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1.
Ann Fam Med ; 16(Suppl 1): S72-S79, 2018 04.
Artículo en Inglés | MEDLINE | ID: mdl-29632229

RESUMEN

PURPOSE: The methods and costs to enroll small primary care practices in large, regional quality improvement initiatives are unknown. We describe the recruitment approach, cost, and resources required to recruit and enroll 500 practices in the Northwest and Midwest regional cooperatives participating in the Agency for Healthcare Research and Quality (AHRQ)-funded initiative, EvidenceNOW: Advancing Heart Health in Primary Care. METHODS: The project management team of each cooperative tracked data on recruitment methods used for identifying and connecting with practices. We developed a cost-of-recruitment template and used it to record personnel time and associated costs of travel and communication materials. RESULTS: A total of 3,669 practices were contacted during the 14- to 18-month recruitment period, resulting in 484 enrolled practices across the 6 states served by the 2 cooperatives. The average number of interactions per enrolled practice was 7, with a total of 29,100 hours and a total cost of $2.675 million, or $5,529 per enrolled practice. Prior partnerships predicted recruiting almost 1 in 3 of these practices as contrasted to 1 in 20 practices without a previous relationship or warm hand-off. CONCLUSIONS: Recruitment of practices for large-scale practice quality improvement transformation initiatives is difficult and costly. The cost of recruiting practices without existing partnerships is expensive, costing 7 times more than reaching out to familiar practices. Investigators initiating and studying practice quality improvement initiatives should budget adequate funds to support high-touch recruitment strategies, including building trusted relationships over a long time frame, for a year or more.


Asunto(s)
Atención Primaria de Salud/organización & administración , Mejoramiento de la Calidad/economía , United States Agency for Healthcare Research and Quality/economía , Costos y Análisis de Costo , Investigación sobre Servicios de Salud/economía , Investigación sobre Servicios de Salud/métodos , Humanos , Atención Primaria de Salud/economía , Atención Primaria de Salud/estadística & datos numéricos , Estados Unidos
2.
Appl Clin Inform ; 8(2): 322-336, 2017 04 05.
Artículo en Inglés | MEDLINE | ID: mdl-28378025

RESUMEN

BACKGROUND: Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching. OBJECTIVES: We sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites. METHODS: We compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites. RESULTS: Several attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001). CONCLUSIONS: Overall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare.


Asunto(s)
Demografía , Registro Médico Coordinado/métodos , Femenino , Humanos , Masculino , Sistemas de Identificación de Pacientes , Factores de Tiempo
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