The health coverage of low-income workers represents an area of continuing disparities in the United States system of health insurance. Using the 2001 California Health Interview Survey, we estimate the effect of low-income wage earners' citizenship and gender on the odds of obtaining primary employment-based health insurance (EBHI), dependent EBHI, public program coverage, and coverage from any source. We find that noncitizen men and women who comprise 40% of California's low-income workforce, share the disadvantage of much lower rates of insurance coverage, compared to naturalized and U.S.-born citizens. However, poor coverage rates of noncitizen men, regardless of permanent residency status, result from the cumulative disadvantage in obtaining dependent EBHI and public insurance. If public policies designed to provide a health care safety net fail to address the health care coverage needs of low-wage noncitizens, health disparities will continue to increase in this group that contributes essentially to the U.S. economy.

Objective The FlywheelMS study will explore the use of a real-world health record data set generated by PicnicHealth, a patient-centric health records platform, to improve understanding of disease course and patterns of care for patients with multiple sclerosis (MS). Materials and Methods The FlywheelMS study aims to enroll 5000 adults with MS in the United States to create a large, deidentified, longitudinal data set for clinical research. PicnicHealth obtains health records, including paper charts, electronic health records, and radiology imaging files from any healthcare site. Using a large-scale health record processing pipeline, PicnicHealth abstracts standard and condition-specific data elements from structured (eg, laboratory test results) and unstructured (eg, narrative) text and maps these to standardized medical vocabularies. Researchers can use the resulting data set to answer empirical questions and study participants can access and share their harmonized health records using PicnicHealth’s web application. Results As of November 24, 2020, more than 4176 participants from 49 of 50 US states have enrolled in the FlywheelMS study. A median of 200 pages of records have been collected from 14 different doctors over 8 years per participant. Abstraction precision, established through inter-abstractor agreement, is as high as 97.8% when identifying and mapping data elements to a standard ontology. Conclusion Using a commercial health records platform, the FlywheelMS study is generating a real-world, multimodal data set that could provide valuable insights about patients with MS. This approach to data collection and abstraction is disease-agnostic and could be used to address other clinical research questions in the future.

Background: This study explored the application of healthcare failure mode and effect analysis (HFMEA) to identify and evaluate risk-associated factors in the intensive care unit (ICU) through a clinical-based expert knowledge (decision) for the physiological monitor operational maintenance process. Methods and intervention: A mixed qualitative and quantitative proactive approach to explore the HFMEA process by analyzing 20 units of physiological monitors in the ICU. An HFMEA expert team of six people was formed to perform a risk-based analysis and evaluate the potential hazard index, mitigating the hazard scores and risks. Results: From the main processes and possible failure reasons, one high-risk hazard index greater than or equal to 8 of the standard score was found. This standard score indicates the signed manufacturer’s contract for maintenance was the hazard index failure mode on the parts not regularly replaced according to the contract. This systematic hazard index failure mode shows the highest hazard scores in the possible failure reason category, established as a standard maintenance procedure. In addition, the HFMEA expert analysis of the 20 units of physiological monitors within 6 months of the original and remanufactured part maintenance results in operational availability from 90.9% for self-repair to 99.2% for contract manufacturer repair. Conclusions: This study concludes a systematic reference in malpractices caused by maintenance negligence. The HFMEA expert team agrees that hazard failure scores greater than or equal to 8 are vital assessments and evaluations for decision-making, especially in maintaining healthcare intensive unit care physiological monitors.

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