Diabetes
Sample PBM Hemoglobin A1c Initiative: Medivo Program Protocols
Client: Sample PBM
Test: Hemoglobin A1c
Dates: Data is valid from 8/1/2009-5/1/2010
PBM contracted with the home testing subsidiary of a major U.S. laboratory and Medivo to conduct a Hemoglobin A1c test for a targeted list of its members. PBM used a home test kit specimen collection method, with state by state physician oversight provided using Medivo’s online platform and technology to facilitate lab test review/approval/results by Medivo’s credentialed and contracted physician network.
PROCESS
- Sample PBM identifies eligible patients to participate in the program. Criteria for participation is based on patients’ Rx records including at least one prescription for a diabetes medication.
- PBM provides the lab with a file of Eligible Participants. The lab sends the list via a secure data connection to Medivo, whose physicians review and approve the requests for HgA1c tests.
- The lab prints out each authorization form, inserts it in a Lab Home Test Kit, and mails the eligible patient the kits.
- Patient receives the Lab Test Kit, obtains their sample and returns the Lab Test Kit with a signed authorization/consent form. Similarly, the patient may receive the kit and opt to not complete the kit.
- The lab receives, validates, and processes the Lab Test Kit sample. The Lab enters the results into the vCare platform where the results are reviewed by the Medivo physician network.
- The physicians release the results per approved protocols.
- The lab test results are sent to the PBM which then provides copies of the report to the patient within the context of education and awareness materials to improve diabetes care.
- PBM Staff, per Medivo protocols, calls and provides counseling for patients whose test results are above identified ranges.
- PBM provides Medivo with this counseling report. The report lists the patients with their tests results above the identified range who were contacted or attempted to be contacted.
- All result communications are documented in the vCare database for regulatory, compliance, and audit purposes.
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Summary Findings:
At the conclusion of a successful testing program, we found the following data features, which are highlighted below:
- When stratified by age and risk category (Table 1), male and female results showed no distinct trend as risk category increased. Female participant results were approx. 7% higher in the at-risk category than males, while male results were approximately 5% higher for the diabetes category
- However, when stratification increases to accommodate age category (Tables 2 & 3), noticeable and expected patterns emerge. Given the larger participant pool in the middle age categories (ages 30-70), we see fairly reliable trending toward increased risk with each advancing age category. Male participants in the 30-70 age range demonstrated systematically declining results in the normal category as well as increased results in the risk categories (both in at-risk and diabetic) with age. Female participants did not demonstrate upward trending of risk by age, and in fact, showed fairly even results in the risk categories (implying that age was not a marked determinant for risk within the participant pool)
- When sorted by state and sex (Table 4), DE, SD, MN, NM, WA, TX, OR, VT, WY and VA had the highest percentage of diabetic results overall. Accompanying the total results, male participant data also indicated higher than average diabetic results in these same states.
- When risk was finally sorted at the deepest level by state, age, and sex (Charts 1, 2, 3 and Table 4), geography did not appear to be a strong determinant of risk, as much as age. When categorized together and sorted for the highest combined results from the at-risk and diabetic groups, all highest risk participants were in the 50-70 age category, with the predominance between 50-60 years. However, there was variation of less than 2% in highest risk results between the highest and lowest state, age and sex grouping, implying that there were no clear high risk groups in the participant pool.