Opportunities at the intersection of work and health: Developing the occupational data for health information model

Marovich, Stacey; Luensman, Genevieve B.; Wallace, Barbara; Storey, Eileen

doi:10.1093/jamia/ocaa070

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…One such option, which is currently under investigation for use among mesothelioma patients, is to use the Occupational Data for Health (ODH) modules created by U.S. National Institute for Occupational Safety and Health (NIOSH) researchers. These ODH modules suggest using standard data elements, structure, and vocabulary in health information technology systems to collect self-reported data related to one’s “usual” work, past and present jobs, employment, and retirement status, including deployments to combat zones [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Laying the Foundation for a Mesothelioma Patient Registry: Development of Data Collection Tools

Gaitens

Culligan

Friedberg

et al. 2023

IJERPH

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Mesothelioma, a cancer of mesothelial cells that line the chest, lungs, heart, and abdomen, is a relatively rare disease. In the United States, approximately 3000 individuals are diagnosed with mesothelioma annually. The primary risk factor for mesothelioma is occupational asbestos exposure which can occur decades prior to disease development, though in approximately 20% of cases, known asbestos exposure is lacking. While several other countries have developed mesothelioma registries to collect key clinical and exposure data elements to allow better estimation of incidence, prevalence, and risk factors associated with disease development, no national mesothelioma registry exists in the U.S. Therefore, as part of a larger feasibility study, a patient exposure questionnaire and a clinical data collection tool were created using a series of key informant interviews. Findings suggest that risk factor and clinical data collection via an on-line questionnaire is feasible, but specific concerns related to confidentiality, in the context of employer responsibility for exposure in the unique U.S. legal environment, and timing of enrollment must be addressed. Lessons learned from piloting these tools will inform the design and implementation of a mesothelioma registry of national scope.

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Section: Discussionmentioning

confidence: 99%

Laying the Foundation for a Mesothelioma Patient Registry: Development of Data Collection Tools

Gaitens

Culligan

Friedberg

et al. 2023

IJERPH

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“…NIOSH developed Occupational Data for Health (ODH), a structured framework for capturing self‐reported patient work information to provide EHR vendors with methods for including I&O and other work‐related variables in EHRs. The model also includes collection of information on employed household contacts for data systems focusing on minors 47 . When patient record systems will be upgraded to include work‐related information and become fully integrated into hospital patient records is unclear.…”

Section: Discussionmentioning

confidence: 99%

Assessing the role of social determinants of health in health disparities: The need for data on work

Silver,

Sweeney,

Sanderson

et al. 2023

American J Industrial Med

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BackgroundWork is a key social determinant of health. Without the collection of work‐related information in public health data systems, the role of social determinants in creating and reinforcing health disparities cannot be fully assessed.MethodsThe Centers for Disease Control and Prevention (CDC) maintains or supports a number of public health surveillance and health monitoring systems, including surveys, case‐based disease and exposure systems, vital status records, and administrative data systems. We evaluated a convenience sample of these systems for inclusion of information in three work‐related domains: employment status, industry and occupation, and working conditions.ResultsWhile 12 of 39 data systems were identified as collecting work‐related data, this information was often minimal (e.g., only employment status), restricted to a subset of respondents, or only gathered periodically. Information on working conditions was particularly sparse.ConclusionHistorically, the limited and inconsistent collection of work‐related information in public health data systems has hindered understanding of the role work plays in health disparities. Current CDC data modernization efforts present opportunities to enhance the identification and mitigation of health disparities by prioritizing inclusion of an expanded set of work‐related data elements.

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“…Nowadays, the benefits of incorporating occupation-related information in the Electronic Health Record (EHR), such as a more accurate diagnosis or better health outcomes, have been widely characterized [2, 3], and promoted by agencies such as the National Institute for Occupational Safety and Health (NIOSH) or European Agency for Safety and Health at Work (EU-OSHA) [4]. Although the relevance of this Social Determinant of Health (SDOH) [5] has been identified and studied at multiple health levels (i.e., mental health [6], health inequality [7], or self-rated health [8]), its study is overshadowed by other SDOH including gender, race, or ethnicity [9, 10]; compromising its content [11], variability [12] and quality [13]. Moreover, most SDOH are stored as free-text unstructured data [14] making them difficult to handle and use.…”

Section: Introductionmentioning

confidence: 99%

Occupation Recognition and Exploitation in Rheumatology Clinical Notes: Employing Deep Learning Models for Named Entity Recognition and Knowledge Discovery in Electronic Health Records

Madrid-García,

Pérez-Sancristóbal,

Leticia-Leon

et al. 2024

Preprint

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Occupation is considered a Social Determinant of Health (SDOH) and its effects have been studied at multiple levels. Although the inclusion of such data in the Electronic Health Record (EHR) is vital for the provision of clinical care, specially in rheumatology where work disability prevention is essential, occupation information is often either not routinely documented or captured in an unstructured manner within conventional EHR systems. Encouraged by recent advances in natural language processing and deep learning models, we propose the use of novel architectures (i.e., transformers) to detect occupation mentions in rheumatology clinical notes of a tertiary hospital, and to whom those occupations belongs. We also aimed to evaluate the clinical and demographic characteristics that influence the collection of this SDOH; and the association between occupation and patients' diagnosis. Bivariate and multivariate logistic regression analysis were conducted for this purpose. A Spanish pre-trained language model, RoBERTa, fine-tuned with biomedical texts was used to detect occupations. The best model achieved a F1-score of 0.725 identifying occupation mentions. Moreover, highly disabling mechanical pathology diagnoses (i.e., back pain, muscle disorders) were associated with a higher probability of occupation collection. Ultimately, we determined the professions most closely associated with thirteen categories of musculoskeletal disorders.

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Opportunities at the intersection of work and health: Developing the occupational data for health information model

Cited by 8 publications

References 24 publications

Laying the Foundation for a Mesothelioma Patient Registry: Development of Data Collection Tools

Laying the Foundation for a Mesothelioma Patient Registry: Development of Data Collection Tools

Assessing the role of social determinants of health in health disparities: The need for data on work

Occupation Recognition and Exploitation in Rheumatology Clinical Notes: Employing Deep Learning Models for Named Entity Recognition and Knowledge Discovery in Electronic Health Records

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