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Sources of Information Used in Selection of Surgeons
Caroline S. Carlin, PhD; John Kralewski, PhD; and Megan Savage, BS

Sources of Information Used in Selection of Surgeons

Caroline S. Carlin, PhD; John Kralewski, PhD; and Megan Savage, BS
This article explores the information (scorecards, referrals, prior experience, location) influencing the choice of orthopedic surgeon and how this influence varies by patient characteristics.
Because so few actually used the scorecard, we did not investigate what characteristics were associated with scorecard use. Therefore, we focused on awareness of the scorecard, willingness to switch physician for a financial incentive, and the  selection process. The first 2 questions are dichotomous yes/no answers, modeled as a probit equation. We used multinomial logit to model the physician choice process. Both probit and multinomial logit estimations were completed using Stata 12. We explored nesting in physician choice, but found no improvement in fit or evidence of nesting in the choice process. We also tested methods15 of accounting for potentially endogenous variables (eg, access to the Internet and awareness of the scorecard), but found no evidence of bias due to endogeneity.

When modeling awareness of the physician scorecard (Table 2), we found that the participant characteristic that has the greatest statistical significance is access to the Internet. Holding all other characteristics at their true values, moving the population from “no Internet access” to “Internet access” nearly doubled the average predicted probability of awareness of the scorecard, leading to the +6.5 percentagepoint (pp) marginal effect (ME) shown in the second column of Table 2. We also identified marginal statistical significance in the census tract’s median income. When we computed the marginal effect of moving the population from the lowest to highest median income quartile, we found a +9.1 pp ME, more than doubling the probability of being aware of the scorecard.

Willingness to change surgeons (Table 3) appears to be sensitive to respondent’s sex, their access to the Internet, and the education level in their census tract. Females were much less likely to be willing to switch; the average predicted probability of being willing to switch drops by a –9.9 pp ME when moving from a male to a female status. Those who live in a highly educated census tract were much less willing to switch; moving from a baseline education to a highly educated census tract caused the willingness to change surgeons to drop by a –14.4 pp ME. In contrast, those with access to the Internet were more flexible in provider choice. Moving from “no access” to “Internet access” generated a +8.5 pp ME.

Two of our respondent characteristics appear to have significantly impacted the physician selection process (Table 4): rural/urban residence and access to the Internet both have a χ2 P value less than .05. The marginal effects in Table 5 show us that those who live in an urban area were much less likely to rely on referrals from families and friends (–11.2 pp ME), and much more likely to rely on physician referrals (+15.6 pp ME). Those with access to the Internet were much less likely to rely on the physician’s location in their selection (–12.5 pp ME), with an increased reliance on previous physician experience (+5.4 pp ME) and other factors (+5.5 pp ME) to influence their choice.


It is interesting that we found no predictive value of age or type of insurance product (commercial, Medicare, Medicaid) when modeling the 3 dependent variables—awareness of the scorecard, willingness to switch providers for a lower copay without sacrificing quality, and the factors influencing choice of surgeon. On the other hand, Internet access, a variable rarely included in this literature, was strongly associated with all 3 dependent variables. It is possible that this is a causal factor—the greater informational resources that come with access to the Internet may alter the choices and attitudes of the respondent. With Internet access, the consumer can more easily find cost and quality information,4 as well as search for other, non-clinical quality dimensions such as practice size, presence of urgent care facilities, and hospital affiliation. This suggests that improving access to the Internet, or reducing search costs in other ways, such as employer-facilitated provision of quality information,6 may improve awareness and use of quality information, leading to increased patient-driven market forces. Alternatively, Internet access may be a proxy for (latent) respondent characteristics such as generalized differences in the way they engage with their environment. This is an area that deserves additional investigation. If it is a causal factor, this has great implications for the impact of market forces as Internet access increases in prevalence, particularly through mobile devices.

This study is unusual in its inclusion of rural versus urban status, which we find is an important factor in the source of informationused by the respondent in physician selection. The greater importance of family and friend referrals in rural locations implies stronger reliance on social fabric in rural areas, while the urban respondents had a much greater tendency touse professional referrals in their choice of surgeon. This suggests that, as we seek to increase the impact of market forces to drive improvements in cost and quality, different strategies may be needed by geography.

By selecting first observances of a claim for hip and knee surgery, this project was designed to highlight instances of orthopedic surgeon selection when an existing relationship is unlikely. This was intended to maximize the probability that quality and cost data provided by the health plan would be salient, in a setting where the patient had time for research, physician cost and quality rating information had been recently publicized, and the data were easily accessible on the Internet. Yet only 11% of the respondents indicated that they knew about the physician rating program, consistent with surveys of awareness in a more general population.5 Since nearly 70% of the respondents indicated that they have access to the Internet at home or office, the media for transmission of the cost and quality information does not appear to be the primary limiting factor. Given these conditions, we must conclude that either the health plan enrollees in this study do not place a high value on cost and quality data when selecting their surgeon, or that the message regarding the availability of the information was ineffective.

The availability of “product quality” information is a key feature of a functioning market. Therefore, if healthcare cost and quality improvements are to be driven by market forces, patient awareness and use of physician quality information are necessary. This work identifies factors that are important in patient awareness of quality information, and explores the physician selection process more broadly. Our results suggest that easy access to information through the Internet is a key component of awareness of quality information and willingness to change providers, and reduces the patient’s reliance on proximity to physician. This implies that other efforts that reduce search cost may be important, such as employer provision of information orstate-based public and private initiatives, such as those tied to the Robert Wood Johnson Foundation’s Aligning Forces for Quality initiative. We also find that cultural differences, here captured in rural versus urban residence, should influence communication strategies as they influence which factors are important to decision makers.


Because we focused on orthopedic surgery, the population was fairly old (average age 68.6 years), so these results may not be generalizable to a younger population. Age and type of coverage (Medicare vs commercial insurance) were not found to be statistically significant in any model, but there may be a subtle impact not detected. To capture a more age-diverse population, we originally attempted to survey new mothers about their choice of pediatrician, because these were also new relationships formed in a non-emergent setting, but—perhaps due to the demands of new motherhood—the response rate for this population was abnormally low (under 10%) and the data for new mothers had to be excluded.

Author Affiliations: From Medica Research Institute (CSC, JK, MS), Minneapolis, MN; University of Minnesota (CSC, JK), Minneapolis, MN.

Funding Source: This project was funded by an internal award from the Medica Research Institute.

Author Disclosures: The authors (CSC, JK, MS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JK, MS); acquisition of data (CSC, JK, MS); analysis and interpretation of data (CSC, JK, MS); drafting of the manuscript (CSC, JK, MS); critical revision of the manuscript for important intellectual content (CSC, MS); statistical analysis (CSC, MS); obtaining funding (JK); administrative, technical, or logistic support (MS); and supervision (CSC, JK).

Address correspondence to: Caroline S. Carlin, PhD, Research Investigator, Medica Research Institute, MR-CW295, PO Box 9310, Minneapolis, MN 55440. E-mail:
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