Description
AIM:
Evaluating physician referral network characteristics can help to understand how physicians and hospitals interact to provide patient services within the US healthcare system and ultimately how this may influence patient outcomes.
METHOD:
We used the 2012-2013 national Physician Referral data from the Centers for Medicare & Medicaid Services (CMS), which consists of 73,071,804 pairs of referrals from one health provider to another in calendar year 2012 and the first two quarters of year 2013 within 30 days of care. These referrals are from 642,144 national-wide physicians and 4,811 hospitals. We obtained information for each provider, physician or hospital, from CMS.
We then generated a nationwide referral network. We described the network with graphs and potential important network characteristics using graph theory and social network theory. Further, we described the sub-network by Exponential random graph models (ERGM). The ERGM coefficients from such models can reflect the properties of the network nodes and help illustrate how the network outcomes are influenced.
RESULTS:
Our results show that 1) the graphs and characteristics vary substantially across the geographic areas and 2) graphs and the characteristics depicting the same area are strongly associated. The ERGM model shows that physicians in cardiology, diagnostic radiology and geriatric medicine are more likely to send and receive referrals than physicians in family care and internal medicine in certain hospitals.
CONCLUSION:
We demonstrate the use of graph-based approaches to describe and evaluate nationwide physician referral networks. Further work will study how these network characteristics are associated with hospital outcomes.
Included in
Biostatistics Commons, Health and Medical Administration Commons, Statistical Models Commons
Using Graphs to Characterize Nationwide Physician Referral Networks
AIM:
Evaluating physician referral network characteristics can help to understand how physicians and hospitals interact to provide patient services within the US healthcare system and ultimately how this may influence patient outcomes.
METHOD:
We used the 2012-2013 national Physician Referral data from the Centers for Medicare & Medicaid Services (CMS), which consists of 73,071,804 pairs of referrals from one health provider to another in calendar year 2012 and the first two quarters of year 2013 within 30 days of care. These referrals are from 642,144 national-wide physicians and 4,811 hospitals. We obtained information for each provider, physician or hospital, from CMS.
We then generated a nationwide referral network. We described the network with graphs and potential important network characteristics using graph theory and social network theory. Further, we described the sub-network by Exponential random graph models (ERGM). The ERGM coefficients from such models can reflect the properties of the network nodes and help illustrate how the network outcomes are influenced.
RESULTS:
Our results show that 1) the graphs and characteristics vary substantially across the geographic areas and 2) graphs and the characteristics depicting the same area are strongly associated. The ERGM model shows that physicians in cardiology, diagnostic radiology and geriatric medicine are more likely to send and receive referrals than physicians in family care and internal medicine in certain hospitals.
CONCLUSION:
We demonstrate the use of graph-based approaches to describe and evaluate nationwide physician referral networks. Further work will study how these network characteristics are associated with hospital outcomes.