Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study

Ruben Amarasingham, Parag C Patel, Kathleen Toto, Lauren L Nelson, Timothy S Swanson, Billy J Moore, Bin Xie, Song Zhang, Kristin S Alvarez, Ying Ma, Mark H Drazner, Usha Kollipara, Ethan A Halm, Ruben Amarasingham, Parag C Patel, Kathleen Toto, Lauren L Nelson, Timothy S Swanson, Billy J Moore, Bin Xie, Song Zhang, Kristin S Alvarez, Ying Ma, Mark H Drazner, Usha Kollipara, Ethan A Halm

Abstract

Objective: To test a multidisciplinary approach to reduce heart failure (HF) readmissions that tailors the intensity of care transition intervention to the risk of the patient using a suite of electronic medical record (EMR)-enabled programmes.

Methods: A prospective controlled before and after study of adult inpatients admitted with HF and two concurrent control conditions (acute myocardial infarction (AMI) and pneumonia (PNA)) was performed between 1 December 2008 and 1 December 2010 at a large urban public teaching hospital. An EMR-based software platform stratified all patients admitted with HF on a daily basis by their 30-day readmission risk using a published electronic predictive model. Patients at highest risk received an intensive set of evidence-based interventions designed to reduce readmission using existing resources. The main outcome measure was readmission for any cause and to any hospital within 30 days of discharge.

Results: There were 834 HF admissions in the pre-intervention period and 913 in the post-intervention period. The unadjusted readmission rate declined from 26.2% in the pre-intervention period to 21.2% in the post-intervention period (p=0.01), a decline that persisted in adjusted analyses (adjusted OR (AOR)=0.73; 95% CI 0.58 to 0.93, p=0.01). In contrast, there was no significant change in the unadjusted and adjusted readmission rates for PNA and AMI over the same period. There were 45 fewer readmissions with 913 patients enrolled and 228 patients receiving intervention, resulting in a number needed to treat (NNT) ratio of 20.

Conclusions: An EMR-enabled strategy that targeted scarce care transition resources to high-risk HF patients significantly reduced the risk-adjusted odds of readmission.

Keywords: Decision support, clinical; Health services research; Healthcare quality improvement; Information technology.

Figures

Figure 1
Figure 1
Patient selection and review process.
Figure 2
Figure 2
Thirty-day readmission rates by month.

References

    1. Goodman DC, Fisher ES, Chang C-H. After hospitalization: a Dartmouth atlas report on post-acute care for Medicare beneficiaries. Hanover, NH: The Dartmouth Institute for Health Policy and Clinical Practice, 28 September 2011
    1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009;360:1418–28
    1. Phillips CO, Wright SM, Kern DE, et al. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure. JAMA 2004;291:1358–67
    1. Seow H, Phillips CO, Rich MW, et al. Isolation of health services research from practice and policy: the example of chronic heart failure management. J Am Geriatr Soc 2006;54:535–40
    1. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011;306:1688–98
    1. Burke RE, Coleman EA. Interventions to decrease hospital readmissions: keys for cost-effectiveness. JAMA Intern Med 2013;173:695–8
    1. Amarasingham R, Moore BJ, Tabak YP,, et al. An automated model to identify heart failure patients at risk for readmission or death using electronic medical record data. Med Care 2010;48:981–8
    1. Centers for Medicare & Medicaid Services. ICD-9 codes included in range 403.00–405.99. (accessed 24 Jul 2012)
    1. Centers for Medicare & Medicaid Services. ICD-9 codes included in range 428.0–428.9. (accessed 24 Jul 2012)
    1. Centers for Medicare & Medicaid Services. ICD-9 codes included in range 410.00–410.92. (accessed 24 Jul 2012)
    1. Centers for Medicare & Medicaid Services. ICD-9 codes included in range 480.0–508.9. (accessed 24 Jul 2012)
    1. Voss R, Gardner R, Baier R, et al. The care transitions intervention: translating from efficacy to effectiveness. Arch Intern Med 2011;171:1232–7
    1. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 2009;150:178–87
    1. Society of Hospital Medicine. SHM Project BOOST: Better Outcomes for Older adults through Safe Transitions. (accessed 20 Jul 2012)
    1. Boutwell AE, Johnson MB, Rutherford P, et al. An early look at a four-state initiative to reduce avoidable hospital readmissions. Health Aff 2011;30:1272–80
    1. Hansen LO, Young RS, Hinami K, et al. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 2011;155:520–8
    1. Keenan PS, Normand S-LT, Lin Z, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circulation 2008(1):29–37
    1. Tabak YP, Johannes RS, Silber JH. Using automated clinical data for risk adjustment: development and validation of six disease-specific mortality predictive models for pay-for-`performance. Med Care 2007;45:789–805
    1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41–55
    1. D'Agostino R., Jr Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998;17:2265–81
    1. Diggle PJ, Liang KY, Zeger SL. Analysis of longitudinal data. Oxford: Oxford University Press, 1994
    1. White H. Maximum likelihood estimation of misspecified models. Econometrica 1982;50:1–25
    1. Hernandez AF, Greiner MA, Fonarow GC, et al. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA 2010;303:1716–22
    1. Anderson C, Deepak BV, Amoateng-Adjepong Y, et al. Benefits of comprehensive inpatient education and discharge planning combined with outpatient support in elderly patients with congestive heart failure. Congest Heart Fail 2005;11:315–21
    1. Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA 2011;305:675–81

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться