scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

Electronic Data Collection for Rockfall Analysis

2003 ◽  
Vol 1821 (1) ◽  
pp. 97-103 ◽  
Author(s):  
Derrick Bellamy ◽  
Vanessa Bateman ◽  
Eric C. Drumm ◽  
William M. Dunne ◽  
Christopher Vandewater ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Personal Digital Assistants ◽  
Data Input ◽  
Department Of Transportation ◽  
Electronic Data ◽  
Electronic Data Collection ◽  
Rockfall Hazard ◽  
Pencil And Paper ◽  
Hazard Rating

Rockfall analysis traditionally has used conventional stationery tools, that is, pencil and paper, for data collection. Traditional methodologies are being revisited with the advent of personal digital assistants (PDAs) or pen-based computers that enable field data to be collected electronically. The advantages over data collection with pencil and paper include automatic error and data integrity checks during data input and the elimination of manual data entry. PDAs also allow automatic branching to solicit data input on the basis of previous data entered and support for code or scripting, which can be used to create unique files names from the data entered. These advantages are illustrated in an electronic data collection methodology as implemented within a rockfall hazard rating system for the Tennessee Department of Transportation.

scholarly journals Going Digital: Added Value of Electronic Data Collection in 2018 Afghanistan Health Survey

2021 ◽  
Author(s):  
Christina Mergenthaler ◽  
Rajpal Singh Yadav ◽  
Sohrab Safi ◽  
Ente Rood ◽  
Sandra Alba
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Household Survey ◽  
Added Value ◽  
User Friendliness ◽  
Electronic Data ◽  
Electronic Data Collection ◽  
Time Investment ◽  
Data Points ◽  
Nationally Representative

Abstract Background: Through a nationally representative household survey in Afghanistan, we conducted an operational study in two relatively secure provinces comparing effectiveness of electronic data collection (EDC) with paper data collection (PDC). Methods: In Panjshir and Parwan provinces, household survey data were collected using paper questionnaires in 15 clusters, versus OpenDataKit (ODK) software on electronic tablets in 15 clusters. Added value was evaluated from three perspectives: efficient implementation, data quality, and acceptability. Efficiency was measured through financial expenditures and time stamped data. Data quality was measured by examining completeness. Acceptability was studied through focus group discussions with survey staff.Results: Training, printing, material procurement, wages and transportation costs were 68% more expensive in electronic clusters compared to paper clusters, due to upfront one-time investment for survey programming. Enumerators spent significantly less time administering surveys in electronic cluster households (248 minutes survey time) compared to paper (289 minutes), for an average savings of 41 minutes per household (95% CI: 25 – 55). EDC offered a savings of 87 days for data management over PDC.Among 49 tracer variables (meaning responses were required from all respondents), small differences were observed between PDC and EDC. 2.2% of the cleaned dataset’s tracer data points were missing in electronic surveys (1,216/ 56,073 data points), compared to 3.2% in paper surveys (1,953/ 60,675 data points). In pre-cleaned datasets, 3.9% of tracer data points were missing in electronic surveys (2,151/ 55,092 data points) compared to 3.2% in paper surveys (1,924/ 60,113 data points). Enumerators from Panjsher and Parwan preferred EDC over PDC due to time savings, user-friendliness, improved data security, and less conspicuity when traveling; however approximately half of enumerators trained from all 34 provinces reported feeling unsafe due to Taliban presence. Community and household respondent skepticism could be resolved by enumerator reassurance. Enumerators shared that in the future, they prefer collecting data digitally when possible.Conclusions: EDC offers clear gains in efficiency over PDC for data collection and management time, although costs are relatively comparable even without the programming investment. However, serious field staff concerns around Taliban threats and general insecurity mean that EDC should only be conducted in relatively secure areas.

Abstract 239: 911 Caller Description of Seizure-Like Symptoms and Delays to Starting Telecommunicator CPR

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Kara Kronemeyer ◽  
Kameron Shee ◽  
Vatsal Chikani ◽  
Normandy Villa ◽  
Lesley Osborn ◽  
...  
Keyword(s):  
Categorical Variables ◽  
Continuous Variables ◽  
Chi Square ◽  
Process Measures ◽  
Younger Patients ◽  
Improve Accuracy ◽  
Bystander Cardiopulmonary Resuscitation ◽  
Data Points ◽  
Chi Square Analysis ◽  
Hospital Cardiac Arrest

Background: Bystander cardiopulmonary resuscitation (BCPR) improves survival after out-of-hospital cardiac arrest (OHCA). Identifying delays to starting Telecommunicator CPR (TCPR) may improve outcomes. Identifying terms callers use to describe seizure-like symptoms may improve accuracy and expedite TCPR. Methods: A total of 586 confirmed OHCA calls from 3 regional 911 centers in Arizona were reviewed between 2013 to 2016. Frequency of terms callers use to describe seizure-like symptoms were assessed. Demographics and TCPR process measures were compared between the seizure and non-seizure cohorts using Chi-square analysis for categorical variables and Kruskal-Wallis test for continuous variables. Other data points were time to start of seizure description, time to end of description, and time to start of seizure intervention. Results: There were 545 calls after exclusions. Twenty-six (.05%) had seizure-like symptoms described. Of these, “seizure” or “seizing” were used in 22 (84.6%) calls, “shaking” in 6 (23.1%), “cramping up” in 2 (7.7%) and convulsing in 2 (7.7%). Descriptions were more common in witnessed arrests [65.4% (17/26) vs. 34.6% (9/26); p=0.045] and in younger patients [median age=57 (QI=45, Q3=68) vs. 66 (Q1=51, Q3=77); p=0.036.] In calls with descriptions, telecommunicators were less likely to recognize OHCA [56.0% (14/25) vs. 74.5% (382/513), .031% (17/545) missing; (p=0.041] but bystanders were not less likely to start compressions [42.3% (11/26) vs. 57.6% (289/501), .033% (18/545) missing; p=0.122]. Median time to recognition in calls with descriptions was delayed vs. calls without descriptions [142 s (Q1=74 s, Q3=194 s), n=13, vs. 63 s (Q1=40 s, Q3=112 s), n=336; p=0.005], as was time to first chest compression [262 s (Q1=182 s, Q3=291 s), n=6 vs. 154 s (Q1=110 s, Q3=206 s), n=155; p=0.011]. Median times to start of description, end of description, and start of intervention were respectively: 33 s (Q1=20 s, Q3=40 s; 54 s (Q1=37 s, Q3=138 s; and 50 s (Q1=38 s, Q3=162 s). Conclusion: Description of seizure-like symptoms were uncommon and were associated with reduced and delayed OHCA recognition and delayed start of compressions.

Usability Evaluation of an Offline Electronic Data Capture App in a Prospective Multicentre Dementia Registry – digiDEM Bayern: a Mixed Method Study (Preprint)

2021 ◽  
Author(s):  
Michael Reichold ◽  
Miriam Hess ◽  
Peter L. Kolominsky-Rabas ◽  
Elmar Gräßel ◽  
Hans-Ulrich Prokosch
Keyword(s):  
Data Collection ◽  
Technology Acceptance ◽  
Mobile Devices ◽  
Mixed Method ◽  
User Group ◽  
Electronic Data ◽  
Thinking Aloud ◽  
Electronic Data Collection ◽  
Usability Problems ◽  
Dementia Registry

BACKGROUND Digital registries have shown to provide an efficient way better to understand the clinical complexity and long-term progression of diseases. The paperless way of electronic data collection during a patient interview saves both: time and resources. In the prospective multicenter 'Digital Dementia Registry Bavaria - digiDEM Bayern', interviews are also conducted on-site in rural areas with unreliable internet connectivity. It must be ensured that electronic data collection can still be performed there, and it is no need to fall back on paper-based questionnaires. Therefore, the EDC system REDCap offers, in addition to a web-based data collection solution, the option to collect data offline via an app and synchronize it afterward. OBJECTIVE This study evaluates the usability of the REDCap app as an offline electronic data collection option for a lay user group and examines the necessary technology acceptance using mobile devices for data collection. Thereby, the feasibility of the app-based offline data collection in the dementia registry project was evaluated before going live. METHODS The study was conducted with an exploratory mixed-method in the form of an on-site usability test with the 'Thinking Aloud' method combined with a tailored semi-standardized online questionnaire including System Usability Score (SUS). The acceptance of mobile devices for the data collection was surveyed based on the technology acceptance model (TAM) with five categories. RESULTS Using the Thinking Aloud method, usability problems were identified and solutions were derived therefore. The evaluation of the REDCap app resulted in a SUS score of 74, which represents 'good' usability. After evaluating the technology acceptance questionnaire, it can be stated that the lay user group is open to mobile devices as interview tools. CONCLUSIONS The usability evaluation results show that a lay user group like the data collecting partners in the digiDEM project can handle the REDCap app well overall. The usability test provided statements about positive aspects and was able to identify usability problems of the REDCap app. In addition, the current technology acceptance in the sample showed that heterogeneous groups of different ages with different experiences in handling mobile devices are also ready for the use of app-based EDC systems. Based on the results, it can be assumed that the offline use of an app-based EDC system on mobile devices is a viable solution to collect data in a registry-based research project.

Sign in / Sign up

Export Citation Format

Share Document