scholarly journals Postpartum Hemorrhage: Differences in Definition, Data and Incidence

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Diana Walther ◽  
Patricia Halfon ◽  
David Desseauve ◽  
Yvan Vial ◽  
Bernard Burnand ◽  
...  
Keyword(s):  
Blood Loss ◽  
Electronic Health Records ◽  
Data Quality ◽  
Data Availability ◽  
University Hospital ◽  
Diagnostic Code ◽  
Health Records ◽  
Discharge Data ◽  
Diagnostic Codes ◽  
Electronic Health

IntroductionPostpartum hemorrhage (PPH) remains a major cause of morbidity and mortality worldwide. Geo-temporal comparisons of in-hospital PPH incidence remain a challenge due to differences in definition, data quality and the absence of accurate, validated indicators. Objectives and ApproachTo compare the incidence of PPH using different definitions to assess the need for a validated indicator. Singleton births from 2014-2016 at Lausanne University Hospital, Switzerland, were included. PPH was defined based on 1) clinical diagnosis using International Classification of Diseases (ICD-10-GM) PPH diagnostic codes, 2) volume of blood loss ≥500ml for vaginal births and ≥1000ml for cesareans 3) peripartum Hb change >2g/dl in vaginal births and ≥4g/dl in cesareans and 4) fulfillment of criteria from definition one, two or three. Data were extracted from hospital discharge data and linked with electronic health records. ResultsThere were 2529, 2660 and 2715 singleton births in 2014, 2015 and 2016, respectively, 28.8% were cesareans. Peripartum change in Hb was available for 17% of births. The incidence (95% CI) of PPH in 2014, 2015 and 2016 was, respectively: 1)6.0% (5.1, 7.0), 6.3% (5.4, 7.3) and 7.9% (6.9, 9.0) based on diagnostic codes; 2)7.9% (6.8, 9.0), 7.1% (6.2, 8.2) and 7.2% (6.3, 8.3) based on blood loss volumes; 3)2.4% (1.8, 3.1), 2.7% (2.1, 3.4) and 3.5% (2.9, 4.3) based on change in Hb; 4)11.3% (10.1, 12.6), 10.4% (9.3, 11.6) and 11.0% (9.9, 12.3) based on the combined definition. Differences in PPH incidence by year between definitions one and four, two and four and three and four were all statistically significant (McNemar p-values Conclusion/ImplicationsIncidence varied widely according to definition and data availability, not to mention data quality. Our results highlight the need for a validated PPH indicator to enable monitoring. Future prospects include the validation of a diagnostic code based PPH indicator aided by text mining in electronic health records.

Satisfaction and perceived usefulness with newly-implemented Electronic Health Records System among pediatricians at a university hospital

2019 ◽  
Vol 169 ◽  
pp. 51-57 ◽  
Author(s):  
Fahad Alsohime ◽  
Mohamad-Hani Temsah ◽  
Ayman Al-Eyadhy ◽  
Fahad A. Bashiri ◽  
Mowafa Househ ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Perceived Usefulness ◽  
University Hospital ◽  
Health Records ◽  
Electronic Health

scholarly journals Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e029314 ◽  
Author(s):  
Kaiwen Ni ◽  
Hongling Chu ◽  
Lin Zeng ◽  
Nan Li ◽  
Yiming Zhao
Keyword(s):  
Qualitative Study ◽  
Electronic Health Records ◽  
Clinical Research ◽  
Data Quality ◽  
Healthcare Professionals ◽  
Barriers And Facilitators ◽  
Health Records ◽  
Improve Data Quality ◽  
Electronic Health

ObjectivesThere is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals’ experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context.SettingFour tertiary hospitals in Beijing, China.ParticipantsNineteen healthcare professionals with experience in using EHR data for clinical research participated in the study.MethodsA qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.ResultsThe main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation.ConclusionsThese results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.

Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

2016 ◽  
Vol 22 (4) ◽  
pp. 1017-1029 ◽  
Author(s):  
Lua Perimal-Lewis ◽  
David Teubner ◽  
Paul Hakendorf ◽  
Chris Horwood
Keyword(s):  
Electronic Health Records ◽  
Data Quality ◽  
Performance Metrics ◽  
Process Mining ◽  
Hospital Performance ◽  
Process Models ◽  
Patient Journey ◽  
Health Records ◽  
Electronic Health

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

Sign in / Sign up

Export Citation Format

Share Document