Adherence to lab requests by patients with diabetes: The Diabetes Study of Northern California (DISTANCE)

Am J Manag Care. Author manuscript; available in PMC 2011 October 10.

Published in final edited form as:

Am J Manag Care. 2011 May; 17(5): 339–344.

PMCID: PMC3189790

NIHMSID: NIHMS326595

Adherence to lab requests by patients with diabetes: The Diabetes Study of Northern California (DISTANCE)

Howard H. Moffet, MPH, Melissa M. Parker, MS, Urmimala Sarkar, MD, MPH, Dean Schillinger, MD, Alicia Fernandez, MD, Nancy Adler, PhD, Alyce S. Adams, PhD, and Andrew J. Karter, PhD

Howard H. Moffet, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612 510-891-5902, fax: 510-891-2836;

Contributor Information.

Corresponding author.

Howard H. Moffet: Howard.H.Moffet/at/kp.org; Melissa M. Parker: Melissa.parker/at/kp.org; Urmimala Sarkar: usarkar/at/medsfgh.ucsf.edu; Dean Schillinger: dschillinger/at/medsfgh.ucsf.edu; Alicia Fernandez: afernandez/at/medsfgh.ucsf.edu; Nancy Adler: Nancy.Adler/at/UCSF.edu; Alyce S. Adams: Alyce.S.Adams/at/kp.org; Andrew J. Karter: andy.j.karter/at/kp.org

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Abstract

Objective

We sought to estimate rates and predictors of completion of clinical laboratory tests by diabetes patients subsequent to provider referrals.

Study design

Prospective cohort study

Methods

Among 186,306 adult members with diabetes in Kaiser Permanente Northern California, we searched the electronic medical records (7/1/2008 – 6/30/2009) of each patient for the first outpatient order for lab tests commonly used to measure risk factor control or adverse effects of pharmacotherapy: glycosylated hemoglobin (A1c), low-density lipoprotein (LDL), serum creatinine (SC), urinary albumin (UA) or creatine kinase (CK) (CK only among persons using statins). We measured lab attendance as completion of the order within 6 months of the order date, counting the days to result, and looked for variations by subgroups.

Results

Lab attendance ranged from 86% for A1c to 73% for SC. Time to attendance had a median of 7–10 days and mean of 25–30 days. Attendance was more likely among women, older patients or for orders subsequent to a face-to-face provider visit and less likely if ordered by a pharmacist, but most variations, even by copayment, were small or not clinically substantive. In subanalyses, we observed no clinically significant variations by race, socioeconomic status, trust in provider or patient-provider communication, and no association with depression, health literacy or English fluency.

Conclusions

The fact that one in seven patients did not complete labwork within six months of the provider referral may help explain why healthcare services appear to fall short of optimal diabetes care.

Keywords: diabetes mellitus, laboratory techniques and procedures, electronic health record, adherence, HbA1c

Clinical laboratory testing of blood and urine provide essential, objective evidence for making clinical decisions in diabetes care management.¹ Testing is important not only for routine monitoring, but to determine the need for and consequences of treatment intensification, which may be needed to achieve clinical goals^2,3 or to detect adverse drug reactions in order to prevent clinical harm.⁴

Routine tests of glycosolated hemoglobin (A1c), low-density lipoprotein (LDL), serum creatinine (SC) and urinary albumin (UA) are recommended for patients with diabetes.⁵ Rates of testing in diabetes care are routinely reported in journals⁶ or published by health plans as quality measures (e.g. HEDIS), with variations and disparities noted. For example, Schmittdiel, et. al., found that measurement of A1c and LDL within 6 months after treatment intensification occurred less than half the time.⁷ Racial disparities in A1c testing have also been reported in population-based studies,^8,9 although differences are greatly attenuated or absent in managed care settings with fully insured patients.¹⁰ Non-adherence to laboratory requests could contribute to higher rates of poor diabetes control among vulnerable patient populations. However, it is unknown whether disparities in rates of testing are attributable to variations in ordering by providers or to lab attendance by patients after a test is ordered.

This paper examines adherence to clinical laboratory requests. We calculated rates of laboratory attendance by patients with diabetes subsequent to provider referrals for lab tests in an electronic medical record (EMR) system. We hypothesized that a small segment of the patient population fails to fulfill lab orders and that non-fulfillment would not vary by test. We further hypothesized that vulnerable groups (e.g., minority racial/ethnic groups or those with less education, lower income, limited English proficiency, or inadequate health literacy) would be at highest risk of non-utilization due to latent barriers (e.g., lack of transportation, inadequate understanding of the clinical importance) as well as those with low trust in physician or experiencing poor provider-patient communication.

Methods

This study was conducted among patients in the Diabetes Registry at Kaiser Permanente Northern California (“Kaiser”). We identified a cohort of 186,306 adult diabetes patients with continuous health plan membership during the study period of July 1, 2008 – December 31, 2009. At Kaiser, electronic order entry for lab tests (a component of the EMR) facilitates an analysis of patient adherence to lab orders by tracking separately a) the provider’s order to the lab for each test and b) the patient’s adherence to the provider’s request to attend the lab and provide specimen(s). Kaiser is a closed system, lab orders are not transferred outside of Kaiser and paper lab slips are no longer used, so we were able to accurately capture the outcome for 100% of the cohort. Patients merely go to at any Kaiser laboratory, where the lab order awaits and the ordered test will be conducted on the specimen(s) provided.

In this cohort, we searched the electronic medical records of each patient for the first occurrence during July 1, 2008, to June 30, 2009, of an outpatient lab order for the tests of interest: glycosolated hemoglobin (A1c), low-densitiy lipoprotein (LDL), serum creatinine (SC) or urinary albumin (UA). In order to contrast “routine” diabetes tests with a test that might be more urgent, we also tracked creatine kinase (CK) among patients using a statin (dispensed within the previous six months); this test is often ordered to detect an asymptomatic medication side effect or, more commonly in this clinical environment, to diagnose a possible adverse reaction among symptomatic patients.

We used individual lab orders as well as lab panel orders (e.g., diabetes panel, lipid panel) to capture orders for each test. We excluded standing orders (estimated to be about 5% of A1c test cohort) which cannot be easily ascertained as they do not appear as new orders in the system; we excluded any order with an “expected completion date” (estimated to be 9% of A1c test cohort) for which the provider has entered a future date for order completion and presumably has requested the patient to attend the lab around that future date, that is, not immediately. Thus, all included orders were eligible for immediate fulfillment. Each patient could be counted in more than one lab test cohort.

Our outcome of interest was patient attendance at a Kaiser lab within six months for each identified test order. To measure attendance, we identified the first test result subsequent to a given order and used the date that the specimen (either blood or urine) was collected as the attendance date. We censored follow-up at 6 months after the order date, even though some orders may be valid for up to a year; although we don’t know when the provider intended the patient to attend the lab, we assumed that six month was a generous window for completion. Thus, attendance was affirmative if a result was entered within six months. We calculated lab attendance as a percentage of lab orders that were completed and counted the number of days between order and attendance.

We evaluated differences by sex, age, type of provider and visit (face-to-face versus other) and lab copayment. In sub-analyses of patients who had participated in the 2005–2006 Diabetes Study of Northern California (DISTANCE) Survey (n=20,188),¹¹ we further evaluated differences by race, income, education, employment, depressive symptoms,¹² health literacy,¹³ English fluency, trust in provider¹⁴ and provider’s communication quality (patient reports that provider explains things in a way patient can understand).¹⁵

This study was approved by the Institutional Review Board of the Kaiser Foundation Research Institute.

Results

Lab attendance ranged from (highest to lowest) 86.2% for A1c (number of tests, N = 138,017) to 73.4% for SC (N=139,962) (Table 1). Time to attendance had a median of 7–11 days and mean of 25–30 days. The attendance for CK, which we assumed might have some urgency, had a pattern identical to A1c. Survival curves of open lab orders for each test were largely indistinguishable (not shown).

Table 1

Patterns of laboratory test attendance (7/1/2008 – 6/30/2009) and differences in attendance rates by characteristics among 186,306 patients. with diabetes and the subcohort of 20,188 patients who completed the Diabetes Study of Northern California (more ...)

In unadjusted analyses, women were slightly more likely to attend, but the male-female differences were minimal (differences were under 1.6% for any test) (p<0.05). Older patients (≥65 years) were more likely to attend than younger patients (age 19–34 years) (p<0.001); the rate difference was 12–17%. Orders resulting from face-to-face visits were more likely to result in attendance (p<0.05), though differences were small (6–13%). Orders entered by a pharmacist were less likely to be completed (1–7% lower). There was no evidence that copayments were a significant barrier.

In subanalyses among patients who completed the DISTANCE Survey, none of the test completion rates differed substantively across any of the factors we considered (<7% difference for any test), although some differences reached statistical significance (for example, variations in LDL rates were statistically significant with the lowest rates among Caucasians). Some of the social patterns were not as predicted (e.g., more education, higher income or being employed had some association with lower attendance), but none of the differences were large enough to be considered clinically substantive. We observed no statistically significant differences in lab attendance by depressive symptoms, health literacy or English fluency.

Discussion

This is the first large study to use EMR-based lab orders to estimate rates of lab attendance by patients given a provider referral. A significant proportion of patients (14–27%) did not get to the lab within six months and the time to attendance had little variation by test, even comparing routine A1c to a potentially urgent CK. We observed no substantive social disparities in attendance. While we cannot generalize these results observed in a managed care setting to other populations, our findings do suggest that any disparities in rates of clinical testing may be attributable to variations in referrals, not patient adherence. Much has been made of the concept of “clinical inertia” – the finding that medication changes are not made in a prompt manner – but delays in patients’ presenting for laboratory testing represent one more barrier to timely care. Clinical action cannot be taken on lab tests that have not been completed.

We did observe that older patients attended the lab at higher rates than younger patients, perhaps in part because older patients have more appointments (perhaps because of advanced disease) and thus more opportunities to attend the lab while they are at the medical center or fewer competing demands (especially among those not employed). Patients were more likely to attend the lab if the order was entered during a face-to-face visit, suggesting that the provider may have engaged in shared decision making or emphasized the importance of the lab test. These findings imply that PCPs play an important role in increasing patient adherence to lab testing for chronic disease care.

Our hypothesis that the results would not vary by test was largely correct. However, other hypotheses regarding differences by socioeconomic status were not supported; differences were not substantive and often related to attendance contrary to our prediction (i.e., higher SES was associated with lower lab attendance). Moreover, we observed no substantive or statistically significant differences by depression, health literacy or English fluency.

Kaiser reported annual testing rates in 2009 for commercial and Medicare members for A1c at 91.4% and 95.1%, respectively, and LDL-C rates of 89.2% and 94.5%, respectively. This indicates that a higher proportion of patients in this health plan do get to the lab within a year. It is unclear how providers handle the lapse in data for those who fail to attend the lab. Of greater concern are those patients who repeatedly fail to complete requested labs. In the Translating Research Into Action for Diabetes (TRIAD), Gregg et al reported that 11.6% of patients had persistent gaps (≥3 years) for lipid testing; the rate was 4.2% for A1c.¹⁶ Consistent with our findings, ethnicity, education, trust in provider and depression were not associated with persistent gaps in labs; however, income was associated.

Limitations

This study has some limitations. First, not all lab orders are accounted for as we excluded standing orders and orders with expected dates; thus, we cannot make any conclusions about rates of ordering lab tests, only rates of completion for specific lab tests. Second, when a patient goes to the lab, all open orders should be completed. However, we don’t know which test, if any, motivated the patient to attend or to what extent the patient is actually aware of outstanding orders. With electronic ordering, there is no paper lab order for the patient to carry as a reminder (or to lose). Thus, we suspect that patients may go to the lab when they know there is one or more open orders, but not necessarily to fulfill an order for a specific test. We do not know what instructions, if any, the provider may have given the patient about when to go to the lab, so we cannot make any conclusions about how quickly the patients attended. We do not know if patients in this study completed the ordered lab work after the 6 month observation window, and thus a portion of labs considered not complete could have been later completed, albeit in an untimely fashion. In subanalyses among patients contacted for the DISTANCE Survey, non-response bias may be present, but the overall lack of variations minimizes this concern.

Conclusions

Most patients with diabetes attended the lab for commonly ordered tests within 6 months of a provider order. The fact that one in seven patients did not complete labwork within six months of the provider referral may help explain why healthcare services appear to fall short of optimal diabetes care. Understanding the frequency with which lab orders are fulfilled, by whom and why, would facilitate interventions and quality improvement efforts to maximize the effectiveness of these referrals; these results suggest new avenues for innovative research focused on overcoming barriers to optimal diabetes care. Further research is needed to understand the clinical implications of uncompleted labwork.

Take away points

One in seven patients in this diabetes registry cohort did not complete labwork within six months of the provider requesting it; this may help explain why healthcare services appear to fall short of optimal diabetes care.
The median time from referral to lab attendance was at least one week and the mean was nearly a month.
No substantive race or other social disparities in attendance were observed; this suggests that disparities in testing which have been observed in population-based studies may be attributable to variations in referral rather than patient adherence.

Acknowledgments

Funding source: This work was supported by the National Institute of Diabetes, Digestive and Kidney Diseases [Grant numbers RC1 DK086178, R01DK080726, R01DK65664]. The Diabetes Research and Training Center was also supported by the National Institute of Health [P60 DK20595].

Contributor Information

Howard H. Moffet, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612 510-891-5902, fax: 510-891-2836.

Melissa M. Parker, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612 510-891-5966, fax: 510-891-2836.

Urmimala Sarkar, UCSF Division of General Internal Medicine, Center for Vulnerable Populations, San Francisco General Hospital. 415 206-4273, fox 415 206-5586.

Dean Schillinger, UCSF Division of General Internal Medicine, Center for Vulnerable Populations, San Francisco General Hospital. 415 206-8940, fax 415 206-5586.

Alicia Fernandez, University of California, San Francisco, Division of General Internal Medicine, San Francisco General Hospital, Ward 13, Building 10, 1001 Potrero Ave, San Francisco, CA 94110, phone (415) 206-5394, fax (415) 206-5586.

Nancy Adler, Departments of Psychiatry and Pediatrics and Center for Health and Community, University of California San Francisco, 3333 California Street, Suite 465, San Francisco, CA 94118, 415-476-7759.

Alyce S. Adams, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, 510-891-5921.

Andrew J. Karter, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612; 206-855-9551; FAX 206-855-9550.

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