scholarly journals Automating Clinical Score Calculation within the Electronic Health Record

2017 ◽  
Vol 08 (02) ◽  
pp. 369-380 ◽  
Author(s):  
Christopher Aakre ◽  
Mikhail Dziadzko ◽  
Mark Keegan ◽  
Vitaly Herasevich
Keyword(s):  
Electronic Health Record ◽  
Clinical Examination ◽  
Data Retrieval ◽  
Clinical Score ◽  
Structured Data ◽  
Unstructured Data ◽  
Health Record ◽  
Clinical Judgments ◽  
Clinical Scores ◽  
Electronic Health

Summary Objectives: Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records. Methods: We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings. Results: We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable. Conclusion: Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments – partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study. Citation: Aakre C, Dziadzko M, Keegan MT, Herasevich V. Automating clinical score calculation within the electronic health record: A feasibility assessment. Appl Clin Inform 2017; 8: 369–380 https://doi.org/10.4338/ACI-2016-09-RA-0149

The Electronic Health Record and the Clinical Examination

2018 ◽  
Vol 102 (3) ◽  
pp. 475-483 ◽  
Author(s):  
Helene F. Hedian ◽  
Jeremy A. Greene ◽  
Timothy M. Niessen
Keyword(s):  
Electronic Health Record ◽  
Clinical Examination ◽  
Health Record ◽  
Electronic Health

scholarly journals Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 570-579 ◽  
Author(s):  
Na Hong ◽  
Andrew Wen ◽  
Feichen Shen ◽  
Sunghwan Sohn ◽  
Chen Wang ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Data ◽  
Large Scale ◽  
Structured Data ◽  
Health Record ◽  
Data Normalization ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Clinical Resource

Abstract Objective To design, develop, and evaluate a scalable clinical data normalization pipeline for standardizing unstructured electronic health record (EHR) data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification. Methods We established an FHIR-based clinical data normalization pipeline known as NLP2FHIR that mainly comprises: (1) a module for a core natural language processing (NLP) engine with an FHIR-based type system; (2) a module for integrating structured data; and (3) a module for content normalization. We evaluated the FHIR modeling capability focusing on core clinical resources such as Condition, Procedure, MedicationStatement (including Medication), and FamilyMemberHistory using Mayo Clinic’s unstructured EHR data. We constructed a gold standard reusing annotation corpora from previous NLP projects. Results A total of 30 mapping rules, 62 normalization rules, and 11 NLP-specific FHIR extensions were created and implemented in the NLP2FHIR pipeline. The elements that need to integrate structured data from each clinical resource were identified. The performance of unstructured data modeling achieved F scores ranging from 0.69 to 0.99 for various FHIR element representations (0.69–0.99 for Condition; 0.75–0.84 for Procedure; 0.71–0.99 for MedicationStatement; and 0.75–0.95 for FamilyMemberHistory). Conclusion We demonstrated that the NLP2FHIR pipeline is feasible for modeling unstructured EHR data and integrating structured elements into the model. The outcomes of this work provide standards-based tools of clinical data normalization that is indispensable for enabling portable EHR-driven phenotyping and large-scale data analytics, as well as useful insights for future developments of the FHIR specifications with regard to handling unstructured clinical data.

scholarly journals The Electronic Health Record Objective Structured Clinical Examination

2017 ◽  
Vol 92 (1) ◽  
pp. 87-91 ◽  
Author(s):  
Frances E. Biagioli ◽  
Diane L. Elliot ◽  
Ryan T. Palmer ◽  
Carla C. Graichen ◽  
Rebecca E. Rdesinski ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Clinical Examination ◽  
Objective Structured Clinical Examination ◽  
Health Record ◽  
Electronic Health

Electronic Health Record for Forensic Dentistry

2008 ◽  
Vol 47 (01) ◽  
pp. 8-13 ◽  
Author(s):  
T. Dostálová ◽  
P. Hanzlíček ◽  
Z. Teuberová ◽  
M. Nagy ◽  
M. Pieš ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Data Entry ◽  
Structured Data ◽  
Health Record ◽  
Forensic Dentistry ◽  
Disaster Victim Identification ◽  
Health Records ◽  
Victim Identification ◽  
Electronic Health

Summary Objectives: To identify support of structured data entry for electronic health record application in forensic dentistry. Methods: The methods of structuring information in dentistry are described and validation of structured data entry in electronic health records for forensic dentistry is performed on several real cases with the interactive DentCross component. The connection of this component to MUDR and MUDRLite electronic health records is described. Results: The use of the electronic health record MUDRLite and the interactive DentCross component to collect dental information required by standardized Disaster Victim Identification Form by Interpol for possible victim identification is shown. Conclusions: The analysis of structured data entry for dentistry using the DentCross component connected to an electronic health record showed the practical ability of the DentCross component to deliver a real service to dental care and the ability to support the identification of a person in forensic dentistry.

scholarly journals Assessing quality and agreement of structured data in automatic versus manual abstraction of the electronic health record for a clinical epidemiology study

2021 ◽  
pp. 263208432110612
Author(s):  
Joseph Grant Brazeal ◽  
Alexander V Alekseyenko ◽  
Hong Li ◽  
Mario Fugal ◽  
Katie Kirchoff ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Epidemiology ◽  
Epidemiological Studies ◽  
Structured Data ◽  
Health Record ◽  
Automated Identification ◽  
History Of ◽  
Electronic Health ◽  
Automated Methods

Objective We evaluate data agreement between an electronic health record (EHR) sample abstracted by automated characterization with a standard abstracted by manual review. Study Design and Setting We obtain data for an epidemiology cohort study using standard manual abstraction of the EHR and automated identification of the same patients using a structured algorithm to query the EHR. Summary measures of agreement (e.g., Cohen’s kappa) are reported for 12 variables commonly used in epidemiological studies. Results Best agreement between abstraction methods is observed among demographic characteristics such as age, sex, and race, and for positive history of disease. Poor agreement is found in missing data and negative history, suggesting potential impact for researchers using automated EHR characterization. EHR data quality depends upon providers, who may be influenced by both institutional and federal government documentation guidelines. Conclusion Automated EHR abstraction discrepancies may decrease power and increase bias; therefore, caution is warranted when selecting variables from EHRs for epidemiological study using an automated characterization approach. Validation of automated methods must also continue to advance in sophistication with other technologies, such as machine learning and natural language processing, to extract non-structured data from the EHR, for application to EHR characterization for clinical epidemiology.

Preserving medical correctness, readability and consistency in de-identified health records

2016 ◽  
Vol 23 (4) ◽  
pp. 291-303 ◽  
Author(s):  
Kostas Pantazos ◽  
Soren Lauesen ◽  
Soren Lippert
Keyword(s):  
Electronic Health Record ◽  
Structured Data ◽  
Free Text ◽  
Health Record ◽  
Health Records ◽  
Phone Number ◽  
Patient Health ◽  
Electronic Health ◽  
E Mail

A health record database contains structured data fields that identify the patient, such as patient ID, patient name, e-mail and phone number. These data are fairly easy to de-identify, that is, replace with other identifiers. However, these data also occur in fields with doctors’ free-text notes written in an abbreviated style that cannot be analyzed grammatically. If we replace a word that looks like a name, but isn’t, we degrade readability and medical correctness. If we fail to replace it when we should, we degrade confidentiality. We de-identified an existing Danish electronic health record database, ending up with 323,122 patient health records. We had to invent many methods for de-identifying potential identifiers in the free-text notes. The de-identified health records should be used with caution for statistical purposes because we removed health records that were so special that they couldn’t be de-identified. Furthermore, we distorted geography by replacing zip codes with random zip codes.

Business: The Electronic Health Record: Front Line Experience

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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