The American Journal of Managed Care June 2016
Variations in Patient Response to Tiered Physician Networks
Objectives: Prior studies found that tiered provider networks channel patients to preferred providers in certain contexts. This paper evaluates whether the effects of tiered physician networks vary for different types of patients.
Study Design: Cross-sectional analysis of fiscal year 2009 to 2010 administrative enrollment and claims data on nonelderly beneficiaries in Massachusetts Group Insurance Commission health plans.
Methods: Main outcome measures are physician market share among new patients and the percent of physician’s patients who switch away. We utilized estimated fixed effects linear regression models that were stratified by patient characteristics.
Results: Physicians with the worst tier rankings had lower market share among new patients who are older and sicker, or male, representing losses in market share of 10% and 15%, respectively, than other tiered physicians. A poor tier ranking did not affect physician market share of new patients who are female or younger. There was no effect of a physician’s tier ranking on the proportion of patients who switch to other doctors among any groups of patients.
Conclusions: Loyalty to their own physicians is pervasive across groups of patients. Physicians with poor tier rankings lost market share among new patients who are older and sicker, and among new male patients. Together, these findings suggest that tiered network designs have the potential for the greatest impact on value in healthcare over time, as more patients seek new relationships with physicians.
Am J Manag Care. 2016;22(6):420-425
- Patients’ loyalty to their own physicians is pervasive, as all patients were no more likely to switch away from worst-tiered physicians than any other physicians.
- Physicians with the worst tier rankings lost market share among new older, sicker patients.
- Tiered networks have promise as one of a set of mechanisms to increase the value of healthcare spending.
- Targeting these interventions at patients when they are choosing a new doctor may be more effective than strategies that interrupt existing care relationships.
Increases in patient out-of-pocket medical costs are known to decrease the amount of medical care that patients use,1 but the majority of evidence on patient response to changes in physician cost sharing is based on price changes that affect all physician services, not just a select, or tiered, group of physicians. Tiered network plans are relatively common in private health insurance—19% of all firms report offering a tiered network in the health plan with the largest enrollment2—but the evidence on the impact of tiered physician networks on patient choices is sparse. In previous work, I found that physicians with nonpreferred tier rankings and a $10- to $20-higher co-payment for an office visit earned a 12% lower market share of new patients than their top- or average-tiered colleagues, but were not more likely to have their existing patients switch away.3
Tiered networks can also focus on hospitals, and inter-tier cost-sharing differences are usually much larger. Scanlon et al found that workers in one union were more likely to select a preferred hospital for medical visits, which allowed them to avoid 5% coinsurance (equivalent to approximately $400 in out-of-pocket payments), while workers in a second union and all patients admitted for a surgical diagnosis were no more likely to choose within the preferred tier of hospitals.4 More recently, Frank et al evaluated the Blue Cross Blue Shield of Massachusetts tiered hospital network, where patients saved $900 out of pocket ($690) if they chose a top-tiered (middle-tiered) hospital relative to the worst-tiered hospitals, finding increased patient use of hospitals on the preferred and middle tiers relative to the nonpreferred tier.5
A related form of physician network design are “narrow networks” (also called “limited networks”) that exclude physicians with the worst performance on quality and cost-efficiency measures from the network, and provide little to no reimbursement to patients for healthcare services received from out-of-network providers. Following the narrowing of a PPO network in Las Vegas, Nevada, that increased patient out-of-pocket costs to see excluded physicians from a $10 co-payment to 40% of allowed charges, nearly 9 out of 10 patients of affected physicians switched away.6 However, sicker patients were observed to be more loyal to excluded physicians.
There is no evidence on whether the impact of tiered physician networks varies across patients, and the expected impact is uncertain. On one hand, older and sicker patients are likely to use more medical care and, thus, would have a greater net benefit from receiving care from better-tiered physicians. However, physician concerns about reliability of measurement (eg, if the measures used to evaluate performance do not adequately account for additional complexities and resources needed to treat sicker populations) and misclassification risk may make avoidance of higher-risk patients a potential unintended consequence of tiering.7 Information about response gradients across patients is also important if tiered networks are to achieve their greatest impact, as they will need to channel sicker patients—who are associated with the highest proportion of medical spending—to higher-value providers. In contrast, savings due to tiered networks will be less if only the youngest, healthiest patients respond. More evidence can also inform the implementation of tiered networks and the focus of marketing and outreach.
This paper analyzes data from 5 commercial health plans in Massachusetts to test whether the impact of tiered physician networks varies across different groups of patients. Specifically, I assessed whether tier ranking affects physician market share among new patients or the proportion of patients who switch to other physicians for male versus female patients, older versus younger patients, and sicker versus healthier patients.
The Massachusetts Group Insurance Commission (GIC) is a quasi-state agency that provides health benefits to over 400,000 state and municipal employees, as well as retirees and their dependents. In fiscal year (FY) 2009 and FY2010, a 3-tiered physician network was included in all GIC non-Medicare health plans, with approximately 20% of physicians in the top tier, 65% classified in the middle, and 15% assigned to the bottom (or worst-performing) tier at each plan. In FY2009, the range of office visit co-payments was from $10 to $15 for the preferred tier, $20 to $25 for the middle tier, and $25 to $35 for the worst-performing tier; co-payments increased by $5 to $10 at all levels in FY2010.
Tier rankings were assigned based on physician quality performance first, and efficiency performance second, using a common, cross-health plan database of performance profiles. Physicians with insufficient data were assigned to the middle tier. Tiered networks in all health plans included cardiologists, endocrinologists, gastroenterologists, obstetricians/gynecologists, orthopedists, and rheumatologists. Plans could choose to implement tiered networks for additional specialties, and several also tiered allergist/immunologists, dermatologists, general surgeons, internal medicine, neurologists, ophthalmologists, and otolaryngology (for additional detail on tiering methodology and specialties, see the eAppendix [available at www.ajmc.com]).
Administrative enrollment and claims data, for all non-Medicare individuals enrolled in 5 of the 6 GIC health plans over July 2006 to June 2010 (FY2007-FY2010), were obtained. Data included patient age and gender, diagnosis code, and the providing physician’s name, practice tax identifier, and tier ranking. Data also included a physician identifier variable—unique to each physician—and specialty designation for the providing physician on each claim. This was the same physician identifier used to construct the tiered physician networks by the plans, and allowed for reliable observation of unique physicians across claims data from different health plans. Claims where this identifier was unavailable were dropped (6% of claims).
The analysis sample consisted of physicians in a tiered network in FY2009 and FY2010; the sample included 15,401 physician-year observations, of which 6236 were included in multiple plans’ tiered networks. The main outcomes were the physician’s market share among new patients, stratified by patient characteristics, and the percent of a physician’s patients that switch to another doctor, also stratified by patient characteristics.
Following methods published previously, all office visits with tiered physicians were assessed for whether they were with a new physician or if the patient was a “potential switcher.”3 Patients who switched health plans during this period were excluded, because switches could be based (at least in part) on tiering (eg, to switch to a plan where one’s physician has a better tier ranking) and subsequently bias the results. A patient is defined as “new” if neither the patient nor any other person in the patient’s family had a visit within the same specialty in the previous 2 years. “Potential switchers” are patients who had at least 2 visits in prior years with a specific tiered physician and then had an office visit with any physician of the same specialty type in FY2009 or FY2010. Patients who saw a new, different doctor are classified as switchers. Patients who return to see the previous doctor are classified as non-switchers.
The effect of tiering on these outcomes was assessed within 6 subgroups of patients: 1) “older” patients, who were 46 years or older—the median patient age of patients of tiered physicians; 2) “younger” patients, who were younger than the median age; 3) “sicker” patients, who had a diagnosis in the study year or earlier in one of the 70 condition categories identified for risk adjustment in the Medicare CMS-Hierarchical Condition Categories (HCC) models8—this method for risk-adjustment was used because it focused on identifying individuals expected to have higher than average medical expenditures, and because it does not require information about the Diagnosis-Related Group assigned to hospitalizations, which was not available in our data; 4) “healthier” patients, who did not have any of these prior diagnoses; 5) males; and 6) females.
Each physician’s market share of new patients by plan and patient group was calculated (eg, Dr Smith’s share of new male patients in health plan X, Dr Smith’s share of new male patients in health plan Y) as the physician’s total number of new patients within a patient group and plan (eg, the number of Dr Smith’s new male patients enrolled in health plan X) divided by the total number of new patients in that group and enrolled in that plan (eg, the total number of new male patients enrolled in health plan X). The percent of a physician’s patients by plan and patient group who switched away was equal to the number of switchers among each group of patients and each plan (eg, the number Dr Smith’s male patients in plan X who switched away) divided by the number of potential switchers in each group and plan (eg, the number Dr Smith’s male patients in plan X who were potential switchers). To avoid misclassifying visits where a patient is seeking a second opinion as a switch, in sensitivity analyses a requirement was imposed that a switch entail a minimum of 2 visits with the new physician in a year.