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Canadian Respiratory Journal 2015In Canada, although medical insurance is generally universal, significant differences exist in the provision of home oxygen therapy across the country. (Review)
Review
BACKGROUND
In Canada, although medical insurance is generally universal, significant differences exist in the provision of home oxygen therapy across the country.
OBJECTIVE
To systematically compare the terms of reference for home oxygen across Canada, with a focus on the clinical inclusion criteria to the programs.
METHODS
The authors searched the terms of reference of the 10 Canadian provinces and three territories, focusing on general eligibility criteria for home oxygen (including blood gas criteria, and eligibility criteria for ambulatory and nocturnal oxygen), and compared the eligibility criteria to the widely accepted criteria of the Nocturnal Oxygen Therapy Trial (NOTT) trial, the clinical recommendations of the Canadian Thoracic Society and the results of Cochrane reviews.
RESULTS
The terms of reference for nine provinces were retrieved. All jurisdictions have similar criteria for long-term oxygen therapy, with slight differences in the thresholds of prescription and the clinical criteria defining 'pulmonary hypertension' or 'cor pulmonale'. The use of oxyhemoglobin saturation as a criterion for funding is inconsistent. All nine provinces fund nocturnal oxygen, all with different clinical criteria. Funding for portable oxygen widely varies across provinces, whether the ambulatory equipment is offered to patients on long-term oxygen therapy or to those who have isolated exercise-induced desaturation. The terms of reimbursement are very heterogeneous.
CONCLUSIONS
Heterogeneity exists in the criteria for eligibility to home oxygen programs and funding across Canada. Terms of prescription and reimbursement of oxygen are not necessarily supported by available evidence from the current literature in several Canadian jurisdictions.
Topics: Canada; Eligibility Determination; Humans; Insurance Coverage; Oxygen Inhalation Therapy; Pulmonary Disease, Chronic Obstructive
PubMed: 26306331
DOI: 10.1155/2015/280604 -
Journal of the American Medical... Jun 2022To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries.
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.
Topics: Artificial Intelligence; COVID-19; Eligibility Determination; Humans; Natural Language Processing; Patient Selection
PubMed: 35426943
DOI: 10.1093/jamia/ocac051 -
Clinical and Experimental Medicine Oct 2023The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are... (Review)
Review
The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.
Topics: Humans; Artificial Intelligence; Eligibility Determination; Natural Language Processing; Patient Selection; Research Design; Clinical Trials as Topic
PubMed: 36602707
DOI: 10.1007/s10238-022-00975-1 -
PloS One 2021Research on children and youth on the autism spectrum reveal racial and ethnic disparities in access to healthcare and utilization, but there is less research to...
Racial and ethnic disparities in benefits eligibility and spending among adults on the autism spectrum: A cohort study using the Medicare Medicaid Linked Enrollees Analytic Data Source.
BACKGROUND
Research on children and youth on the autism spectrum reveal racial and ethnic disparities in access to healthcare and utilization, but there is less research to understand how disparities persist as autistic adults age. We need to understand racial-ethnic inequities in obtaining eligibility for Medicare and/or Medicaid coverage, as well as inequities in spending for autistic enrollees under these public programs.
METHODS
We conducted a cross-sectional cohort study of U.S. publicly-insured adults on the autism spectrum using 2012 Medicare-Medicaid Linked Enrollee Analytic Data Source (n = 172,071). We evaluated differences in race-ethnicity by eligibility (Medicare-only, Medicaid-only, Dual-Eligible) and spending.
FINDINGS
The majority of white adults (49.87%) were full-dual eligible for both Medicare and Medicaid. In contrast, only 37.53% of Black, 34.65% Asian/Pacific Islander, and 35.94% of Hispanic beneficiaries were full-dual eligible for Medicare and Medicare, with most only eligible for state-funded Medicaid. Adjusted logistic models controlling for gender, intellectual disability status, costly chronic condition, rural status, county median income, and geographic region of residence revealed that Black beneficiaries were significantly less likely than white beneficiaries to be dual-eligible across all ages. Across these three beneficiary types, total spending exceeded $10 billion. Annual total expenditures median expenditures for full-dual and Medicaid-only eligible beneficiaries were higher among white beneficiaries as compared with Black beneficiaries.
CONCLUSIONS
Public health insurance in the U.S. including Medicare and Medicaid aim to reduce inequities in access to healthcare that might exist due to disability, income, or old age. In contrast to these ideals, our study reveals that racial-ethnic minority autistic adults who were eligible for public insurance across all U.S. states in 2012 experience disparities in eligibility for specific programs and spending. We call for further evaluation of system supports that promote clear pathways to disability and public health insurance among those with lifelong developmental disabilities.
Topics: Adolescent; Adult; Aged; Autistic Disorder; Cohort Studies; Cross-Sectional Studies; Eligibility Determination; Ethnicity; Female; Health Expenditures; Health Services Accessibility; Humans; Information Storage and Retrieval; Male; Medicaid; Medicare; Middle Aged; Minority Groups; United States; Young Adult
PubMed: 34032811
DOI: 10.1371/journal.pone.0251353 -
BMC Medical Ethics Dec 2017The scarcity of human organs requires the transplant community to make difficult allocation decisions. This process begins at individual medical centers, where... (Review)
Review
BACKGROUND
The scarcity of human organs requires the transplant community to make difficult allocation decisions. This process begins at individual medical centers, where transplant teams decide which patients to place on the transplant waiting list. Each transplant center utilizes its own listing criteria to determine if a patient is eligible for transplantation. These criteria have historically considered preexisting affective and psychotic disorders to be relative or absolute contraindications to transplantation. While attitudes within the field appear to be moving away from this practice, there is no data to confirm that eligibility criteria have changed.
MAIN BODY
There are no nationwide guidelines detailing the manner in which affective and psychotic disorders should impact transplant eligibility. Individual transplant centers thus form their own transplant eligibility criteria, resulting in significant inter-institution variability. Data from the 1990s indicates that the majority of transplant programs considered certain psychiatric illnesses, such as active schizophrenia, to be absolute contraindications to transplantation. A review of literature reveals that no comprehensive data has been collected on the topic since that time. Furthermore, the limited data available about current practices suggests that psychiatric illness continues to be viewed as a contraindication to transplantation at some transplant centers. In light of this finding, we review psychiatric literature that examines the impact of affective and psychotic disorders on transplant outcomes and conclude that the presence of these disorders is not an accurate predictor of transplant success. We then discuss the requirements of justice as they relate to the creation of a just organ allocation system.
CONCLUSION
We conclude that transplant eligibility criteria that exclude patients with affective and psychotic disorders on the basis of their psychiatric diagnosis alone are unjust. Just listing criteria must incorporate only those factors that have a causative effect on posttransplant morbidity and mortality. Justice also demands that we eliminate current inter-institution practice variations in favor of national transplant eligibility criteria. Given the limited data available about current practices, we call for an updated study investigating the manner in which affect and psychotic disorders impact transplant eligibility determinations.
Topics: Eligibility Determination; Guidelines as Topic; Humans; Organ Transplantation; Patient Selection; Psychotic Disorders; Social Justice; United States; Waiting Lists
PubMed: 29216883
DOI: 10.1186/s12910-017-0235-4 -
Health Services Research Aug 2021To estimate the incremental associations between the implementation of expanded Medicaid eligibility and prerelease Medicaid enrollment assistance on Medicaid enrollment...
OBJECTIVE
To estimate the incremental associations between the implementation of expanded Medicaid eligibility and prerelease Medicaid enrollment assistance on Medicaid enrollment for recently incarcerated adults.
DATA SOURCES/STUDY SETTING
Data include person-level merged, longitudinal data from the Wisconsin Department of Corrections and the Wisconsin Medicaid program from 2013 to 2015.
STUDY DESIGN
We use an interrupted time series design to estimate the association between each of two natural experiments and Medicaid enrollment for recently incarcerated adults. First, in April 2014 the Wisconsin Medicaid program expanded eligibility to include all adults with income at or below 100% of the federal poverty level. Second, in January 2015, the Wisconsin Department of Corrections implemented prerelease Medicaid enrollment assistance at all state correctional facilities.
DATA COLLECTION/EXTRACTION METHODS
We collected Medicaid enrollment, and state prison administrative and risk assessment data for all nonelderly adults incarcerated by the state who were released between January 2013 and December 2015. The full sample includes 24 235 individuals. Adults with a history of substance use comprise our secondary sample. This sample includes 12 877 individuals. The primary study outcome is Medicaid enrollment within the month of release.
PRINCIPAL FINDINGS
Medicaid enrollment in the month of release from state prison grew from 8 percent of adults at baseline to 36 percent after the eligibility expansion (P-value < .01) and to 61 percent (P-value < .01) after the introduction of enrollment assistance. Results were similar for adults with a history of substance use. Black adults were 3.5 percentage points more likely to be enrolled in Medicaid in the month of release than White adults (P-value < .01).
CONCLUSIONS
Medicaid eligibility and prerelease enrollment assistance are associated with increased Medicaid enrollment upon release from prison. States should consider these two policies as potential tools for improving access to timely health care as individuals transition from prison to community.
Topics: Eligibility Determination; Humans; Interrupted Time Series Analysis; Medicaid; Poverty; Prisoners; United States; Wisconsin
PubMed: 33565117
DOI: 10.1111/1475-6773.13634 -
Journal of the National Cancer Institute Nov 2022
Topics: Humans; Patient Selection; Population Groups; Drug Development; Eligibility Determination
PubMed: 36047853
DOI: 10.1093/jnci/djac155 -
Health Services Research Feb 2022To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland.
OBJECTIVE
To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland.
DATA SOURCES
Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets.
STUDY DESIGN
Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve.
DATA COLLECTION/EXTRACTION METHODS
One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations.
PRINCIPAL FINDINGS
We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power.
CONCLUSIONS
A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.
Topics: Aged; Aged, 80 and over; Eligibility Determination; Fee-for-Service Plans; Hospitalization; Humans; Maryland
PubMed: 34648179
DOI: 10.1111/1475-6773.13891 -
Pediatrics Feb 2022To examine inpatient vaccine delivery across a national sample of children's hospitals.
OBJECTIVES
To examine inpatient vaccine delivery across a national sample of children's hospitals.
METHODS
We conducted a retrospective cohort study examining vaccine administration at 49 children's hospitals in the Pediatric Health Information System database. Children <18 years old admitted between July 1, 2017, and June 30, 2019, and age eligible for vaccinations were included. We determined the proportion of hospitalizations with ≥1 dose of any vaccine type administered overall and by hospital, the type of vaccines administered, and the demographic characteristics of children who received vaccines. We calculated adjusted hospital-level rates for each vaccine type by hospital. We used logistic and linear regression models to examine characteristics associated with vaccine administration.
RESULTS
There were 1 185 667 children and 1 536 340 hospitalizations included. The mean age was 5.5 years; 18% were non-Hispanic Black, and 55% had public insurance. There were ≥1 vaccine doses administered in 12.9% (95% confidence interval: 12.8-12.9) of hospitalizations, ranging from 1% to 45% across hospitals. The most common vaccines administered were hepatitis B and influenza. Vaccine doses other than the hepatitis B birth dose and influenza were administered in 1.9% of hospitalizations. Children had higher odds of receiving a vaccine dose other than the hepatitis B birth dose or influenza if they were <2 months old, had public insurance, were non-Hispanic Black race, were medically complex, or had a length of stay ≥3 days.
CONCLUSIONS
In this national study, few hospitalizations involved vaccine administration with substantial variability across US children's hospitals. Efforts to standardize inpatient vaccine administration may represent an opportunity to increase childhood vaccine coverage.
Topics: Adolescent; Child; Child, Preschool; Cohort Studies; Eligibility Determination; Female; Hospitalization; Hospitals, Pediatric; Humans; Infant; Male; Retrospective Studies; Vaccination
PubMed: 35001100
DOI: 10.1542/peds.2021-053925 -
Journal of Biomedical Informatics Apr 2011Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on...
Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive, we transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques. We evaluated our approach in three ways. First, we assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second, we demonstrated the practicality of the annotation process in a feasibility study. Finally, we demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study. We therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form.
Topics: Clinical Trials as Topic; Computational Biology; Databases, Factual; Eligibility Determination; Information Storage and Retrieval; Semantics; Vocabulary, Controlled
PubMed: 20851207
DOI: 10.1016/j.jbi.2010.09.007