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The American Journal of Managed Care July 2009
Adherence to Osteoporosis Medications After Patient and Physician Brief Education: Post Hoc Analysis of a Randomized Controlled Trial
Aimee Der-Huey Shu, MD; Margaret R. Stedman, MPH; Jennifer M. Polinski, MPH, MS; Saira A. Jan, MS, PharmD; Minal Patel, MD, MPH; Colleen Truppo, RN, MBA; Laura Breiner, RN, BSN; Ya-ting Chen, PhD; Thomas W. Weiss, DrPH; and Daniel H. Solomon, MD, MPH
Lipid Profile Changes Associated With Changing Available Formulary Statins: Removing Higher Potency Agents
Daniel S. Longyhore, PharmD; Casey McNulty Stockton, PharmD; and Marie Roke Thomas, PhD
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Automated Messaging to Improve Compliance With Diabetes Test Monitoring
Stephen F. Derose, MD, MS; Randall K. Nakahiro, PharmD; and Frederick H. Ziel, MD
Medicaid Beneficiaries With Congestive Heart Failure: Association of Medication Adherence With Healthcare Use and Costs
Dominick Esposito, PhD; Ann D. Bagchi, PhD; James M. Verdier, JD; Deo S. Bencio, BS; and Myoung S. Kim, PhD
Medication Adherence and Use of Generic Drug Therapies
Becky A. Briesacher, PhD; Susan E. Andrade, ScD; Hassan Fouayzi, MS; and K. Arnold Chan, MD
A Multiattribute Decision Model for Bipolar Disorder: Identification of Preferred Mood-Stabilizing Medications
Brandon T. Suehs, PharmD; and Tawny L. Bettinger, PharmD, BCPP
Impact of Workplace Health Services on Adherence to Chronic Medications
Bruce W. Sherman, MD; Sharon Glave Frazee, PhD; Raymond J. Fabius, MD, CPE; Rochelle A. Broome, MD; James R. Manfred, RPh; and Jeffery C. Davis, MBA

Automated Messaging to Improve Compliance With Diabetes Test Monitoring

Stephen F. Derose, MD, MS; Randall K. Nakahiro, PharmD; and Frederick H. Ziel, MD

A randomized controlled trial was conducted to compare the effectiveness of automated telephone and mail outreach to prompt compliance with periodic diabetes laboratory testing.

Objectives: To evaluate the use of automated systems to prompt patients with diabetes mellitus to obtain overdue laboratory tests for its effectiveness in promoting test compliance and to compare letters, telephone messages, and combinations.

Study Design: Randomized controlled trial.

Methods: All subjects (N = 13,057) were adult members of Southern California Kaiser Permanente with diabetes and with no record of glycosylated hemoglobin, low-density lipoprotein cholesterol, and urinary microalbumin tests in more than 1 year. The effectiveness of automated telephone calls and letters was compared versus a no-contact control group using the following 5 intervention groups: letter, call, letter that is followed by a call 4 weeks later, call that is followed by a letter 4 weeks later, and letter-callletter
combination. Messages were in English and in Spanish. Adherence to all testing was compared at 8 weeks and 12 weeks after initial contact using X2 test and logistic regression analysis.

Results: The proportions of each study group compliant with all tests were 18% to 19% among controls, 21% for a letter or a call, 25% for a lettercall or call-letter, and 26% for a letter-call-letter; letter-call and call-letter were significantly different versus controls (P <.001), and letter-call-letter was not significantly different versus letter-call. Older age was associated with compliance (P <.001).

Conclusions: The pairing of automated letters and telephone calls in any order was more effective than any single intervention in promoting compliance with diabetes monitoring tests. The relative cost of the letter-call and call-letter approaches to outreach should be considered to determine which is preferred in any given situation.

(Am J Manag Care. 2009;15(7):425-431)

Chronic disease care often requires periodic monitoring of disease status. Noncompliance with monitoring can lead to less optimal control of risk factors.

  • A randomized trial was conducted to compare the use of automated telephone calls and mailed letters versus a no-contact control group among 13,057 patients with diabetes mellitus who had not had routine diabetes monitoring tests in more than 1 year.
  • The combination of a letter and a telephone call in either order increased compliance with laboratory testing by 6% to 7%, a 50% increase over controls.
  • Automated telephone calls and letters can be effective in changing the behavior of noncompliant patients with diabetes.
About one-third of all persons in the United States older than 65 years have 3 or more chronic conditions,1 and 23% of persons older than 60 years have diabetes mellitus.2 Chronic disease care involves periodic monitoring of disease status and risk factors. In diabetes care, periodic (usually semiannual) measurement of blood glucose level control, lipid levels, and urinary protein excretion is used to adjust therapy and to change healthcare behavior to reduce morbidity and mortality.3 The National Committee for Quality Assurance and other agencies often include annual disease-monitoring tests among measures of quality and performance.4

Compliance with disease-monitoring tests traditionally requires periodic patient visits. Outreach to noncompliant patients can be time-consuming if healthcare providers must maintain a list of patients with chronic disease, track those who are noncompliant, and attempt contact themselves. Automated outreach to patients can alleviate some of the burden on providers and bypass the need for an office visit. Although automated systems admittedly are less personal, they can be reliable and may be more cost-effective overall in reaching a portion of noncompliant patients.

The most common forms of outreach are by mail and telephone, and these are often effective in certain settings such as childhood immunizations.5 There is less information available on the combination of mail and telephone outreach, especially in chronic disease. The combined use of mail and telephone outreach is promising because these interventions can complement one another through different means of communication. We evaluated the effectiveness of an automated outreach system to prompt patients with diabetes to have recommended monitoring tests. Response rates to mailed letters and telephone messages used singly and in combination were compared to determine the most effective method of outreach.


This randomized controlled trial evaluated 5 automated messaging strategies to prompt compliance with diabetes laboratory monitoring tests. The study was staggered over 2 consecutive 3-month periods, referred to as study phases, to coincide with quarterly outreach efforts and to maximize sample size. Compliance rates were compared between intervention groups and the no-contact control group. The Southern California Kaiser Permanente Institutional Review Board approved the study. Informed consent was waived. All analyses were performed using SAS 9.1 (SAS Institute, Cary, NC) and STATA 9.2 (StataCorp LP, College Station, TX).


Study subjects were all members of Southern California Kaiser Permanente (referred to as the health plan), an integrated healthcare system that provides comprehensive care to 3.1 million members at 12 medical centers and at hundreds of satellite clinics throughout southern California. All members have similar coverage benefits and copayments for healthcare services, including office visits and laboratory tests. About 93% of members have a pharmacy benefit.

All adult members of the health plan with diabetes were potentially eligible for the study. Members with diabetes were identified by a diabetes case identification database that has been used by the health plan since 1999 for individual patient care and for population outreach. Cases were identified by a combination of the following: (1) International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes (250.x, 357.2, 362.0, 366.41, or 648.0), (2) glycosylated hemoglobin (A1C) laboratory test result exceeding 7.5%, or (3) dispensation of oral hypoglycemics or insulin. Women with gestational diabetes were excluded. Data on lipid levels, urinary microalbumin levels, A1C levels, and other test results were updated daily. Health plan enrollment and death data were updated monthly and weekly, respectively, to remove nonactive members. Similar case identification criteria were evaluated in 1997 by comparison with a diabetes clinic electronic record developed at a health plan medical center: the sensitivity was 93%, and the positive predictive value was 95%.6

Health plan members with diabetes were passively enrolled if they met the following criteria: (1) age older than 18 years; (2) no A1C, low-density lipoprotein cholesterol (LDL-C), and urinary microalbumin tests in more than 365 days; and (3) a birthday within the next 3 months. Members are routinely targeted for preventive medicine outreach close to their birthday, as the experience of the health plan suggests improved response near birthdays, and annual outreach efforts can be organized around these dates.


The goal of the intervention was to increase diabetes laboratory monitoring in a resistant population. Outreach could occur by telephone or by mail using a fully automated system already in use. Components of the outreach system included frequently updated administrative databases containing members’ contact information, the diabetes case identification database, the laboratory test result database, an algorithm to determine when a member needed laboratory tests, a telephone messaging and mail generating system, and a team to prepare and deliver the outreach.

Telephone calls began with a standard greeting saying that the message to follow was from Kaiser Permanente. The message was in English and informed the recipient to call a toll-free number to receive a message from his or her health plan. Members who called in used an interactive menu to select English or Spanish and retrieved the message by inputting their medical record number. The message was just over 100 words and about 40 seconds in length. The member was informed that he or she may have diabetes and was due for laboratory tests that had already been ordered. The tests were named, and the member was directed to go to his or her local health plan laboratory for the tests. A busy signal resulted in up to 2 more attempts to make telephone contact on subsequent days. Telephone calls were made between the hours of 10 am and 8 pm. Ninety-five percent of all health plan members have a telephone number on record. The message scripts may be viewed in eAppendix 1 and eAppendix 2 available at

Mailed letters on Kaiser Permanente letterhead were personally addressed, informed the member that health plan records indicated that he or she may have diabetes, provided a number to call if that was in error, stated the importance of monitoring tests, and directed the addressee to obtain these tests at his or her local health plan laboratory. The text of the letter occupied the top half of the page, and a “laboratory slip” for the needed tests was placed at the bottom of the page. One side of the letter was in English, and the other side was in Spanish. Locations and hours of local laboratories were provided, and the letter was electronically signed by the nurse lead of the local medical center diabetes care management program. Almost 100% of all health plan members had an address on record. A copy of the letter may be viewed in eAppendix 3 (available at

Telephone calls and mailed letters were used alone and in combination among the following 5 intervention groups: (1) letter once only (letter), (2) telephone call once only (call), (3) letter that is followed by a telephone call at 4 weeks for nonresponse (letter-call), (4) telephone call that is followed by a letter at 4 weeks for nonresponse (call-letter), and (5) letter that is followed by a telephone call at 4 weeks for nonresponse, followed by a second letter at 8 weeks for continued nonresponse (letter-call-letter). The letter-call-letter combination was chosen as the most extensive outreach in the belief that the response would be best among all possible triplet combinations. The control group received no contact. Standard outreach at the time was by letter, but because the effectiveness was unknown, permission was obtained to delay standard contact for 4 months in the control group. Subjects who remained noncompliant after the first phase of the study were not entered into the second phase. Laboratory test orders were placed automatically into a regionwide ordering system; each order was active for 90 days. All control group and study group subjects had orders placed at the beginning of the outreach effort; control group subjects had no information that an order had been placed for them.


The primary outcome, termed compliance, was completion of all 3 laboratory tests (A1C, LDL-C, and urinary microalbumin) by 12 weeks after the date of the first attempt at subject contact. The 12-week mark was chosen because it was 4 weeks after the last letter in the letter-call-letter intervention, allowing for comparison across all intervention arms. Additional outcomes included compliance at 8 weeks for all but the letter-call-letter group. All test results and dates were available electronically from a centralized laboratory reporting system. If a subject died or disenrolled before the end of follow-up, the subject was maintained in his or her group.


Just under 2 weeks before the intervention start date, which corresponded with the health plan’s quarterly-batch outreach effort, eligible health plan members were randomized into the study groups. The results of randomization were communicated back to the health plan’s outreach team to alter the usual automated outreach process, which already used telephone calls and letters for other programs. Randomization into study arms in the proportions described herein was computer generated.

Sample Size

In phase 1, interventions starting with a mailed letter (letter, letter-call, letter-call-letter) were compared with controls, and in phase 2, interventions starting with a telephone call (call, call-letter) were compared with controls. Preliminary data suggested a laboratory test compliance rate of 40% for a letter that is followed by a call. We sought to have sufficient power to detect a difference to 35% (letter group) and to 45% (letter-call-letter group). Using a significance level of P >.05 and 80% power, a 2-sided test of proportions and equal-sized groups required 1574 subjects per group. The control group response rate was estimated to be 20%. The control group was set at approximately one-fifth of the anticipated intervention group size (n = 325). When the time came for the phase 1 population outreach, there were more eligible patients than anticipated, and these were randomly divided into the intervention arms until power was close to 90%. In phase 2, we aimed for 90% power to detect a difference between 35% (call group) and 40% (call-letter group), which required 2008 subjects per group. In phase 2, the control group was set at 70% of the intervention group size to detect a 5% difference in compliance, from 30% to 35%. The control group was increased in size for phase 2 in response to a smaller-than-expected difference between the control and letter groups in phase 1.

Statistical Analysis

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