Hidden Structure of Care Coordination in Heart Failure Care Transitions: A Mixed-method Network Analysis of Clinical Notes

Abstract (English)

Introduction: Fragmentation during heart failure care transitions occurs disproportionately among patients with lower socio-economic position. Care coordination mitigates care fragmentation, but the interdependent relationships among clinicians that enable coordinated care are poorly understood. This explanatory sequential mixed-methods study explores how patients’ clinician network characteristics relate to their social context and clinical outcomes following heart failure care transitions.

Methods: We conducted a bipartite social network analysis to describe clinician networks for 1,269 patients first hospitalized with heart failure in a southeastern U.S. health system. Networks were constructed using electronic health record notes one year before, during, and one year after the index hospitalization (January 2015- February 2020). We then used stratified purposive sampling to select 11 adults with diverse socio-economic positions but similar illness severity and comorbidity. For these patients, we conducted qualitative chart reviews of clinical notes. Each patient’s clinician network size, density, and centrality were integrated with qualitative findings to explore clinician networks’ relationship to patients’ social context and outcomes.

Results: Patients with higher socio-economic positions used fewer acute care services and lived longer; their clinicians, particularly outpatient clinicians, tended to have denser and more centrally located team networks before the index hospitalization that persisted after the index hospitalization; their telephone notes indicated more regular and reciprocal communication patterns between patients and clinicians.

Conclusions: Early involvement and better communication among clinician networks with greater density and centrality may help to explain better care transition outcomes observed among patients with higher socio-economic positions.

Acknowledgements (English)

We would like to thank Cory Miller, Preeti Bala, Stanton Daries, and Joseph Kelly from the Duke Heart Center for facilitating the de-identification and extraction of data from the electronic health records. We also appreciate Alyse Freilich for her editorial support.