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Background/Aims In April 2020 the British Society for Rheumatology (BSR) issued a risk stratification guide to identify patients at the highest risk of COVID-19 requiring shielding. This guidance was based on patients' age, comorbidities, and immunosuppressive therapies - including biologics that are not captured in primary care records. This meant rheumatologists needed to manually review outpatient letters to score patients' risk. The process required considerable clinician time, with shielding decisions not always transparently communicated. Our aim was to develop an automated shielding algorithm by text-mining outpatient letter diagnoses and medications, reducing the need for future manual review. Methods Rheumatology outpatient letters from Salford Royal Hospital, a large UK tertiary hospital, were retrieved between 2013-2020. The two most recent letters for each patient were extracted, created before 01.04.2020 when BSR guidance was published. Free-text diagnoses were processed using Intelligent Medical Objects software1 (Concept Tagger), which utilised interface terminology for each condition mapped to a SNOMED-CT code. We developed the Medication Concept Recognition tool (MedCore Named Entity Recognition) to retrieve medications type, dose, duration and status (active/past) at the time of the letter. The medication status was established based on the heading where they appeared (e.g. past medications, current medications), but incorporated additional information such as medication stop dates. The age, diagnosis and medication variables were then combined to output the BSR shielding score. The algorithm's performance was calculated using clinical review as the gold standard. Results To allow for the comparison with manual decisions, we focused on all 895 patients who were reviewed clinically. 64 patients (7.1%) had not consented for their data to be used for research as part of the national opt-out scheme. After removing duplicates, 803 patients were used to run the algorithm. 11,558 free-text diagnoses were extracted and mapped to SNOMED CT, with 15,003 free-text medications (that included past, present and any planned treatment). The automated shielding algorithm demonstrated a sensitivity of 80.3% (95% CI: 74.7, 85.2%) and specificity of 92.2% (95% CI: 89.7, 94.2%). Positive likelihood ratio was 10.3 (95% CI: 7.7, 13.7), negative likelihood ratio was 0.21 (95% CI: 0.16, 0.28), F1 score was 0.81. False positive rate was 7.9%, whilst false negative rate was 19.7%. Further evaluation of false positives/negatives revealed clinician interpretation of BSR guidance and misclassification of medications status were important contributing factors. Conclusion An automated algorithm for risk stratification has several advantages including reducing clinician time for manual review to allow more time for direct care, improving efficiency and transparently communicating decisions based on individual risk. With further development, it has the potential to be adapted for future public health initiatives that requires prompt automated review of hospital outpatient letters.
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When the Americans with Disabilities Act was originally enacted in 1990, and later amended in 2008, technology had not yet advanced to where it is today. In the past decade, sophisticated computer applications and programs have become commonplace. These advances in technology, have enabled millions of employees to work from home since the onset of the Covid-19 pandemic in March 2020. During the pandemic, more than half of the national labor force worked remotely. By most estimates, a significant percentage of the workforce will continue to work remotely, at least part time, even after the pandemic ends. This Article argues that people with disabilities, like their nondisabled colleagues, should enjoy the benefits of our new remote workplace culture. For employees with disabilities, Title I of the Americans with Disabilities (ADA) protects their right to accommodations in the workplace. Over the years, courts have been called upon to resolve disputes between disabled employees and their employers regarding whether or not an employee's request to work remotely is a "reasonable accommodation” under Title I. An examination of the cases from every federal circuit court of appeals over the last decade reveals that most courts rule in favor of employers. However, due to recent changes in the workplace as a result of the Covid-19 pandemic, including greater reliance on communication technologies, the author argues that more courts should recognize remote work as a reasonable workplace accommodation for qualified employees. While it is true that not all employees—with or without disabilities—want to work from home, and not all jobs can be done remotely, increasing opportunities for remote work as a reasonable accommodation furthers the goal of the ADA to promote employment and economic self-sufficiency of disabled people. Remote work opportunities also may challenge the ongoing and systemic ableism that exists within many workplaces today. Further, while discussions of the future of remote work have been a "hot topic” during the pandemic, this Article is the first to systemically review and analyze the state of remote work as a disability accommodation under the ADA. This Article incorporates legal analysis and social science evidence in support of its argument for remote work as a reasonable accommodation. This Article concludes with recommendations for changes to the applicable EEOC regulations which would clarify that remote work or "telework,” the term used in the current regulations, is a reasonable accommodation for qualified employees under Title I of the ADA. Such changes are necessary to re-envision remote work as the future of disability accommodations under the ADA. © 2022 Cornell Law School. All rights reserved.