Original Research

The Effect of Insurance Type on Patient Access to Ankle Fracture Care Under the Affordable Care Act

Publish date: September 13, 2018

References

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MATERIALS AND METHODS

The study population included board-certified orthopedic surgeons who belonged to the Orthopaedic Trauma Association (OTA) from 8 representative states; 4 states with expanded Medicaid eligibility (California, Massachusetts, New York, Ohio) and 4 states without expanded Medicaid eligibility (Florida, North Carolina, Georgia, Texas). These states were selected due to their ability to represent diverse healthcare marketplaces throughout the country. Using the OTA website’s “Find a Surgeon” search tool,¹⁶ we created a list of surgeons for each state and matched each surgeon with a random number. The list of surgeons was ordered according to the value of the surgeon’s associated random number, and surgeons were called in ascending order. We excluded disconnected or inaccurate numbers from the calling list. Surgeons who did not manage ankle fractures were removed from the dataset. Approximately 30 orthopedic trauma surgeons per state were contacted.

Each office was called to make an appointment for the caller’s mother. Every surgeon’s office was specifically asked if the surgeon would accept the patient to be evaluated for an ankle fracture that occurred out-of-state. The caller had a standardized protocol to limit intra- and inter-office variations (Appendix). The scenario involved a request to be evaluated for an unstable ankle fracture, with the patient having Medicaid, Medicare, or BlueCross insurance. The scenario required 3 separate calls to the same surgeon in order to obtain data regarding each insurance-type. The calls were separated by at least 1 week to avoid caller recognition by the surgeon’s office.

Appendix

Scenario

1. Date of Birth: Medicaid–2/07/55; BlueCross PPO–2/09/55; Medicare–7/31/45.

2. Ankle fracture evaluated by primary care physician 1 or 2 days ago

3. Not seen previously by your clinic or hospital, she would be a new patient

4. Asked how early she could be scheduled for an appointment

5. Script:

“I’m calling for my mother who injured her ankle a few days ago. Her family doctor took an X-ray and believes she has a fracture and needs surgery. Is Dr. X accepting new patients for evaluation and treatment of ankle fractures?” If YES →

“I was wondering if you take Medicaid/Medicare/BlueCross plan?” If YES →

“When is your soonest available appointment?”

The date of each phone call and date of appointment, if provided, were recorded. If the office did not give an appointment, we asked for reasons why. If an appointment was denied for a patient with Medicaid, we asked for a referral to another office that accepted Medicaid. We considered barriers to obtaining an initial appointment, such as requiring a referral from a primary care physician (PCP), as an unsuccessful attempt at making an appointment. We determined the waiting period for an appointment by calculating the time between the date of the call and the date of the appointment. Appointments were not scheduled to ensure that actual patients were not disadvantaged. For both appointment success rates and waiting periods, we stratified the data into 2 groups: states with expanded Medicaid eligibility (California, Massachusetts, New York, Ohio) and states without expanded Medicaid eligibility (Florida, North Carolina, Georgia, Texas).

We obtained Medicaid reimbursement rates for open reduction and internal fixation of an ankle fracture by querying each state’s reimbursement rate using Current Procedural Terminology code 27822.

Chi-square test or Fisher’s exact test was used to analyze acceptance rate differences based on the patient’s type of insurance. To compare the waiting periods for an appointment, we used an independent samples t-test after applying natural log-transformation, as the data was not normally distributed. We performed logistic regression analysis to detect whether reimbursement was a significant predictor of successfully making an appointment for patients, and a linear regression analysis was used to evaluate whether reimbursement predicted waiting periods. Unless otherwise stated, all statistical testing was performed two-tailed at an alpha-level of 0.05.

This study was approved by the Institutional Review Board of Yale University School of Medicine (HIC No. 1363).

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