Reports From the Field

Multidisciplinary Diabetes Care in a Safety Net Clinic: Lessons Learned from a Quality Improvement Initiative

Journal of Clinical Outcomes Management. 2018 May;25(5):206-210

References

1. Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of diabetes and its burden in the United States, 2014. Atlanta, GA: U.S. Department of Health and Human Services; 2014. Accessed at www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdf.

2. Casagrande SS, Cowie CC. Health insurance coverage among people with and without diabetes in the US adult population. Diabetes Care 2012;35:2243–9.

3. American Diabetes Association. Statistics about diabetes. Arlington, VA; 2017.

4. Eeg-Olofsson K, Eliasson B, Zethelius B, et al. HbA1c reduction and risk of cardiovascular diseases in type 2 diabetes: an observational study from the Swedish NDR. Diabetes 2012.

5. Baxter M, Hudson R, Mahon J, et al. Estimating the impact of better management of glycaemic control in adults with type 1 and type 2 diabetes on the number of clinical complications and the associated financial benefit. Diabet Med 2016;33:1575–81.

6. Harris County Healthcare Alliance. The State of Health in Houston/Harris County 2015-2016. Accessed 17 Mar 2015 at http://houstonstateofhealth.org/soh_doc/.

7. Chalkidou K, Tunis S, Whicher D, et al. The role for pragmatic randomized controlled trials (pRCTs) in comparative effectiveness research. Clin Trials (London, England) 2012;9:436–46.

8. Tricoci P, Allen JM, Kramer JM, et al. Scientific evidence underlying the ACC/AHA clinical practice guidelines. JAMA 2009;301:831–41.

9. Wagner EH, Austin BT, Von Korff M. Organizing care for patients with chronic illness. Milbank Qty 1996:511–44.

10. Ogrinc G, Davies L, Goodman D, et al. SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process. BMJ Qual Safety 2016;25:986–92.

11. Standards of Medical Care in Diabetes—2015 Abridged for Primary Care Providers. American Diabetes Association. Clin Diabetes 2015;33:97–111.

12. Gelman A, Hill J. Data analysis using regression and multilevelhierarchical models. New York: Cambridge University Press; 2007.

13. SAS Institute I. Base SAS Procedures Guide: Statistical procedures. In: SAS Institute I, editor. Cary, NC; 2011.

14. Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for general causal inference. Boston: Houghton Mifflin; 2002.

15. Kane R. Understanding health care outcomes research. 2nd ed. In: Learning JB, editor. Burlington, MA: Jones and Bartlett; 2006: 44–6.

Diffusion of treatment may account for the overall HbA1c reduction regardless of treatment group. Diffusion refers to the unintended spread of a treatment effect when participants receive some or all treatments from an intervention to which they were not assigned, making outcomes descriptions of all study groups more challenging [14]. During the implementation period, other physicians and nurses in the clinic were aware of the multidisciplinary care model being piloted, and may have taken the initiative to connect their patients with clinical pharmacists, dieticians, certified diabetes educators, and clinical case managers. Pragmatic interventions are intended to maintain the internal validity of randomized control trials, yet they are meant to be implemented as close as possible to real-world settings in order to help patients, clinicians, and payers making informed health care decisions [8]. In this regard, participants in the control group could be exposed to the intervention through staff contact between the assigned groups implementing some of the intervention under study. In that case, the diffusion of treatment would be likely to attenuate differences in the outcomes between treatment and control groups [15].

This study has several limitations. We studied a small sample of patients that reflected the primary care population in one clinic in a safety net system with minority, underserved, and high-risk patients. Although attempts were made to keep the intervention limited to the intervention pod, diffusion of treatment might have impacted the internal validity of this intervention.

In summary, our results support the need for further systematic research work to weigh the impact of unblinded designs, simplified recruitment and outcome measurement, and real-world behaviors (such as noncompliance, cross over, and dropout) on evidence-based and multidisciplinary clinical interventions.

Acknowledgements: The authors would like to thank Krystal Gamarra, MSW, LCSW, and Hope Galvan, MS, RN, CVRN-BC, CDE for assistance with project implementation and data collection throughout this process.

Corresponding author: Maria C. Mejia de Grubb, MD, MPH, 3751 Kirby Dr, Suite 600, Houston, TX 77098, mcgrubb@bcm.edu.

Financial disclosures: None.