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Electronic Medical Records as a Tool in Cli nical Pharmacology: Opportunities and Ch allenges


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Electronic Medical Records as a Tool in Cli nical Pharmacology: Opportunities and Ch allenges

報告人:蘇為碩 宋柏融 李曉婷 陳凱普 洪詩涵 2014/05/19





Electronic Medical Records

EMR v.s. Traditional Medical Records

Passive v.s. Active

EMR Capabilities

Preventive medical alerts

Aggregate outcomes for many patients

Public health surveillance

The era of big data


EMR with DNA information

Expansion of EMR systems

Combines DNA biorepositories with electronic medical record (E MR) systems for large-scale, high-throughput genetic research wi th the ultimate goal of returning genomic testing results to patie nts in a clinical care setting.



EMR as a Tool in Clinical Pharmacology

Clinical Pharmacology

Science of drugs and their clinical use

Connects the gap between medical practice and laboratory sc ience

What information can EMR provide?

Longitudinal record of health status

Ex: case of rofecoxib and myocardial infarction

Possibly with DNA information


EMR as a Tool in Clinical Pharmacology

EMR-based phenotyping

Manual v.s. Automated

Inclusion of longitudinal data

Enables studies of variability in phenotype

Gene-Disease associations


EMR-based discovery in clinical pharmacology

Approach to EMR-based phenotype


Traditional & Modern EMR-based phenotyping

Traditional approach for phenotyping has been successfully used in an EMR environment, but it is cumbersome and time-consumi ng and generally cannot generate very large cohorts.

Recent efforts have been focused on developing electronic algori thms for determining specific phenotypes from EMRs.

The criterion that we and others have adopted is that algorithms should have very high positive predictive values (PPVs, generally

>90–95%) to be able to identify cohorts for case–control studies.


A general processing

Developing an algorithm and deploy it in an EMR system until a set of cases (generally 50–100) is identified for manual review.

This manual review then determines the PPV and deficiencies in the electronic phenotyping algorithm.

The algorithm is then refined and the process iterated until the threshold PPV is attained.


Phenotype algorithms

Structured data, e.g., ICD-9 codes, lab results

Narrative data, e.g., various types of clinical notes, text messages between patients and care providers

ICD: The International Classification of Diseases (ICD) is the

standard diagnostic tool for epidemiology, health management and

clinical purposes.


Natural-language processing

Natural-language processing technologies that can extract structur ed information (e.g., smoker: yes or no) from unstructured narrativ e clinical text have been used in such algorithms.

The use of natural-language processing is especially important to d etect diseases and events occurring at facilities outside the recordi ng center and entered as part of the patient’s past medical history and for discovery of rare events that may not be represented in typ ical coding systems.


Features of EMRs

Longitudinal data ( 縱貫性研究 ) - Studies of variability in phenotype -- disease complications

-- tempo of disease progression -- response to drug exposures

Structured medication data

- inpatient  Physician order entries and drug administration records

- outpatient  Resides in the narrative text of clinic notes or interoffice communications


Advantage of longitudinal data

The ability to distinguish among related diagnoses - Data from the multiple visits represented in an EMR

Preliminary data indicate that phenotype definitions developed i n one EMR can be successfully deployed in others


Drug exposures-manual method

Change over time

-- Intolerance, insurance status -- Patient/provider preferences -- Desire to reach clinical targets -- Compliance

Manual method:

experts review different sources -- cumbersome and costly

-- limited sample size


Drug exposures-informatics method

These methods have successfully exploited the longitudinal nature of the E MR to identify drug-response phenotypes

-- cardiovascular events during clopidogrel therapy after coronary stenting.

Rigorous characterization of phenotype, misclassification bias is minimized  greater insights into the true genetic architecture underlying

treatment response


Drug exposures-informatics method

-- an application to Warfarin



Among investigators with expertise in multiple disciplines

Biomedical informatics

Clinical pharmacology Epidemiology



Gene–disease associations

The genome-wide association study (GWAS) paradigm

whole genome association study

associations of phenotypes to single-nucleotide polymorphisms (SNPs)


P: phenotypes

Figure from Chao, KM

Incorporate genome data with EMG





Common in following diseases: atrial fibrillation ( 心房顫動 ), Cro hn’s disease, multiple sclerosis ( 多發性硬化症 ), rheumatoid art hritis ( 類風濕關節炎 ), or type 2 diabetes

-> common 21 SNPs


P: phenotypes (diseases)

Figure from Chao, KM



Application of EMRs to pharmacogenomics

Discuss phenotypes with treated drugs to patient’s genomic data (SNPs)


Breast Cancer: during tamoxifen treatment, SNPs of Estrogen-rec eptor implicated as risk factors for venous thromboembolic dise

ase ( 靜脈血栓 )


Phenome scanning

Phenome-wide association study (PheWAS)

Concept: inverse of the GWAS paradigm

examines the relationship between a single genetic variant and a large range of phenotypes




GWAS: examined 1,317 hypothyroidism cases and 5,053 controls -> FOXE1 SNPs

PheWAS: FOXE1 SNPs -> identified thyroiditis ( 甲狀腺炎 ), nodul ar and multinodular goiters ( 甲狀腺腫大 ), and thyrotoxicosis ( 甲狀腺毒症 )




Methods to identify valid cases and controls are being refined

The use of these SNPs for pharmacogenomics has lagged further behind

the phenotyping for drug response is necessarily more compl ex

no human can be expected to keep track




Information exchange between EMRs

discovery of new drug actions and of genomic influences on dise ase phenotypes and drug responses

Genomic variation → prevention, prognosis and treatment

Application in Point of care system




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