Physician Documentation Behaviors in Electronic Health Records as a Potential Source of Noise for Early Detection of Heart Failure

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

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Identifying Heart Failure from Electronic Health Records: A Systematic Evidence Review

10.1101/2021.02.01.21250933 ◽

2021 ◽

Author(s):

Rebecca T. Levinson ◽

Jennifer R. Malinowski ◽

Suzette J. Bielinski ◽

Luke V. Rasmussen ◽

Quinn S. Wells ◽

...

Keyword(s):

Heart Failure ◽

Electronic Health Records ◽

Academic Medical Centers ◽

Significant Heterogeneity ◽

Health Records ◽

Algorithm Development ◽

Medical Centers ◽

Academic Medical ◽

Evidence Review ◽

Electronic Health

ABSTRACTBackgroundHeart failure (HF) is a complex syndrome associated with significant morbidity and healthcare costs. Electronic health records (EHRs) are widely used to identify patients with HF and other phenotypes. Despite widespread use of EHRs for phenotype algorithm development, it is unclear if the characteristics of identified populations mirror those of clinically observed patients and reflect the known spectrum of HF phenotypes.MethodsWe performed a subanalysis within a larger systematic evidence review to assess the different methods used for HF algorithm development and their application to research and clinical care. We queried PubMed for articles published up to November 2020. Out of 318 studies screened, 25 articles were included for primary analysis and 15 studies using only International Classification of Diseases (ICD) codes were evaluated for secondary analysis. Results are reported descriptively.ResultsHF algorithms were most often developed at academic medical centers and the V.A. One health system was responsible for 8 of 10 HF algorithm studies. HF and congestive HF were the most frequent phenotypes observed and less frequently, specific HF subtypes and acute HF. Diagnoses were the most common data type used to identify HF patients and echocardiography was the second most frequent. The majority of studies used rule-based methods to develop their algorithm. Few studies used regression or machine learning methods to identify HF patients. Validation of algorithms varied considerably: only 52.9% of HF and 44.4% of HF subtype algorithms were validated, but 75% of acute HF algorithms were. Demographics of any study population were reported in 68% of algorithm studies and 53% of ICD-only studies. Fewer than half reported demographics of their HF algorithm-identified population. Of those reporting, most identified majority male (>50%) populations, including both algorithms for HF with preserved ejection fraction.ConclusionThere is significant heterogeneity in phenotyping methodologies used to develop HF algorithms using EHRs. Validation of algorithms is inconsistent but largely relies on manual review of patient records. The concentration of algorithm development at one or two sites may reduce potential generalizability of these algorithms to identify HF patients at non-academic medical centers and in populations from underrepresented regions. Differences between the reported demographics of algorithm-identified HF populations those expected based on HF epidemiology suggest that current algorithms do not reflect the full spectrum of HF patient populations.

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Retrospective analysis of electronic health records of patients with heart failure: the first Russian experience

Russian Journal of Cardiology ◽

10.15829/1560-4071-2021-4502 ◽

2021 ◽

Vol 26 (5) ◽

pp. 4502

Author(s):

S. R. Gilyarevsky ◽

D. V. Gavrilov ◽

A. V. Gusev

Keyword(s):

Heart Failure ◽

Electronic Health Records ◽

Treatment Quality ◽

Health Records ◽

Brain Natriuretic Peptides ◽

Number Of Patients ◽

Patients With Heart Failure ◽

Electronic Health ◽

Therapy Quality

The article presents the first experience of analyzing the treatment quality of hospitalized patients with heart failure based on electronic health records (EHR). We analyzed EHR of patients hospitalized in three large hospitals in Kirov. The results of the analysis indicated insufficient detailed information in the EHR, which complicates analyzing the accuracy of diagnosis and therapy quality. In particular, attention is drawn to the disproportionate number of patients with heart failure with reduced and preserved ejection fractionю This, apparently, is due to the low prevalence of assessing brain natriuretic peptides and conducting Doppler echocardiography. A separate part of the analysis is devoted to assessing the therapy quality in patients with concomitant diabetes. Despite the study limitations, the presented results can be useful for improving the quality of EHR filling for performing further observational clinical trials.

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Risk factors for incident heart failure in age‐ and sex‐specific strata: a population‐based cohort using linked electronic health records

European Journal of Heart Failure ◽

10.1002/ejhf.1350 ◽

2019 ◽

Vol 21 (10) ◽

pp. 1197-1206 ◽

Cited By ~ 16

Author(s):

Alicia Uijl ◽

Stefan Koudstaal ◽

Kenan Direk ◽

Spiros Denaxas ◽

Rolf H. H. Groenwold ◽

...

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Electronic Health Records ◽

Population Based ◽

Health Records ◽

Incident Heart Failure ◽

Electronic Health ◽

Age And Sex

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Electronic Health Records as Valuable Data Sources in the Health Care Quality Improvement Process

Health Services Research and Managerial Epidemiology ◽

10.1177/2333392819852879 ◽

2019 ◽

Vol 6 ◽

pp. 233339281985287 ◽

Cited By ~ 1

Author(s):

Katja Wikström ◽

Maija Toivakka ◽

Päivi Rautiainen ◽

Hilkka Tirkkonen ◽

Teppo Repo ◽

...

Keyword(s):

Quality Improvement ◽

Early Detection ◽

Electronic Health Records ◽

Regional Differences ◽

Health Care Quality ◽

Data Retrieval ◽

Care Quality ◽

Health Records ◽

Valuable Data ◽

Electronic Health

Background: In North Karelia, Finland, the regional electronic health records (EHRs) enable flexible data retrieval and area-level analyses. The aim of this study was to assess the early detection of type 2 diabetes (T2D) in the region and to evaluate the performed activities in order to improve the processes between the years 2012 and 2017. Methods: Patients with T2D were identified from the EHRs using the ICD-10 codes registered during any visit to either primary or specialized care. The prevalence of T2D was calculated for the years 2012, 2015, and 2017 on the municipality level. The number of people found in the EHRs with diabetes was compared with the number found in the national register of medication reimbursement rights. Results: In 2012, the age-adjusted prevalence of T2D in North Karelia varied considerably between municipalities (5.5%-8.6%). These differences indicate variation in the processes of early diagnosis. The findings were discussed in the regional network of health professionals treating patients with T2D, resulting in sharing experiences and best practices. In 2017, the differences had notably diminished, and in most municipalities, the prevalence exceeded 8%. The regional differences in the prevalence and their downward trend were observed both in the EHRs and in the medication reimbursement rights register. Conclusion: Clear differences in the prevalence of T2D were detected between municipalities. After visualizing these differences and providing information for the professionals, the early detection of T2D improved and the regional differences decreased. The EHRs are a valuable data source for knowledge-based management and quality improvement.

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