Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record

ABSTRACT Background An important component of internal medicine residency is clinical immersion in core rotations to expose first-year residents to common diagnoses. Objective Quantify intern experience with common diagnoses through clinical documentation in an electronic health record. Methods We analyzed all clinical notes written by postgraduate year (PGY) 1, PGY-2, and PGY-3 residents on medicine service at an academic medical center July 1, 2012, through June 30, 2014. We quantified the number of notes written by PGY-1s at 1 of 3 hospitals where they rotate, by the number of notes written about patients with a specific principal billing diagnosis, which we defined as diagnosis-days. We used the International Classification of Diseases 9 (ICD-9) and the Clinical Classification Software (CCS) to group the diagnoses. Results We analyzed 53 066 clinical notes covering 10 022 hospitalizations with 1436 different ICD-9 diagnoses spanning 217 CCS diagnostic categories. The 10 most common ICD-9 diagnoses accounted for 23% of diagnosis-days, while the 10 most common CCS groupings accounted for more than 40% of the diagnosis-days. Of 122 PGY-1s, 107 (88%) spent at least 2 months on the service, and 3% were exposed to all of the top 10 ICD-9 diagnoses, while 31% had experience with fewer than 5 of the top 10 diagnoses. In addition, 17% of PGY-1s saw all top 10 CCS diagnoses, and 5% had exposure to fewer than 5 CCS diagnoses. Conclusions Automated detection of clinical experience may help programs review inpatient clinical experiences of PGY-1s.

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sureLDA: A multidisease automated phenotyping method for the electronic health record

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa079 ◽

2020 ◽

Vol 27 (8) ◽

pp. 1235-1243 ◽

Cited By ~ 1

Author(s):

Yuri Ahuja ◽

Doudou Zhou ◽

Zeling He ◽

Jiehuan Sun ◽

Victor M Castro ◽

...

Keyword(s):

Feature Selection ◽

Electronic Health Record ◽

Topic Model ◽

De Novo ◽

Association Studies ◽

High Accuracy ◽

Health Record ◽

Electronic Health Record Data ◽

Accuracy And Precision ◽

Electronic Health

Abstract Objective A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes. Materials and Methods Surrogate-guided ensemble latent Dirichlet allocation (sureLDA) is a label-free multidimensional phenotyping method. It first uses the PheNorm algorithm to initialize probabilities based on 2 surrogate features for each target phenotype, and then leverages these probabilities to constrain the LDA topic model to generate phenotype-specific topics. Finally, it combines phenotype-feature counts with surrogates via clustering ensemble to yield final phenotype probabilities. Results sureLDA achieves reliably high accuracy and precision across a range of simulated and real-world phenotypes. Its performance is robust to phenotype prevalence and relative informativeness of surogate vs nonsurrogate features. It also exhibits powerful feature selection properties. Discussion sureLDA combines attractive properties of PheNorm and LDA to achieve high accuracy and precision robust to diverse phenotype characteristics. It offers particular improvement for phenotypes insufficiently captured by a few surrogate features. Moreover, sureLDA’s feature selection ability enables it to handle high feature dimensions and produce interpretable computational phenotypes. Conclusions sureLDA is well suited toward large-scale electronic health record phenotyping for highly multiphenotype applications such as phenome-wide association studies .

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Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies

BMC Infectious Diseases ◽

10.1186/s12879-016-2020-2 ◽

2016 ◽

Vol 16 (1) ◽

Cited By ~ 4

Author(s):

Kathryn L. Jackson ◽

Michael Mbagwu ◽

Jennifer A. Pacheco ◽

Abigail S. Baldridge ◽

Daniel J. Viox ◽

...

Keyword(s):

Staphylococcus Aureus ◽

Electronic Health Record ◽

Genetic Association ◽

Methicillin Resistant Staphylococcus Aureus ◽

Association Studies ◽

Genetic Association Studies ◽

Health Record ◽

Methicillin Resistant ◽

Electronic Health

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Business: The Electronic Health Record: Front Line Experience

Perspectives on Administration and Supervision ◽

10.1044/aas21.1.18 ◽

2011 ◽

Vol 21 (1) ◽

pp. 18-22

Author(s):

Rosemary Griffin

Keyword(s):

Health Care ◽

Information Technology ◽

Electronic Health Record ◽

Financial Incentives ◽

Best Practice ◽

Health Care Systems ◽

The United States ◽

Health Record ◽

Health Trends ◽

Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

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