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Descriptive examination of secure messaging in a longitudinal cohort of diabetes patients in the ECLIPPSE study

Descriptive examination of secure messaging in a longitudinal cohort of diabetes patients in the... Abstract The substantial expansion of secure messaging (SM) via the patient portal in the last decade suggests that it is becoming a standard of care, but few have examined SM use longitudinally. We examined SM patterns among a diverse cohort of patients with diabetes (N = 19 921) and the providers they exchanged messages with within a large, integrated health system over 10 years (2006-2015), linking patient demographics to SM use. We found a 10-fold increase in messaging volume. There were dramatic increases overall and for patient subgroups, with a majority of patients (including patients with lower income or with self-reported limited health literacy) messaging by 2015. Although more physicians than nurses and other providers messaged throughout the study, the distribution of health professions using SM changed over time. Given this rapid increase in SM, deeper understanding of optimizing the value of patient and provider engagement, while managing workflow and training challenges, is crucial. secure messaging, patient portals, longitudinal studies, electronic health record, diabetes INTRODUCTION Kaiser Permanente and other healthcare systems launched patient portals1 to grant access to electronic medical record data in the late 1990s.2 Most U.S. healthcare systems were financially incentivized to follow with the passage of the 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act.3–5 At Kaiser Permanente Northern California (KPNC), greater than three-fourths of patients are registered for the portal, and more than half were sending secure messages by 2014.6 National data show that other systems are catching up, with over 90% of hospitals offering patient portals, yet only 15%-30% of patients are using them currently.7–9 Key to understanding patient portal uptake will be deeper examination of secure messaging (SM), given that it is one of the most used features.6,10,11 Previous studies suggest that SM bridges communication between in-person encounters and has the benefit of being automatically documented in the electronic health record.12,13 Small studies suggest that patients and providers are messaging at higher volumes over time.14,15 Among patients with diabetes at KPNC, patient portal use, and SM specifically, has been associated with improved patient-provider communication, medication adherence, and glycemic control.16 There is a large body of literature documenting disparities in portal use and secure messaging use in the United States, both before and after HI-TECH Act implementation—particularly by race and ethnicity,17,18 socioeconomic status,19 and health literacy.20 These differences do not appear to be due to a lack of patient interest in portal functionality,21 as most patients report a desire to engage in online communication tools to increase convenience of care. Instead, qualitative data have documented clear barriers to portal use based on needs and preferences for in-person support (for both technical support and from healthcare providers), poor usability features of portal interfaces, and concerns about privacy and security.17,22,23 Despite this existing knowledge, real-world data on longitudinal trends in portal and SM use are lacking, even as use is rapidly increasing. It is critical that we better understand patterns of SM in routine practice to plan for staffing, workflow and training needs, and identify engagement strategies for patient and provider subgroups that might use these platforms differently, particularly now during the pandemic, as key touchpoints for remote care access. We therefore examined 10-year SM patterns and changes at KPNC (an early adopter of patient portals) among a large, diverse cohort of patients and providers. MATERIALS AND METHODS Setting KPNC is a nonprofit, integrated healthcare delivery system serving 4.5 million patients with 9368 physicians.24 KPNC members are largely representative of the Northern California population, except for the extremes of income.25 Members have been able to utilize the patient portal (kp.org) since 1999, with the SM feature enabled since 2005. Patients and providers exchange SMs via the portal. Providers typically respond to messages within 48 hours. Message threads, defined as messages sent or received under the same subject line, are closed after 30 days of inactivity, at which point a new thread can be initiated. Cohort This analysis examines SM data from the ECLIPPSE (Employing Computational Linguistics to Improve Provider-Patient Secure Email) study, an interdisciplinary study from 2015 to 2019 examining SM exchange among diabetes patients and physicians.26 This study is a secondary analysis of the ECLIPPSE study dataset (January 1, 2006 to December 31, 2015), which includes all SM data from a cohort of patients with diabetes and their providers. Patients included completed an in-depth survey about themselves and their diabetes care in 2006,16,26,27 This survey was an ethnically stratified, random sample of all KPNC Diabetes Registry participants 30-75 years of age.27 We examined all SMs by patients and providers, excluding (1) system-generated messages, (2) questionnaires, and (3) messages without content. We determined the total denominator of active patients in the cohort (eg, with an active Kaiser medical record number) in each year of follow-up, removing patients without KPNC coverage. Providers who sent at least 1 SM to any patient in the cohort within the time frame were included. Measures Secure message data Our primary outcome was engagement in SM threads, examined (1) in aggregate across the 10-year period and (2) annually from 2006 to 2015. We chose to primarily report SM results at the thread level, as it includes all consecutive messages responsive to the same subject line and best represents a specific “conversation” between patients and providers. When examining patterns of messaging within patient and provider subgroups, we categorized thread-level exchanges in both the overall and annual datasets. These categorical thread measures were less sensitive to outliers and built on cutpoints derived from previous literature.18,28 In descriptive analyses, we also examined total numbers of messages summing across threads (eg, sent and received annually and the proportion of patient- vs provider-generated messages) to measure the change in volume over time. Patient-level data Using self-reported survey items, we captured the following baseline patient characteristics: age, sex, race and ethnicity, annual household income, educational attainment, limited health literacy,20,29,30 and health status. Using electronic health record data, we created two derived variables: comorbidity (Charlson comorbidity score) and utilization rates (annual outpatient visits). Provider-level data Provider-level characteristics included type (MD/DO physicians, including endocrinologists and nurse practitioners; registered nurses; medical assistants; physician assistants; and other providers such as nutritionists, pharmacists, and physical therapists) and sociodemographic variables, including sex, birth year, and race and ethnicity. Because this dataset lacked a dynamic denominator of providers (1) practicing at KPNC over time and (2) messaging outside of our patient cohort, all provider-level analyses were exploratory. We included these analyses given the absence of provider-level messaging data in the literature. Statistical analyses First, we summarized the raw thread and message data, across the entire 10-year period and annually. We specifically summarized the total number of messages, and the total proportion of threads that were patient initiated vs provider initiated and calculated the average thread length. We then examined the aggregated 10-year patient and provider thread data separately. For both sets of analyses, we determined whether there were significant differences in thread counts within patient and provider subgroups, using t tests for continuous variables and chi-square tests for categorical variables. Finally, to further determine whether there were differences in the number of threads over time based on patient characteristics, we employed paired McNemar’s tests comparing patients who sent ≥2 threads in 2006 vs 2015, using the same cutpoints as described previously. All analyses represent unadjusted comparisons using SAS version 9.4 (SAS Institute, Cary, NC). This study was approved by the KPNC and University of California, San Francisco Institutional Review Boards. RESULTS At the SM level, 1 050 577 messages (within 547 226 threads) were exchanged between patients and providers between 2006 and 2015. The fixed patient cohort started with 19 921 active patients in 2006, and by 2015, 13 530 active patients remained. By 2015, 67% of the sample sent at least 1 SM during the study period (Figure 1). In 2006, 19 921 patients engaged in 18 983 messages (0.95 messages/patient) and 13 530 participants in 2015 engaged in 182 067 messages (13.46 messages/patient). Figure 1. Open in new tabDownload slide The total number of active patients remaining in the fixed cohort each year, and the number of active patients sending messages each year. Figure 1. Open in new tabDownload slide The total number of active patients remaining in the fixed cohort each year, and the number of active patients sending messages each year. The average length of a thread was 1.92 ± 1.38 messages (range, 1-49 messages); it decreased from 2.12 ± 1.09 in 2006 to 1.80 ± 1.38 in 2015. Overall, 49% of threads were patient initiated. Our sample was diverse (Table 1): 43% were ≥60 years of age at baseline, 46% had a household income <$50 000, 38% had completed less than or equal to high school, and 63% identified as a racial/ethnic minority . Among patients who had ≥1 SM thread, 60% had ≥20 total threads over the study period. Overall, there were significant differences across subgroups in SM behavior at the thread level. White and Asian patients were more likely to have exchanged ≥20 total threads with providers, as were those who were female, were younger, had higher income or educational attainment, had adequate health literacy, and had higher comorbidity scores (Table 1). Table 1. Characteristics of patients who engaged in different amounts of secure messaging over the 10-year study period (2006-2015) . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 a Sample includes all survey respondents in initial cohort. b Data missing for 15% of sample. c Data missing for 2% of sample. d Data missing for 33% of sample. e Data missing for 17% of sample. Open in new tab Table 1. Characteristics of patients who engaged in different amounts of secure messaging over the 10-year study period (2006-2015) . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 a Sample includes all survey respondents in initial cohort. b Data missing for 15% of sample. c Data missing for 2% of sample. d Data missing for 33% of sample. e Data missing for 17% of sample. Open in new tab The proportion of patients engaged in ≥2 threads dramatically increased across all sociodemographic groups in absolute terms (Figure 2). Among individuals making <$50 000/year at baseline, 6% engaged in ≥2 threads in 2006, compared with 51% in 2015 (P < .001). Descriptively, patients with lower income, limited health literacy, and racial and ethnic minorities showed the greatest relative increases in the odds of engaging in ≥2 threads (Figure 2). Figure 2. Open in new tabDownload slide Changes in patients engaging in ≥2 threads in 2006 vs 2015, by patient sociodemographic characteristics. Figure 2. Open in new tabDownload slide Changes in patients engaging in ≥2 threads in 2006 vs 2015, by patient sociodemographic characteristics. The provider sample included 15 727 providers (Table 2) who messaged with a patient in the cohort. Fifty-one percent were MD/DO physicians (16% primary care physicians, 0.4% endocrinologist physicians, 2.9% nurse practitioners) and 6.4% were registered nurses. Other providers included 1.5% who were physician assistants and 18% who were medical assistants. The remaining 23% included pharmacists (3.56% of sample), physical therapists (3.43%), optometrists (1.86%), psychologists (1.61%), social workers (1.46%), and other provider types representing <1% of the sample. Male, middle-aged (46-56 years of age at baseline), and Asian providers had the highest rates of SM threads (Table 2). Although all providers messaged more over time, the distribution of health professions messaging changed; non-MDs/DOs demonstrated a greater increase over time relative to MDs/DOs (Figure 3). Figure 3. Open in new tabDownload slide Numbers of MD/DO, nurse, and other providers who engaged in at least 5 or more message threads annually, from 2006-2015. Dynamic provider denominator not available. Figure 3. Open in new tabDownload slide Numbers of MD/DO, nurse, and other providers who engaged in at least 5 or more message threads annually, from 2006-2015. Dynamic provider denominator not available. Table 2. Characteristics of providers engaged in secure messaging over the 10-year study period . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 The data represent providers as they messaged with patient in our cohort, without a full denominator of all providers in the Kaiser Permanente Northern California system. a Younger providers were more likely to enter the study later, making this age group more difficult to interpret over time. Open in new tab Table 2. Characteristics of providers engaged in secure messaging over the 10-year study period . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 The data represent providers as they messaged with patient in our cohort, without a full denominator of all providers in the Kaiser Permanente Northern California system. a Younger providers were more likely to enter the study later, making this age group more difficult to interpret over time. Open in new tab DISCUSSION The goal of this study was to characterize changes in SM and SM users within a fixed cohort of patients with diabetes exchanging SMs with their providers. We examined longitudinal patterns of SM use among an aging,31,32 diverse18,33,34 patient population with chronic disease—within a healthcare system that implemented SM well before federal policy incentives were introduced. Overall, we observed a 14-fold increase in message volume per patient over the 10-year observation window. A much broader set of patients and providers used this mode of communication over time, supporting other research suggesting that SM is increasingly becoming a standard of care.35 These findings align with increased broadband and mobile internet connectivity nationally,36,37 in California households,25 and among KPNC members.38 Previous studies have investigated SM use among diabetes patients during a shorter time frame (6-36 months),39 using similar annual thread cutpoints.40 To our knowledge, no previous study has examined the annual increase in messaging longitudinally, overall or within patient subgroups. While we observed significant, steady increases in patient engagement with SM, we also noted increases in provider engagement, with physicians still involved in a majority of SMs but other professionals experiencing rapid increases. How the changing distribution of providers messaging affects care (eg, causal impact on subsequent in-person utilization patterns) remains a research topic with active exploration.41 Our work builds on and supports previous literature about patient-level differences in SM, which finds highest rates of portal use among patients managing chronic conditions,32,42 and lower rates among racial and ethnic minorities,18,33 patients with lower socioeconomic status,32,42,43 and individuals with limited health literacy.20 Our findings may suggest that differences in SM use are narrowing over time, but the nature of our analysis was descriptive and not confirmatory and thus future research is warranted. Multiple factors may be influencing these patterns, including Kaiser Permanente’s broad marketing to increase awareness about portals and system-level efforts to support patients’ use, such as the use of onsite technology centers at clinics.44 Other research has highlighted the importance of integrated workflows and provider recommendations to support patient use of portals,17,18 which are likely more established in settings like Kaiser Permanente. However, disparities can still persist, driven by additional system-level factors, such as patient-provider interactions across sociodemographic domains and45 suboptimal usability of portal interfaces,23 as well as structural factors, such as insufficient broadband access in communities.46 Promoting equitable access for all patients will likely require multilevel interventions to address such barriers.47,48 While our findings cannot provide insights into the quality of communication occurring via SM, we did observe a slight decline in the average thread length over time. More work is needed to understand whether this represents a change in SM exchange interactivity, an important marker of interpersonal care and high-quality communication. Limitations The patient portal was primarily available in English during this study period, and we only included English SMs. There were a few operational changes in SM policies over time (eg, other healthcare team members could view and respond to messages) that could have contributed to SM patterns. Because this descriptive study characterizes SM in a specific, race-stratified sample of survey responders with diabetes, caution is needed when comparing the findings with those from other populations. We were also unable to refresh our dataset through mid-2020 given the time-consuming cleaning and coding efforts to match patient and provider SM data. While we had missingness in self-reported health and income survey data, we do not think that it was substantially larger than other similar survey efforts Finally, we had incomplete denominator data of healthcare providers practicing at KPNC during our study period, undermining our ability to examine total provider messaging volume across all their encounters. CONCLUSION This study highlights the rapid proliferation in SM exchange between patients and providers, suggesting that SM has become a standard of care in health systems that have been offering the service for a significant time. This likely portends trends that will be observed nationally given the recent widespread expansion of patient portals nationally. Our study can provide insights for workforce-, workflow-, and training-related planning. Considering the ubiquity of SM, better understanding of how to optimize patient and provider interest and engagement in leveraging this technology to its fullest is crucial. Although social differences in SM use may be declining, specific patient subgroups (eg, low heath literacy) are using portals less. New solutions must be developed to enable all patients to utilize health communication technology optimally based on their preferences for care.49 Our study finds that healthcare providers are engaging in an ever-increasing volume of SMs over time and that the task is expanding beyond physicians. This growing demand for clinicians’ time must be met with novel strategies and additional resources to enable them to deliver high-quality care. FUNDING This work was part of a larger parent study (ECLIPPSE study) funded by the National Library of Medicine (Grant No. R01LM12355). DS and AJK were also supported by The Health Delivery Systems Center for Diabetes Translational Research, funded by the National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. P30DK092924). AJK was also supported by the National Institute on Aging (Grant No. R01AG063391) and National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. R01 DK103721). CRL and WB were supported by the National Library of Medicine (Grant No. R01LM013045). WB was also supported by the Agency for Healthcare Research and Quality (Grant No. K12HS026383),and the National Center for Advancing Translational Sciences (KL2TR001870) throughout various parts of the research and writing process. CONFLICT OF INTEREST STATEMENT The authors have no competing interests to report. REFERENCES 1 HealthIT.gov. What is a Patient Portal? 2015 . https://www.healthit.gov/providers-professionals/faqs/what-patient-portal Accessed December 4, 2019. 2 McCarthy D Mueller K Wrenn J. Kaiser Permanente: Bridging the Quality Divide With integrated practice, group accountability, and Health Information Technology Case Study. Case Study: Organized Health Care Delivery System . 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The digital divide and patient portals: internet access explained differences in patient portal use for secure messaging by age, race, and income . Med Care 2016 ; 54 ( 8 ): 772 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 34 Bush RA Richardson AC Cardona-Grau D Din H Kuelbs CL Chiang GJ. Patient portal usage in pediatric urology: is it meaningful use for everyone? Urol Pract 2018 ; 5 ( 4 ): 279 – 85 . Google Scholar Crossref Search ADS PubMed WorldCat 35 Institute of Medicine Committee on Quality of Health Care in America . Crossing the Quality Chasm: A New Health System for the 21st Century . Washington, DC : National Academies Press , 2001 . Google Scholar PubMed OpenURL Placeholder Text Google Preview WorldCat COPAC 36 Pew Research Center. Internet/Broadband Fact Sheet . 2019 . https://www.pewresearch.org/internet/fact-sheet/internet-broadband/ Accessed September 14, 2020. 37 Pew Research Center. Mobile Fact Sheet . 2019 . https://www.pewresearch.org/internet/fact-sheet/mobile/ Accessed September 14, 2020. 38 Gordon N Lin T. The Kaiser Permanente Northern California adult member health survey . Perm J 2016 ; 20 ( 4 ): 15 – 225 |. Google Scholar PubMed OpenURL Placeholder Text WorldCat 39 Kuo A Dang S. Secure messaging in electronic health records and its impact on diabetes clinical outcomes: a systematic review . Telemed J E Health 2016 ; 22 ( 9 ): 769 – 77 . Google Scholar Crossref Search ADS PubMed WorldCat 40 Harris LT Koepsell TD Haneuse SJ Martin DP Ralston JD. Glycemic control associated with secure patient-provider messaging within a shared electronic medical record: a longitudinal analysis . Diabetes Care 2013 ; 36 ( 9 ): 2726 – 33 . Google Scholar Crossref Search ADS PubMed WorldCat 41 Garrido T Meng D Wang JJ Palen TE Kanter MH. Secure e-mailing between physicians and patients: transformational change in ambulatory care . J Ambul Care Manage 2014 ; 37 ( 3 ): 211 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 42 Wakefield DS Kruse RL Wakefield BJ , et al. Consistency of patient preferences about a secure internet-based patient communications portal: contemplating, enrolling, and using . Am J Med Qual 2012 ; 27 ( 6 ): 494 – 502 . Google Scholar Crossref Search ADS PubMed WorldCat 43 Mold F Hendy J Lai Y-L de Lusignan S. Electronic consultation in primary care between providers and patients: systematic review . JMIR Med Inform 2019 ; 7 ( 4 ): e13042 . Google Scholar Crossref Search ADS PubMed WorldCat 44 Kaiser Permanente. Technology and Wellness Center . 2020 . https://thrive.kaiserpermanente.org/care-near-you/northern-california/sanfrancisco/departments/health-education/technology-and-wellness-center/ Accessed October 6, 2020. 45 Lyles CR Karter AJ Young BA , et al. Provider factors and patient-reported healthcare discrimination in the Diabetes Study of California (DISTANCE) . Patient Educ Couns 2011 ; 85 ( 3 ): e216–24 . doi: 10.1016/j.pec.2011.04.031[published Online First: Epub Date]|. Google Scholar Crossref Search ADS PubMed WorldCat 46 Perzynski AT Roach MJ Shick S , et al. Patient portals and broadband internet inequality . J Am Med Inform Assoc 2017 ; 24 ( 5 ): 927 – 32 . Google Scholar Crossref Search ADS PubMed WorldCat 47 Antonio MG Petrovskaya O Lau F. Is research on patient portals attuned to health equity? A scoping review . J Am Med Inform Assoc 2019 ; 26 ( 8–9 ): 871 – 83 . Google Scholar Crossref Search ADS PubMed WorldCat 48 Grossman LV Masterson Creber RM Benda NC Wright D Vawdrey DK Ancker JS. Interventions to increase patient portal use in vulnerable populations: a systematic review . J Am Med Inform Assoc 2019 ; 26 ( 8–9 ): 855 – 70 . Google Scholar Crossref Search ADS PubMed WorldCat 49 Lyles CR Tieu L Sarkar U , et al. A randomized trial to train vulnerable primary care patients to use a patient portal . J Am Board Fam Med 2019 ; 32 ( 2 ): 248 – 58 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Descriptive examination of secure messaging in a longitudinal cohort of diabetes patients in the ECLIPPSE study

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Oxford University Press
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Copyright © 2021 American Medical Informatics Association
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1067-5027
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1527-974X
DOI
10.1093/jamia/ocaa281
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Abstract

Abstract The substantial expansion of secure messaging (SM) via the patient portal in the last decade suggests that it is becoming a standard of care, but few have examined SM use longitudinally. We examined SM patterns among a diverse cohort of patients with diabetes (N = 19 921) and the providers they exchanged messages with within a large, integrated health system over 10 years (2006-2015), linking patient demographics to SM use. We found a 10-fold increase in messaging volume. There were dramatic increases overall and for patient subgroups, with a majority of patients (including patients with lower income or with self-reported limited health literacy) messaging by 2015. Although more physicians than nurses and other providers messaged throughout the study, the distribution of health professions using SM changed over time. Given this rapid increase in SM, deeper understanding of optimizing the value of patient and provider engagement, while managing workflow and training challenges, is crucial. secure messaging, patient portals, longitudinal studies, electronic health record, diabetes INTRODUCTION Kaiser Permanente and other healthcare systems launched patient portals1 to grant access to electronic medical record data in the late 1990s.2 Most U.S. healthcare systems were financially incentivized to follow with the passage of the 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act.3–5 At Kaiser Permanente Northern California (KPNC), greater than three-fourths of patients are registered for the portal, and more than half were sending secure messages by 2014.6 National data show that other systems are catching up, with over 90% of hospitals offering patient portals, yet only 15%-30% of patients are using them currently.7–9 Key to understanding patient portal uptake will be deeper examination of secure messaging (SM), given that it is one of the most used features.6,10,11 Previous studies suggest that SM bridges communication between in-person encounters and has the benefit of being automatically documented in the electronic health record.12,13 Small studies suggest that patients and providers are messaging at higher volumes over time.14,15 Among patients with diabetes at KPNC, patient portal use, and SM specifically, has been associated with improved patient-provider communication, medication adherence, and glycemic control.16 There is a large body of literature documenting disparities in portal use and secure messaging use in the United States, both before and after HI-TECH Act implementation—particularly by race and ethnicity,17,18 socioeconomic status,19 and health literacy.20 These differences do not appear to be due to a lack of patient interest in portal functionality,21 as most patients report a desire to engage in online communication tools to increase convenience of care. Instead, qualitative data have documented clear barriers to portal use based on needs and preferences for in-person support (for both technical support and from healthcare providers), poor usability features of portal interfaces, and concerns about privacy and security.17,22,23 Despite this existing knowledge, real-world data on longitudinal trends in portal and SM use are lacking, even as use is rapidly increasing. It is critical that we better understand patterns of SM in routine practice to plan for staffing, workflow and training needs, and identify engagement strategies for patient and provider subgroups that might use these platforms differently, particularly now during the pandemic, as key touchpoints for remote care access. We therefore examined 10-year SM patterns and changes at KPNC (an early adopter of patient portals) among a large, diverse cohort of patients and providers. MATERIALS AND METHODS Setting KPNC is a nonprofit, integrated healthcare delivery system serving 4.5 million patients with 9368 physicians.24 KPNC members are largely representative of the Northern California population, except for the extremes of income.25 Members have been able to utilize the patient portal (kp.org) since 1999, with the SM feature enabled since 2005. Patients and providers exchange SMs via the portal. Providers typically respond to messages within 48 hours. Message threads, defined as messages sent or received under the same subject line, are closed after 30 days of inactivity, at which point a new thread can be initiated. Cohort This analysis examines SM data from the ECLIPPSE (Employing Computational Linguistics to Improve Provider-Patient Secure Email) study, an interdisciplinary study from 2015 to 2019 examining SM exchange among diabetes patients and physicians.26 This study is a secondary analysis of the ECLIPPSE study dataset (January 1, 2006 to December 31, 2015), which includes all SM data from a cohort of patients with diabetes and their providers. Patients included completed an in-depth survey about themselves and their diabetes care in 2006,16,26,27 This survey was an ethnically stratified, random sample of all KPNC Diabetes Registry participants 30-75 years of age.27 We examined all SMs by patients and providers, excluding (1) system-generated messages, (2) questionnaires, and (3) messages without content. We determined the total denominator of active patients in the cohort (eg, with an active Kaiser medical record number) in each year of follow-up, removing patients without KPNC coverage. Providers who sent at least 1 SM to any patient in the cohort within the time frame were included. Measures Secure message data Our primary outcome was engagement in SM threads, examined (1) in aggregate across the 10-year period and (2) annually from 2006 to 2015. We chose to primarily report SM results at the thread level, as it includes all consecutive messages responsive to the same subject line and best represents a specific “conversation” between patients and providers. When examining patterns of messaging within patient and provider subgroups, we categorized thread-level exchanges in both the overall and annual datasets. These categorical thread measures were less sensitive to outliers and built on cutpoints derived from previous literature.18,28 In descriptive analyses, we also examined total numbers of messages summing across threads (eg, sent and received annually and the proportion of patient- vs provider-generated messages) to measure the change in volume over time. Patient-level data Using self-reported survey items, we captured the following baseline patient characteristics: age, sex, race and ethnicity, annual household income, educational attainment, limited health literacy,20,29,30 and health status. Using electronic health record data, we created two derived variables: comorbidity (Charlson comorbidity score) and utilization rates (annual outpatient visits). Provider-level data Provider-level characteristics included type (MD/DO physicians, including endocrinologists and nurse practitioners; registered nurses; medical assistants; physician assistants; and other providers such as nutritionists, pharmacists, and physical therapists) and sociodemographic variables, including sex, birth year, and race and ethnicity. Because this dataset lacked a dynamic denominator of providers (1) practicing at KPNC over time and (2) messaging outside of our patient cohort, all provider-level analyses were exploratory. We included these analyses given the absence of provider-level messaging data in the literature. Statistical analyses First, we summarized the raw thread and message data, across the entire 10-year period and annually. We specifically summarized the total number of messages, and the total proportion of threads that were patient initiated vs provider initiated and calculated the average thread length. We then examined the aggregated 10-year patient and provider thread data separately. For both sets of analyses, we determined whether there were significant differences in thread counts within patient and provider subgroups, using t tests for continuous variables and chi-square tests for categorical variables. Finally, to further determine whether there were differences in the number of threads over time based on patient characteristics, we employed paired McNemar’s tests comparing patients who sent ≥2 threads in 2006 vs 2015, using the same cutpoints as described previously. All analyses represent unadjusted comparisons using SAS version 9.4 (SAS Institute, Cary, NC). This study was approved by the KPNC and University of California, San Francisco Institutional Review Boards. RESULTS At the SM level, 1 050 577 messages (within 547 226 threads) were exchanged between patients and providers between 2006 and 2015. The fixed patient cohort started with 19 921 active patients in 2006, and by 2015, 13 530 active patients remained. By 2015, 67% of the sample sent at least 1 SM during the study period (Figure 1). In 2006, 19 921 patients engaged in 18 983 messages (0.95 messages/patient) and 13 530 participants in 2015 engaged in 182 067 messages (13.46 messages/patient). Figure 1. Open in new tabDownload slide The total number of active patients remaining in the fixed cohort each year, and the number of active patients sending messages each year. Figure 1. Open in new tabDownload slide The total number of active patients remaining in the fixed cohort each year, and the number of active patients sending messages each year. The average length of a thread was 1.92 ± 1.38 messages (range, 1-49 messages); it decreased from 2.12 ± 1.09 in 2006 to 1.80 ± 1.38 in 2015. Overall, 49% of threads were patient initiated. Our sample was diverse (Table 1): 43% were ≥60 years of age at baseline, 46% had a household income <$50 000, 38% had completed less than or equal to high school, and 63% identified as a racial/ethnic minority . Among patients who had ≥1 SM thread, 60% had ≥20 total threads over the study period. Overall, there were significant differences across subgroups in SM behavior at the thread level. White and Asian patients were more likely to have exchanged ≥20 total threads with providers, as were those who were female, were younger, had higher income or educational attainment, had adequate health literacy, and had higher comorbidity scores (Table 1). Table 1. Characteristics of patients who engaged in different amounts of secure messaging over the 10-year study period (2006-2015) . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 a Sample includes all survey respondents in initial cohort. b Data missing for 15% of sample. c Data missing for 2% of sample. d Data missing for 33% of sample. e Data missing for 17% of sample. Open in new tab Table 1. Characteristics of patients who engaged in different amounts of secure messaging over the 10-year study period (2006-2015) . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 . Total Threads (n = 20,188) (%)a . 0 Threads (n = 7902) (%) . 1-10 Threads (n = 2802) (%) . 11-20 Threads (n = 2159) (%) . >20 Threads (n = 7325) (%) . P Value . Sex .002 Female 51 49 52 54 52 Male 49 51 48 46 48 Age at baseline <.0001 21-49 y 19 16 19 20 23 50-59 y 32 28 32 35 36 60-69 y 31 34 27 30 29 70-79 y 17 22 18 16 11 Race/ethnicity <.0001 White 23 17 22 21 31 Black 18 21 17 18 14 Hispanic/Latinx 19 25 18 17 14 Asian or Pacific Islander 27 22 30 33 30 Other/Unknown 12 14 12 11 11 Annual household income at baselineb <.0001 <$50 000/y 49 67 49 40 32 $50 000-$80 000 25 19 28 29 28 More than 80 000/y 27 13 24 31 40 Educationc <.0001 Completed high school or less 46 57 46 41 35 Some college 25 24 25 24 26 College graduate or more 29 19 29 35 39 Health literacyd <.0001 Limited 56 69 57 55 45 Adequate 44 31 43 45 55 Self-reported healthe <.0001 Good or better 66 62 67 71 68 Fair or poor 34 38 33 29 32 Comorbidity score <.0001 1 44 49 38 42 42 2 27 28 23 24 28 ≥3 29 23 40 35 30 a Sample includes all survey respondents in initial cohort. b Data missing for 15% of sample. c Data missing for 2% of sample. d Data missing for 33% of sample. e Data missing for 17% of sample. Open in new tab The proportion of patients engaged in ≥2 threads dramatically increased across all sociodemographic groups in absolute terms (Figure 2). Among individuals making <$50 000/year at baseline, 6% engaged in ≥2 threads in 2006, compared with 51% in 2015 (P < .001). Descriptively, patients with lower income, limited health literacy, and racial and ethnic minorities showed the greatest relative increases in the odds of engaging in ≥2 threads (Figure 2). Figure 2. Open in new tabDownload slide Changes in patients engaging in ≥2 threads in 2006 vs 2015, by patient sociodemographic characteristics. Figure 2. Open in new tabDownload slide Changes in patients engaging in ≥2 threads in 2006 vs 2015, by patient sociodemographic characteristics. The provider sample included 15 727 providers (Table 2) who messaged with a patient in the cohort. Fifty-one percent were MD/DO physicians (16% primary care physicians, 0.4% endocrinologist physicians, 2.9% nurse practitioners) and 6.4% were registered nurses. Other providers included 1.5% who were physician assistants and 18% who were medical assistants. The remaining 23% included pharmacists (3.56% of sample), physical therapists (3.43%), optometrists (1.86%), psychologists (1.61%), social workers (1.46%), and other provider types representing <1% of the sample. Male, middle-aged (46-56 years of age at baseline), and Asian providers had the highest rates of SM threads (Table 2). Although all providers messaged more over time, the distribution of health professions messaging changed; non-MDs/DOs demonstrated a greater increase over time relative to MDs/DOs (Figure 3). Figure 3. Open in new tabDownload slide Numbers of MD/DO, nurse, and other providers who engaged in at least 5 or more message threads annually, from 2006-2015. Dynamic provider denominator not available. Figure 3. Open in new tabDownload slide Numbers of MD/DO, nurse, and other providers who engaged in at least 5 or more message threads annually, from 2006-2015. Dynamic provider denominator not available. Table 2. Characteristics of providers engaged in secure messaging over the 10-year study period . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 The data represent providers as they messaged with patient in our cohort, without a full denominator of all providers in the Kaiser Permanente Northern California system. a Younger providers were more likely to enter the study later, making this age group more difficult to interpret over time. Open in new tab Table 2. Characteristics of providers engaged in secure messaging over the 10-year study period . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 . Total Threads (n = 15727) (%) . 1-10 Threads (n = 8603) (%) . 11-20 Threads (n = 2239) (%) . >20 Threads (n = 4880) (%) . P Value . Sex <.0001 Female 65 72 60 54 Male 35 28 40 46 Birth year <.001 1925-1958 24 23 25 24 1959-1969 26 24 25 32 1970-1976 24 22 25 27 1977-1996a 26 31 25 17 Race/ethnicity <.0001 White 48 49 49 44 Black 4.7 5.7 4.4 3.2 Hispanic/Latinx 11 14 9.7 6.6 Asian/Pacific Islander 26 22 25 34 Other/unknown 10 9.5 11 12 Provider type <.0001 MD/DO 51 36 58 74 Nurse 9 13 6 5 Other 40 51 36 21 The data represent providers as they messaged with patient in our cohort, without a full denominator of all providers in the Kaiser Permanente Northern California system. a Younger providers were more likely to enter the study later, making this age group more difficult to interpret over time. Open in new tab DISCUSSION The goal of this study was to characterize changes in SM and SM users within a fixed cohort of patients with diabetes exchanging SMs with their providers. We examined longitudinal patterns of SM use among an aging,31,32 diverse18,33,34 patient population with chronic disease—within a healthcare system that implemented SM well before federal policy incentives were introduced. Overall, we observed a 14-fold increase in message volume per patient over the 10-year observation window. A much broader set of patients and providers used this mode of communication over time, supporting other research suggesting that SM is increasingly becoming a standard of care.35 These findings align with increased broadband and mobile internet connectivity nationally,36,37 in California households,25 and among KPNC members.38 Previous studies have investigated SM use among diabetes patients during a shorter time frame (6-36 months),39 using similar annual thread cutpoints.40 To our knowledge, no previous study has examined the annual increase in messaging longitudinally, overall or within patient subgroups. While we observed significant, steady increases in patient engagement with SM, we also noted increases in provider engagement, with physicians still involved in a majority of SMs but other professionals experiencing rapid increases. How the changing distribution of providers messaging affects care (eg, causal impact on subsequent in-person utilization patterns) remains a research topic with active exploration.41 Our work builds on and supports previous literature about patient-level differences in SM, which finds highest rates of portal use among patients managing chronic conditions,32,42 and lower rates among racial and ethnic minorities,18,33 patients with lower socioeconomic status,32,42,43 and individuals with limited health literacy.20 Our findings may suggest that differences in SM use are narrowing over time, but the nature of our analysis was descriptive and not confirmatory and thus future research is warranted. Multiple factors may be influencing these patterns, including Kaiser Permanente’s broad marketing to increase awareness about portals and system-level efforts to support patients’ use, such as the use of onsite technology centers at clinics.44 Other research has highlighted the importance of integrated workflows and provider recommendations to support patient use of portals,17,18 which are likely more established in settings like Kaiser Permanente. However, disparities can still persist, driven by additional system-level factors, such as patient-provider interactions across sociodemographic domains and45 suboptimal usability of portal interfaces,23 as well as structural factors, such as insufficient broadband access in communities.46 Promoting equitable access for all patients will likely require multilevel interventions to address such barriers.47,48 While our findings cannot provide insights into the quality of communication occurring via SM, we did observe a slight decline in the average thread length over time. More work is needed to understand whether this represents a change in SM exchange interactivity, an important marker of interpersonal care and high-quality communication. Limitations The patient portal was primarily available in English during this study period, and we only included English SMs. There were a few operational changes in SM policies over time (eg, other healthcare team members could view and respond to messages) that could have contributed to SM patterns. Because this descriptive study characterizes SM in a specific, race-stratified sample of survey responders with diabetes, caution is needed when comparing the findings with those from other populations. We were also unable to refresh our dataset through mid-2020 given the time-consuming cleaning and coding efforts to match patient and provider SM data. While we had missingness in self-reported health and income survey data, we do not think that it was substantially larger than other similar survey efforts Finally, we had incomplete denominator data of healthcare providers practicing at KPNC during our study period, undermining our ability to examine total provider messaging volume across all their encounters. CONCLUSION This study highlights the rapid proliferation in SM exchange between patients and providers, suggesting that SM has become a standard of care in health systems that have been offering the service for a significant time. This likely portends trends that will be observed nationally given the recent widespread expansion of patient portals nationally. Our study can provide insights for workforce-, workflow-, and training-related planning. Considering the ubiquity of SM, better understanding of how to optimize patient and provider interest and engagement in leveraging this technology to its fullest is crucial. Although social differences in SM use may be declining, specific patient subgroups (eg, low heath literacy) are using portals less. New solutions must be developed to enable all patients to utilize health communication technology optimally based on their preferences for care.49 Our study finds that healthcare providers are engaging in an ever-increasing volume of SMs over time and that the task is expanding beyond physicians. This growing demand for clinicians’ time must be met with novel strategies and additional resources to enable them to deliver high-quality care. FUNDING This work was part of a larger parent study (ECLIPPSE study) funded by the National Library of Medicine (Grant No. R01LM12355). DS and AJK were also supported by The Health Delivery Systems Center for Diabetes Translational Research, funded by the National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. P30DK092924). AJK was also supported by the National Institute on Aging (Grant No. R01AG063391) and National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. R01 DK103721). CRL and WB were supported by the National Library of Medicine (Grant No. R01LM013045). WB was also supported by the Agency for Healthcare Research and Quality (Grant No. K12HS026383),and the National Center for Advancing Translational Sciences (KL2TR001870) throughout various parts of the research and writing process. CONFLICT OF INTEREST STATEMENT The authors have no competing interests to report. REFERENCES 1 HealthIT.gov. What is a Patient Portal? 2015 . https://www.healthit.gov/providers-professionals/faqs/what-patient-portal Accessed December 4, 2019. 2 McCarthy D Mueller K Wrenn J. Kaiser Permanente: Bridging the Quality Divide With integrated practice, group accountability, and Health Information Technology Case Study. Case Study: Organized Health Care Delivery System . 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J Am Board Fam Med 2019 ; 32 ( 2 ): 248 – 58 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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Journal of the American Medical Informatics Association – Oxford University Press

Published: Nov 24, 2020

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