Figure 1: Clinical performance and cost estimates of emerging products are challenging to assess.

Clinical trials and meta-analyses that support the efficacy of a given innovation take time to complete

and will likely be available after the period when adoption would lead to the greatest competitive

advantage. Likewise, data on actual operating costs will not be available. Leveraging current

knowledge of individual adoption theory can minimize clinician rejection.

•

•

Category Item # Product Inspiration Vendor Health

Communication 1 PerfectServe PerfectServe, Inc.

5 AirStrip CARDIOLOGY AirStrip Technologies

6 SurgiChart SurgiChart, LLC

8 MedXCom Giffen Solutions, Inc. In vitro

Diagnostics 14 Piccolo Xpress Abaxis

18 Lactate Scout + EKF Diagnostics

20 Cerebral Array I and II Randox Laboratories Ltd.

21 IschemiaCare Ischemia Care, LLC Imaging 42 MobiUS SP1 MobiSante, Inc.

Total MD RN Avg. Exp. >10 IN EA EM

1 11 3 8 10.6 5 2 3 5

2 4 3 1 4.5 0 0 2 2

3 8 1 6 8.6 3 1 0 5

4 10 4 7 13.3 4 0 3 7

Seniority Innovation Style

Facto

r

Count

•

•

•

•

•

•

References

Cost

No Clinical Efficacy Technology Rejection

Possible to mitigate prior to implementation

Additional Cost Drivers

• Wasted resources • Harm to patients Difficult to mitigate

prior to implementation

• Productivity Loss • Turnover

SPEED (33%) HOLISM (12%) ACUITY (24%) INFO (30%)

1. Coye MJ, Kell J. How Hospitals Confront New Technology. Health Affairs. 2006 January 1, 2006;25(1):163-73.

2. Christensen CM, Kaufman SP, Shih WC. Innovation Killers: How Financial Tools Destroy Your Capacity to Do New Things. Harvard Business

Review. 2008:98-105.

3. Amabile TM. A Model of Creativity And Innovation in Organizations. Research in Organizational Behavior. 1988;10:123-67.

4. Damanpour F. The Adoption of Technological, Administrative and Ancillary Innovations: Impact of Organizational Factors. J Management.

1987;13(4):675-88.

5. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly.1989;13(3):319-40.

6. Goodhue DL, Thompson RL. Task-Technology Fit and Individual Performance. MIS Quarterly. 1995 Jun., 1995;19(2):213-36.

7. Naiman MI. Systematically Gathering Clinician Opinions on Health Care Technology. Chicago: University of Illinois at Chicago; 2013.

8. Rogers EM. Diffusion of Innovations, Fifth Edition. New York, NY: Free Press; 2003

Figure 2: Individual and organizational variables

that are influenced by opinion gathering.

D) Instrument Deployment (Q-sorts) Figure 3: A) Qualitative methods generated

data on potential areas for radical

innovation. Interviews identified favorable

technology characteristics and focus groups

identified specific clinical challenges. B)

Market analysis identified products that met

ergonomic and clinical needs identified

qualitatively. C) Generic descriptions of

current products were developed. D)

Participants were asked to rank the 43

products in terms of what they felt were

“most likely” and “most unlikely” to improve

care in their department. These Q-sorts were

then subjected to factor analysis and

interpreted.

A) Qualitative B) Market Analysis C) Instrument Development (43 Items Total)

Most Unlikely Uncertain Most Likely

-3 -2 -1 0 +1 +2 +3