Transition Networks in a Cohort of Patients with Congestive Heart Failure: A Novel Application of Informatics Methods to Inform Care Coordination.
BACKGROUND
Unnecessary hospital readmissions are one source of escalating costs that may be reduced through improved care coordination, but how best to design and evaluate coordination programs is poorly understood. Measuring patient flow between service visits could support decisions for coordinating care, particularly for conditions such as congestive heart failure (CHF) which have high morbidity, costs, and hospital readmission rates.


OBJECTIVE
To determine the feasibility of using network analysis to explore patterns of service delivery for patients with CHF in the context of readmissions.


METHODS
A retrospective cohort study used de-identified records for patients ≥18 years with an ICD-9 diagnosis code 428.0-428.9, and service visits between July 2011 and June 2012. Patients were stratified by admission outcome. Traditional and novel network analysis techniques were applied to characterize care patterns.


RESULTS
Patients transitioned between services in different order and frequency depending on admission status. Patient-to-service CoUsage networks were diffuse suggesting unstructured flow of patients with no obvious coordination hubs. In service-to-service Transition networks a specialty heart failure service was on the care path to the most other services for never admitted patients, evidence of how specialist care may prevent hospital admissions for some patients. For patients admitted once, transitions expanded for a clinic-based internal medicine service which clinical experts identified as a Patient Centered Medical Home implemented in the first month for which we obtained data.


CONCLUSIONS
We detected valid patterns consistent with a targeted care initiative, which experts could understand and explain, suggesting the method has utility for understanding coordination. The analysis revealed strong but complex patterns that could not be demonstrated using traditional linear methods alone. Network analysis supports measurement of real world health care service delivery, shows how transitions vary between services based on outcome, and with further development has potential to inform coordination strategies.
Columbia University Medical Center , New York, NY, United States.
J A
JA
Merrill
Division of Health and Life Sciences, Intel Corporation, Santa Clara , CA, United States.
B M
BM
Sheehan
Institute of Software Research, Carnegie Mellon University , Pittsburgh, PN, United States.
K M
KM
Carley
Memorial Sloan Kettering Cancer Center , New York, NY, United States.
P D
PD
Stetson
D000368:Aged; D015331:Cohort Studies; D005260:Female; D006333:Heart Failure; D006801:Humans; D008297:Male; D008490:Medical Informatics; D010360:Patient Transfer / Q000379:methods*; D012189:Retrospective Studies
Care coordination; healthcare systems; heart failure; network analysis; patient care management