The Feasibility of Using Electronic Health Records to Describe Demographic and Clinical Indicators of Migrant and Seasonal Farmworkers

Abstract

Few extensive, national clinical databases exist on the health of migrant and seasonal farmworkers (MSFWs). Electronic health records (EHRs) are increasingly utilized by Federally Qualified Health Centers (FQHCs) and have the potential to improve clinical care and complement current surveillance and epidemiologic studies of underserved working populations, such as MSFWs. The aim of this feasibility study was to describe the demographics and baseline clinical indicators of patients at an FQHC by MSFW status. We described 2012 patient demographics, social history, medical indicators, and diagnoses by MSFW status from the de-identified EHR database of a large, multi-site Colorado Migrant Health Center (MHC). Included in the study were 41,817 patients from 2012: 553(1.3%) MSFWs; 20,665(49.4%) non-MSFWs; and 20,599 (49.3%) who had no information in the MSFW field. MSFWs were more often male, married, employed, Hispanic, and Spanish-speaking compared to non-MSFWs. The most frequent diagnoses for all patients were hypertension, overweight/obesity, lipid disorder, type 2 diabetes, or a back disorder. Although there were significant missing values, this feasibility study was able to analyze medical data in a timely manner and show that Meaningful Use requirements can improve the usability of EHR data for epidemiologic research of MSFWs and other patients at FQHCs. The results of this study were consistent with current literature available for MSFWs. By reaching this vulnerable working population, EHRs may be a key data source for occupational injury and illness surveillance and research.

Knowledge, attitude and behaviour of dental health care providers towards health electronic record systems in Saudi Arabia

“…The results of the current study predicted that around 95% of the population agreed that EHR system would help in maintaining the dental and medical history, medication lists and patients’ demographic details. Consistent to it, Socias et al (2016) conducted a study to evaluate the feasibility of using EHRs to describe the demographic and clinical indicators of seasonal and migrant farmworkers. Thus, the study reported that EHR data have a significant positive impact on patient care; however, EHR needs to be improvised with regard to their quality and usability.…”

“…The study proved the efficiency of EHR by showing its production, application and utilisation of information in the health management systems. Another similar study conducted by Socias et al (2016) determined the feasibility of using EHRs in describing the demographic and clinical indicators of patients. The results showed EHRs to be key data source for occupational injury and illness surveillance and research by reaching the vulnerable population.…”

Recognition of the Relationship Between Patients’ Work and Health

“…25,26 Currently, the most widely used occupational surveillance data sources likely substantially and systematically undercount certain work-related diseases and injuries, certain types of workplaces, and certain populations of workers. 27–29 The EHR provides new opportunities to improve surveillance by capturing cases seen by PCPs, especially for workers reluctant to report their case to workers’ compensation or to their employer or where the relation to work was not previously recognized.…”

Electronic Health Records for Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Scoping Review

“…In further work, it has been found possible to utilise a workplace EHR for describing injuries in a worker population [31]. Many factors such as comorbidities, psychosocial and clinical factors that have been found relevant for describing injuries are the same as those that could be used for predicting outcomes in WMSDs.…”

“…Some aspects include missing data, unstructured data, interoperability issues between datasets and collection of the right data. Missing data is a challenge for many EHR studies [23], missingness of desirable research variables can be nearly half what is required [27,31]. Missing data is not, however, only a challenge for EHRs, and exists in claims databases [8,34,38,41] as well as in studies utilising patient questionnaires or PROMs [42,43].…”