Journal of the American Medical Informatics Association : JAMIA · 2015
Identifying homelessness using health information exchange data.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zech John, Husk Gregg, Moore Thomas, Kuperman Gilad J, Shapiro Jason S
Аннотация
BACKGROUND: Homeless patients experience poor health outcomes and consume a disproportionate amount of health care resources compared with domiciled patients. There is increasing interest in the federal government in providing care coordination for homeless patients, which will require a systematic way of identifying these individuals.
OBJECTIVE: We analyzed address data from Healthix, a New York City-based health information exchange, to identify patterns that could indicate homelessness.
METHODS: Patients were categorized as likely to be homeless if they registered with the address of a hospital, homeless shelter, place of worship, or an address containing a keyword synonymous with "homelessness."
RESULTS: We identified 78,460 out of 7,854,927 Healthix patients (1%) as likely to have been homeless over the study period of September 30, 2008 to July 19, 2013. We found that registration practices for these patients varied widely across sites.
CONCLUSIONS: The use of health information exchange data enabled us to identify a large number of patients likely to be homeless and to observe the wide variation in registration practices for homeless patients within and across sites. Consideration of these results may suggest a way to improve the quality of record matching for homeless patients. Validation of these results is necessary to confirm the homeless status of identified individuals. Ultimately, creating a standardized and structured field to record a patient's housing status may be a preferable approach.
OBJECTIVE: We analyzed address data from Healthix, a New York City-based health information exchange, to identify patterns that could indicate homelessness.
METHODS: Patients were categorized as likely to be homeless if they registered with the address of a hospital, homeless shelter, place of worship, or an address containing a keyword synonymous with "homelessness."
RESULTS: We identified 78,460 out of 7,854,927 Healthix patients (1%) as likely to have been homeless over the study period of September 30, 2008 to July 19, 2013. We found that registration practices for these patients varied widely across sites.
CONCLUSIONS: The use of health information exchange data enabled us to identify a large number of patients likely to be homeless and to observe the wide variation in registration practices for homeless patients within and across sites. Consideration of these results may suggest a way to improve the quality of record matching for homeless patients. Validation of these results is necessary to confirm the homeless status of identified individuals. Ultimately, creating a standardized and structured field to record a patient's housing status may be a preferable approach.