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Integrating Formal Technology Assessment into an Integrated Healthcare Delivery System: Smart Innovation

Part of: Special Collection - Methods

Published online by Cambridge University Press:  31 March 2020

Erik J. Landaas Open the ORCID record for Erik J. Landaas [Opens in a new window] ,
Geoffrey S. Baird ,
Ryan N. Hansen ,
David R. Flum  and
Sean D. Sullivan
Erik J. Landaas
Affiliation:
The Comparative Health Outcomes, Policy, and Economics Institute, University of Washington, Seattle, WA, USA
Geoffrey S. Baird
Affiliation:
Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
Ryan N. Hansen
Affiliation:
The Comparative Health Outcomes, Policy, and Economics Institute, University of Washington, Seattle, WA, USA
David R. Flum
Affiliation:
Department of Surgery, University of Washington, Seattle, WA, USA
Sean D. Sullivan
Affiliation:
The Comparative Health Outcomes, Policy, and Economics Institute, University of Washington, Seattle, WA, USA
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Abstract

Objectives

We designed, developed, and implemented a new hospital-based health technology assessment (HB-HTA) program called Smart Innovation. Smart Innovation is a decision framework that reviews and makes technology adoption decisions. Smart Innovation was meant to replace the fragmented and complex process of procurement and adoption decisions at our institution. Because use of new medical technologies accounts for approximately 50 percent of the growth in healthcare spending, hospitals and integrated delivery systems are working to develop better processes and methods to sharpen their approach to adoption and management of high cost medical innovations.

Methods

The program has streamlined the decision-making process and added a robust evidence review for new medical technologies, aiming to balance efficiency with rigorous evidence standards. To promote system-wide adoption, the program engaged a broad representation of leaders, physicians, and administrators to gain support.

Results

To date, Smart Innovation has conducted eleven HB-HTAs and made clinician-led adoption decisions that have resulted in over $5 million dollars in cost avoidance. These are comprised of five laboratory tests, three software-assisted systems, two surgical devices, and one capital purchase.

Conclusions

Smart Innovation has achieved cost savings, avoided uncertain or low-value technologies, and assisted in the implementation of new technologies that have strong evidence. The keys to its success have been the program's collaborative and efficient decision-making systems, partnerships with clinicians, executive support, and proactive role with vendors.

Keywords

Hospital-basedTechnologyAssessmentDeviceAdoption
Type
Method
Information
International Journal of Technology Assessment in Health Care , Volume 36 , Issue 1 , 2020 , pp. 58 - 63
Copyright
Copyright © Cambridge University Press 2020

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References

Chew, M, Sharrock, K (2007) Medical milestones: Celebrating key advances since 1840. BMJ 334(s), 1–22.Google Scholar
Adedeji, W (2016) The treasure called antibiotics. Ann Ib Postgrad Med 14(2), 56.Google ScholarPubMed
Sorenson, C, Drummond, M, Khan, BB (2013) Medical technology as a key driver of rising health expenditure: Disentangling the relationship. Clinicoecon Outcomes Res 5(1), 223.CrossRefGoogle ScholarPubMed
Baker, LC (2001) Managed care and technology adoption in health care: Evidence from magnetic resonance imaging. J Health Econ 20(3), 395–421.CrossRefGoogle ScholarPubMed
Jarvik, JG, Gold, LS, Comstock, BA et al. (2015) Association of early imaging for back pain with clinical outcomes in older adults. JAMA 313(11), 1143–1153.CrossRefGoogle ScholarPubMed
Congressional Budget Office (2008) Technological change and the growth of health care spending. Available at: https://www.cbo.gov/sites/default/files/110th-congress-2007-2008/reports/01-31-techhealth.pdf. Accessed 2008.Google Scholar
Hartman, M, Martin, AB, Espinosa, N et al. The National Health Expenditure Accounts T (2018) National health care spending in 2016: Spending and enrollment growth slow after initial coverage expansions. Health Aff (Millwood) 37(1), 150-60.CrossRefGoogle ScholarPubMed
Centers for Medicare and Medicaid Services (2019) Hospital value-based purchasing. Available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Hospital-Value-Based-Purchasing-.html. Accessed 2019.Google Scholar
Quentin, W, Scheller-Kreinsen, D, Blumel, M et al. (2013) Hospital payment based on diagnosis-related groups differs in Europe and holds lessons for the United States. Health Aff (Millwood) 32(4), 713–723.CrossRefGoogle ScholarPubMed
Gagnon, MP (2014) Hospital-based health technology assessment: Developments to date. Pharmacoeconomics. 32(9), 819–824.CrossRefGoogle ScholarPubMed
McGregor, M, Brophy, JM (2005) End-user involvement in health technology assessment (HTA) development: A way to increase impact. J Int Technol Health Care 21(2), 263–267.CrossRefGoogle ScholarPubMed
Sampietro-Colom, L, Martin, J (2016) Hospital-based health technology assessment: The next frontier. New York: Springer Publishing.CrossRefGoogle Scholar
Sullivan, SD, Watkins, J, Sweet, B et al. (2009) Health technology assessment in health-care decisions in the United States. Value Health 12(Suppl 2), S39–S44.CrossRefGoogle ScholarPubMed
Millenson, LJ, Slizewski, E (1986) How do hospital executives spell technology assessment? “P-l-a-n-n-i-n-g.”. Health Management Quarterly: HMQ 4(1), 4–8.Google Scholar
Coye, MJ, Kell, J (2006) How hospitals confront new technology. Health Aff 25(1),163–173.CrossRefGoogle ScholarPubMed
Gutowski, C, Maa, J, Hoo, KS et al. (2011) Health technology assessment at the University of California-San Francisco. J Healthc Manag 56(1), 15–30.Google ScholarPubMed
Halmesmaki, E, Pasternack, I, Roine, R (2016) Hospital-based health technology assessment (HTA) in Finland: A case study on collaboration between hospitals and the national HTA unit. Health Res Policy Syst 14(1), 25.CrossRefGoogle ScholarPubMed
University of Washington (2019) UW Medicine annual financial report. Available at: https://s3-us-west-2.amazonaws.com/uw-s3-cdn/wp-content/uploads/sites/12/2019/02/06104924/2019-02-B-6.pdf. Accessed 2019.Google Scholar
Center for Medicare and Medicaid Innovation (2019) Our innovation models. Available at: https://innovation.cms.gov/. Accessed 2019.Google Scholar
UW Medicine (2019) Care transformation. Available at: https://depts.washington.edu/uwmedptn/strategies-programs/. Accessed 2019.Google Scholar
Sampietro-Colom, L, Lach, K, Pasternack, I et al. (2015) Guiding principles for good practices in hospital-based health technology assessment units. Int J Technol Assess Health Care 31(6), 457–465.CrossRefGoogle ScholarPubMed
Landaas, EJ, Franklin, G, Thompson, J et al. (2016) Expanding evidence-based technology assessment for coverage in Washington state. Int J Technol Assess Health Care 32(3), 140–146.CrossRefGoogle ScholarPubMed
Balshem, H, Helfand, M, Schünemann, HJ et al. (2011) GRADE Guidelines: 3. Rating the quality of evidence. J Clin Epidemiol 64(4), 401–406.CrossRefGoogle ScholarPubMed
Sorenson, C, Drummond, M, Burns, LR (2013) Evolving reimbursement and pricing policies for devices in Europe and the United States should encourage greater value. Health Aff (Millwood) 32(4), 788–796.CrossRefGoogle ScholarPubMed
Paxton, EW, Inacio, MC, Kiley, M-L (2012) The Kaiser Permanente implant registries: Effect on patient safety, quality improvement, cost effectiveness, and research opportunities. Perm J 16(2), 36.Google Scholar