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J Am Med Inform Assoc ; 29(7): 1161-1171, 2022 Jun 14.
Article in English | MEDLINE | ID: covidwho-1795239


OBJECTIVE: To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries. MATERIALS AND METHODS: Criteria2Query (C2Q) 2.0 was developed to enable real-time user intervention for criteria selection and simplification, parsing error correction, and concept mapping. The accuracy, precision, recall, and F1 score of enhanced modules for negation scope detection, temporal and value normalization were evaluated using a previously curated gold standard, the annotated eligibility criteria of 1010 COVID-19 clinical trials. The usability and usefulness were evaluated by 10 research coordinators in a task-oriented usability evaluation using 5 Alzheimer's disease trials. Data were collected by user interaction logging, a demographic questionnaire, the Health Information Technology Usability Evaluation Scale (Health-ITUES), and a feature-specific questionnaire. RESULTS: The accuracies of negation scope detection, temporal and value normalization were 0.924, 0.916, and 0.966, respectively. C2Q 2.0 achieved a moderate usability score (3.84 out of 5) and a high learnability score (4.54 out of 5). On average, 9.9 modifications were made for a clinical study. Experienced researchers made more modifications than novice researchers. The most frequent modification was deletion (5.35 per study). Furthermore, the evaluators favored cohort queries resulting from modifications (score 4.1 out of 5) and the user engagement features (score 4.3 out of 5). DISCUSSION AND CONCLUSION: Features to engage domain experts and to overcome the limitations in automated machine output are shown to be useful and user-friendly. We concluded that human-computer collaboration is key to improving the adoption and user-friendliness of natural language processing.

COVID-19 , Artificial Intelligence , Eligibility Determination/methods , Humans , Natural Language Processing , Patient Selection
Isr J Health Policy Res ; 10(1): 16, 2021 02 19.
Article in English | MEDLINE | ID: covidwho-1090615


The rapid rollout of Israel's vaccination program has led to considerable international interest. In this brief commentary we consider how the criteria for vaccination priority groups differ between Israel and selected European countries. We argue that following the Israeli approach of using broad criteria for prioritization- i.e. having fewer groups and a lower age threshold- could have several beneficial effects, including more manageable logistics and fewer roll out delays, as well as potentially reducing pressure on hospitals. With an increasing supply of vaccines becoming available rapidly in much of Europe, countries could consider following the approach of Israel and adopting broader priority criteria going forward.

COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , Health Policy , Health Priorities , Immunization Programs/organization & administration , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19 Vaccines/supply & distribution , Eligibility Determination/methods , Europe/epidemiology , Humans , Israel/epidemiology , Middle Aged
Health Aff (Millwood) ; 39(10): 1822-1831, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-695660


The recent coronavirus disease 2019 (COVID-19) global pandemic has resulted in unprecedented job losses in the United States, disrupting health insurance coverage for millions of people. Several models have predicted large increases in Medicaid enrollment among those who have lost jobs, yet the number of Americans who have gained coverage since the pandemic began is unknown. We compiled Medicaid enrollment reports covering the period from March 1 through June 1, 2020, for twenty-six states. We found that in these twenty-six states, Medicaid covered more than 1.7 million additional Americans in roughly a three-month period. Relative changes in Medicaid enrollment differed significantly across states, although enrollment growth was not systemically related to job losses. Our results point to the important effects of state policy differences in the response to COVID-19.

Coronavirus Infections/epidemiology , Eligibility Determination/statistics & numerical data , Employment/statistics & numerical data , Insurance Coverage/statistics & numerical data , Medicaid/statistics & numerical data , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , COVID-19 , Cohort Studies , Coronavirus Infections/prevention & control , Databases, Factual , Eligibility Determination/methods , Employment/economics , Female , Humans , Incidence , Insurance, Health/organization & administration , Male , Medically Uninsured/statistics & numerical data , Needs Assessment , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Retrospective Studies , Risk Assessment , Time Factors , United States