Personalized diabetes management recommendations at hospital discharge based on a computerized, pre‐hospitalization clinical profile analysis: A prospective, electronic health records–based study

2019 ◽  
Vol 34 (4) ◽  
Author(s):  
Yishay Wasserstrum ◽  
Anat Peles‐Bortz ◽  
Sara Dabahi ◽  
Irina Gringauz ◽  
Amir Tirosh ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Hospital Discharge ◽  
Diabetes Management ◽  
Profile Analysis ◽  
Clinical Profile ◽  
Health Records ◽  
Electronic Health ◽  
Management Recommendations

scholarly journals Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246119
Author(s):  
Diana Walther ◽  
Patricia Halfon ◽  
Romain Tanzer ◽  
Bernard Burnand ◽  
Moira Robertson ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Hospital Discharge ◽  
Postpartum Hemorrhage ◽  
Cumulative Incidence ◽  
Hospital Discharge Data ◽  
Detection Accuracy ◽  
Health Records ◽  
Discharge Data ◽  
Maternity Hospitals ◽  
Electronic Health

Introduction Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safety improvement. These indicators are usually based on hospital discharge data though their accuracy is seldom assessed. We aimed to measure postpartum hemorrhage and severe postpartum hemorrhage using electronic health records and hospital discharge data separately and compare the detection accuracy of these methods to manual chart review, and to examine the temporal trends in cumulative incidence of these potentially avoidable adverse outcomes. Materials and methods We analyzed routinely collected data of 7904 singleton deliveries from a large Swiss university hospital for a three year period (2014–2016). We identified postpartum hemorrhage and severe postpartum hemorrhage in electronic health records by text mining discharge letters and operative reports and calculating drop in hemoglobin from laboratory tests. Diagnostic and procedure codes were used to identify cases in hospital discharge data. A sample of 334 charts was reviewed manually to provide a reference-standard and evaluate the accuracy of the other detection methods. Results Sensitivities of detection algorithms based on electronic health records and hospital discharge data were 95.2% (95% CI: 92.6% 97.8%) and 38.2% (33.3% to 43.0%), respectively for postpartum hemorrhage, and 87.5% (85.2% to 89.8%) and 36.2% (26.3% to 46.1%) for severe postpartum hemorrhage. Postpartum hemorrhage cumulative incidence based on electronic health records decreased from 15.6% (13.1% to 18.2%) to 8.5% (6.7% to 10.5%) from the beginning of 2014 to the end of 2016, with an average of 12.5% (11.8% to 13.3%). The cumulative incidence of severe postpartum hemorrhage remained at approximately 4% (3.5% to 4.4%). Hospital discharge data-based algorithms provided significantly underestimated incidences. Conclusions Hospital discharge data is not accurate enough to assess the incidence of postpartum hemorrhage at hospital or national level. Instead, automated algorithms based on structured and textual data from electronic health records should be considered, as they provide accurate and timely estimates for monitoring and improvement in obstetrical safety. Furthermore, they have the potential to better code for postpartum hemorrhage thus improving hospital reimbursement.

Electronic health records: Context matters!

2016 ◽  
Vol 34 (2) ◽  
pp. 163-165 ◽  
Author(s):  
William B. Ventres ◽  
Richard M. Frankel
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health
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