Modeling the Adoption of Identification Standards in the U.S. Healthcare Supply Chain

1. Modeling the Adoption of Identification Standards in the U.S. Healthcare Supply Chain Angelica Burbano Ph.D Student – Dpt. of Industrial Engineering University of Arkansas Advisor Prof. Ronald Rardin PhD

2. Outline • Motivation • Background • Research questions • Proposed work plan • Literature review • Causal loop diagram • Diffusion modeling framework • Conclusions

3. Motivation • This research topic emerged as a way to approach a problem I observed during my work as a graduate research assistant at the Center for Innovation on Healthcare Logistics (CIHL). • Data standards also referred to as identification standards have been around since 1974 when the Universal Product Code (UPC) was developed within the grocery industry.

4. Motivation • Think about the check-out process at the grocery store. • Currently the healthcare supply chain lacks of identification standards for the products that flow through the chain and for the locations associated with this product flow.

5. The problem • The adoption of identification standards and its associated technology in healthcare supply chain has been slow over the past twenty five years despite the evidence of the benefits that can be achieved. • This slow adoption process is preventing healthcare supply chain from reaching the process efficiencies other industries have realized and most importantly, it is affecting the healthcare delivery process.

6. Outline • Motivation • Background • Research questions • Proposed work plan • Literature review • Causal loop diagram • Diffusion modeling framework • Conclusions

7. Healthcare Supply Chain Fragmentation Manufacturer Distributor Provider End User Manufacturing Distribution Warehousing Usage Patient Disposal Figure 1. Healthcare Supply Chain (Product Flow) Source: Adapted from EHCR 1996 p.3

8. Identification standards • Unique identification of products and locations. • Identification standards are a building block for the efficient product flow and its associated transactions on a given supply chain. • Many to identify a product: • Pharmaceuticals NDC • Medical Devices and supplies UPN • Industry HIBCC, GS1 standards

9. Identification standards Location identifier Manufacturer Distributor Provider End User Manufacturing Distribution Warehousing Usage Patient Product identifier Disposal Figure 1. Healthcare Supply Chain (Product Flow) Source: Adapted from EHCR 1996 p.3

10. Technology • Technology allows capturing the product related information as it moves through the supply chain and also helps to store and process product related information as well as the transactions associated with it. • Auto Identification and Data Capture technology (e.g barcodes and RFID) • Information Systems (e.g Material Management Information systems MMIS or ERP systems)

11. External Between transacting members Business Process RosettaNet HL7, EANCOM, EDI Transaction (messages) Catalog UNSPSC Classification Internal Identification GS1 ( GTIN, GLN..) HIBCC Technology Auto ID DC, Information Systems, EDI Figure 2. Supply Chain Standards Source: Adapted from Hubner 2008 p.108

12. Technology • According a 2007 American Hospital Association AHA survey less than 16 percent of hospitals were fully using barcode technology for supply chain management purposes; the use of RFID is less than 3 percent. • Technology has been referenced as one of the major barriers to identification standards adoption (Natchmann and Pohl 2009).

13. Previous attempts • 1983 HIBCC was established to promote the adoption of identification standards. • 1989 The use of barcode technology for point of care applications was promoted among hospitals. • 1996 The EHCR study identified more than 6 billion dollars on saving with the introduction of barcode driven processes within hospitals.

14. Previous attempts • 2004 FDA rule for human drug and blood products mandates that manufacturers print a barcode (one dimensional) on every product at the unit of use. • FDA Amendments Act of 2007 was signed into law; this Act includes the establishment of a unique device identification system. • 2008 (25 years after the first attempt) An industry movement towards GS1 standards system (GTIN, GLN) adoption was initiated.

15. Outline • Motivation • Background • Research questions • Proposed work plan • Literature review • Causal loop diagram • Diffusion modeling framework • Conclusions

16. Research questions (Real world) • What is preventing healthcare supply chain members and healthcare providers in particular from adopting identification standards and its supporting technologies? What are the major barriers? •  What are the cost implications and benefits for the healthcare supply chain members and stakeholders to adopt identification standards? How can they reach equilibrium? •  What actions (strategies, incentives, policies) are required to increase the number of healthcare supply chain members and healthcare providers adopting identification standards?

17. Research questions (theoretical) • How does the identification standards adoption and diffusion process compare with the adoption of other technologies within the healthcare industry in general. (e.g. the diffusion of medical technologies or Electronic Health Records EHR)? • How does the identification standards adoption process compare with the same adoption process in other industries such as retail? • How could existing diffusion models be extended or modified to model the identification standards adoption process?

18. The research project • This research project will investigate the identification standards adoption process within healthcare supply chain and develop a theoretical model based on the diffusion of innovations theory; a system dynamics modeling approach will be used to model this process.

19. Outline • Motivation • Background • Research questions • Proposed work plan • Literature review • Causal loop diagram • Diffusion modeling framework • Conclusions

20. Lr / cld • A literature review includes healthcare supply chain, identification standards and its associated technology, diffusion of innovations theory and systems dynamics methodology. • The causal loop diagram will formalize the findings regarding the possible causes (hypothesis) for the slow adoption process and will help to identify the feedback structure of the system (healthcare supply chain).

21. Relevant findings • Inter-firm diffusion of process innovations (Davies 1979) • Models for innovation diffusion (Mahajan and Peterson 1985) • Inter and intra firm effects in the diffusion of new process technology (Battisti and Stoneman 2003) • Information technology innovations: General diffusion patterns and its relationships to innovation characteristics ( Teng et al 2002)

22. Relevant Findings • A model of the linked adoption of complementary technologies (Smith 2004) • The effects on new technologies on productivity: An intra firm diffusion based assessment (Fuentelsaz et al 2009) • Electronic Health Record adoption • Coordinating quality care: A policy model to simulate adoption of EHR (Otto and Simon 2009) • Systems analysis of Electronic Health Record adoption in the U.S. healthcare system. Erdil, Nadiye Ozlem, Ph.D. Dissertation, State University of New York at Binghamton, 2009

23. Diffusion modeling framework • Theoretical development (model formulation) • Simulation model • Interventions design • Expected results

24. Theoretical development • An adaptation of the diffusion theory (diffusion of innovations) and its associated mathematical models will be required to capture the complexity of the identification standards adoption process within the healthcare supply chain. • This complexity is related to the organizational nature of the adopting members and the multiple set of innovations (technologies) to be modeled.

25. Bass diffusion model Single population (member) Single innovation

26. Diffusion model(Model formulation) Healthcare Provider Distributor Manufacturer Multiple members (Intra-firm diffusion) Contingent innovations ( Identification standards, Auto ID DC, IS)

27. Simulation model • The simulation model will be validated using available industry data; the GS1 identification standards adoption process as a reference. The model will be calibrated to reproduce meaningful results according to the theoretical development. • The main purpose of the simulation is to test the impact of different policy interventions (e.g. costs and benefits of each intervention, the impact on adoption rate over the time horizon by member )

28. Interventions design • It will be required to develop a mechanism to formulate interventions and also to implement and test those interventions in the current VENSIM model. • These interventions can include local, industry and/or government interventions. For example it could be possible to explore the impact of an FDA a regulation such as the Unique Device Identification UDI on the identification standards adoption process within the medical device industry segment.

29. Expected results • Results from the application of the proposed model to the GS1 identification standards adoption process should provide: • Better understanding of the current system • Identification of the effect of different policies on the system as well as the definition of a possible equilibrium point ( if a 4 game players is defined ) • It will be possible to test for example if the current sunrise dates for location identifiers by 2010 and product identifiers by 2012 can be achieved under current conditions • An industry adoption curve with an estimated timeline can be developed (critical mass)

30. Questions aburbano@uark.edu