scholarly journals Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sophie Relph ◽  
◽  
Maria Elstad ◽  
Bolaji Coker ◽  
Matias C. Vieira ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Quality ◽  
Data Management ◽  
Randomised Control Trial ◽  
Data Dictionary ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Cluster Randomised ◽  
Quality Checks ◽  
Data Harmonisation

Abstract Background The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. Methods The DESiGN Trial is a cluster randomised control trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age foetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from foetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. Results Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites; each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1–4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0–1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. Conclusions Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units. In this manuscript, we describe how we managed this and provide a detailed data dictionary covering the harmonisation of variable names and values that will be helpful for other researchers working with these data. Trial registration Primary registry and trial identifying number: ISRCTN 67698474. Registered on 02/11/16.

scholarly journals Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial - Data management experience from the DESiGN Trial team.

2021 ◽  
Author(s):  
Sophie Relph ◽  
Maria Elstad ◽  
Bolaji Coker ◽  
Matias C Vieira ◽  
Natalie Moitt ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Cluster Randomised Controlled Trial ◽  
Data Dictionary ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Cluster Randomised ◽  
Quality Checks ◽  
Randomised Controlled ◽  
The Uk ◽  
Data Harmonisation

Abstract Background The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. Methods The DESiGN Trial is a cluster randomised controlled trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age fetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from fetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. Results Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites (each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1-4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0-1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. Conclusions Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units in the UK. In this manuscript we describe how we managed this and provide a detailed data dictionary which covers the harmonisation of variable names and variables that will be helpful for other researchers working with these data. Trial Registration: Primary registry and trial identifying number: ISRCTN 67698474. Registered 02/11/16. https://doi.org/10.1186/ISRCTN67698474

A Methodology for Determining Patients’ Eligibility for Clinical Trials

1993 ◽  
Vol 32 (04) ◽  
pp. 317-325 ◽  
Author(s):  
C. A. Kemper ◽  
N. M. Lane ◽  
R. W. Carlson ◽  
M. A. Musen ◽  
S. W. Tu
Keyword(s):  
Clinical Trials ◽  
Computer System ◽  
Practice Guidelines ◽  
Hiv Positive ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Data Intensive ◽  
Eligibility Determination ◽  
Accrual Rate ◽  
The One

AbstractThe task of determining patients’ eligibility for clinical trials is knowledge and data intensive. In this paper, we present a model for the task of eligibility determination, and describe how a computer system can assist clinical researchers in performing that task. Qualitative and probabilistic approaches to computing and summarizing the eligibility status of potentially eligible patients are described. The two approaches are compared, and a synthesis that draws on the strengths of each approach is proposed. The result of applying these techniques to a database of HIV-positive patient cases suggests that computer programs such as the one described can increase the accrual rate of eligible patients into clinical trials. These methods may also be applied to the task of determining from electronic patient records whether practice guidelines apply in particular clinical situations.

scholarly journals P0363 / #645: AN INFORMATICS APPROACH TO IMPROVE THE EFFICIENCY OF CLINICAL BIG-DATA MANAGEMENT AND EXTRACTED DATA QUALITY FOR CLINICAL TRIALS: A SCOTTISH PICU’S PERSPECTIVE.

2021 ◽  
Vol 22 (Supplement 1 3S) ◽  
pp. 191-191
Author(s):  
M. Van Dijke ◽  
I. Piper ◽  
D. Armstrong ◽  
J. Richardson ◽  
L. Reilly ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Big Data ◽  
Data Quality ◽  
Data Management

scholarly journals Accuracy and agreement of national spine register data for 474 patients compared to corresponding electronic patient records

2022 ◽  
Author(s):  
Ole Kristian Alhaug ◽  
Simran Kaur ◽  
Filip Dolatowski ◽  
Milada Cvancarova Småstuen ◽  
Tore K. Solberg ◽  
...  
Keyword(s):  
Data Quality ◽  
Perioperative Complications ◽  
Public Hospitals ◽  
Kappa Statistics ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Asa Classification ◽  
Moderate Agreement ◽  
Patient Reported ◽  
Spine Register

Abstract Purpose Data quality is essential for all types of research, including health registers. However, data quality is rarely reported. We aimed to assess the accuracy of data in a national spine register (NORspine) and its agreement with corresponding data in electronic patient records (EPR). Methods We compared data in NORspine registry against data in (EPR) for 474 patients operated for spinal stenosis in 2015 and 2016 at four public hospitals, using EPR as the gold standard. We assessed accuracy using the proportion correctly classified (PCC) and sensitivity. Agreement was quantified using Kappa statistics or interaclass correlation coefficient (ICC). Results The mean age (SD) was 66 (11) years, and 54% were females. Compared to EPR, surgeon-reported perioperative complications displayed weak agreement (kappa (95% CI) = 0.51 (0.33–0.69)), PCC of 96%, and a sensitivity (95% CI) of 40% (23–58%). ASA classification had a moderate agreement (kappa (95%CI) = 0.73 (0.66–0.80)). Comorbidities were underreported in NORspine. Perioperative details had strong to excellent agreements (kappa (95% CI) ranging from 0.76 ( 0.68–0.84) to 0.98 (0.95–1.00)), PCCs between 93% and 99% and sensitivities (95% CI) between 92% (0.84–1.00%) and 99% (0.98–1.00%). Patient-reported variables (height, weight, smoking) had excellent agreements (kappa (95% CI) between 0.93 (0.89–0.97) and 0.99 (0.98–0.99)). Conclusion Compared to electronic patient records, NORspine displayed weak agreement for perioperative complications, moderate agreement for ASA classification, strong agreement for perioperative details, and excellent agreement for height, weight, and smoking. NORspine underreported perioperative complications and comorbidities when compared to EPRs. Patient-recorded data were more accurate and should be preferred when available.

Electronic Patient Records in Medical Practice: A Multidisciplinary Endeavor

1999 ◽  
Vol 38 (04/05) ◽  
pp. 287-288 ◽  
Author(s):  
J. van der Lei ◽  
P. W. Moorman ◽  
M. A. Musen
Keyword(s):  
Medical Practice ◽  
Patient Records ◽  
Electronic Patient Records

Postmarketing Surveillance Based on Electronic Patient Records: The IPCI Project

1999 ◽  
Vol 38 (04/05) ◽  
pp. 339-344 ◽  
Author(s):  
J. van der Lei ◽  
B. M. Th. Mosseveld ◽  
M. A. M. van Wijk ◽  
P. D. van der Linden ◽  
M. C. J. M. Sturkenboom ◽  
...  
Keyword(s):  
General Practitioner ◽  
General Practitioners ◽  
Validation Studies ◽  
Resource Planning ◽  
Routine Practice ◽  
Patient Records ◽  
Postmarketing Surveillance ◽  
Electronic Patient Records ◽  
Use Of Data ◽  
Data Requirements

AbstractResearchers claim that data in electronic patient records can be used for a variety of purposes including individual patient care, management, and resource planning for scientific research. Our objective in the project Integrated Primary Care Information (IPCI) was to assess whether the electronic patient records of Dutch general practitioners contain sufficient data to perform studies in the area of postmarketing surveillance studies. We determined the data requirements for postmarketing surveil-lance studies, implemented additional software in the electronic patient records of the general practitioner, developed an organization to monitor the use of data, and performed validation studies to test the quality of the data. Analysis of the data requirements showed that additional software had to be installed to collect data that is not recorded in routine practice. To avoid having to obtain informed consent from each enrolled patient, we developed IPCI as a semianonymous system: both patients and participating general practitioners are anonymous for the researchers. Under specific circumstances, the researcher can contact indirectly (through a trusted third party) the physician that made the data available. Only the treating general practitioner is able to decode the identity of his patients. A Board of Supervisors predominantly consisting of participating general practitioners monitors the use of data. Validation studies show the data can be used for postmarketing surveillance. With additional software to collect data not normally recorded in routine practice, data from electronic patient record of general practitioners can be used for postmarketing surveillance.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

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