
doi: 10.1017/cem.2015.6
pmid: 25989813
AbstractObjectivesAs an aging population continues to place strain on the health care system, many older adults are living with unmet social and medical needs. In response, Emergency Medical Services (EMS) have initiated programs that encourage paramedics to refer patients in need to community based support services. This qualitative study explores frontline paramedic experiences with referral programs to identify opportunities and challenges in their practice.MethodsThis study used an intepretivist qualitative study design involving interviews of frontline paramedics employed in a region where referral programs were in place. Interviews were semi-structured and one-on-one. Data were transcribed verbatim and analyzed using inductive open coding throughout, then grouped to identify themes. Data collection and analysis were conducted simultaneously and flexibly until saturation.ResultsTwenty-three interviews were conducted representing 6 regions. When participating with referral programs the data revealed that frontline paramedics appear to experience (a) role confusion, (b) an inadequate knowledge base, (c) inadequate feedback, (d) undefined accountability, and (e) strong patient advocacy.ConclusionsIn a strained health care system, EMS and paramedics have an opportunity to better serve patients by initiating referrals for patients they encounter with unmet social and medical needs. However, referral programs face a number of challenges that, if left poorly addressed, may threaten the success of such programs.
Adult, Male, Emergency Medical Services, Allied Health Personnel, Emergency Medical Technicians, Education, Professional, Humans, Female, Health Services Research, Qualitative Research
Adult, Male, Emergency Medical Services, Allied Health Personnel, Emergency Medical Technicians, Education, Professional, Humans, Female, Health Services Research, Qualitative Research
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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