Communication About Diabetes Risk Factors During Between-Visit Encounters
Published Online: December 18, 2012
Courtney R. Lyles, PhD; Lou Grothaus, MS; Robert J. Reid, MD, PhD; Urmimala Sarkar, MD, MPH; and James D. Ralston, MD, MPH
Clear and actionable patient-provider communication is associated with improvement in health behaviors and outcomes for patients with diabetes.1 Quality interpersonal interactions between patients and their providers, as well as targeted diabetes- specific communication, have been linked to better self-care2,3 and improved glycemic control.4 Moreover, supporting patient-provider diabetes communication could have a significant impact on population health, given that a large portion of patients with diabetes (35%-45% of those aged >40 years) exhibit poor control of 1 or more risk factors for micro- and macro-vascular complications: blood glucose, blood pressure, and/or cholesterol.5
The US healthcare system, with its focus on brief in-person office visits, does not adequately address the ongoing needs of patients with chronic conditions including diabetes.6 Between-visit contacts, including both phone visits and secure messaging, may enhance patient-provider communication. Studies have shown that patients are satisfied with alternate communication forms, and phone and secure messaging use continues to increase in systems that promote multi-modal access.7,8 Several trials of proactive care management between office visits also demonstrated that both phone and secure messaging can contribute to better control of glycated hemoglobin (A1C), blood pressure, and cholesterol. 9-14 Observational studies have also observed similar effects.15,16
In healthcare organizations that actively support phone and secure messaging communications between patient and providers, betweenvisit encounters amount to up to half of all patient encounters in primary care.17,18 No study to our knowledge has examined diabetes-specific communication during between-visit encounters. Specifically, among patients with chronic illnesses such as diabetes, it is unclear whether between-visit encounters focus on chronic disease self-management or on other health issues. For patients with diabetes, between-visit encounters may support managing blood glucose, blood pressure, and cholesterol— collectively considered to be risk factors for poor diabetes outcomes. The goals of this paper are to estimate (for patients with diabetes who are enrolled in a healthcare system with a shared electronic medical record [EMR]): 1) what percent use phone visits or secure messaging, 2) how often diabetes risk factors are discussed during each type of encounter, and 3) what patient characteristics are associated with a) having a phone or secure message encounter and b) discussing diabetes control during between-visit encounters.
Group Health Cooperative is an integrated delivery system that operates in Washington and Idaho. Over 300,000 members receive care from 25 Group Health Cooperative–owned facilities and over 1000 physicians. Group Health has offered patient access to a shared EMR since 2003, which includes multiple features: viewing after-visit summaries, viewing medical history and diagnoses, making appointments, refilling prescriptions, viewing doctor’s notes, viewing laboratory test results, and/or secure messaging with healthcare providers. Group Health also promotes scheduled phone visits as an alternative to in-person visits,7,19 but these were not specifically structured a priori during the study period.
We conducted a survey in September 2009 among adults with diabetes within 5 primary care clinics in western Washington. We sampled from a pool of 910 potential participants who met the Healthcare Effectiveness Data and Information Set definition for type 1 or type 2 diabetes,20 were continuously enrolled for 24 months, and were paneled to a primary care physician with whom they had 2 or more visits in the past 2 years. We stratified the sample so that one-half had used at least 1 of the EMR features on the patient website (www.ghc.org) on 2 or more occasions at least 30 days apart in the past 2 years. This captured users who were active rather than those who simply logged onto the website once. We also asked respondents for permission to link their EMR data, including diagnoses, laboratory results, and visit counts. Group Health’s institutional review board approved the study.
The primary variables of interest were survey items that asked about communication with healthcare providers about risk factors for diabetes complications (termed “risk factor communication”) in the year prior to the survey. We created 3 separate questions for communication about each risk factor (blood sugar/glucose, blood pressure, and cholesterol), and each item had options for how the discussion occurred (not mutually exclusive): at an in-person visit, on the phone, or secure message (eAppendix, available at www.ajmc.com). Using the linked utilization data from EMR records, we calculated the numbers of in-person, phone, and secure message encounters during the concurrent period, combining any phone or secure message visits into a joint category of “between-visit encounters.” We then created an indicator of any risk factor communication during a between-visit encounter, defined as discussion about any of the 3 risk factors via phone or secure message.
Covariates included age categories (<50, 50-64, or >65 years), sex, race (white, black, Asian, or other; which included Hispanic/Latino and those specifying 2 or more races), and education (high school or less, some college, or college graduate or more)—all self-reported from the survey. We assessed insulin use from automated pharmacy data and diabetes severity (measured by the Diabetes Complications and Severity Index21) from the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes and laboratory data. Finally, A1C, systolic and diastolic blood pressure, and low-density lipoprotein (LDL) values were extracted from automated laboratory data. We calculated the average yearly results for these values and dichotomized according to standard definitions for clinical control: A1C <7%, blood pressure <130/80 mm Hg, and LDL <100 mg/dL. Standard cleaning was performed to ensure that nonsensical values were excluded.
We first computed the proportion of patients with each encounter type in the past year (in-person, phone, secure message, or any between-visit encounter). We then computed the proportion who reported discussing each diabetes risk factor with their provider by encounter type, and compared the proportions with χ2 tests. In addition, we compared the proportions reporting discussions across risk factors (eg, glucose vs blood pressure, glucose vs cholesterol), also using χ2 tests. Next, we described the amount of overlap in discussions by encounter type—that is, reports of risk factor communication only at in-person visits, only during between-visit encounters, during both, or during neither.
We then analyzed patient demographics and health characteristics by encounter type and by self-reported risk factor communication. First, we examined the percent of respondents with any between-visit encounter by various patient demographics (eg, gender, age, education) and health characteristics (eg, insulin use, control of A1C, blood pressure, and LDL), comparing groups using χ2 tests. For example, we compared the percentages with a between-visit encounter among men and women. We then limited the analysis to those with at least 1 between-visit encounter and compared those reporting any between-visit risk factor discussions by the same patient and health characteristics. For example, we compared the percentages of men versus women reporting any between-visit risk factor discussions with their provider. Because our goal was to describe patterns within these crosssectional data, we completed unadjusted analyses.
The longer, written version of the survey had a 68% response rate (592 of the 873 eligible individuals without language, hearing/vision, or other impairments). Our final analysis sample included the 501 patients (57% of those eligible) who answered the risk factor communication outcomes of interest and who gave permission to access their EMR data. Overall, more than three-fourths (77%) of these respondents had a between-visit encounter in the preceding year. There was an average of 8.3 in-person visits (primary and specialty care), compared with 3.1 phone visits (among the 63% who had any phone encounters), and 7.1 secure message encounters (among the 41% who messaged with providers, counting a single secure message encounter as 1 e-mail thread22). In addition, half of the respondents were 65 years or older, half were male, 35% had a college education or more, and 64% were white (Table 1). Half of respondents were on insulin therapy and had on average 1.1 diabetes complications (standard deviation = 1.3). In terms of intermediate diabetes outcomes, 33% of patients had A1C <7%, 43% had blood pressure <130/80 mm Hg, and 73% had LDL <100 mg/dL; 13% were in control of all 3 risk factors simultaneously. Survey non-respondents (also shown in Table 1) were more likely to be younger, more educated, and non-white, without secure message use in the previous year; however, there were no significant differences in non-response to the communication outcomes of interest by granting permission to view medical records or by clinical control of A1C, BP, or LDL.
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