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Published in: BMC Health Services Research 1/2024

Open Access 01-12-2024 | Research

Treatment approaches and costs associated with diabetes clinical metrics as measured by Healthcare Effectiveness Data and Information Set (HEDIS)

Authors: Jamil Alkhaddo, Jillian M. Rung, Ameer Khowaja, Yue Yin, Shannon B. Richards, Charlotte Drury-Gworek, Samina Afreen, Caitlan Rossi, Susan Manzi

Published in: BMC Health Services Research | Issue 1/2024

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Abstract

Background

The clinical outcomes of diabetes can be influenced by primary care providers’ (PCP) treatment approaches. This study explores the association between PCP approaches to management and performance measured by established diabetes metrics and related costs.

Methods

In phase one, Electronic Medical Records were used to extract diabetes related metrics using Healthcare Effectiveness Data and Information Set (HEDIS), for patients with diabetes who had office visits to 44 PCP practices from April 2019 to March 2020. Using those metrics and scoring system, PCP practices were ranked and then categorized into high- and low-performing groups (top and bottom 25%, n = 11 each), with a total of 19,059 clinic visits by patients with a diagnosis of diabetes. Then extensive analysis was performed to evaluate a correlation between treatment approaches and diabetes outcomes across the top and bottom performing practices. In phase 2, patients with diabetes who were attributed to the aforementioned PCP practices were identified in a local health plan claims data base (a total of 3,221 patients), and the allowed amounts from their claims were used to evaluate differences in total and diabetes-related healthcare costs by providers’ performance.

Results

Comparing 10,834 visits in high-performing practices to 8,235 visits in low-performing practices, referrals to certified diabetes care and education specialists and provider-to-provider electronic consults (e-consults) were higher in high-performing practices (Z = 6.06, p < .0001), while traditional referrals were higher in low-performing practices (Z = -6.94, p < .0001). The patient-to-provider ratio was higher in the low-performing group (M = 235.23) than in the high-performing group (M = 153.26) (Z = -2.82, p = .0048). Claims data analysis included 1,825 and 1,396 patients from high- and low-performing providers, respectively. The patient-to-provider ratio was again higher in the low-performing group (p = .009, V = 0.62). Patients receiving care from lower-performing practices were more likely to have had a diabetes-related hospital observation (5.7% vs. 3.9%, p = .02; V = 0.04) and higher diabetes-related care costs (p = .002; d = − 0.07); these differences by performance status persisted when controlling for differences in patient and physician characteristics. Patients seeing low-performing providers had higher Charlson Comorbidity Index scores (Mdn = 3) than those seeing high-performing providers (Mdn = 2).

Conclusions

Referrals to the CDCES and e-Consult were associated with better measured diabetes outcomes, as were certain aspects of cost and types of hospital utilization. Higher patients to providers ratio and patients with more comorbidities were observed in low performing group.
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Literature
1.
go back to reference Pantalone K, Misra-Hebert A, Hobbs T, et al. The probability of A1C goal attainment in patients with uncontrolled type 2 diabetes in a large Integrated Delivery System: a prediction model. Diabetes Care. 2020;43(8):1910–9.CrossRefPubMedPubMedCentral Pantalone K, Misra-Hebert A, Hobbs T, et al. The probability of A1C goal attainment in patients with uncontrolled type 2 diabetes in a large Integrated Delivery System: a prediction model. Diabetes Care. 2020;43(8):1910–9.CrossRefPubMedPubMedCentral
2.
go back to reference Siminerio LM, Piatt G, Zgibor JC. Implementing the chronic care model for improvements in diabetes care and education in a rural primary care practice. Diabetes Educ. 2005;31(2):225–34.CrossRefPubMed Siminerio LM, Piatt G, Zgibor JC. Implementing the chronic care model for improvements in diabetes care and education in a rural primary care practice. Diabetes Educ. 2005;31(2):225–34.CrossRefPubMed
3.
go back to reference Janes GR. Ambulatory medical care for diabetes. In: Group NDD, editor. Diabetes in America. Bethesda, Md: National Institutes of Health; 1995. pp. 95–1468. Janes GR. Ambulatory medical care for diabetes. In: Group NDD, editor. Diabetes in America. Bethesda, Md: National Institutes of Health; 1995. pp. 95–1468.
4.
go back to reference Peterson KA, Brown MT, Warren-Boulton E. Responding to the challenges of primary diabetes care through the National Diabetes Education Program. Diabetes Care. 2015;38:343–4.CrossRefPubMed Peterson KA, Brown MT, Warren-Boulton E. Responding to the challenges of primary diabetes care through the National Diabetes Education Program. Diabetes Care. 2015;38:343–4.CrossRefPubMed
9.
go back to reference Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.CrossRefPubMed Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.CrossRefPubMed
10.
go back to reference Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676–82.CrossRefPubMed Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676–82.CrossRefPubMed
11.
go back to reference H. Wickham. ggplot2: elegant graphics for data analysis. Springer- New York, 2016. H. Wickham. ggplot2: elegant graphics for data analysis. Springer- New York, 2016.
19.
go back to reference Vimalananda VG, et al. Electronic consultations (E-consults) and their outcomes: a systematic review. J Am Med Inf Assoc. 2020;27(3):471–9.CrossRef Vimalananda VG, et al. Electronic consultations (E-consults) and their outcomes: a systematic review. J Am Med Inf Assoc. 2020;27(3):471–9.CrossRef
20.
go back to reference Patel PS, et al. Electronic Consultation: an effective alternative to In-Person clinical care for patients with diabetes Mellitus. J Diabetes Sci Technol. 2019;13(1):152–3.CrossRefPubMed Patel PS, et al. Electronic Consultation: an effective alternative to In-Person clinical care for patients with diabetes Mellitus. J Diabetes Sci Technol. 2019;13(1):152–3.CrossRefPubMed
21.
go back to reference Ashrafzadeh S, Hamdy O. Patient-driven Diabetes Care of the future in the Technology era. Cell Metab. 2019;29(3):564–75.CrossRefPubMed Ashrafzadeh S, Hamdy O. Patient-driven Diabetes Care of the future in the Technology era. Cell Metab. 2019;29(3):564–75.CrossRefPubMed
23.
go back to reference Powers MA, et al. Diabetes self-management education and support in adults with type 2 diabetes: a Consensus Report of the American Diabetes Association, the Association of Diabetes Care & Education Specialists, the Academy of Nutrition and Dietetics, the American Academy of Family Physicians, the American Academy of PAs, the American Association of Nurse Practitioners, and the American pharmacists Association. Diabetes Educ. 2020;46(4):350–69.CrossRefPubMed Powers MA, et al. Diabetes self-management education and support in adults with type 2 diabetes: a Consensus Report of the American Diabetes Association, the Association of Diabetes Care & Education Specialists, the Academy of Nutrition and Dietetics, the American Academy of Family Physicians, the American Academy of PAs, the American Association of Nurse Practitioners, and the American pharmacists Association. Diabetes Educ. 2020;46(4):350–69.CrossRefPubMed
24.
go back to reference Li R, Shrestha SS, Lipman R, et al. Diabetes self-management education and training among privately insured persons with newly diagnosed diabetes–United States, 2011–2012. MMWR Morb Mortal Wkly Rep. 2014;63(46):1045–9. Li R, Shrestha SS, Lipman R, et al. Diabetes self-management education and training among privately insured persons with newly diagnosed diabetes–United States, 2011–2012. MMWR Morb Mortal Wkly Rep. 2014;63(46):1045–9.
25.
go back to reference James TL. Improving referrals to Diabetes Self-Management Education in Medically Underserved adults. Diabetes Spectr. 2021;34(1):20–6. James TL. Improving referrals to Diabetes Self-Management Education in Medically Underserved adults. Diabetes Spectr. 2021;34(1):20–6.
27.
go back to reference Bodenheimer T. Coordinating Care—A perilous journey through the Health Care System. N Engl J Med. 2008;358:1064–71.CrossRefPubMed Bodenheimer T. Coordinating Care—A perilous journey through the Health Care System. N Engl J Med. 2008;358:1064–71.CrossRefPubMed
28.
go back to reference Kerr EA, et al. Beyond comorbidity counts: how do comorbidity type and severity influence diabetes patients’ treatment priorities and self-management? J Gen Intern Med. 2007;22(12):1635–40.CrossRefPubMedPubMedCentral Kerr EA, et al. Beyond comorbidity counts: how do comorbidity type and severity influence diabetes patients’ treatment priorities and self-management? J Gen Intern Med. 2007;22(12):1635–40.CrossRefPubMedPubMedCentral
29.
go back to reference Glycemic Targets. Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021;44(Suppl 1):S73–s84. Glycemic Targets. Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021;44(Suppl 1):S73–s84.
30.
go back to reference Harris J, Haltbakk J, Dunning T, et al. How patient and community involvement in diabetes research influences health outcomes: a realist review. Health Expect. 2019;22(5):907–20.CrossRefPubMedPubMedCentral Harris J, Haltbakk J, Dunning T, et al. How patient and community involvement in diabetes research influences health outcomes: a realist review. Health Expect. 2019;22(5):907–20.CrossRefPubMedPubMedCentral
Metadata
Title
Treatment approaches and costs associated with diabetes clinical metrics as measured by Healthcare Effectiveness Data and Information Set (HEDIS)
Authors
Jamil Alkhaddo
Jillian M. Rung
Ameer Khowaja
Yue Yin
Shannon B. Richards
Charlotte Drury-Gworek
Samina Afreen
Caitlan Rossi
Susan Manzi
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2024
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-024-10745-2

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