Use of Health Information Technology to Improve Medication Adherence

Abstract

Objective

To evaluate the effectiveness of a health information technology (HIT)-based adherence intervention using speech recognition software to promote adherence to inhaled corticosteroids (ICS) among individuals with asthma who are members of a large health maintenance organization.

Study Design

Pragmatic randomized clinical trial.

Methods

Adults with asthma enrolled in a large managed care organization (N=8517) were randomized to receive either usual care (UC) or an interactive voice recognition (IVR) intervention designed to prompt medication refills and improve ICS adherence. The primary outcome was ICS adherence as measured by modified medication possession ratio (mMPR) calculated from the electronic medical record (EMR). Secondary measures included survey and EMR-based measures of asthma morbidity.

Results

Our primary analyses found that ICS adherence increased modestly but significantly for participants in the intervention group relative to those in usual care group (Δ=0.02, 95% confidence interval=0.01, 0.03), with a baseline adherence of 0.42 in both groups. No difference was observed in asthma morbidity measures. In post hoc analyses of participants receiving 2 or more direct IVR contacts or detailed messages the intervention effect was more marked. The overall effect was triple that observed in the primary analyses (0.06 vs. 0.02) and significant differences were observed between groups in asthma control.

Conclusions

An HIT-based adherence intervention shows potential for supporting medication adherence in chronic diseases such as asthma. However, additional research is needed to determine how best to enhance the reach and effectiveness of such interventions.

Introduction

Despite the proven efficacy of anti-inflammatory therapy in the management of asthma, patient nonadherence is common.^1,2,3,4 The clinical implications of this non-adherence include treatment failure; unnecessary and dangerous intensification of therapy; and excess health care costs, hospitalizations, and deaths.⁵

Relatively few studies have examined strategies to improve adherence with respiratory medications.^6,7,8,9 A review of primarily adult-focused adherence interventions stressed the need for innovative approaches to assist patients in following chronic medication regimens,¹⁰ while others have called for strategies that leverage health information technologies (HIT) to promote and sustain medication adherence.¹¹

Interactive Voice Recognition (IVR) technology has been widely used to deliver automated health education via telephone and to remind patients about appointments or health screening activities.^12,13,14 Such applications have shown evidence of significant impact on both behavioral and clinical outcomes.^15,16 The use of speech recognition software can further enhance the acceptance and effectiveness of this form of telephone-based interaction.^17,18 Linking IVR applications with electronic medical records (EMR) offers additional opportunities to provide personalized adherence messages triggered by a patient’s own refill patterns. A low-cost, HIT-based adherence intervention, if successful, would have immediate application for improving chronic disease management across a wide range of medical conditions.

We report the main results of a randomized clinical trial designed to test the effectiveness of an HIT-based intervention using speech recognition software to promote adherence to inhaled corticosteroids (ICS) among adults with asthma.

Methods

Study design

We conducted a pragmatic clinical trial¹⁹ among patients receiving care in a routine clinical setting in which 8517 adults with asthma were randomized to receive either usual care (UC) or an IVR intervention designed to improve ICS adherence. The study was approved by the Institutional Review Boards of each participating institution.

Research setting

Kaiser Permanente (KP) is a group-model health maintenance organization that provides comprehensive, prepaid health care service to about 450,000 members of the Northwest region (KPNW) and 230,000 members of the Hawai’i region (KPH). KPNW serves a population that is largely Caucasian (≈91%), while the KPH population includes about 27% Caucasians, 33% Asians, 12% native Hawaiians or Pacific Islanders, and about 24% of mixed heritage. Both KPNW and KPH utilize an EMR that includes pharmacy dispensings.

Study population

The target patient population consisted of adult KPNW and KPH members with asthma who met the eligibility criteria in Table 1. To assure maximum generalizability, we did not exclude individuals with co-morbid physical or mental health conditions.

Table 1.

Eligibility Criteria

Inclusion Criteria

Treatment for asthma during the 12-month period prior to randomization. ≥ 1 dispensing of a respiratory medication [corresponding to Generic Product Identifier (GPI) class 44 (anti-asthma drugs, including inhaled steroids, leukotriene antagonists, beta2-agonists, and ipratopium bromide)] at a KPNW or KPH outpatient pharmacy during the 12-month period prior to randomization. Aged 18 years and older as of the time of randomization. Continuous KP membership from the start of the baseline year until the time of randomization. Willing to participate in the study.

Exclusion Criteria

Individuals meeting the above criteria were only included in the final analysis sample if they ever received (or for usual care participants would have qualified for) an intervention call.

For research-related logistical reasons (e.g., to eliminate the need for multiple rounds of introductory mailings, to simplify the programming that would be required with a rolling enrollment, etc.) we “pre-randomized” a patient pool who made up our potential sample. However, in keeping with how the intervention would be used in clinical practice, our analysis protocol prespecified an inclusion criterion that only those individuals who ever received (or for usual care participants who would have qualified for) intervention calls were included in the analysis samples. Thus, while included in the randomization pool, patients who never qualified for a call did not receive an intervention and were not part of our primary (intention-to-treat) analysis; this design does not introduce bias because pre-randomized control group patients were handled in the same way.

In order to be able to study adherence among both new ICS users and pre-existing ICS users, the target population included members without an ICS dispensing prior to randomization. This paper focuses on adherence among pre-existing ICS users, who were the primary focus of the grant and were defined as having at least one ICS dispensing in the 12 months prior to their qualifying ICS dispensing or order. We also briefly describe findings for new ICS users.

Recruitment and randomization (Figure 1)

Figure 1.

Derivation of Study Sample

Of 15,164 individuals who were sent an invitation letter, 1100 (7.3%) opted out of the study and the remaining 14,064 were randomized to either the intervention or usual care arms, with randomization stratified by region and the clinic facility to which each patient was paneled. Over the 18 months of intervention calling, 8517 individuals qualified for one or more calls, of whom 6905 were pre-existing ICS users and 1612 were new ICS users. The primary reasons for not qualifying for a call were the lack of a triggering ICS dispensing or orders among potential new ICS users (52%) and perfect adherence to a monthly ICS regimen (33%).

Intervention

The intervention included three basic IVR call types, each of which typically lasted 2–3 minutes: a refill reminder call, a tardy refill call, and an initiator/restart call. Each month, we scanned the EMR to determine who was eligible for which type of call.

The refill reminder call went to participants whose last ICS dispensing was at least one month ago and who had less than 30 days supply left assuming appropriate use. The call reminded patients that they were due for a refill and offered a transfer to the automated pharmacy refill line and/or information about KP’s online refill service. The tardy refill call went to individuals who were more than one month past their projected refill date. It not only reminded patients that they were due for an ICS refill, but also assessed asthma control, explored ICS adherence barriers, and provided tailored educational messages. Patients in poor control who declined to be transferred to the automated pharmacy refill line were offered the option to speak to a live pharmacist. Finally, the initiator/restart call was designed to provide support to patients who were either starting ICS for the first time (new users) or were lapsed users. These calls went to individuals with an ICS order or dispensing in the previous month and no other ICS dispensing in the previous six months, and were similar to the tardy refill call in that they included probes for asthma control and adherence barriers and offered tailored educational messages.

When possible, the calling program left messages on answering machines or with another household member if the target participant was not at home. As part of the first direct contact with each participant, the scripted IVR call asked for permission to leave detailed messages that included the name of the target asthma medication. Lacking this, the phone messages simply noted it was the Breathe Easy Medication Reminder Program calling and asked the participant to call back on a toll-free number.

Data collection

We surveyed 2000 randomly selected individuals prior to randomization and again at the end of the study. Additional data, including our primary adherence measures, were derived from the electronic medical record.

Study measurements

Adherence

We used a modification of the Medication Possession Ratio (MPR)^20,21,22 as our primary outcome measure. The MPR is computed as the number of days’ supply of medication dispensed during a given time window divided by the time between the first dispensing in the window and the end of the window. Our modified MPR (mMPR) also accounted for medication that was on hand at the start of the window and ignored any days’ supply that would extend beyond the end of the window. We also assumed that medications were used as directed and that a new ICS canister was not started until any medication on hand was exhausted.²² Since all participants were prior ICS users or had an ICS order prior to randomization, we assumed they met criteria for persistent asthma and hence should have been taking ICS throughout their entire intervention period.²³ This enabled us to calculate an mMPR for everyone, and not just for those with at least one dispensing. Finally, since the implied adherence associated with medication on hand at the start of randomization could not be related to intervention status, we ignored this initial period in our calculations. A baseline mMPR was computed for the 12 months prior to the start of call eligibility in a similar manner. In computing the mMPR, we treated all ICS medications, including combination agents containing an ICS, as equivalent. The mMPR is an indication of the proportion of days during our observation window that patients had ICS medication available. Thus an mMPR of 1.0 suggests that a patient was dispensed enough medication to cover all the days during the window, while an mMPR of 0.5 suggests that a patient was dispensed enough ICS medication to cover half of the window of observation.

Other EMR-based measures

We used data from the EMR for the year prior to qualifying for calling (the “baseline” year) to capture the following information: age, race, gender, smoking status, co-morbid COPD (defined as either a visit coded for 490, 491, 492, 496 during the baseline year or the presence of one of these codes on the patient’s problem list), acute asthma health care utilization (urgent care, emergency department care, or hospitalization), oral steroid use, short acting beta agonist (SABA) usage, and total number of distinct medications filled in the past 12 months. We classified smoking status based on the most recently available EMR data prior to the start of call eligibility.

Survey measures

The baseline and follow-up surveys captured information related to race, asthma-specific health status (i.e., the Juniper mini-Asthma Quality of Life Questionnaire [AQLQ]),²⁴ The follow-up survey also included the Asthma Therapy Assessment Questionnaire [ATAQ] asthma control index),²⁵ and a series of questions (for those randomized to the active intervention arm) related to satisfaction with the intervention.

Intervention process data

For each call we captured the date and type of the call, participant responses during the call, and information on transfers.

Composite and other measures

We used self-reported race from the surveys if available, and otherwise relied on EMR-based race information where available. We used geocoding to map zip code information to block-level income data as a proxy for household income.

Statistical analysis

For our primary analysis, we excluded 474 pre-existing users (6.9% of those who qualified for calls) who either appeared to be daily users of oral steroids (n=215) or had fewer than three months of follow-up data (n=259). We excluded based on length of follow-up since we felt that the mMPR would be too unreliable in this group,²⁰ and on daily oral steroid use since these individuals would be unlikely to gain additional benefit from ICS for preventive purposes. The proportions excluded were very similar in the two intervention groups (7.0% vs. 6.7%, p=0.68). A sensitivity analysis that included these individuals gave similar results.

For the analysis of mMPR scores we used a general linear model (with assumed normal errors) that adjusted for performance site (KPH vs. KPNW), gender, age (18–45; 46–60, 61+ years), baseline SABA use (0, 1–2, 3+ canisters), co-morbid COPD status (any diagnosis of 492/496, a diagnosis of 490/491 but not 492.496, no COPD), and approximate tertiles of baseline mMPR (<0.20, 0.20–0.55, ≥0.55). We used duration of follow-up as a weighting variable to reflect the fact that these adherence measures become more accurate and reliable with longer follow-up. Results are also expressed for subgroups defined by age, gender, tertiles of baseline mMPR, baseline SABA usage, and co-morbid COPD status. Appropriate subgroup by treatment interaction terms were used to formally test whether treatment effects differed across subgroups. We also conducted logistic regression analyses of the proportion of individuals with good adherence (defined as an mMPR≥0.8).

For the analysis of SABA usage and acute asthma health care utilization we used over dispersed Poisson regression analysis to analyze the rate of usage (canisters/year and acute visits/year, respectively) while adjusting for the same set of baseline covariates, although for SABA use we restricted to those without co-morbid COPD since SABA use is very common in this latter group and hence might not be affected by the intervention.

To assess the impact of the intervention on asthma-related quality of life and self-reported asthma control, we utilized data from the 1535 individuals who completed the follow-up survey. The analytic model for these analyses was similar to that for the mMPR data.

Finally, we conducted post hoc analyses comparing usual care participants with those intervention participants whom we reached directly or left detailed messages with at least two times.

Our a priori power calculations showed near 100% power to detect differences of 0.04 in adherence, and 85% power to detect differences of 0.5 on the 7 point mini AQLQ score; we did not perform power calculations for other secondary outcomes. All analyses were done in Stata, version 10 (Stata Corporation LP, College Station, TX).

Results

Sample characteristics

Baseline characteristics of the intervention and usual care groups were very similar (Table 2). Overall, 34% of participants in the primary analysis sample were male, with a mean age of 54 years (range 18–98 years). One-third had co-morbid COPD, 14% had an emergency department visit or hospitalization for asthma, and almost half had some oral steroid dispensing. Twenty three percent received three or more SABA canisters during the baseline year. The mean baseline ICS mMPR was 0.42.

Table 2.

Characteristics¹ of the Primary Analysis Sample – Pre-existing ICS Users

		Intervention Group	Usual Care	Total
Electronic Medical Record Data
N		(3171)	(3260)	(6431)
Age (years)		53.7 (15.3)	53.5 (15.3)	53.6 (15.3)
18–45		30.2%	30.8%	30.5%
46–60		36.4%	37.6%	37.0%
61 or older		33.4%	31.6%	32.5%
Male		32.2%	35.3%	33.8%
Race
White		51.5%	48.4%	50.0%
African American		1.6%	1.6%	1.6%
Asian		10.8%	11.9%	11.4%
Native Hawaiian/Pacific Islander		4.6%	4.0%	4.3%
American Indian/Alaskan Native		0.5%	0.6%	0.5%
Mixed		6.2%	7.2%	6.7%
Unknown		24.9%	26.3%	25.6%
Estimated Household Income
< $40,000		26.9%	26.7%	26.8%
$40–60,000		44.9%	45.3%	45.1%
≥$60,000		27.4%	27.1%	27.2%
Level 3		0.8%	0.9%	0.8%
Smoking status
Current		8.4%	7.9%	8.2%
Former		9.5%	8.7%	9.1%
Never		43.1%	43.6%	43.3%
Unknown		39.1%	39.9%	39.5%
Co-morbid COPD2		33.3%	33.3%	33.3%
ED visit or hospitalization for asthma3		13.9%	13.4%	13.7%
Any oral steroid use (burst pack)		46.5%	45.8%	46.1%
β-agonist usage (canisters)
0		44.1%	43.5%	43.8%
1		22.0%	21.4%	21.7%
2		11.6%	12.5%	12.0%
3 or more		22.4%	22.7%	22.5%
# total different medications dispensed4
0–5		31.6%	31.2%	31.4%
6–12		31.2%	30.4%	30.8%
13 or more		37.2%	38.5%	37.9%
ICS mMPR5		0.42 (0.30)	0.42 (0.31)	0.42 (0.31)

¹Based on chart information for the 12 months prior to call eligibility

²Includes visit or problem list entry for ICD-9 categories 490, 491, 492, 496 in baseline year

³Any listed dx for ED or primary discharge dx for hospitalization in baseline year

⁴Number of unique generic medication names dispensed during the baseline year

⁵Modified medication possession ratio (see Methods)

Intervention process data

A total of 11,714 IVR calls were delivered to 3171 different individuals in the active intervention arm. Of these, 27% were simple refill reminder calls, 61% were tardy refill calls, and 12% were initiator/restart calls. Overall, 91% of intervention participants received at least one tardy refill call, 38% received at least one refill reminder call, and 44% received an initiator/restart call. Of the calls we attempted, we successfully reached the target participant 39% of the time, left a detailed message 15% of the time, and left a non-detailed message 30% of the time. Thus 84% of all calls resulted in some sort of message being left for the participant and over half were specifically able to mention the reason for the call. On an individual basis, 55% of intervention participants were reached directly or received a detailed message on two or more occasions.

Primary Analyses – Pre-existing ICS Users

ICS adherence increased significantly post randomization for participants in the intervention group relative to those in usual care (Table 3), although the magnitude of the difference (Δ=0.02, 95% confidence interval=0.01, 0.03) was small. Baseline adherence was 0.42 in both groups. The drops in mean mMPR from baseline to follow-up that were observed in both treatment groups likely reflect an upward bias in the baseline mMPR, since all participants were required to have at least one ICS dispensing during baseline. The intervention effects did not differ significantly by baseline mMPR, age, gender, baseline SABA use or baseline COPD status. We observed no significant intervention effects on the proportion of good adherers (defined as an mMPR≥0.8), either overall or separately in those with and without good adherence at baseline (data not shown). We also observed no significant intervention effects on reliever medication (SABA) use, quality of life, asthma control, or the rate of acute asthma health care utilization.

Table 3.

Primary analysis of modified medication possession ratio (mMPR) for pre-existing ICS users

		Intervention	Usual Care	Δ1	Sig2
Overall		(3171)	(3260)
	Baseline2	0.42±0.30	0.42±0.30
	Follow-up	0.40±0.32	0.38±0.32
	Change	−0.02±0.24	−0.04±0.24	0.02 (0.01, 0.03)	0.002
Change in mMPR by tertiles of baseline3 mMPR
< 0.20		0.07±0.19	0.07±0.20	0.01 (−0.01, 0.03)	0.39
0.20 – 0.55		−0.03±0.23	−0.06±0.22	0.03 (0.01, 0.04)	0.007
0.55 – 1.00		−0.11±0.26	−0.12±0.25	0.02 (−0.00, 0.04)	0.12
Change in mMPR by gender
male		−0.01±0.24	−0.04±0.24	0.03 (0.01, 0.05)	0.001
female		−0.03±0.24	−0.04±0.23	0.01 (−0.00, 0.03)	0.15
Change in mMPR by age
≤ 45		−0.04±0.22	−0.05±0.23	0.01 (−0.01, 0.03)	0.26
45–60		−0.03±0.24	−0.05±0.23	0.02 (0.00, 0.04)	0.045
>60		0.00±0.25	−0.02±0.25	0.02 (0.00, 0.04)	0.036
Change in mMPR by baseline SABA use
0 canisters		−0.03±0.23	−0.05±0.22	0.02 (0.00, 0.04)	0.039
1–2 canisters		−0.02±0.23	−0.03±0.23	0.01 (−0.02, 0.03)	0.62
3+ canisters		0.00±0.27	−0.04±0.27	0.04 (0.01, 0.06)	0.003
Change in mMPR by co-morbid COPD status
no COPD		0.00±0.29	−0.04±0.26	0.02 (0.00, 0.03)	0.022
490/491 only		−0.02±0.24	−0.04±0.23	0.01 (−0.01, 0.04)	0.29
492/496		−0.03±0.23	−0.04±0.23	0.04 (0.00, 0.08)	0.033

¹Net intervention effect, expressed as mean and 95% confidence interval

²Two-tailed significance level for overall treatment effect based on linear regression analysis adjusting for site and (as appropriate) age, gender, co-morbid COPD status, baseline SABA use, and baseline mMPR categories

³Baseline defined as 12 months prior to start of (eligibility for) calls for each participant

Post hoc analyses – Pre-existing ICS Users

In post hoc analyses limiting intervention participants to those reached directly or with detailed messages two or more times (55% of intervention participants), the apparent effects of the intervention were much more pronounced (Table 4). The overall effect was tripled from that seen in Table 3 (0.06 vs. 0.02), and highly significant effects were seen in all subgroups studied. We also observed a significant improvement in the proportion of individuals with good control 23% vs. 17% overall and 10% vs. 7% among those not in good control initially (both p-values <0.007 based on multivariate logistic regression analyses). We did not see corresponding improvements in our other outcomes, and in fact the rate of acute asthma health care utilization increased significantly in this subset of intervention participants (relative risk =1.06, p=0.038).

Table 4.

Post hoc analysis of modified medication possession ratio (mMPR) for primary analysis sample (pre-existing ICS users) limiting to intervention participants with 2 or more direct contacts or detailed messages

		Intervention	Usual Care	Δ1	Sig2
Overall		(1758)	(3260)
	Baseline2	0.46±0.31	0.42±0.30
	Follow-up	0.47±0.33	0.38±0.32
	Change	0.00±0.24	−0.04±0.24	0.06 (0.04, 0.07)	<0.001
Change in mMPR by tertiles of baseline3 mMPR
< 0.20		0.11±0.20	0.07±0.20	0.04 (0.01, 0.07)	0.004
0.20 – 0.55		0.01±0.23	−0.06±0.22	0.07 (0.04, 0.09)	<0.001
0.55 – 1.00		−0.08±0.24	−0.12±0.25	0.06 (0.03, 0.08)	<0.001
Change in mMPR by gender
male		0.02±0.24	−0.04±0.24	0.08 (0.05, 0.10)	<0.001
female		−0.00±0.24	−0.04±0.23	0.05 (0.03, 0.06)	<0.001
Change in mMPR by age
≤ 45		−0.00±0.20	−0.05±0.23	0.07 (0.04, 0.10)	<0.001
45–60		−0.00±0.25	−0.05±0.23	0.06 (0.04, 0.08)	<0.001
>60		0.01±0.25	−0.02±0.25	0.04 (0.02, 0.07)	<0.001
Change in mMPR by baseline SABA use
0 canisters		−0.01±0.23	−0.05±0.22	0.05 (0.033, 0.07)	<0.001
1–2 canisters		0.00±0.22	−0.03±0.23	0.04 (0.013, 0.06)	0.003
3+ canisters		0.04±0.27	−0.04±0.27	0.08 (0.055, 0.11)	<0.001
Change in mMPR by co-morbid COPD status
no COPD		−0.00±0.23	−0.04±0.26	0.05 (0.04, 0.07)	<0.001
490/491 only		0.01±0.24	−0.04±0.23	0.06 (0.03, 0.08)	<0.001
492/496		0.02±0.29	−0.04±0.23	0.07 (0.03, 0.11)	0.001

¹Net intervention effect, expressed as mean and 95% confidence interval

²Two-tailed significance level for overall treatment effect based on linear regression analysis adjusting for site and (as appropriate) age, gender, co-morbid COPD status, baseline SABA use, and baseline mMPR categories

³Baseline defined as 12 months prior to start of (eligibility for) calls for each participant

Post intervention survey results – Pre-existing ICS Users

559 intervention participants (56% of those completing the follow-up survey) remembered receiving the intervention calls and provided feedback on the intervention. Of these, roughly 50% indicated that the calls were helpful and that the service should be continued in the future; one third reported they felt their asthma was in better control as a result of receiving the calls.

Results in New ICS Users

An intention-to-treat analyses for the secondary analysis sample of new ICS users failed to show a significant intervention effect. Post hoc analyses were not conducted in this group.

Discussion

The results from this large, pragmatic clinical trial suggest that the use of IVR phone calls had a modest but significant effect on overall ICS adherence. However, no outcome difference was observed between treatment groups for SABA use, quality of life, asthma control, or in the rate of acute asthma health care utilization.

The magnitude of the observed intervention effect, although small, may still have important public health implications. For instance it is well known that a 2 mmHg drop in blood pressure, on a population basis, has important public health implications in terms of long-term cardiovascular risk reduction. Williams et al⁵ found that each 25% increase in the proportion of time without an ICS medication resulted in a doubling of the rate of prednisone use and asthma-related hospitalization. In this trial we failed to see a reduction in either SABA use (a short-term measure of morbidity) or urgent asthma health care utilization. This suggests that the clinical benefit of small improvements in adherence may be negligible for most patients. Future interventions are likely to have more impact if accompanied by more intensive and targeted strategies for higher risk patients. These strategies might include other, perhaps coordinated, HIT activities like email and text messaging. One potential way forward is to obtain information about patients’ preferences for HIT reminders and test whether incorporating those preferred methods yield favorable results.

Nonadherence with asthma controller therapy is common,^5,3,26,4 and the current study similarly found that at baseline 65% of existing ICS users used less than 50% of prescribed therapy and only 16% of patients used 80% or more of prescribed therapy. The causes of nonadherence with asthma therapy are multiple, including patient’s beliefs about their asthma and therapy (e.g., not needing as much medication, concerns about steroids, etc.), a failure to understand the regimen, structural barriers to adherence (cost, transportation), as well as factors that contribute to erratic adherence such as regimen complexity and forgetting. The limited effect of the intervention may be attributable to the fact that the current study was primarily designed to address only one of these factors—forgetting, by serving as a reminder and prompt to refill prescriptions. Because of this, our intervention’s results for ICS use in patients with asthma may not be directly generalizable to other medications or diseases. The transferability of our findings to new conditions and treatments may largely depend on how similar those new areas are to asthma with regard to forgetting as a reason for non–adherence.

The absence of a larger intervention effect also can be due to the fact that many individuals in the intervention group actually received little or no real intervention. For only 54% of our IVR calls did we either speak with the participant or leave a detailed message. When we limited the analyses to those individuals whom we either contacted directly or left detailed messages for two or more times (55% of intervention participants), we found much stronger effects in both adherence and improved asthma morbidity that persisted across a wide range of subgroups. While we acknowledge the limitations of such post hoc analyses and recognize that this subset of intervention participants had much higher levels of baseline adherence than did those who were excluded from this analysis (mMPR scores 0.46 vs. 0.36, p<.0001), these results nonetheless suggest that the calls, if received, were useful.

The large sample size, randomized design, and pragmatic nature of the trial (with limited exclusion criteria), are strengths of the study and support the generalizability of the findings. While we had to rely on pharmacy dispensing records to infer adherence, such measures have become increasingly more common and have been shown to correlate with patient outcomes.^3,27 In addition, such measures have high face validity when measured over long periods of time since the only way to achieve high levels of adherence is to persistently refill medications, which in turn is strongly suggestive of ongoing use of those medications. Also, while our databases do not capture dispensing from non Kaiser Permanente pharmacies, the majority of KP members have some form of prescription benefit and previous studies have shown that most of them fill their prescriptions at KP pharmacies. Furthermore, the use of non-KP pharmacies should be distributed evenly across the two treatment groups and hence not bias our treatment comparisons. Reassuringly, although the specifics of the methods and populations included differ, our adherence findings are broadly similar to other studies that have examined adherence with inhaled corticosteroids using pharmacy refill patterns in adults.^27,28

In summary, the impact of this HIT-based IVR adherence intervention may have been limited by participants’ willingness to take the calls. Our qualitative data suggest that continuing to find ways to make the calls personalized, streamlined, non-redundant, and actionable may further increase utility and participation. In the future, inclusion of mail, email or Internet-based platforms may be necessary to reach the broadest populations. In addition, HIT strategies that more fully integrate physicians, pharmacists and other clinical staff into adherence promotion systems have the potential to address more complex barriers to adherence than could be addressed by this intervention.

Take-Away Points.

This study demonstrated that a health information technology (HIT) –based intervention utilizing automated refill reminders resulted in a small but significant overall improvement in asthma medication adherence among managed care patients. For that subset of patients who accepted the reminder phone calls the benefit on medication adherence was larger and resulted in an improvement in asthma control. Future HIT strategies that more fully integrate physicians, pharmacists and other clinical staff into adherence promotion systems have the potential to address more complex barriers to adherence and assist patients in the successful long-term management of chronic disease.