With "big data" comes big responsibility: outreach to North Carolina Medicaid patients with 10 or more computed tomography scans in 12 months.

A review of the article by Biola et al., ‘With "big data" comes big responsibility: outreach to North Carolina Medicaid patients with 10 or more computed tomography scans in 12 months'

Introduction

Increased amounts of medical radiation exposure can lead to unintended health consequences and increased healthcare costs and according to the National Council on Radiation Protection and Measurement (NCRP), these rates have increased significantly since 1980, with most of the increase attributed to Computed tomography (CT) scans. Increased medial radiation exposure is the result of different providers conducting uncoordinated imaging on the same patient; this study evaluated the effectiveness of North Carolina Division of Medical Assistance (DMA)’s initiative to educate Medicaid recipients about the risks associated with increased radiation exposure. Big Data defined - can be a large data set with structure or without. The term is generically used to refer to databases consisting of a few hundred entries to several millions. However, there is not one clear definition of the actual size in which the term "Big Data" is actually used.

Methods

The prospective longitudinal study examined North Carolina Medicaid claims data and extracted recipient data that met the following conditions:

• Patients ages 18–64 years
• Patients in the North Carolina Medicaid pro¬gram during at least part of calendar year 2010
• Patients who had claims for 10 or more CT scans between January 1, 2010, and December 31, 2010
• Patients who did not have a cancer diagnosis

These provisions resulted in 2,646 Medicaid patients being identified. After additional exclusions were made, the study group was narrowed down to 565 patients that either received an educational letter (if they received 10+ CT scans in 2010) or a phone call (if they received 20+ CT scans in 2010).

Results

On July 1, 2011, a letter was mailed to 565 patients that included the following information:

• The number of CT scans he or she received in 2010
• Outlined the risks and benefits of CT scans
• Suggested that he or she contact a primary care provider

The study identified a drop in CT claims in late 2010, however, this drop was universal and was not attributed to the education letters mailed by the study. In addition to letters, 20 of 55 patients with 20+ CT scans in 2010 received a phone call in addition to the letter and had a conversation with one of the study physicians about the risks and benefits of their individual CT utilization. The study found that of these 20 patients, 85.0% had a lower number of CT scans in the year after the phone call, compared with 76.6% of the patients who did not speak with a physician on the phone.

Discussion

The goal of this study was to evaluate whether patient education could decrease subsequent radiation exposure. The authors believed the study was both successful and unsuccessful – successful in that the DMA was able to work with Community Care of North Carolina (CCNC) to gather and use “big data” to educate patients about the risks and benefits of radiation exposure; the study was unsuccessful in that the mailed education letters and follow-up phone calls did not result in decreased radiation exposure and the authors conclude that an educational effort with the providers would probably have been more effective.

Conclusion

While the goal to educate Medicaid recipients and have that education result in lowered radiation exposure was not accomplished, the authors did show that a statewide effort to gather and use Medicaid healthcare data to implement a patient safety and educational outreach program could be accomplished. The authors believe that “big data” can be used in many ways and policymakers should support these efforts.

Comments

I chose this article because it examined Medicaid data and used those results to try an implement a statewide educational effort to patient behaviors that they believed would have resulted in changed patient behaviors. Although the effort was unsuccessful, it was interesting how the authors showed how “big data” can be used in the promotion of healthcare quality and improved healthcare practices.