Electronic Health Record Databases

Horton, Daniel B.; Bhullar, Harshvinder; Carty, Lucy; Cunningham, Francesca; Ogdie, Alexis; Sultana, Janet; Trifirò, Gianluca

doi:10.1002/9781119413431.ch13

Cited by 9 publications

(7 citation statements)

References 352 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Claims data contains claims information on patient utilization of prescription fills and medical services for the purposes of documenting administrative and healthcare billing reimbursement, while EHR data are medical charts in a digitized format and recorded by providers for the purposes of patient clinical care. [24][25][26] Administrative Claims Data and Augmented Data Proctor and colleagues used the Centers for Medicare & Medicaid Services Chronic Conditions Data Warehouse to identify TG Medicare patients in 2013 (n=4098). 17 This study utilized a CP of ICD-9 diagnosis codes that were relevant to TG status, then subsequently addressed concerns of coding errors by validating their method through a specific logic.…”

Section: Resultsmentioning

confidence: 99%

The Use of Computational Phenotypes within Electronic Healthcare Data to Identify Transgender People in the United States: A Narrative Review

Beltran

Lett

Poteat

et al. 2023

Preprint

View full text Add to dashboard Cite

Purpose: With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies. Methods: Authors searched the National Library of Medicine’s PubMed, Scopus, and the American Psychological Association Psyc Info’s databases to identify studies published in the United States that applied CPs to identify TG people within electronic health care data. Results: Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n=5), hierarchy of code mechanisms (n=4), key text-strings (n=2), or self-surveys (n=1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text-strings. However, if key text-strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone. Conclusion: CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text-strings. CPs with high validity are essential to identifying TG patients when self-reported gender identity is not available. Still, self-reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.

show abstract

Section: Resultsmentioning

confidence: 99%

The Use of Computational Phenotypes within Electronic Healthcare Data to Identify Transgender People in the United States: A Narrative Review

Beltran

Lett

Poteat

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…6,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] Twelve of these studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n = 5), hierarchy of code mechanisms (n = 4), key text-strings (n = 2), or self-surveys Claims data contains claims information on patient utilization of prescription fills and medical services for the purposes of documenting administrative and healthcare billing reimbursement, while EHR data are medical charts in a digitized format and recorded by providers for the purposes of patient clinical care. [25][26][27]…”

Section: Resultsmentioning

confidence: 99%

Computational phenotyping within electronic healthcare data to identify transgender people in the United States: A narrative review

Beltran,

Lett,

Poteat

et al. 2023

Pharmacoepidemiology and Drug

View full text Add to dashboard Cite

PurposeWith the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype (CP) algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies.MethodsAuthors searched the National Library of Medicine's PubMed, Scopus, and the American Psychological Association PsycInfo's databases to identify studies published in the United States that applied CPs to identify TG people within electronic healthcare data.ResultsTwelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n = 5), hierarchy of code mechanisms (n = 4), key text‐strings (n = 2), or self‐surveys (n = 1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text‐strings. However, if key text‐strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone.ConclusionCPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text‐strings. CPs with high validity are essential to identifying TG patients when self‐reported gender identity is not available. Still, self‐reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.

show abstract

“… Ji et al (2010) developed a new interestingness measure, exclusive causal-leverage, based on an experience-based fuzzy recognition-primed decision (RPD) model. On the basis of this new measure, a new association rule algorithm is proposed to discover infrequent causal relationships in electronic health databases ( Horton et al, 2019 ). In addition, Soni and Vyas (2010) used the associative method to construct a classifier for predictive analysis in healthcare data mining.…”

Section: Artificial Intellectual Technologiesmentioning

confidence: 99%

A survey of data element perspective: Application of artificial intelligence in health big data

Xiong¹,

Chen²,

Xu³

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

Artificial intelligence (AI) based on the perspective of data elements is widely used in the healthcare informatics domain. Large amounts of clinical data from electronic medical records (EMRs), electronic health records (EHRs), and electroencephalography records (EEGs) have been generated and collected at an unprecedented speed and scale. For instance, the new generation of wearable technologies enables easy-collecting peoples’ daily health data such as blood pressure, blood glucose, and physiological data, as well as the application of EHRs documenting large amounts of patient data. The cost of acquiring and processing health big data is expected to reduce dramatically with the help of AI technologies and open-source big data platforms such as Hadoop and Spark. The application of AI technologies in health big data presents new opportunities to discover the relationship among living habits, sports, inheritances, diseases, symptoms, and drugs. Meanwhile, with the development of fast-growing AI technologies, many promising methodologies are proposed in the healthcare field recently. In this paper, we review and discuss the application of machine learning (ML) methods in health big data in two major aspects: (1) Special features of health big data including multimodal, incompletion, time validation, redundancy, and privacy. (2) ML methodologies in the healthcare field including classification, regression, clustering, and association. Furthermore, we review the recent progress and breakthroughs of automatic diagnosis in health big data and summarize the challenges, gaps, and opportunities to improve and advance automatic diagnosis in the health big data field.

show abstract

Electronic Health Record Databases

Cited by 9 publications

References 352 publications

The Use of Computational Phenotypes within Electronic Healthcare Data to Identify Transgender People in the United States: A Narrative Review

The Use of Computational Phenotypes within Electronic Healthcare Data to Identify Transgender People in the United States: A Narrative Review

Computational phenotyping within electronic healthcare data to identify transgender people in the United States: A narrative review

A survey of data element perspective: Application of artificial intelligence in health big data

Contact Info

Product

Resources

About