1. Adoption of Health Information Exchanges and Physicians’ Referral Patterns: Are they Mutually Reinforcing?
SAEEDE EFTEKHARI*, School of Management, State University of New York at Buffalo
NIAM YARAGHI, School of Business, University of Connecticut; The Brookings Institution, Washington DC
RAM GOPAL, School of Business, University of Connecticut
R. RAMESH, School of Management, State University of New York at Buffalo

2. Health Information Exchange (HIE)
Physician Visits Records
Lab Test Results
Hospital Records
Prescription Information
Insurance Information
A Health information exchange system is a platform in which patients’ medical information, such as discharge information from hospitals or lab test results are saved electronically. When a patient visit a doctor, given the patient consent, the patient’s medical history can be shared with the physician if needed.
Patients’ Electronic Data
Physician Accessibility of Patient Data

3. Referral
In a referral process, usually there is a set of possible choices of specialists to refer to.
The refereeing physicians consider the following factors:
Availability of doctors
Physicians’ satisfaction with prior referrals
Physicians’ communication
The availability of the referred physician, physicians’ satisfaction with prior referrals are the most important factors reported in the literature.

4. Our basic question:
Is HIE membership impacting the referral network with regards to the primary care physicians’ choice of specialist?
HIE member ?
We essentially argue that primary care physicians who are HIE members tend to select the specialists who are also HIE members rather than non-members when they refer their patients.
HIE member
Non-member

5. Why HIE members tend to refer to HIE members?
HIE facilitate the communication between doctors in a referral process.
The referred physician will be able to provide a better care to the patient.
HIE reduces the amount of administrative work.

6. Reverse relationship exists too
Non-member
member
While the basic argument of this research is that the referral patterns of physicians are influenced by HIE adoption, we recognize that the reverse relationship would exist as well; it will be more likely for physicians to become HIE members if they interact with other HIE members in the referral network. The reasons behind this argument are as follows. First, physicians may decide to adopt a new technology even if they do not perceive any benefits from it as long as the
physicians with whom they communicate adopt.
Second, the interaction of physicians with HIE members will encourage the non-members to adopt HIE because they perceive the value of the HIE system through this interaction. In particular, the importance of accessing and integrating medical data becomes more significant when healthcare providers need to exchange patients with each other.

7. Reverse relationship also exist
Why?
Social influence
Increased perceived value of HIE as a result of more interaction with HIE members.
Non-member
member
Becomes member

8. Hypotheses H1: HIE adoption Referring to HIE members
H2: The interaction between a HIE non-member and a member HIE adoption

9. How to test? Secondary data
(i) HIE adoption in the Western New York (Healthelink)
(ii) Panel referral network data (CMS)
Social Network Analysis
SIENA: Simulation Investigation for Empirical Network Analysis

10. The referral network is dynamicP1 S3 S2 S1 T1 T2
The referral network is dynamic in the sense primary care physicians can change the network state by changing their positions in the network. Imagine a very simple network consisting of one primary care physician P1 and three specialist s1, s2, and s3 at T1. This network is dynamic and the new state of the network depends on the action of P1.

11. The referral network is dynamicP1 S3 S2 S1 T1 T2
Creating a new tie P1 S3 S2 S1

12. Terminating an existing tie
The referral network is dynamic P1 S3 S2 S1 T1 T2
Terminating an existing tie P1 S3 S2 S1

13. Remaining in the same state
The referral network is dynamic P1 S3 S2 S1 T1 T2
Remaining in the same state P1 S3 S2 S1

14. Basic assumption of actor-oriented model
Creating?
Terminating?
Remaining in the same state?
The basic assumption of the actor oriented model is that actors chose the action to maximize their objective function. The utility is a weighted function of actors’ attributes such as age and structural features of the network. SIENA estimates parameters. Similar to the coefficients of a regression model; each parameter represents the strength to which the corresponding factor is influencing the referral network.
𝑼𝒕𝒊𝒍𝒊𝒕𝒚 𝒂𝒄𝒕𝒐𝒓 𝒊 𝒏𝒆𝒕𝒘𝒐𝒓𝒌 = 𝜷 𝟎 ( 𝒆𝒙𝒑𝒆𝒓𝒊𝒆𝒏𝒄𝒆 𝒋 × 𝒙 𝒊𝒋 )+ 𝜷 𝟏 ( 𝒐𝒖𝒕𝒅𝒆𝒈𝒓𝒆𝒆 𝒊 )+e

15. How does SIENA estimate parameters?
To estimate the parameters, SIENA needs at least two observed network. It assumes that the transition from T1 to T2 happens through a few min-steps. It starts with a set of initial Beta, at each min-step one actors is randomly selected, the course of action is taken to maximize the utility function.
The transition process starts with a set on initial parameters {β}

16. How SIENA estimate parameters:
This process continues, and finally the resulted simulated network at the end of timeline (which is time point T2) is compared with the real observed network at T2. If they are enough close, the parameters are reported as model parameters. Otherwise, parameters get updated and the next iteration starts.
The simulated network at T2 is compared with real observed network and parameters get updated

17. Co-evolution: P1 S3 S2 S1 1 P1 S3 S2 S1 1 1 1 1 T2 T1 P1 S3 S2 S1 P1 S3 S2 S1 P1 S3 S2 S1
Similar to modelling network, siena is able to model the individual’s characteristics as well. We model HIE adoption and referral network alongside each other. So, at each ministep, either HIE adoption or referral network is modeled. 1 1 1

18. Results of modeling referral network (2013-2014)
Rate parameter for referral network
3.929*** (0.136)
Density
-3.389*** (0.057)
4-cycle
0.028*** (0.001)
Popularity
0.025*** (0.004)
Dyadic covariate effect for gender
0.109* (0.061)
Dyadic covariate effect for practice
1.082*** (0.101)
Popularity effect for experience
-0.005** (0.003)
Activity effect for HIE
0.040 (0.093)
Popularity effect for HIE
-0.309*** (0.099)
Homophily effect for HIE
1.506*** (0.199)
Consider a primary care physician i who needs to choose between two specialists. Let Xa and Xb denote the network realizations as a result of these two choices, respectively. Then, 𝑓 𝑖 𝑛𝑒𝑡 𝑥 𝑏 - 𝑓 𝑖 𝑛𝑒𝑡 𝑥 𝑎 , is the log odds ratio for choosing between these two alternatives. As an example, consider the estimated parameter for Homophily effect for HIE. Suppose that specialists j and k are potential specialist options for primary care physician i who is an HIE member. Let specialist j be a HIE member and specialist k a non-member. Assume the two specialists are similar with regard all other attributes. Then, the only explanatory variable that significantly changes based on these two options is 𝑠 𝑖9 𝑛𝑒𝑡 = 𝑗 𝑥 𝑖𝑗 𝐻𝐼𝐸 𝑖 𝐻𝐼𝐸 𝑗 . By connecting primary care physician i with specialist k instead of j, the gain to this explanatory variable is 1, and hence, the gain to the objective function is simply 𝛽 9 . Thus, 𝛽 9 is the log odds ratio of the probability that k is chosen to the probability that j is chosen. Specifically, the parameter for the “homophily effect for HIE” indicates that for a primary care physician who is an HIE member, the probability to refer to a specialist who is also an HIE member is 𝑒𝑥𝑝 =4.5 times more than the probability to refer to a non-member specialist.

19. Results of modeling HIE adoption for primary care physicians (2013-2014)
Rate parameter for adoption by primary care physicians
1.087*** (0.284)
Out-degree
(0.066)
Covariate Effect of EHR
3.667*** (1.149)
Covariate Effect of Gender
0.827 (0.738)
Covariate Effect of Exp
-0.114** (0.046)
Ties with HIE member physicians
1.867*** (0.525)

20. Results of modeling HIE adoption for specialists (2013-2014)
Rate parameter for adoption by primary care physicians
0.424*** (0.097)
In-degree
(0.071)
Covariate Effect of EHR
2.449** (0.994)
Covariate Effect of Gender
1.548 (1.066)
Covariate Effect of Exp
0.066* (0.039)
Ties with HIE member physicians
1.204*** (0.296)

21. This study …
Uncovers homophily relationships of HIE member physicians in the referral network;
Supports the social influence on HIE diffusion;
Provides important insights on the benefits of HIE to the overall healthcare system;
Explores dominant factors shaping the referral network.

22. Thank you for your attention