ArticleACS omega2023
Mitigation of Chemical Reporting Liabilities through Systematic Modernization of Chemical Hazard and Safety Data Management Systems.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Deploying artificial intelligence for occupational and environmental health in waste management: a systematic review.International journal of public health · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the United States alone, approximately 2 billion tons of hazardous material products are manufactured each year for both household and industrial applications and contribute to thousands of worker chemical exposures with as many as 50,000 deaths from prolonged exposure each year. The potential hazards and impacts of these chemicals for human health and the environment are primarily communicated to the public through Safety Data Sheets (SDSs) from the chemical vendors or distributors. These documents provide a standardized approach for how and what information is provided to product users to assist them with assessment of precautionary measures, hazard mitigation, emergency response or cleanup procedures, and environmental, health, and safety (EHS) management. Despite the criticality for hazard communication (HAZCOM) precision, legacy SDS management and industry business practices leave the overall ability to effectively manage chemicals vulnerable to significant liability through a lack of full constituent disclosure, injection of data quality errors through various handling of SDS information and manual data entry, and the lack of direct SDS-to-product association. Chemical spills and accidents often require individuals to look for the appropriate SDS on a local computer, online, or in workplace binders; each of which results in information returned that is often found to be outdated or incorrect. Workplace HAZCOM violations remain among the top citations during EHS inspections by regulatory agencies. More important, however, is the lack of precise association of SDS to hazardous products that can occur through chemical management lifecycles. Incorrect SDSs can yield significant liability, as subsequent environmental and occupational health analyses and reporting are based upon incorrect and, in some cases, entirely different chemical formulations. This paper focuses on the need for a paradigm shift in our chemical management systems and how a standardized management system and various recent technological advances can be incorporated into Environmental Management System operations to reduce or eliminate these liabilities. The following advancements can be used to enhance the lifecycle management of workplace chemicals, reduce potential exposure and spill risks, reduce workplace hazards, and increase the efficiency and accuracy of environmental reporting through a more streamlined systems approach. EHS system enhancement applications discussed in this paper include the following: the need for a centralized universal SDS repository with full chemical disclosure of all product constituents and a nationally adopted machine language SDS standard. The use of artificial intelligence/machine learning in environmental systems and how they can be used as a medium to transition toward an automated standard by reverse-engineering and partitioning SDS components into machine-encoded text that can be validated and uploaded to a centralized repository. Algorithmic and meta-algorithmic approaches to SDS requirement and data validation, hazard characteristic code calculations, and determination of potentially less hazardous substitutions. Application of Natural Language Processing methods for real-time updates from scientific journals, regulatory agencies, and other reputable sources to produce "living" SDSs capable of informing users of relevant regulatory updates, news, and research. Embedded SDSs or SDS links in product barcodes with QR code reader technology to retrieve precise SDSs for each product in emergency situations. Use of advanced QR codes embedding authentication layers, authenticity verification, and alerts of potential product or inventory problems or discrepancies. Benefits of radio frequency identification technology in providing accurate SDS associations while also minimizing manual tracking of hazardous material and hazardous waste containers and monitoring for expired shelf life, incompatible storage, temperature sensitivities, and other inventory concerns.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.