Implementing and Evaluating a Telemedicine Program for Vulnerable Patients with Diabetes

Urmimala Sarkar Assistant Professor Division of General Internal Medicine Center for Vulnerable Populations Health-IT-Enabled Self-management Support For Vulnerable Patients With Diabetes

1Invited by Dr. Zayas-Caban to discuss my AHRQ-funded workPrimary care physicianUnderserved patientsLow incomeLimited English proficiencyLimited health literacyHuge potential to leverage technology to improve healthReduce/ eliminate health disparitiesMedical innovation and technological advancement should start with diverse populationsWhy I am at medicine x7 out of 10 deaths among Americans each year are from chronic diseases75% of our health care dollars go to treatment of chronic diseasesProfound disparities by educational attainment and race/ ethnicity for incidence and for outcomesCompared to non-Hispanic whites, the risk of diagnosed diabetes is 18% higher among Asian Americans66% higher among Latinos77% higher among non-Hispanic African-Americansburden of chronic diseasescomplexity of managing chronic conditionsDiabetes daily tasksTaking medications Timing and planning of meals and exerciseChecking blood sugarAdjustment of medications in response to numbersBeing vigilant for problems (checking feet)Regular trips to doctors office, pharmacy, and lab

Benazapril 5mg4What I want to emphasize here is that self-managing a chronic condition places a HIGH DEMAND on people.

If you cant read, you rely on med appearance, but that can vary

2nd bullet

All of these pills are benazapril 5 mg. Anytime the pharmacy gets a better deal from a different generic drug supplier, your patients can get a new pill. And usually, the pharmacist wouldnt mention it.

Adhering to medications is challengingAutomated telephone self-management (ATSM)Engage patients between visitsRemotelyLiteracy and language appropriateTailor with rulesScreen for those who need live telephone follow-upCAN TECHNOLOGY HELP?6Patients respond via touch-tone commands, and based on their answers, patients hear automated health education messages in the form of narrativesExample response

Example Exercise Narrative Improving Diabetes Efforts Across Language and Literacy (IDEALL)

Patients answering out of range on an item receive a call back from a language-concordant care manager within 3 days. Care managers provide education and engage in collaborative goal-setting to form patient-centered action plans. Finally, care managers notify primary care providers regarding any concerning safety or access issues.

7ATSM: weekly automated calls English, Cantonese, or SpanishTriggers for live telephone follow-up from NPTopics rotated Self-care (diet, exercise, medication adherence)Psychosocial issues (such as depressive symptoms)Access to preventive services (such as eye care)ATSM vs. group visits vs. usual careATSM group vs usual careimprovements in self-management behavior (P < 0.05)fewer bed days/month (-1.7 days, P = 0.05) less interference with daily activities (OR 0.37, P = 0.02)

Improving Diabetes Efforts Across Language and Literacy (IDEALL)We measured 1-year changes in structure (Patient Assessment of Chronic Illness Care [PACIC]), communication processes (Interpersonal Processes of Care [IPC]), and outcomes (behavioral, functional, and metabolic). RESULTS: Compared with the usual care group, the ATSM and GMV groups showed improvements in PACIC, with effect sizes of 0.48 and 0.50, respectively (P < 0.01). Only the ATSM group showed improvements in IPC (effect sizes 0.40 vs. usual care and 0.25 vs. GMV, P < 0.05). Both SMS arms showed improvements in self-management behavior versus the usual care arm (P < 0.05), with gains being greater for the ATSM group than for the GMV group (effect size 0.27, P = 0.02). The ATSM group had fewer bed days per month than the usual care group (-1.7 days, P = 0.05) and the GMV group (-2.3 days, P < 0.01) and less interference with daily activities than the usual care group (odds ratio 0.37, P = 0.02). We observed no differences in A1C change. CONCLUSIONS: Patient-centered SMS improves certain aspects of diabetes care and positively influences self-management behavior. ATSM seems to be a more effective communication vehicle than GMV in improving behavior and quality of life.

8In the last bullet pls put the confidence interval instead of P valueadd the shortened citation, Schillinger et al, Diabetes Care, YEAR.9SMART Steps: Partnering to Put Research Into PracticeSan Francisco Health Plan (SFHP): nonprofit Medicaid managed-care plan Recruitment and implementationEvaluation by UCSF CVP, funded by AHRQWait-list randomized trial

9out of range responsesLive call within 3 daysCare managers not licensed nurse practitionersLanguage-concordantProvide education Trained to generate patient-centered action plans Safety or access issuesPCP notification tailored by clinic siteSMART StepsPatientCase ManagerATSMPCP10Engagement in ATSM% completing callsDifferences by languageCompare intervention (combined) vs. waitlist in change from baseline to 6-month:Summary of Diabetes Self-Care Activities Quality of life (SF-12)

OutcomesToobert 2000, Ware 199611Participants With 6-Month F/U (n=249)CharacteristicIntervention (n=125)Wait-List (n=124)Age in years, mean (SD)56.6 (7.9)54.9 (8.6)Women, %7772Latino, %Black / African-American, %Asian / Pacific Islander, %White / Caucasian, %2666062010627Born Outside the U.S., %8685Cantonese-speaking, %Spanish-speaking, %542055198th grade education or less, %3947Limited health literacy, %4740Income $20,000 / Yr, %6160Hgb A1c >8.0%, %3024Participant characteristics did not differ between the intervention and wait-list groups. They averaged about 55 years in age, and over 70% were women. A quarter were Latino and 60% were Asian, and 85% were born outside the U.S. Just over half chose Cantonese for their ATSM calls and 20% Spanish. Fewer than half had been educated past the 8th grade, and about half had limited health literacy on the validated Chew screening instrument. Two-thirds earned less than $20000/year. A quarter had a hemoglobin A1c >8.0%, a marker for poor control.12

This graph shows the proportion of participants completing calls across each of the 27 weeks of ATSM. The black line is all participants; Cantonese-speakers are in green, Spanish-speakers in blue, and English-speakers in red. As you can see, engagement remains high throughout the 27 weeks, moreso for Cantonese-speakers.

This pattern of engagement is quite different than you typically see with apps or websites, and I think its because content was different every call. Usually engagement plummets over time.13Change in Quality of Life at 6 Mos (n=249)Adjusted* Difference(95% CI)Standardized Effect Size*p-valuePhysical ComponentSF-12 2.0 (0.1,3.9)0.250.04MentalComponentSF-121.3 (-1.0,3.6)0.140.26SMARTSteps 6-month change in SF-12 physical component score is comparable to effects of other educational and behavioral interventions.60,61Although self-management interventions have traditionally focused on surrogate outcomes such as hemoglobin A1c,22 HRQOL is both clinically relevant and meaningful as a patient-centered outcome.22-25,62 Hemoglobin A1c may not correlate with quality of life, with increasing recognition that cardiometabolic targets must be adjusted based on individual patient factors such as medical and social comorbidities, values, and preferences. 23,24,40,62 Studies also suggest that HRQOL can predict future utilization among people with chronic medical conditions, an important consideration for Medicaid plans with increasingly complex and expanding populations.26-30

In tables, we present raw (unadjusted) values at baseline and 1 year and calculate differences for each intervention arm relative to usual care and for ATSM versus GMV, adjusting only for baseline values for each scale. To enable interpretation of effects that involve scales, we also calculated standardized effect sizes. For continuous variables, we used linear regression; for dichotomous variables, we used logistic regression. For bed days, we used negative binomial models to calculate log mean differences and generated incidence rate ratios.

From the standardize effect size we can say that there was more of an impact on the physical component than the mental component.

14Change in Self-Care at 6 Mos (n=249)Adjusted* Difference(95% CI)Standardized Effect Size*p-valueOverall Self-Care0.2 (0.1, 0.04)0.29