and are less likely to lead to respondent fatigue. However, they may be more sensitive to recall bias, and they entail a large amount of highly error-prone post-processing after data collection in order to link elicited names to unique identifiers. Here, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and entered as rapidly as name-generator-based data; DieTryin can be used to run network-structured economic games, as well as collect and process standard social network data and round-robin Likert-scale peer ratings. DieTryin automates photograph standardization, survey tool compilation, and data entry. We present a complete methodological workflow using DieTryin to teach end-users its full functionality.

Comparing the accuracy and speed of four data-checking methods.

Double entry locates and corrects more data-entry errors than does visual checking or reading the data out loud with a partner. However, many researchers do not use double entry, because it is substantially slower. Therefore, in this study we examined the speed and accuracy of solo read aloud, which has never before been examined and might be faster than double entry. To compare these four methods, we deliberately introduced errors while entering 20 data sheets and then asked 412 randomly assigned undergraduates to locate and correct these errors. Double entry was significantly and substantially more accurate than the other data-checking methods. However, the double-entry participants still made some errors. Close examination revealed that whenever double-entry participants made errors, they made the two sets of entries match, sometimes by introducing new errors into the dataset. This suggests that double entry can be improved by focusing attention on making entries match the original data sheets (rather than each other), perhaps by using a new person for mismatch correction. Solo read aloud was faster than double entry, but not as accurate. Double entry remains the gold standard in data-checking methods. However, solo read aloud was often substantially more accurate than partner read aloud and was more accurate than visual checking for one type of data. Therefore, when double entry is not possible, we recommend that researchers use solo read aloud or visual checking.

Reducing defects in the datasets of clinical research studies: conformance with data quality metrics.

BACKGROUND: A dataset is indispensable to answer the research questions of clinical research studies. Inaccurate data lead to ambiguous results, and the removal of errors results in increased cost. The aim of this Quality Improvement Project (QIP) was to improve the Data Quality (DQ) by enhancing conformance and minimizing data entry errors. METHODS: This is a QIP which was conducted in the Department of Biostatistics using historical datasets submitted for statistical data analysis from the department's knowledge base system. Forty-five datasets received for statistical data analysis, were included at baseline. A 12-item checklist based on six DQ domains (i) completeness (ii) uniqueness (iii) timeliness (iv) accuracy (v) validity and (vi) consistency was developed to assess the DQ. The checklist was comprised of 12 items; missing values, un-coded values, miscoded values, embedded values, implausible values, unformatted values, missing codebook, inconsistencies with the codebook, inaccurate format, unanalyzable data structure, missing outcome variables, and missing analytic variables. The outcome was the number of defects per dataset. Quality improvement DMAIC (Define, Measure, Analyze, Improve, Control) framework and sigma improvement tools were used. Pre-Post design was implemented using mode of interventions. Pre-Post change in defects (zero, one, two or more defects) was compared by using chi-square test. RESULTS: At baseline, out of forty-five datasets; six (13.3%) datasets had zero defects, eight (17.8%) had one defect, and 31(69%) had ≥2 defects. The association between the nature of data capture (single vs. multiple data points) and defective data was statistically significant (p = 0.008). Twenty-one datasets were received during post-intervention for statistical data analysis. Seventeen (81%) had zero defects, two (9.5%) had one defect, and two (9.5%) had two or more defects. The proportion of datasets with zero defects had increased from 13.3 to 81%, whereas the proportion of datasets with two or more defects had decreased from 69 to 9.5% (p = < 0.001). CONCLUSION: Clinical research study teams often have limited knowledge of data structuring. Given the need for good quality data, we recommend training programs, consultation with data experts prior to data structuring and use of electronic data capturing methods.

Impact of retrospective data verification to prepare the ICON6 trial for use in a marketing authorization application.

BACKGROUND: The ICON6 trial (ISRCTN68510403) is a phase III academic-led, international, randomized, three-arm, double-blind, placebo-controlled trial of the addition of cediranib to chemotherapy in recurrent ovarian cancer. It investigated the use of placebo during chemotherapy and maintenance (arm A), cediranib alongside chemotherapy followed by placebo maintenance (arm B) and cediranib throughout both periods (arm C). Results of the primary comparison showed a meaningful gain in progression-free survival (time to progression or death from any cause) when comparing arm A (placebo) with arm C (cediranib). As a consequence of the positive results, AstraZeneca was engaged with the Medical Research Council trials unit to discuss regulatory submission using ICON6 as the single pivotal trial. METHODS: A relatively limited level of on-site monitoring, single data entry and investigator's local evaluation of progression were used on trial. In order to submit a license application, it was decided that (a) extensive retrospective source data verification of medical records against case report forms should be performed, (b) further quality control checks for accuracy of data entry should be performed and (c) blinded independent central review of images used to define progression should be undertaken. To assess the value of these extra activities, we summarize the impact on both efficacy and safety outcomes. RESULTS: Data point changes were minimal; those key to the primary results had a 0.47% error rate (36/7686), and supporting data points had a 0.18% error rate (109/59,261). The impact of the source data verification and quality control processes were analyzed jointly. The conclusion drawn for the primary outcome measure of progression-free survival between arm A and arm C was unchanged. The log-rank test p-value changed only at the sixth decimal place, the hazard ratio does not change from 0.57 with the exception of a marginal change in its upper bound (0.74-0.73) and the median progression-free survival benefit from arm C remained at 2.4 months. Separately, the blinded independent central review of progression scans was performed as a sensitivity analysis. Estimates and p values varied slightly but overall demonstrated a difference in arms, which is consistent with the initial result. Some increases in toxicity were observed, though these were generally minor, with the exception of hypertension. However, none of these increases were systematically biased toward one arm. CONCLUSION: The conduct of this pragmatic, academic-sponsored trial was sufficient given the robustness of the results, shown by the results remaining largely unchanged following retrospective verification despite not being designed for use in a marketing authorization. The burden of such comprehensive retrospective effort required to ensure the results of ICON6 were acceptable to regulators is difficult to justify.

"Struggling with practices" - a qualitative study of factors influencing the implementation of clinical quality registries for cardiac rehabilitation in England and Denmark.

BACKGROUND: The use of clinical quality registries as means for data driven improvement in healthcare seem promising. However, their use has been shown to be challenged by a number of aspects, and we suggest some may be related to poor implementation. There is a paucity of literature regarding barriers and facilitators for registry implementation, in particular aspects related to data collection and entry. We aimed to illuminate this by exploring how staff perceive the implementation process related to the registries within the field of cardiac rehabilitation in England and Denmark. METHODS: A qualitative, interview-based study with staff involved in collecting and/or entering data into the two case registries (England N = 12, Denmark N = 12). Interviews were analysed using content analysis. The Consolidated Framework for Implementation Research was used to guide interviews and the interpretation of results. RESULTS: The analysis identified both similarities and differences within and between the studied registries, and resulted in clarification of staffs´ experiences in an overarching theme: ´Struggling with practices´ and five categories; the data entry process, registry quality, resources and management support, quality improvement and the wider healthcare context. Overall, implementation received little focused attention. There was a lack of active support from management, and staff may experience a struggle of fitting use of a registry into a busy and complex everyday practice. CONCLUSION: The study highlights factors that may be important to consider when planning and implementing a new clinical quality registry within the field of cardiac rehabilitation, and is possibly transferrable to other fields. The results may thus be useful for policy makers, administrators and managers within the field and beyond. Targeting barriers and utilizing knowledge of facilitating factors is vital in order to improve the process of registry implementation, hence helping to achieve the intended improvement of care processes and outcomes.

Differential reinforcement of high rates of behaviour to increase work productivity in adults with intellectual disability.

BACKGROUND: Due to deficits in adaptive and cognitive functioning, productivity may pose challenges for individuals with intellectual disability in the workplace. METHOD: Using a changing-criterion embedded in a multiple baseline across participants design, we examined the effects of differential reinforcement of high rates of behaviour (DRH) on the rate of data entry (i.e., productivity) in four adults with intellectual disability. RESULTS: Although the DRH procedure increased the rate of correct data entry in all four participants, none of the participants achieved the criterion that we set with novice undergraduate students. CONCLUSIONS: Our results indicate that DRH is an effective intervention to increase rate of correct responding in individuals with intellectual disability, but that achieving the same productivity as workers without disability may not always be possible.

Data Analysis Using R Programming.

Beginning RR is an open-source, freely available, integrated software environment for data manipulation, computation, analysis, and graphical display. The R environment consists of *a data handling and storage facility, *operators for computations on arrays and matrices, *a collection of tools for data analysis *graphical capabilities for analysis and display, and *an efficient, and continuing developing programming algebra-like programming language which consists of loops, conditionals, user-defined functions, and input and output capabilities. Many R programs are available for biostatistical analysis in Genetic Epidemiology. Typical examples are shown.

Does Computerized Physician Order Entry Benefit from Dynamic Structured Data Entry? A Quasi-Experimental Study.

BACKGROUND: With advancements in information technology, computerized physician order entry (CPOE) and electronic Medical Records (eMR), have become widely utilized in medical settings. The predominant mode of CPOE in Taiwan is free text entry (FTE). Dynamic structured data entry (DSDE) was introduced more recently, and has increasingly drawn attention from hospitals across Taiwan. This study assesses how DSDE compares to FTE for CPOE. METHODS: A quasi-experimental study was employed to investigate the time-savings, productivity, and efficiency effects of DSDE in an outpatient setting in the gynecological department of a major hospital in Taiwan. Trained female actor patients were employed in trials of both entry methods. Data were submitted to Shapiro-Wilk and Shapiro-Francia tests to assess normality, and then to paired t-tests to assess differences between DSDE and FTE. RESULTS: Relative to FTE, the use of DSDE resulted in an average of 97% time saved and 55% more abundant and detailed content in medical records. In addition, for each clause entry in a medical record, the time saved is 133% for DSDE compared to FTE. CONCLUSION: The results suggest that DSDE is a much more efficient and productive entry method for clinicians in hospital outpatient settings. Upgrading eMR systems to the DSDE format would benefit both patients and clinicians.

A nationwide survey on gynecologic endoscopic surgery in Japan, 2014-2016.

AIM: Since 2014, Japan Society of Gynecologic and Obstetric Endoscopy and minimally invasive therapy (JSGOE) conducted a nationwide survey on gynecologic endoscopic surgery. We aimed to evaluate the current status and complications associated with endoscopic surgery by Japan gynecologic and obstetric endoscopy-database registry system (JOE-D). METHODS: Electrical medical records concerning the endoscopic surgery were generated from the daily use of reporting system. The subjects were all patients who underwent gynecologic endoscopic surgery. In addition to assessment of actual numbers, diagnosis, and operative methods, adverse events were registered. RESULTS: Total 203 970 patients performed laparoscopic, hysteroscopic and falloposcopic surgery for 3 years, 2014-2016. The numbers of endoscopic surgeries conducted in 2016 were increased more than 67 000, 13 000 or 450 cases, respectively. Incidence rates of complications involving these three types of surgeries in each year were approximately 3.1%. Incidences of intraoperative complications were relatively high in malignant diseases, laparoscopic-assisted vaginal hysterectomy (LAVH) and myomectomy (LAM). In total laparoscopic hysterectomy/laparoscopic hysterectomy (TLH/LH) performed from 2014 to 2016, ureteral injury as intra and postoperative complication occurred in 0.35%. In the past 3 years, the rates of vascular injury, urinary tract, and bowel injury as intraoperative complications caused by laparoscopic surgery were approximately 0.1%. In the hysteroscopic surgery, the rates of total intra- and postoperative complications were 0.78%. CONCLUSION: We exhibited the current status by the nationwide survey of gynecologic endoscopic surgery all over Japan. Severe intra or postoperative complications were identified over the 3 years at a rate of 0.04%.

Omission of quality assurance during data entry in public health research from India: Is there an elephant in the room?

As the adage, "Garbage in, Garbage out" goes, data entry errors may lead to erroneous results and conclusions. Quality assurance during data entry is one of the most neglected components of research and is conspicuously missing in most of the reporting standards. In this study, we reviewed research studies published in Indian Journal of Public Health and Indian Journal of Community Medicine during 2014-2016 and determined the proportion of papers reporting on quality assurance during data entry. Of 110 papers, only 6 (5.5%) papers explicitly included a statement about data quality assurance, with two studies reported to have performed double entry and validation, considered the gold standard in quality-assurance of data entry. This is highly unacceptable. We hereby appeal to the community of researchers, peer reviewers, and journal editors in India to pay attention to this important aspect of research and make reporting of quality assurance of data entry mandatory in every published paper.

Improving patient safety reporting with the common formats: Common data representation for Patient Safety Organizations.

Medical errors and patient safety issues remain a significant problem for the healthcare industry in the United States. The Institute of Medicine report To Err is Human reported that there were as many as 98,000 deaths per year due to medical error as of 1999. Many authors and government officials believe that the first step on the path to improvement in patient safety is more comprehensive collection and analysis of patient safety events. The belief is that this will enable safety improvements based on data showing the nature and frequency of events that occur, and the effectiveness of interventions. This systematization of healthcare practice can be a step in the right direction toward a value based, safety conscious and effective healthcare system. To help standardize this reporting and analysis, AHRQ created Common Formats for Patient Safety data collection and reporting. This manuscript describes the development of patient safety reporting and learning through the Patient Safety Organizations (PSO)s and the Common Formats and gives readers an overview of how the system is expected to function and the breadth of development of the Common Formats to date.

A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system.

BACKGROUND: Healthcare providers generate a huge amount of biomedical data stored in either legacy system (paper-based) format or electronic medical records (EMR) around the world, which are collectively referred to as big biomedical data (BBD). To realize the promise of BBD for clinical use and research, it is an essential step to extract key data elements from unstructured medical records into patient-centered electronic health records with computable data elements. Our objective is to introduce a novel solution, known as a double-reading/entry system (DRESS), for extracting clinical data from unstructured medical records (MR) and creating a semi-structured electronic health record database, as well as to demonstrate its reproducibility empirically. METHODS: Utilizing the modern cloud-based technologies, we have developed a comprehensive system that includes multiple subsystems, from capturing MRs in clinics, to securely transferring MRs, storing and managing cloud-based MRs, to facilitating both machine learning and manual reading, and to performing iterative quality control before committing the semi-structured data into the desired database. To evaluate the reproducibility of extracted medical data elements by DRESS, we conduct a blinded reproducibility study, with 100 MRs from patients who have undergone surgical treatment of lung cancer in China. The study uses Kappa statistic to measure concordance of discrete variables, and uses correlation coefficient to measure reproducibility of continuous variables. RESULTS: Using the DRESS, we have demonstrated the feasibility of extracting clinical data from unstructured MRs to create semi-structured and patient-centered electronic health record database. The reproducibility study with 100 patient's MRs has shown an overall high reproducibility of 98 %, and varies across six modules (pathology, Radio/chemo therapy, clinical examination, surgery information, medical image and general patient information). CONCLUSIONS: DRESS uses a double-reading, double-entry, and an independent adjudication, to manually curate structured data elements from unstructured clinical data. Further, through distributed computing strategies, DRESS protects data privacy by dividing MR data into de-identified modules. Finally, through internet-based computing cloud, DRESS enables many data specialists to work in a virtual environment to achieve the necessary scale of processing thousands MRs within days. This hybrid system represents probably a workable solution to solve the big medical data challenge.

Debriefing as a form of reflection and catharsis for researchers.

Background The collection of sensitive data can arouse emotional reactions and researchers may have difficulty distancing themselves from personal stories. Debriefing can address the emotional effect of an experience on researchers. Aim To explore the debrief responses of three research assistants who were involved in the review of retrospective charts and medical notes in a study that examined the risk factors for readmission in young people with anorexia nervosa. Discussion Based on a review of the responses, the principal research team reflected on the value and effectiveness of a debrief tool for research assistants entering sensitive quantitative data. The paper highlights these reflections. Conclusion The use of an electronic debrief tool, while not without its challenges, provides an opportunity for individual reflection and a platform for emotional release for researchers engaged in sustained and intensive collection of sensitive data. This type of tool may serve as a guide for research teams and assist them in monitoring the well-being of those collecting sensitive data. We also advocate that a debriefing tool may contribute to closure for research assistants who become emotionally invested and affected by meticulous quantitative data entry. Implications for practice This paper provides recommendations for future use of an electronic debrief tool for researchers collecting sensitive data.

Crossing the digital divide: The workload of manual data entry for integration between mobile health applications and eHealth infrastructure.

Background: Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions, and sustained funding. Manual data linkage processes are commonly used to "integrate" across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood. Methodology: As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel. Results: We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone. Conclusion: Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.

Time Efficiency, Reliability, and User Satisfaction of the Tooth Memo App for Recording Oral Health Information: Cross-Sectional Questionnaire Study.

BACKGROUND: Digitalizing oral health data through an app can help manage the extensive data obtained through oral health surveys. The Tooth Memo app collects data from oral health surveys and personal health information. OBJECTIVE: This study aims to evaluate the evaluate the time efficiency, reliability, and user satisfaction of the Tooth Memo app. METHODS: There are 2 sections in the Tooth Memo app: oral health survey and personal oral health record. For the oral health survey section of the Tooth Memo app, different data entry methods were compared and user satisfaction was evaluated. Fifth-year dental students had access to the oral health survey section in the Tooth Memo app during their clinical work. The time required for data entry, analysis, and summary of oral health survey data by 3 methods, that is, pen-and-paper (manual), Tooth Memo app on iOS device, and Tooth Memo app on Android device were compared among 3 data recorders who entered patients' information on decayed, missing, and filled permanent teeth (DMFT) index and community periodontal index (CPI), which were read aloud from the database of 103 patients by another dental personnel. The interobserver reliability of the 3 different data-entering procedures was evaluated by percent disagreement and kappa statistic values. Laypeople had access to the personal oral health record section of this app, and their satisfaction was evaluated through a Likert scale questionnaire. The satisfaction assessments for both sections of the Tooth Memo app involved the same set of questions on the app design, usage, and overall satisfaction. RESULTS: Of the 103 dental records on DMFT and CPI, 5.2% (177/3399) data points were missing in the manual data entries, but no data on tooth status were missing in the Android and iOS methods. Complete CPI information was provided by all 3 methods. Transferring data from paper to computer took an average of 55 seconds per case. The manual method required 182 minutes more than the iOS or Android methods to clean the missing data and transfer and analyze the tooth status data of 103 patients. The users, that is, 109 fifth-year dental students and 134 laypeople, expressed high satisfaction with using the Tooth Memo app. The overall satisfaction with the oral health survey ranged between 3 and 10, with an average (SD) of 7.86 (1.46). The overall satisfaction with the personal oral health record ranged between 4 and 10, with an average (SD) of 8.09 (1.28). CONCLUSIONS: The Tooth Memo app was more efficacious than manual data entry for collecting data of oral health surveys. Dental personnel as well as general users reported high satisfaction when using this app.

Training Graduate Students to Enter Fieldwork Data Using Asynchronous Online Instruction.

The curr ent study aimed to evaluate the effects of asynchronous online instruction on correct fieldwork data entry for graduate students in behavior analysis preparing to sit for the BACB exam. Previous research has been conducted on using synchronous instructional methods to teach fieldwork data entry. To our knowledge, this is the first examination of a completely asynchronous approach to teaching the new Behavior Analysis Certification Board (BACB) fieldwork requirements (BACB, 2020a). Experimenters focused on the completion of daily fieldwork activities, as well as the completion of monthly fieldwork forms. Participants were 22 graduate students beginning their fieldwork experiences in pursuit of their board certified behavior analyst credential. Most participants did not reach the mastery criterion in baseline after only reviewing the fieldwork resources provided by the BACB for both phases. After undergoing training, all participants scored above the mastery criterion in their completion of both their daily fieldwork logs and monthly forms. • Fieldwork trainees taught to fill in Trackers and monthly forms. • Asynchronous online instruction used to teach data entry using mock fieldwork scenarios. • 18 of 18 participants in the Tracker Training improved from baseline. • 18 of 20 participants in the Monthly Forms Training improved from baseline. • Correct responding for 15 participants generalized to a novel scenario. Data suggest that asynchronous online instruction is an effective method to teach fieldwork data entry. • Social validity data suggest favorable views of the training.

Leveraging Electronic Health Records for Guideline-Based Asthma Documentation.

BACKGROUND: Asthma is the most common pediatric chronic disease; thus, clinical guidelines have been developed for its assessment and management, which rely on systematic symptom documentation. Electronic health records (EHR) have the potential to record clinical data systematically; however, variability in documentation persists. OBJECTIVE: To identify if the use of a structured asthma template is associated with increased guideline-based asthma documentation and clinical outcomes when compared with the use of nonstructured ones. METHODS: We performed a retrospective case-control study comparing the use of nonstructured templates (NSTs) and asthma-structured templates (ASTs) in new patient and first follow-up encounters, evaluated by pediatric pulmonologists between March 2016 and December 2021. Asthma history items were selected following clinical guidelines, summarized in 29 items for new and 22 items for follow-up encounters. Associations with demographic, spirometry, and health care utilization were explored. RESULTS: A total of 546 initial encounters were included; 450 used structured templates. The use of an AST was associated with higher documentation of asthma items in initial and follow-up encounters. Linear regression analysis showed that the use of ASTs was associated with a 28.2% and 39.65% increase in asthma history completeness (in initial and follow-up encounters, respectively), compared with the use of NSTs. AST use was associated with higher rates of systemic steroid prescriptions within 12 months. No other differences were observed after adjusting for asthma severity. CONCLUSIONS: Using asthma-specific structured templates was associated with increased guideline-based asthma documentation. Leveraging the EHR as a clinical and research tool has the potential to improve clinical practice.

Migrating a Well-Established Longitudinal Cohort Database From Oracle SQL to Research Electronic Data Entry (REDCap): Data Management Research and Design Study.

BACKGROUND: Providing user-friendly electronic data collection tools for large multicenter studies is key for obtaining high-quality research data. Research Electronic Data Capture (REDCap) is a software solution developed for setting up research databases with integrated graphical user interfaces for electronic data entry. The Swiss Mother and Child HIV Cohort Study (MoCHiV) is a longitudinal cohort study with around 2 million data entries dating back to the early 1980s. Until 2022, data collection in MoCHiV was paper-based. OBJECTIVE: The objective of this study was to provide a user-friendly graphical interface for electronic data entry for physicians and study nurses reporting MoCHiV data. METHODS: MoCHiV collects information on obstetric events among women living with HIV and children born to mothers living with HIV. Until 2022, MoCHiV data were stored in an Oracle SQL relational database. In this project, R and REDCap were used to develop an electronic data entry platform for MoCHiV with migration of already collected data. RESULTS: The key steps for providing an electronic data entry option for MoCHiV were (1) design, (2) data cleaning and formatting, (3) migration and compliance, and (4) add-on features. In the first step, the database structure was defined in REDCap, including the specification of primary and foreign keys, definition of study variables, and the hierarchy of questions (termed "branching logic"). In the second step, data stored in Oracle were cleaned and formatted to adhere to the defined database structure. Systematic data checks ensured compliance to all branching logic and levels of categorical variables. REDCap-specific variables and numbering of repeated events for enabling a relational data structure in REDCap were generated using R. In the third step, data were imported to REDCap and then systematically compared to the original data. In the last step, add-on features, such as data access groups, redirections, and summary reports, were integrated to facilitate data entry in the multicenter MoCHiV study. CONCLUSIONS: By combining different software tools-Oracle SQL, R, and REDCap-and building a systematic pipeline for data cleaning, formatting, and comparing, we were able to migrate a multicenter longitudinal cohort study from Oracle SQL to REDCap. REDCap offers a flexible way for developing customized study designs, even in the case of longitudinal studies with different study arms (ie, obstetric events, women, and mother-child pairs). However, REDCap does not offer built-in tools for preprocessing large data sets before data import. Additional software is needed (eg, R) for data formatting and cleaning to achieve the predefined REDCap data structure.

The Young Researcher's Guide to Creating Data Mastersheets.

Researchers who have completed a study need to transfer their data from individual patient research files to a mastersheet. This article is a beginner's guide to creating data spreadsheets that can be shared with others and imported into statistical programs. The article discusses choice of spreadsheet software, use of separate worksheets for main data and granular data, use of columns for variables and rows for patient data, coding data, entry of data into cells in the worksheets, and other matters.

[Automated data extraction into a cataract surgery registry : Automatic investigation of results in the registry on ophthalmological analysis (ROPHA)]. / Automatische Datenextraktion zum Katarakt-OP-Register : Automatische Ergebnisermittlung im Register zur ophthalmologischen Analytik (ROPHA).

BACKGROUND: Medical data should be evaluated with respect to quality management, for scientific and research purposes on the basis of registries. These data are stored in the EMR in an unstructured way and are not suitable for automatic evaluation. METHOD: The EMR tomedo (Zollsoft/Jena) and the PACS of FORUM (Zeiss/Oberkochen) manage personal, medical and image data for cataract surgery. New developed input masks gather data from diagnostic tools direct in a structured way. These data are automatically extracted by an ETL process and are stored in our data warehouse. Via Excel specific data can be selected and displayed in the dashboard. All medical data are structured and stored electronically. Few data are entered manually and most of the data are entered by pull-down menus or taken over directly from diagnostic equipment. RESULTS: A total of 2816 cataract surgeries of 1844 patients were selected for a first analysis. Results of IOL type distribution in relation to the patients and to the surgeons are displayed as well as prediction error of refraction for the 4 different groups of IOLs. DISCUSSION: Structured data input into the EMR (electronic medical record) makes data for automatic extraction available thus reducing input errors or confounding data. The result is a high quality of data sets. Input of epidemiological and general medical data also helps to perform health services research. These registry data represent better information for real live data than data from clinical trials. Bureaucratic hurdles in Germany prevent the use of registry data.