Methods Inf Med 2019; 58(02/03): 071-078
DOI: 10.1055/s-0039-1694990
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Risk Adjusting Health Care Provider Collaboration Networks

Ariel E. Chandler
1   Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
,
R. Kannan Mutharasan
2   Department of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
,
Lia Amelia
3   Chapin Hall at the University of Chicago, Chicago, Illinois, United States
,
Matthew B. Carson
4   Galter Health Sciences Library & Learning Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
,
Denise M. Scholtens
5   Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
,
Nicholas D. Soulakis
1   Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
› Author Affiliations
Further Information

Publication History

15 January 2019

28 June 2019

Publication Date:
12 September 2019 (online)

Abstract

Objectives The quality of hospital discharge care and patient factors (health and sociodemographic) impact the rates of unplanned readmissions. This study aims to measure the effects of controlling for the patient factors when using readmission rates to quantify the weighted edges between health care providers in a collaboration network. This improved understanding may inform strategies to reduce hospital readmissions, and facilitate quality-improvement initiatives.

Methods We extracted 4 years of patient, provider, and activity data related to cardiology discharge workflow. A Weibull model was developed to predict the risk of unplanned 30-day readmission. A provider–patient bipartite network was used to connect providers by shared patient encounters. We built collaboration networks and calculated the Shared Positive Outcome Ratio (SPOR) to quantify the relationship between providers by the relative rate of patient outcomes, using both risk-adjusted readmission rates and unadjusted readmission rates. The effect of risk adjustment on the calculation of the SPOR metric was quantified using a permutation test and descriptive statistics.

Results Comparing the collaboration networks consisting of 2,359 provider pairs, we found that SPOR values with risk-adjusted outcomes are significantly different than unadjusted readmission as an outcome measure (p-value = 0.025). The two networks classified the same provider pairs as high-scoring 51.5% of the time, and the same low scoring provider pairs 85.6% of the time. The observed differences in patient demographics and disease characteristics between high-scoring and low-scoring provider pairs were reduced by applying the risk-adjusted model. The risk-adjusted model reduced the average variation across each individual's SPOR scored provider connections.

Conclusions Risk adjusting unplanned readmission in a collaboration network has an effect on SPOR-weighted edges, especially on classifying high-scoring SPOR provider pairs. The risk-adjusted model reduces the variance of providers' connections and balances shared patient characteristics between low- and high-scoring provider pairs. This indicates that the risk-adjusted SPOR edges better measure the impact of collaboration on readmissions by accounting for patients' risk of readmission.

 
  • References

  • 1 Damiani G, Salvatori E, Silvestrini G. , et al. Influence of socioeconomic factors on hospital readmissions for heart failure and acute myocardial infarction in patients 65 years and older: evidence from a systematic review. Clin Interv Aging 2015; 10: 237-245
  • 2 Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 2013; 173 (08) 632-638
  • 3 Ashton CM, Kuykendall DH, Johnson ML, Wray NP, Wu L. The association between the quality of inpatient care and early readmission. Ann Intern Med 1995; 122 (06) 415-421
  • 4 Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial. J Am Geriatr Soc 2004; 52 (05) 675-684
  • 5 DesHarnais SI, McMahon Jr LF, Wroblewski RT, Hogan AJ. Measuring hospital performance. The development and validation of risk-adjusted indexes of mortality, readmissions, and complications. Med Care 1990; 28 (12) 1127-1141
  • 6 Horwitz LI, Partovian C, Lin Z. , et al. Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission. Ann Intern Med 2014; 161 (10, Suppl): S66-S75
  • 7 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. Circ Cardiovasc Qual Outcomes 2008; 1 (01) 29-37
  • 8 Centers for Medicare & Medicaid Services. Measure information about the 30-day all-cause hospital readmission measure, calculated for the Value-Based Payment Modifier Program. 2015 . Available at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/PhysicianFeedbackProgram/Downloads/2014-ACR-MIF.pdf . Accessed December 15, 2018
  • 9 Kansagara D, Englander H, Salanitro A. , et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011; 306 (15) 1688-1698
  • 10 Bae SH, Nikolaev A, Seo JY, Castner J. Health care provider social network analysis: a systematic review. Nurs Outlook 2015; 63 (05) 566-584
  • 11 Sabot K, Wickremasinghe D, Blanchet K, Avan B, Schellenberg J. Use of social network analysis methods to study professional advice and performance among healthcare providers: a systematic review. Syst Rev 2017; 6 (01) 208
  • 12 Newman ME. The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 2001; 98 (02) 404-409
  • 13 Meltzer D, Chung J, Khalili P. , et al. Exploring the use of social network methods in designing healthcare quality improvement teams. Soc Sci Med 2010; 71 (06) 1119-1130
  • 14 Robinson I, Webber J, Eifrem E. Graph Databases: New Opportunities for Connected Data. Sebastopol, CA: O'Reilly Media; 2015
  • 15 Sehgal N. Annual perspective 2014: handoffs and transitions: AHRQ Patient Safety Network; 2015 . Available at: http://psnet.ahrq.gov/perspective.aspx?perspectiveID=170 . Accessed July 18, 2019
  • 16 The Centers for Medicare & Medicaid Services. Medicare CCD. 2017 . Available at: https://innovation.cms.gov/initiatives/Medicare-Coordinated-Care . Accessed July 18, 2019
  • 17 Carson MB, Scholtens DM, Frailey CN, Gravenor SJ, Kricke GE, Soulakis ND. An outcome-weighted network model for characterizing collaboration. PLoS One 2016; 11 (10) e0163861
  • 18 Carson MB, Scholtens DM, Frailey CN. , et al. Characterizing teamwork in cardiovascular care outcomes: a network analytics approach. Circ Cardiovasc Qual Outcomes 2016; 9 (06) 670-678
  • 19 Pope GC, Kautter J, Ellis RP. , et al. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev 2004; 25 (04) 119-141
  • 20 Zhang Z. Parametric regression model for survival data: Weibull regression model as an example. Ann Transl Med 2016; 4 (24) 484
  • 21 Therneau TM. A package for survival analysis in S. Verison 2.38. 2015 . Available at: https://CRAN.R-project.org/package=survival . Accessed July 18, 2019
  • 22 Harrell Jr FE. Regression modeling strategies. Verison 5.1–2. 2018 . Available at: https://cran.r-project.org/web/packages/rms/rms.pdf . Accessed July 18, 2019
  • 23 Kricke GS, Carson MB, Lee YJ. , et al. Leveraging electronic health record documentation for failure mode and effects analysis team identification. J Am Med Inform Assoc 2017; 24 (02) 288-294
  • 24 Fay MP, Shaw PA. Exact and asymptotic weighted logrank tests for interval censored data: the interval R package. J Stat Softw 2010; 36 (02) i02
  • 25 Dunn AG, Westbrook JI. Interpreting social network metrics in healthcare organisations: a review and guide to validating small networks. Soc Sci Med 2011; 72 (07) 1064-1068
  • 26 Saunders ND, Nichols SD, Antiporda MA. , et al. Examination of unplanned 30-day readmissions to a comprehensive cancer hospital. J Oncol Pract 2015; 11 (02) e177-e181
  • 27 Khawaja FJ, Shah ND, Lennon RJ. , et al. Factors associated with 30-day readmission rates after percutaneous coronary intervention. Arch Intern Med 2012; 172 (02) 112-117
  • 28 O'Connor M, Murtaugh CM, Shah S. , et al. Patient characteristics predicting readmission among individuals hospitalized for heart failure. Med Care Res Rev 2016; 73 (01) 3-40
  • 29 Safran C, Bloomrosen M, Hammond WE. , et al; Expert Panel. Toward a national framework for the secondary use of health data: an American Medical Informatics Association white paper. J Am Med Inform Assoc 2007; 14 (01) 1-9