Between health care and social services

Boundary objects and cross-sector collaboration

Bibliographic Data

ID4598925
AuthorsMark D Fleming (0000-0002-3037-704X, Berkeley Public Health Division, corresponding author), Nadia Safaeinili (0000-0003-4792-3091, Berkeley Public Health Division), Michele Knox (0000-0002-5234-8610, Berkeley Public Health Division), Elizabeth A Hernandez (Contra Costa Health Services), Elizabeth Hernandez (0000-0003-2910-9248), Amanda L Brewster (0000-0003-4024-5830, Berkeley Public Health Division)
Year2023
Volume320
Pages115758
Publication date2023-03-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Science & Medicine (JOURNAL)
Journal identifiersISSN: 0277-9536 • E-ISSN: 1873-5347
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.socscimed.2023.115758
PMID36753994
OpenAlexW4319071921
LanguageEN
Citations received13
References cited33

Health care systems throughout the United States are initiating collaborations with social services agencies. These cross-sector collaborations aim to address patients' social needs-such as housing, food, income, and transportation-in health care settings. However, such collaborations can be challenging as health care and social service sectors are composed of distinct missions, institutions, professional roles, and modes of distributing resources. This paper examines how the "high-risk" patient with both medical and social needs is constructed as a shared object of intervention across sectors. Using the concept of boundary object, we illustrate how the high-risk patient category aggregates and represents multiple types of information-medical, social, service utilization, and cost-in ways that facilitate its use across sectors. The high-risk patient category works as a boundary object, in part, by the differing interpretations of "risk" available to collaborators. During 2019-2021, we conducted 75 semi-structured interviews and 31 field observations to investigate a relatively large-scale, cross-sector collaboration effort in California known as CommunityConnect. This program uses a predictive algorithm and big data sets to assign risk scores to the population and directs integrated health care and social services to patients identified as high risk. While the high-risk patient category worked well to foster collaboration in administrative and policy contexts, we find that it was less useful for patient-level interactions, where frontline case managers were often hesitant or unable to communicate information about the risk-based eligibility process. We suggest that the predominance of health care utilization (and its impacts on costs) in constructing the high-risk patient category may be medicalizing social services, with the potential to deepen inequities

Boundary object · Business · Economic growth · Economics · Environmental health · Geography · Health care · Political science · Population · Public health · Public relations · Scale (ratio · Service (business · Social determinants of health · Health Policy Implementation Science · Medicine · Mental Health and Patient Involvement · Nursing · Primary Care and Health Outcomes · Marketing

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