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Journal of General Internal Medicine

, Volume 34, Issue 1, pp 41–48 | Cite as

The Impact of Population-Based Disease Management Services on Health Care Utilisation and Costs: Results of the CAPICHe Trial

  • Paul A. ScuffhamEmail author
  • Joshua M. Byrnes
  • Christine Pollicino
  • David Cross
  • Stan Goldstein
  • Shu-Kay Ng
Original Research

Abstract

Background

Disease management programmes may improve quality of care, improve health outcomes and potentially reduce total healthcare costs. To date, only one very large population-based study has been undertaken and indicated reductions in hospital admissions > 10%.

Objective

We sought to confirm the effectiveness of population-based disease management programmes. The objective of this study was to evaluate the relative impact on healthcare utilisation and cost of participants the Costs to Australian Private Insurance – Coaching Health (CAPICHe) trial.

Design

Parallel-group randomised controlled trial, intention-to-treat analysis

Setting

Australian population

Participants

Forty-four thousand four hundred eighteen individuals (18–90 years of age) with private health insurance and diagnosis of heart failure, chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD), diabetes, or low back pain, with predicted high cost claims for the following 12 months.

Intervention

Health coaching for disease management from Bupa Health Dialog, vs Usual Care.

Main Outcome Measures

Total cost of claims per member to the private health insurer 1 year post-randomisation for hospital admissions, including same-day, medical and prostheses hospital claims, excluding any maternity costs. Analysis was based on the intent-to-treat population.

Results

Estimated total cost 1 year post-randomisation was not significantly different (means: intervention group A$4934; 95% CI A$4823–A$5045 vs control group A$4868; 95% CI A$4680–A$5058; p = 0.524). However, the intervention group had significantly lower same-day admission costs (A$468; 95% CI A$454–A$482 vs A$508; 95% CI A$484–A$533; p = 0.002) and fewer same-day admissions per 1000 person-years (intervention group, 530; 95% CI 508–552 vs control group, 614; 95% CI 571–657; p = 0.002). Subgroup analyses indicated that the intervention group had significantly fewer admissions for patients with COPD and fewer same-day admissions for patients with diabetes.

Conclusions

Chronic disease health coaching was not effective to reduce the total cost after 12 months of follow-up for higher risk individuals with a chronic condition. Statistically significant changes were found with fewer same-day admissions; however, these did not translate into cost savings from a private health insurance perspective.

KEY WORDS

disease management costs private healthcare insurance 

Notes

Acknowledgements

All authors gratefully acknowledge the contribution of Raimundo Gomes MSc, Bupa Australia, for extracting the data from the Bupa Australia claims database.

Funding Information

This trial was funded by the Bupa Health Foundation, Australia, which is an independent foundation with charity status under the Australian Taxation Office rules.

Compliance with Ethical Standards

This study was conducted in accordance with the ethics approval from the Griffith University Human Research Ethics Committe (ref MED/10/11/HREC).

Conflict of Interest

PS, JB and SN were independent consultants and have no conflicts of interest. CP and SG are employees of Bupa Australia. DC is an employee of Bupa Health Dialog, part of Bupa Australia. Coaching was provided by Bupa Health Dialog. Health Dialog in the USA provided the risk scoring algorithm which was adapted for Australia.

Disclaimer

The funders had no role in the trial design, data analysis, interpretation of data, or writing of this report. The corresponding author had full access to the extracted data in the trial and had final responsibility for the decision to submit for publication.

Supplementary material

11606_2018_4682_MOESM1_ESM.docx (65 kb)
ESM 1 (DOCX 65 kb)

References

  1. 1.
    Mays GP, Au M, Claxton G. MARKETWATCH: Convergence And Dissonance: Evolution In Private-Sector Approaches To Disease Management And Care Coordination. Health Aff 2007;26(6):1683–91.CrossRefGoogle Scholar
  2. 2.
    Todd W, Nash D, editors. Disease Management: A Systems Approach to Improving Patient Outcomes. San Francisco: Jossey Bass, 2001.Google Scholar
  3. 3.
    Ellrodt G, Cook DJ, Lee J, et al. Evidence-Based Disease Management. JAMA 1997;278(20):1687–92.  https://doi.org/10.1001/jama.1997.03550200063033 CrossRefPubMedGoogle Scholar
  4. 4.
    Bodenheimer T. Disease management in the American market. BMJ 2000;320(7234):563–6. [published Online First: 2000/02/25]CrossRefGoogle Scholar
  5. 5.
    Buntin M, Jain A, Mattke S, et al. Who Gets Disease Management? J Gen Intern Med 2009;24(5):649–55.  https://doi.org/10.1007/s11606-009-0950-8 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Wennberg DE, Marr A, Lang L, et al. A Randomized Trial of a Telephone Care-Management Strategy. N Engl J Med 2010;363(13):1245–55.  https://doi.org/10.1056/NEJMsa0902321 CrossRefPubMedGoogle Scholar
  7. 7.
    Byrnes J, Goldstein S, Venator B, et al. The impact of population-based disease management services for selected chronic conditions: the Costs to Australian Private Insurance - Coaching Health (CAPICHe) study protocol. BMC Public Health 2012;12(1):114.CrossRefGoogle Scholar
  8. 8.
    AIHW. Admitted patient care 2014–15: Australian hospital statistics. Health services series no. 68. Cat. no. HSE 172. Canberra: AIHW. 2016Google Scholar
  9. 9.
    Little RJ, Long Q, Lin X. A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance. Biometrics 2009;65:640–9.  https://doi.org/10.1111/j.1541-0420.2008.01066.x.CrossRefPubMedGoogle Scholar
  10. 10.
    Sussman JB, Hayward RA. An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials. BMJ 2010;340:c2073.  https://doi.org/10.1136/bmj.c2073 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Akbarzadeh Baghban A, Pourhoseingholi A, Zayeri F, et al. Zero inflated statistical count models for analysing the costs imposed by GERD and dyspepsia. Arab J Gastroenterol 2013;14(4):165–68. doi: https://doi.org/10.1016/j.ajg.2013.09.004 CrossRefPubMedGoogle Scholar
  12. 12.
    Greene W. Accounting for excess zeros and sample selection in poisson and negative binomial regression models. Working Papers EC-94-10. New York University: New York University, Leonard N. Stern School of Business, Department of Economics, 1994.Google Scholar
  13. 13.
    Mihaylova B, Briggs A, O’Hagan A, et al. Review of statistical methods for analysing healthcare resources and costs. Health Econ 2011;20(8):897–916.  https://doi.org/10.1002/hec.1653 [published Online First: 2010/08/28]CrossRefPubMedGoogle Scholar
  14. 14.
    Ng SK, Holden L, Sun J. Identifying comorbidity patterns of health conditions via cluster analysis of pairwise concordance statistics. Stat Med 2012;31:3393–405.  https://doi.org/10.1002/sim.5426 CrossRefPubMedGoogle Scholar
  15. 15.
    Ng SK. A two-way clustering framework to identify disparities in multimorbidity patterns of mental and physical health conditions among Australians. Stat Med 2015;34:3444–60.  https://doi.org/10.1002/sim.6542 CrossRefPubMedGoogle Scholar
  16. 16.
    Eakin EG, Lawler SP, Vandelanotte C, et al. Telephone interventions for physical activity and dietary behavior change: a systematic review. Am J Prev Med 2007;32(5):419–34.  https://doi.org/10.1016/j.amepre.2007.01.004 [published Online First: 2007/05/05]CrossRefPubMedGoogle Scholar
  17. 17.
    Williams ED, Bird D, Forbes AW, et al. Randomised controlled trial of an automated, interactive telephone intervention (TLC Diabetes) to improve type 2 diabetes management: baseline findings and six-month outcomes. BMC Public Health 2012;12:602.  https://doi.org/10.1186/1471-2458-12-602 [published Online First: 2012/08/04]CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Wolever RQ, Dreusicke M, Fikkan J, et al. Integrative health coaching for patients with type 2 diabetes: a randomized clinical trial. Diabetes Educ 2010;36(4):629–39.  https://doi.org/10.1177/0145721710371523 [published Online First: 2010/06/11]CrossRefPubMedGoogle Scholar
  19. 19.
    Gordon LG, Bird D, Oldenburg B, et al. A cost-effectiveness analysis of a telephone-linked care intervention for individuals with Type 2 diabetes. Diabetes Res Clin Pract 2014;104(1):103–11.  https://doi.org/10.1016/j.diabres.2013.12.032 [published Online First: 2014/02/08]CrossRefPubMedGoogle Scholar
  20. 20.
    Sangster J, Church J, Haas M, et al. A Comparison of the Cost-effectiveness of Two Pedometer-based Telephone Coaching Programs for People with Cardiac Disease. Heart Lung Circ 2015;24(5):471–9.  https://doi.org/10.1016/j.hlc.2015.01.008 [published Online First: 2015/02/24]CrossRefPubMedGoogle Scholar
  21. 21.
    Patja K, Absetz P, Auvinen A, et al. Health coaching by telephony to support self-care in chronic diseases: clinical outcomes from The TERVA randomized controlled trial. BMC Health Serv Res 2012;12:147.  https://doi.org/10.1186/1472-6963-12-147 [published Online First: 2012/06/12]CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Hawkes AL, Patrao TA, Atherton J, et al. Effect of a telephone-delivered coronary heart disease secondary prevention program (proactive heart) on quality of life and health behaviours: primary outcomes of a randomised controlled trial. Int J Behav Med 2013;20(3):413–24.  https://doi.org/10.1007/s12529-012-9250-5 [published Online First: 2012/09/27]CrossRefPubMedGoogle Scholar
  23. 23.
    Turkstra E, Hawkes AL, Oldenburg B, et al. Cost-effectiveness of a coronary heart disease secondary prevention program in patients with myocardial infarction: results from a randomised controlled trial (ProActive Heart). BMC Cardiovasc Disord 2013;13:33.  https://doi.org/10.1186/1471-2261-13-33 [published Online First: 2013/05/03]CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Blackberry ID, Furler JS, Best JD, et al. Effectiveness of general practice based, practice nurse led telephone coaching on glycaemic control of type 2 diabetes: the Patient Engagement and Coaching for Health (PEACH) pragmatic cluster randomised controlled trial. BMJ 2013;347:f5272.  https://doi.org/10.1136/bmj.f5272 [published Online First: 2013/09/21]CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Ruggiero L, Riley BB, Hernandez R, et al. Medical assistant coaching to support diabetes self-care among low-income racial/ethnic minority populations: randomized controlled trial. West J Nurs Res 2014;36(9):1052–73.  https://doi.org/10.1177/0193945914522862 [published Online First: 2014/02/27]CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Society of General Internal Medicine 2018

Authors and Affiliations

  • Paul A. Scuffham
    • 1
    Email author
  • Joshua M. Byrnes
    • 1
  • Christine Pollicino
    • 2
  • David Cross
    • 3
  • Stan Goldstein
    • 2
  • Shu-Kay Ng
    • 1
  1. 1.Centre for Applied Health Economics, Menzies Health Institute QueenslandGriffith UniversityBrisbaneAustralia
  2. 2.Bupa AustraliaSydneyAustralia
  3. 3.Bupa Health DialogMelbourneAustralia

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