scholarly journals Electronic Health Records-Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Procedure and Observational Study (Preprint)

JMIR Cardio ◽  
10.2196/22296 ◽  
2020 ◽  
Author(s):  
Alvin Chandra ◽  
Steven T Philips ◽  
Ambarish Pandey ◽  
Mujeeb Basit ◽  
Vaishnavi Kannan ◽  
...  
Keyword(s):  
Observational Study ◽  
Electronic Health Records ◽  
Health Records ◽  
Care Gap ◽  
Pragmatic Research ◽  
Research Procedure ◽  
Electronic Health

scholarly journals Recording of weight in electronic health records: an observational study in general practice

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Lisa D. M. Verberne ◽  
Markus M. J. Nielen ◽  
Chantal J. Leemrijse ◽  
Robert A. Verheij ◽  
Roland D. Friele
Keyword(s):  
General Practice ◽  
Observational Study ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

scholarly journals Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records

Healthcare ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 70
Author(s):  
Xiaowei Yan ◽  
Walter F. Stewart ◽  
Hannah Husby ◽  
Jake Delatorre-Reimer ◽  
Satish Mudiganti ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Cardiometabolic Health ◽  
Health Records ◽  
Care Gap ◽  
Care Gaps ◽  
Primary Care Patients ◽  
Therapeutic Care ◽  
Electronic Health

The objective of this study was to determine the strengths and limitations of using structured electronic health records (EHR) to identify and manage cardiometabolic (CM) health gaps. We used medication adherence measures derived from dispense data to attribute related therapeutic care gaps (i.e., no action to close health gaps) to patient- (i.e., failure to retrieve medication or low adherence) or clinician-related (i.e., failure to initiate/titrate medication) behavior. We illustrated how such data can be used to manage health and care gaps for blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), and HbA1c for 240,582 Sutter Health primary care patients. Prevalence of health gaps was 44% for patients with hypertension, 33% with hyperlipidemia, and 57% with diabetes. Failure to retrieve medication was common; this patient-related care gap was highly associated with health gaps (odds ratios (OR): 1.23–1.76). Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6–6.0) and diabetes (OR = 5.7; 95% CI: 5.4–6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps.

scholarly journals Validation of an algorithm using inpatient electronic health records to determine the presence and severity of cirrhosis in patients with hepatocellular carcinoma in England: an observational study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028571 ◽  
Author(s):  
Robert J Driver ◽  
Vinay Balachandrakumar ◽  
Anya Burton ◽  
Jessica Shearer ◽  
Amy Downing ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Disease ◽  
Observational Study ◽  
Electronic Health Records ◽  
Disease Severity ◽  
Health Records ◽  
Derivation Cohort ◽  
Electronic Health ◽  
Clinical Records ◽  
New Diagnosis

ObjectivesOutcomes in hepatocellular carcinoma (HCC) are determined by both cancer characteristics and liver disease severity. This study aims to validate the use of inpatient electronic health records to determine liver disease severity from treatment and procedure codes.DesignRetrospective observational study.SettingTwo National Health Service (NHS) cancer centres in England.Participants339 patients with a new diagnosis of HCC between 2007 and 2016.Main outcomeUsing inpatient electronic health records, we have developed an optimised algorithm to identify cirrhosis and determine liver disease severity in a population with HCC. The diagnostic accuracy of the algorithm was optimised using clinical records from one NHS Trust and it was externally validated using anonymised data from another centre.ResultsThe optimised algorithm has a positive predictive value (PPV) of 99% for identifying cirrhosis in the derivation cohort, with a sensitivity of 86% (95% CI 82% to 90%) and a specificity of 98% (95% CI 96% to 100%). The sensitivity for detecting advanced stage cirrhosis is 80% (95% CI 75% to 87%) and specificity is 98% (95% CI 96% to 100%), with a PPV of 89%.ConclusionsOur optimised algorithm, based on inpatient electronic health records, reliably identifies and stages cirrhosis in patients with HCC. This highlights the potential of routine health data in population studies to stratify patients with HCC according to liver disease severity.

scholarly journals Estimating Morbidity Rates Based on Routine Electronic Health Records in Primary Care: Observational Study

10.2196/11929 ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. e11929 ◽  
Author(s):  
Mark M J Nielen ◽  
Inge Spronk ◽  
Rodrigo Davids ◽  
Joke C Korevaar ◽  
René Poos ◽  
...  
Keyword(s):  
Primary Care ◽  
Observational Study ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health
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