Diagnostic code agreement for electronic health records and claims data for tuberculosis

OBJECTIVE: To measure the frequency of diseases related to latent tuberculosis infection (LTBI) and tuberculosis (TB), we assessed the agreement between diagnosis codes for TB or LTBI in electronic health records (EHRs) and insurance claims for the same person.METHODS: In a US population-based, retrospective cohort study, we matched TB-related Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) EHR codes and International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) claims codes. Furthermore, LTBI was identified using a published ICD-based algorithm and all LTBI- and TB-related SNOMED CT codes.RESULTS: Of people with the 10 most frequent TB-related claim codes, 50% did not have an exact-matched EHR code. Positive tuberculin skin test was the most frequent unmatched EHR code and people with the 10 most frequent TB EHR codes, 40% did not have an exact-matched claim code. The most frequent unmatched claim code was TB screening encounter. EHR codes for LTBI matched to claims codes for TB testing; pulmonary TB; and nonspecific, positive or adverse tuberculin reaction.CONCLUSION: TB-related EHR codes and claims diagnostic codes often disagree, and people with claims codes for LTBI have unexpected EHR codes, indicating the need to reconcile these coding systems.

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UK phenomics platform for developing and validating EHR phenotypes: CALIBER

10.1101/539403 ◽

2019 ◽

Cited By ~ 3

Author(s):

Spiros Denaxas ◽

Arturo Gonzalez-Izquierdo ◽

Kenan Direk ◽

Natalie Fitzpatrick ◽

Amitava Banerjee ◽

...

Keyword(s):

Primary Care ◽

Electronic Health Records ◽

Population Based ◽

Source Population ◽

Snomed Ct ◽

Health Records ◽

Lifestyle Risk Factors ◽

Electronic Health ◽

Lifestyle Risk ◽

The Uk

ABSTRACTObjectiveElectronic health records are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems and collected for purposes other than medical research. We describe an approach for developing, validating and sharing reproducible phenotypes from national structured Electronic Health Records (EHR) in the UK with applications for translational research.Materials and MethodsWe implemented a rule-based phenotyping framework, with up to six approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements such as blood pressure, medication information and coded diagnoses, symptoms, procedures and referrals, recorded using five controlled clinical terminologies: a) Read (primary care, subset of SNOMED-CT), b) ICD-9, ICD-10 (secondary care diagnoses and cause of mortality), c) OPCS-4 (hospital surgical procedures) and d) Gemscript Drug Codes.ResultsThe open-access CALIBER Portal (https://www.caliberresearch.org/portal) demonstrates phenotyping algorithms for 50 diseases, syndromes, biomarkers and lifestyle risk factors and provides up to six validation layers. These phenotyping algorithms have been used by 40 national/international research groups in 60 peer-reviewed publications.ConclusionHerein, we describe the UK EHR phenomics approach, CALIBER, with initial evidence of validity and use, as an important step towards international use of UK EHR data for health research.

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An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocu022 ◽

2015 ◽

Vol 22 (3) ◽

pp. 649-658 ◽

Cited By ~ 6

Author(s):

Kin Wah Fung ◽

Julia Xu

Keyword(s):

Electronic Health Records ◽

Growth Pattern ◽

Problem List ◽

Snomed Ct ◽

Health Records ◽

The Core ◽

Core Subset ◽

Electronic Health ◽

Systematized Nomenclature Of Medicine ◽

Core Problem

Abstract Objective Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the emergent international health terminology standard for encoding clinical information in electronic health records. The CORE Problem List Subset was created to facilitate the terminology’s implementation. This study evaluates the CORE Subset’s coverage and examines its growth pattern as source datasets are being incorporated. Methods Coverage of frequently used terms and the corresponding usage of the covered terms were assessed by “leave-one-out” analysis of the eight datasets constituting the current CORE Subset. The growth pattern was studied using a retrospective experiment, growing the Subset one dataset at a time and examining the relationship between the size of the starting subset and the coverage of frequently used terms in the incoming dataset. Linear regression was used to model that relationship. Results On average, the CORE Subset covered 80.3% of the frequently used terms of the left-out dataset, and the covered terms accounted for 83.7% of term usage. There was a significant positive correlation between the CORE Subset’s size and the coverage of the frequently used terms in an incoming dataset. This implies that the CORE Subset will grow at a progressively slower pace as it gets bigger. Conclusion The CORE Problem List Subset is a useful resource for the implementation of Systematized Nomenclature of Medicine Clinical Terms in electronic health records. It offers good coverage of frequently used terms, which account for a high proportion of term usage. If future datasets are incorporated into the CORE Subset, it is likely that its size will remain small and manageable.

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Prevalence, incidence, and outcomes across cardiovascular diseases in homeless individuals using national linked electronic health records

European Heart Journal ◽

10.1093/eurheartj/ehaa795 ◽

2020 ◽

Vol 41 (41) ◽

pp. 4011-4020

Author(s):

Atsunori Nanjo ◽

Hannah Evans ◽

Kenan Direk ◽

Andrew C Hayward ◽

Alistair Story ◽

...

Keyword(s):

Risk Factors ◽

Electronic Health Records ◽

Mortality Risk ◽

Homeless People ◽

Arterial Disease ◽

Population Based ◽

Health Records ◽

Coronary Death ◽

Health And Social Care ◽

Electronic Health

Abstract Aims The risk and burden of cardiovascular disease (CVD) are higher in homeless than in housed individuals but population-based analyses are lacking. The aim of this study was to investigate prevalence, incidence and outcomes across a range of specific CVDs among homeless individuals. Methods and results Using linked UK primary care electronic health records (EHRs) and validated phenotypes, we identified homeless individuals aged ≥16 years between 1998 and 2019, and age- and sex-matched housed controls in a 1:5 ratio. For 12 CVDs (stable angina; unstable angina; myocardial infarction; sudden cardiac death or cardiac arrest; unheralded coronary death; heart failure; transient ischaemic attack; ischaemic stroke; subarachnoid haemorrhage; intracerebral haemorrhage; peripheral arterial disease; abdominal aortic aneurysm), we estimated prevalence, incidence, and 1-year mortality post-diagnosis, comparing homeless and housed groups. We identified 8492 homeless individuals (32 134 matched housed individuals). Comorbidities and risk factors were more prevalent in homeless people, e.g. smoking: 78.1% vs. 48.3% and atrial fibrillation: 9.9% vs. 8.6%, P < 0.001. CVD prevalence (11.6% vs. 6.5%), incidence (14.7 vs. 8.1 per 1000 person-years), and 1-year mortality risk [adjusted hazard ratio 1.64, 95% confidence interval (CI) 1.29–2.08, P < 0.001] were higher, and onset was earlier (difference 4.6, 95% CI 2.8–6.3 years, P < 0.001), in homeless, compared with housed people. Homeless individuals had higher CVD incidence in all three arterial territories than housed people. Conclusion CVD in homeless individuals has high prevalence, incidence, and 1-year mortality risk post-diagnosis with earlier onset, and high burden of risk factors. Inclusion health and social care strategies should reflect this high preventable and treatable burden, which is increasingly important in the current COVID-19 context.

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Medium and long-term risks of specific cardiovascular diseases in survivors of 20 adult cancers: a population-based cohort study using multiple linked UK electronic health records databases

The Lancet ◽

10.1016/s0140-6736(19)31674-5 ◽

2019 ◽

Vol 394 (10203) ◽

pp. 1041-1054 ◽

Cited By ~ 53

Author(s):

Helen Strongman ◽

Sarah Gadd ◽

Anthony Matthews ◽

Kathryn E Mansfield ◽

Susannah Stanway ◽

...

Keyword(s):

Cohort Study ◽

Cardiovascular Diseases ◽

Electronic Health Records ◽

Population Based ◽

Health Records ◽

Electronic Health

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Documentation of social determinants in electronic health records with and without standardized terminologies: A comparative study

Proceedings of Singapore Healthcare ◽

10.1177/2010105818785641 ◽

2018 ◽

Vol 28 (1) ◽

pp. 39-47 ◽

Cited By ~ 1

Author(s):

Karen A Monsen ◽

Joyce M Rudenick ◽

Nicole Kapinos ◽

Kathryn Warmbold ◽

Siobhan K McMahon ◽

...

Keyword(s):

Electronic Health Records ◽

Free Text ◽

Snomed Ct ◽

Health Records ◽

Behavioral Determinants ◽

Omaha System ◽

Standardized Terminology ◽

Electronic Health ◽

Data Elements ◽

Improve Health

Background: Electronic health records (EHRs) are a promising new source of population health data that may improve health outcomes. However, little is known about the extent to which social and behavioral determinants of health (SBDH) are currently documented in EHRs, including how SBDH are documented, and by whom. Standardized nursing terminologies have been developed to assess and document SBDH. Objective: We examined the documentation of SBDH in EHRs with and without standardized nursing terminologies. Methods: We carried out a review of the literature for SBDH phrases organized by topic, which were used for analyses. Key informant interviews were conducted regarding SBDH phrases. Results: In nine EHRs (six acute care, three community care) 107 SBDH phrases were documented using free text, structured text, and standardized terminologies in diverse screens and by multiple clinicians, admitting personnel, and other staff. SBDH phrases were documented using one of three standardized terminologies ( N = average number of phrases per terminology per EHR): ICD-9/10 ( N = 1); SNOMED CT ( N = 1); Omaha System ( N = 79). Most often, standardized terminology data were documented by nurses or other clinical staff versus receptionists or other non-clinical personnel. Documentation ‘unknown’ differed significantly between EHRs with and without the Omaha System (mean = 26.0 (standard deviation (SD) = 8.7) versus mean = 74.5 (SD = 16.5)) ( p = .005). SBDH documentation in EHRs differed based on the presence of a nursing terminology. Conclusions: The Omaha System enabled a more comprehensive, holistic assessment and documentation of interoperable SBDH data. Further research is needed to determine SBDH data elements that are needed across settings, the uses of SBDH data in practice, and to examine patient perspectives related to SBDH assessments.

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