Research ArticleOriginal ResearchA

Changes in Office Visit Use Associated With Electronic Messaging and Telephone Encounters Among Patients With Diabetes in the PCMH

David T. Liss, Robert J. Reid, David Grembowski, Carolyn M. Rutter, Tyler R. Ross and Paul A. Fishman
The Annals of Family Medicine July 2014, 12 (4) 338-343; DOI: https://doi.org/10.1370/afm.1642

Article Figures & Data

Figures

  • Figure 1
    Figure 1

    Quarterly numbers of primary care contacts between care teams and patients with diabetes during baseline, PCMH implementation, and postimplementation periods.

    PCMH = patient-centered medical home.

Tables

  • Table 1

    Patient Characteristics at PCMH Baseline and Primary Care Contacts During the Pre-PCMH Baseline Year

    Baseline Year Primary Care Contacts, Mean (SD), No.
    CharacteristicTotal No. (%)Secure Message ThreadsTelephone EncountersOffice Visits
    Total population18,486 (100)3.5 (7.1)6.6 (8.2)3.7 (3.7)
    Age-group
     18–441,637 (9)3.5 (6.9)4.9 (6.0)3.5 (3.4)
     45–543,944 (21)3.5 (6.5)5.6 (7.1)3.6 (3.5)
     55–647,400 (40)3.7 (7.5)6.3 (7.8)3.6 (3.9)
     65–755,505 (30)3.4 (6.9)8.2 (9.7)4.0 (3.7)
    Sex
     Female8,879 (48)3.7 (7.7)7.3 (8.8)4.0 (3.7)
     Male9,607 (52)3.4 (6.5)6.0 (7.6)3.4 (3.7)
    Morbidity (ACG RUB)
     Moderate12,073 (65)2.5 (4.4)4.1 (4.4)2.6 (2.1)
     High3,963 (21)4.8 (9.0)8.4 (7.7)4.9 (3.3)
     Very high2,450 (13)6.7 (11.7)15.7 (14.1)7.4 (6.6)
    Insurance type
     Commercial11,924 (65)3.5 (6.4)5.4 (6.5)3.4 (3.4)
     Medicaid/state subsidized408 (2)2.2 (5.4)5.5 (5.9)3.7 (3.2)
     Medicare6,154 (33)3.7 (8.3)8.9 (10.5)4.3 (4.2)

    • ACG = adjusted clinical groups; PCMH = patient-centered medical home; RUB = resource utilization band.

  • Table 2

    Changes in Office Visits Associated With Proportional Increases in Secure Message Threads and Telephone Encounters

    Model and VariableChange With 10% Increase in Secure Messaging Threads % (95% CI)Change With 10% Increase in Telephone Encounters % (95% CI)
    Adjusted modela
    Full study population1.25 (1.21–1.29)2.74 (2.70–2.77)
    Interaction modela
    Study period
     Baseline1.13 (0.89–1.38)2.93 (2.70–3.15)b
     PCMH implementation (Ref)1.14 (0.93–1.35)2.74 (2.55–2.93)
     Postimplementation1.20 (0.98–1.41)2.57 (2.38–2.77)c
    Age-group, y
     18–441.28 (1.03–1.53)2.93 (2.70–3.17)b
     45–541.21 (0.99–1.43)2.94 (2.74–3.14)b
     55–64 (Ref)1.14 (0.93–1.35)2.74 (2.55–2.93)
     65–751.22 (0.93–1.50)2.81 (2.56–3.06)
    Sex
     Female (Ref)1.14 (0.93–1.35)2.74 (2.55–2.93)
     Male1.18 (0.97–1.39)2.62 (2.43–2.81)c
    Morbidity burden (ACG RUB)
     Moderate (Ref)1.14 (0.93–1.35)2.74 (2.55–2.93)
     High0.84 (0.62–1.06)c2.66 (2.46–2.86)c
     Very high0.69 (0.45–0.93)c2.72 (2.51–2.93)
    Insurance type
     Commercial (Ref)1.14 (0.93–1.35)2.74 (2.55–2.93)
     Medicaid/state-subsidized1.00 (0.63–1.38)3.16 (2.83–3.48)b
     Medicare0.96 (0.68–1.23)2.78 (2.53–3.03)

    • ACG = adjusted clinical group; PCMH = patient-centered medical home; Ref = referent category; RUB=resource utilization band.

    • Note: Results are from log-linear regression models.

    • ↵a Adjusted for age, sex, morbidity burden, insurance type, clinician network, well-care waiver, pharmaceutical coverage, education, income, baseline secure messaging use, hemoglobin A1c level, blood pressure, low-density lipoprotein cholesterol level, study period, calendar quarter, physician secure messaging, and physician telephone encounter use.

    • ↵b Positive effect modification at P ≤.05, compared with referent category.

    • ↵c Negative effect modification at P ≤.05, compared with referent category.

Additional Files

  • Supplemental Appendix

    Supplemental Appendix. Diabetes Case Definition

    Files in this Data Supplement:

  • In Brief

    Changes in Office Visit use Associated With Electronic Messaging and Telephone Encounters Among Patients With Diabetes in the PCMH

    David T. Liss , and colleagues

    Background Telephone- and internet-based communication is increasingly common in primary care, but it is uncertain how these forms of communication affect demand for in-person office visits. This study assesses how, among diabetes patients, use of secure messaging and telephone visits is associated with office visit use.

    What This Study Found Increases in electronic and phone messaging are associated with an increase in primary care office visits for individuals with diabetes.

    Implications

    • Secure messages and telephone encounters could stimulate demand for visits by reducing barriers to access and allowing patients to address previously unmet needs.

  • Annals Journal Club

    Jul/Aug: A Paradoxical Effect?


    The Annals of Family Medicine encourages readers to develop a learning community of those seeking to improve health care and health through enhanced primary care. You can participate by conducting a RADICAL journal club and sharing the results of your discussions in the Annals online discussion for the featured articles. RADICAL is an acronym for Read, Ask, Discuss, Inquire, Collaborate, Act, and Learn. The word radical also indicates the need to engage diverse participants in thinking critically about important issues affecting primary care and then acting on those discussions.1

    HOW IT WORKS

    In each issue, the Annals selects an article or articles and provides discussion tips and questions. We encourage you to take a RADICAL approach to these materials and to post a summary of your conversation in our online discussion. (Open the article online and click on "TRACK Discussion: Submit a comment.") You can find discussion questions and more information online at: http://www.AnnFamMed.org/site/AJC/.

    CURRENT SELECTION

    Article for Discussion

    • Liss DT, Reid RJ, Grembowski D, Rutter CM, Ross TR, Fishman PA. Changes in office visit use associated with electronic messaging and telephone encounters among patients with diabetes in the PCMH. Ann Fam Med. 2014;12(4):338-343.

    Discussion Tips

    This article provides an opportunity to consider findings from one novel component of a patient-centered medical home intervention. The study provides an example of the increasingly commonly used interrupted time series design for studying natural experiments, and requires consideration of contextual factors in interpreting and transporting the findings.

    Discussion Questions

    • What question is asked by this study and why does it matter?
    • How does this study advance beyond previous research and clinical practice on this topic?
    • How strong is the study design for answering the question? What alternatives exist for learning from this kind of real world experiment?
    • To what degree can the findings be accounted for by:
      1. How patients were selected, excluded, or lost to follow-up?
      2. How the main variables were measured?
      3. Confounding (false attribution of causality because 2 variables discovered to be associated actually are associated with a 3rd factor)?
      4. Chance?
      5. How the findings were interpreted?
    • How were potential threats to validity dealt with in the study design and analysis approach?
    • What are the main study findings?
    • How do you interpret the differences between the crude and regression analyses? How do you interpret the attenuation over time of the association between office visits and telephone encounters?
    • How comparable is the study sample to similar patients in your practice? What is your judgment about the transportability of the findings?
    • What contextual factors are important for interpreting the findings (ie the nesting of this study within an ongoing patient-centered medical home initiative within an integrated health care system)?
    • How might this study change your practice? Policy? Education? Research?
    • Who are the constituencies for the findings, and how might they be engaged in interpreting or using the findings?
    • What are the next steps in interpreting or applying the findings?
    • What researchable questions remain?

    References

    1. Stange KC, Miller WL, McLellan LA, et al. Annals Journal Club: It's time to get RADICAL. Ann Fam Med. 2006;4(3):196-197 http://annfammed.org/content/4/3/196.full.

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