Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-05-06T20:37:31.168Z Has data issue: false hasContentIssue false

COMBINING INSTITUTIONAL AND ADMINISTRATIVE DATA TO ASSESS HOSPITAL COSTS FOR PATIENTS RECEIVING VENTRICULAR ASSIST DEVICES

Published online by Cambridge University Press:  31 December 2018

Roslyn Prichard
Affiliation:
St Vincent’s Hospital, Faculty of Health, University of Technology Sydney
Louise Kershaw
Affiliation:
St Vincent's Hospital Sydney
Patricia M. Davidson
Affiliation:
Johns Hopkins University Baltimore, Faculty of Health University of Technology Sydney
Phillip J. Newton
Affiliation:
Western Sydney University
Stephen Goodall
Affiliation:
Centre for Health Economics Research and Evaluation, University of Technology Sydney
Christopher Hayward
Affiliation:
St Vincent's Hospital, Faculty of Health University of Technology Sydneycshayward@stvincents.com.au

Abstract

Objectives:

The aim of this study was to describe patient level costing methods and develop a database of healthcare resource use and cost in patients with AHF receiving ventricular assist device (VAD) therapy.

Methods:

Patient level micro-costing was used to identify documented activity in the years preceding and following VAD implantation, and preceding heart transplant for a cohort of seventy-seven consecutive patients listed for heart transplantation (2009–12). Clinician interviews verified activity, established time resource required for each activity, and added additional undocumented activities. Costs were sourced from the general ledger, salary, stock price, pharmacy formulary data, and from national medical benefits and prostheses lists. Linked administrative data analyses of activity external to the implanting institution, used National Weighted Activity Units (NWAU), 2014 efficient price, and admission complexity cost weights and were compared with micro-costed data for the implanting admission.

Results:

The database produced includes patient level activity and costs associated with the seventy-seven patients across thirteen resource areas including hospital activity external to the implanting center. The median cost of the implanting admission using linked administrative data was $246,839 (interquartile range [IQR] $246,839–$271,743), versus $270,716 (IQR $211,740–$378,482) for the institutional micro-costing (p = .08).

Conclusions:

Linked administrative data provides a useful alternative for imputing costs external to the implanting center, and combined with institutional data can illuminate both the pathways to transplant referral and the hospital activity generated by patients experiencing the terminal phases of heart failure in the year before transplant, cf-VAD implant, or death.

Type
Method
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The authors acknowledge the assistance of St Vincent's Hospital finance department and in particular Melita Howes and Lai Mun Balnave for advice and support during the study. This investigator initiated study was supported by Heartware Inc. (C.H., Salary Support for research staff), the National Health and Medical Research Council (R.P. NHMRC post graduate scholarship APP1133337 and an Australian Government research training program scholarship). None of the other authors have a financial relationship with a commercial entity that has an interest in the subject of the presented manuscript or other conflicts of interest to disclose. The authors also thank the Centre for Health Record Linkage for the data linkage and the New South Wales Ministry of Health, for the use of linked data from the admitted patient and emergency department collections (APDC/EDDC).

References

REFERENCES

1.Kirklin, JK, Naftel, DC, Pagani, FD, et al. Seventh INTERMACS annual report: 15,000 patients and counting. J Heart Lung Transplant. 2015;34:14951504. doi:10.1016/j.healun.2015.10.003.Google Scholar
2.Nunes, AJ, MacArthur, RGG, Kim, D, et al. A systematic review of the cost-effectiveness of long-term mechanical circulatory support. Value Health. 2016;19:494504. doi:10.1016/j.jval.2014.12.020.Google Scholar
3.Kirklin, JK, Pagani, FD, Kormos, RL, et al. Eighth annual INTERMACS report: Special focus on framing the impact of adverse events. J Heart Lung Transplant. 2017;36:10801086. doi:10.1016/j.healun.2017.07.005.Google Scholar
4.Australian Government Department of Health. The prostheses list. www.health.gov.au/internet/main/publishing.nsf/content/health-privatehealth-prostheseslist.htm (accessed December 5, 2016).Google Scholar
5.Birks, E. The comparative use of ventricular assist devices. Tex Heart Inst J 2010;37:13.Google Scholar
6.Sahle, BW, Owen, AJ, Mutowo, MP, et al. Prevalence of heart failure in Australia: A systematic review. BMC Cardiovasc Disord. 2016;16:32. doi:10.1186/s12872-016-0208-4.Google Scholar
7.Australia's Health 2014. Australian Institute of Health and Welfare 2014. 2014; (Cat. no. AUS 178):1578.Google Scholar
8.Xu, X, Grossetta Nardini, HK, Ruger, J. Micro-costing studies in the health and medical literature: Protocol for a systematic review. Syst Rev. 2014;3:47. doi:10.1136/bmj.38737.607558.80.Google Scholar
9.Frappier, J, Tremblay, G, Charny, M, Cloutier, LM. Costing bias in economic evaluations. J Med Econ. 2015;18:596599. doi:10.3111/13696998.2015.1033423.Google Scholar
10.Tan, SS, Bakker, J, Hoogendoorn, ME, et al. Direct cost analysis of intensive care unit stay in four European countries: Applying a standardized costing methodology. Value Health. 2012;15:8186. doi:10.1016/j.jval.2011.09.007.Google Scholar
11.Alvin, MD, Miller, JA, Lubelski, D, et al. Variations in cost calculations in spine surgery cost-effectiveness research. Neurosurg Focus. 2014;36:E1. doi:10.3171/2014.3.FOCUS1447.Google Scholar
12.Mercier, G, Naro, G. Costing hospital surgery services: The method matters. PLoS One. 2014;9:e97290. doi:10.1371/journal.pone.0097290.Google Scholar
13.Frick, KD. Microcosting quantity data collection methods. Med Care. 2009;47(Suppl):S76S81. doi:10.1097/MLR.0b013e31819bc064.Google Scholar
14.Kaplan, RS, Porter, ME. How to solve the cost crisis in health care. Harv Bus Rev. 2011;89:4652.Google Scholar
15.Neumann, PJ. Costing and perspective in published cost-effectiveness analysis. Med Care. 2009;47(Suppl):S28S32. doi:10.1097/MLR.0b013e31819bc09d.Google Scholar
16.Tan, SS, Rutton, FF, Van Ineveld, BM, Redekop, WK, Roijen, LH-V. Comparing methodologies for the cost estimation of hospital services. Eur J Health Econ. 2008;10:18. doi:10.1007/sl0198-008-0101-x.Google Scholar
17.National Hospital Cost Data Collection. Independent Hospital Pricing Authority; 2016. https://www.ihpa.gov.au/publications (accessed December 18, 2017).Google Scholar
18.Mishra, V, Fiane, AE, Winsnes, BA, et al. Cardiac replacement therapies: Outcomes and costs for heart transplantation versus circulatory assist. Scand Cardiovasc J. 2017;51:17. doi:10.1080/14017431.2016.1196826.Google Scholar
19.Marasco, SF, Summerhayes, R, Quayle, M, McGiffin, D, Luthe, M. Cost comparison of heart transplant vs. left ventricular assist device therapy at one year. Clin Transplant. 2016;30:598605. doi:10.1111/ctr.12725.Google Scholar
20.Patel, SR, Sileo, A, Bello, RB, et al. Heart transplantation versus continuous-flow left ventricular assist device: Comprehensive cost at 1 year. J Card Fail. 2015;21:160166. doi:10.1016/j.cardfail.2014.11.007.Google Scholar
21.Duckett, S. Improving accountability for use of blood products. Report to the Australian Red Cross Blood Service. June 2013:124. https://www.ihpa.gov.au/sites/g/files/net636/f/Documents/australian_red_cross_blood_service_-consultation_paper_2016-17.pdf (accessed December 18, 2017).Google Scholar
22.Reeve, R, Haas, M. Estimating the Cost of Emergency Department Presentations in NSW. 2014:1–18. OPUS Library. http://hdl.handle.net/10453/33513 (accessed December 18, 2017).Google Scholar
23.Briggs, A, Grey, A. The distribution of health care costs and their statistical analysis for economic evaluation. J Health Serv Res Policy. 1998;3:233245.Google Scholar
24.Williams, ML, Trivedi, JR, McCants, KC, et al. Heart transplant vs left ventricular assist device in heart transplant-eligible patients. Ann Thorac Surg 2011;91:13301334. doi:10.1016/j.athoracsur.2011.01.062.Google Scholar
25.Damato, A. Improving patient outcomes: Leveraging data to drive innovation in health care – New South Wales’ Activity-Based Funding management portal. BMC Health Services Research. 2015;15(Suppl 2):A1. doi.org/10.1186/1472-6963-15-S2-A1 (accessed November 9, 2018).Google Scholar
26.Duckett, S. Blood money: Pathology cuts can reduce spending without compromising health. https://theconversation.com/blood-money-pathology-cuts-can-reduce-spending-without-compromising-health-54834 (accessed December 18, 2017).Google Scholar
27.Neil, A, Pfeffer, S, Burnett, L. Benchmarking in pathology: Development of a benchmarking complexity unit and associated key performance indicators. Pathology. 2013;45:6670. doi:10.1097/PAT.0b013e32835b77c4.Google Scholar
28.Medicare Benefits Schedule Book Category 6. May 2014:1–113. http://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/007A76CBC3437BF7CA257CCF00051C2E/$File/201403-Cat6.pdf (accessed December 18, 2017).Google Scholar
Supplementary material: File

Prichard et al. supplementary material

Prichard et al. supplementary material 1

Download Prichard et al. supplementary material(File)
File 385.3 KB