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10.1017/S1047951121000974 http://scihub22266oqcxt.onion/10.1017/S1047951121000974 33726868!ä!33726868 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cardiol+Young 2021 ; 31 (11): 1829-1834 Nephropedia Template TP {{tp|p=33726868|t=2021. Data quality methods through remote source data verification auditing: results from the Congenital Cardiac Research Collaborative |pdf=|usr=}}{{33726868}} gab.com Text Data quality methods through remote source data verification auditing: results from the Congenital Cardiac Research Collaborative JO: Cardiol Young http://www.kidney.de/pget.php?&tw=1&pmid=33726868 #FOAvip BACKGROUND: Multicentre research databases can provide insights into healthcare processes to improve outcomes and make practice recommendations for novel approaches. Effective audits can establish a framework for reporting research efforts, ensuring accurate reporting, and spearheading quality improvement. Although a variety of data auditing models and standards exist, barriers to effective auditing including costs, regulatory requirements, travel, and design complexity must be considered. MATERIALS AND METHODS: The Congenital Cardiac Research Collaborative conducted a virtual data training initiative and remote source data verification audit on a retrospective multicentre dataset. CCRC investigators across nine institutions were trained to extract and enter data into a robust dataset on patients with tetralogy of Fallot who required neonatal intervention. Centres provided de-identified source files for a randomised 10% patient sample audit. Key auditing variables, discrepancy types, and severity levels were analysed across two study groups, primary repair and staged repair. RESULTS: Of the total 572 study patients, data from 58 patients (31 staged repairs and 27 primary repairs) were source data verified. Amongst the 1790 variables audited, 45 discrepancies were discovered, resulting in an overall accuracy rate of 97.5%. High accuracy rates were consistent across all CCRC institutions ranging from 94.6% to 99.4% and were reported for both minor (1.5%) and major discrepancies type classifications (1.1%). CONCLUSION: Findings indicate that implementing a virtual multicentre training initiative and remote source data verification audit can identify data quality concerns and produce a reliable, high-quality dataset. Remote auditing capacity is especially important during the current COVID-19 pandemic. Twit Text FOAVip Data quality methods through remote source data verification auditing: results from the Congenital Cardiac Research Collaborative ????Cardiol Young http://www.kidney.de/pget.php?&tw=1&pmid=33726868 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33726868 #FOAvip English Wikipedia <ref>{{Cite pmid|33726868}}</ref> Data quality methods through remote source data verification auditing: results from the Congenital Cardiac Research Collaborative #MMPMID33726868 Pettus JA; Pajk AL; Glatz AC; Petit CJ; Goldstein BH; Qureshi AM; Nicholson GT; Meadows JJ; Zampi JD; Law MA; Shahanavaz S; Kelleman MS; McCracken CM Cardiol Young 2021[Nov]; 31 (11): 1829-1834 PMID33726868show ga BACKGROUND: Multicentre research databases can provide insights into healthcare processes to improve outcomes and make practice recommendations for novel approaches. Effective audits can establish a framework for reporting research efforts, ensuring accurate reporting, and spearheading quality improvement. Although a variety of data auditing models and standards exist, barriers to effective auditing including costs, regulatory requirements, travel, and design complexity must be considered. MATERIALS AND METHODS: The Congenital Cardiac Research Collaborative conducted a virtual data training initiative and remote source data verification audit on a retrospective multicentre dataset. CCRC investigators across nine institutions were trained to extract and enter data into a robust dataset on patients with tetralogy of Fallot who required neonatal intervention. Centres provided de-identified source files for a randomised 10% patient sample audit. Key auditing variables, discrepancy types, and severity levels were analysed across two study groups, primary repair and staged repair. RESULTS: Of the total 572 study patients, data from 58 patients (31 staged repairs and 27 primary repairs) were source data verified. Amongst the 1790 variables audited, 45 discrepancies were discovered, resulting in an overall accuracy rate of 97.5%. High accuracy rates were consistent across all CCRC institutions ranging from 94.6% to 99.4% and were reported for both minor (1.5%) and major discrepancies type classifications (1.1%). CONCLUSION: Findings indicate that implementing a virtual multicentre training initiative and remote source data verification audit can identify data quality concerns and produce a reliable, high-quality dataset. Remote auditing capacity is especially important during the current COVID-19 pandemic. |*COVID-19[MESH] |*Data Accuracy[MESH] |Humans[MESH] |Infant, Newborn[MESH] |Pandemics[MESH] |Retrospective Studies[MESH]Data quality methods through remote source data verification auditing:(epmc).pdf DeepDyve Pubget Overpricing