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Creative Partnerships between PhD Programs and Industry Thomas R. Clancy, PhD, MBA, RN, FAAN Clinical Professor and Associate Dean, Faculty Practices, Partnerships and Professional Development School of Nursing, The University of Minnesota Minneapolis, MN

Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

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Page 1: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Creative Partnerships between

PhD Programs and Industry Thomas R. Clancy, PhD, MBA, RN, FAAN

Clinical Professor and Associate Dean, Faculty Practices, Partnerships and Professional Development

School of Nursing, The University of Minnesota

Minneapolis, MN

Page 2: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Objectives

• Define types of academic/industry partnerships.

• Review faculty roles in industry partnerships.

• Identify key considerations in working with industry partners.

• Discuss examples of industry sponsored research and scholarship.

Page 3: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Associate Dean for Practice, Partnerships and

Professional Development

• Health Systems • Hospitals, clinics, sub-acute

• Corporate • Medical Device

• Technology (EHR)

• Commercial Insurance

• Community • Religious (Charities)

• Federal/State/County (HRSA)

• Private

• Foundations • Private

• Corporate

Page 4: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Academic/Industry Partnership

Roles:

• Expert Consultation

• Joint Research

• Continuing Education and Professional Development

• Product/Service Development

Page 5: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Key Considerations

• Research

• Pre-disclosure • IP Disclosure form

• Intellectual Property • Non-disclosure

agreement

• Copyright

• Provisional patent

• Utility patent

• Institutional agreements

• Academic freedom

• Licensing

• Commercialization

Page 6: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Trends in National Institutes

of Health Appropriations

http://faseb.org/Science-Policy-and-Advocacy/Federal-Funding-Data/NIH-Research-Funding-Trends.aspx

Page 7: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

NIH Research Evaluation and

Commercialization Hubs (REACH)

• Nine million in funding to develop best practices in translating academic innovations into products to improve health.

• Provides education in industry-style project management to commercialize technologies that are poised to launch.

NIH

REACH

Hubs

University

of

Minnesota

Long Island

Bioscience

Hub

University of

Louisville, Kentucky

Boston

Biomed.

Hub

Cleveland

Clinic Innovation

Accelerator

University of CA

BRAID Center

Page 8: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Academic vs Industry Lenses

Grant Application

• Project Narrative

• Specific Aims

• Need

• Methodological Approach

• Work Plan

• Logic Model

• Evaluation Plan

• Outcome Measures

• Sustainability, Replicability

• Dissemination

Business Plan

• Executive Summary

• Opportunity (product

solution)

• Market Analysis

• Company and

Management (SWOT)

• Strategy and

Implementation Plan

• Financial Plan

Page 9: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Expert Consultation

• Application of domain expertise to a product or service. • Determine ROI of a

hands free communication device

• Integration of new technology into nurses workflow

• Design, pilot and evaluate product or service

• Publish and present

Table 4 Comparison of Event Data

Type of Event

Percent of

Total

Events

Average

Delay

(seconds)

Events per

Surgical

Case

Percent of

Total Events

Average

Delay

(seconds)

Events per

Surgical

Case

Percent Difference

in Average Delay

Pre and Post

Percent Difference in

Events per Surgical

Case Pre and Post

Call placed and answered 42.47% 100 2.28 53.87% 65 1.063 -35.0% -53.38%

Call received and answered 23.49% 100 1.21 13.60% 65 0.268 -35.0% -77.85%

Call placed and message left 8.43% 66 0.5 9.07% 46 0.178 -30.3% -64.40%

Call placed, put on hold or transferred 6.13% 231 0.39 4.80% 108 0.094 -53.2% -75.90%Walked to a phone to answer a call 6.13% 173 0.32 1.33% 89 0.026 -48.6% -91.88%

Physically searched for someone 4.77% 207 0.26 0.27% 135 0.005 -34.8% -98.08%

Checked phone mail 4.51% 100 0.26 4.80% 65 0.0947 -35.0% -63.58%

Call placed, put on hold and left a message 2.55% 197 0.15 8.00% 89 0.157 -54.8% 4.67%

Sent a text message 1.02% 183 0.06 0.80% 53 0.0157 -71.0% -73.83%

Sent broadcast page to multiple staff 0.51% 228 0.03 3.47% 75 0.068 -67.1% 126.67%

Average1

118.71 5.46 68.02 1.97 -46.5% -46.8%

1 The difference in the Average Delay per Surgical Case, pre and post Vocera, are significant at a p< .01.

Sample Size

Pre HFMD 189 cases observed

Post HFMD 190 cases observed

PRE-VOCERA POST-VOCERA

Table 3 Comparison of Cycle Time by Area

Surgical Area

Average Minutes per

Surgical Case

Standard

Deviation

Average Minutes

per Surgical Case

Standard

Deviation

Percent

Difference

t test p

value

Pre-operative 121.81 67.4 112.32 44.88 -7.8% 0.0079

Intra-operative 85.4 81.2 83.9 64.8 -1.8% 0.643

Phase 1 recovery 91.82 46 78.69 30.93 -14.3% 0.00012

Phase 2 recovery 96.33 71.6 95.19 72.95 -1.2% 0.728

Total cycle time (Pre, Intra & Post-operative)1

308.6 162.6 293.0 129.0 -5.0% 0.0001091

1 Total cycle time is a weighted average

Sample size:

Pre HFMD - 2784

Post HFMD - 828

PRE-VOCERA POST VOCERA

1 Place call

6 Receive call

2 Leave phone

3 On hold or transfer

7 Walk to a phone

8 Search

5 Check voice mail

4 Placed on hold

11 Text

9 Broadcast page

SURGERY Pt

Pre-operative AreaOperating Room Phase 1 Recovery

Phase 2 Recovery

message

room to room

0

0

0

0

0

0

0

0

0

0

0

07:00:00

February 1, 2012

Page 10: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Joint Research

Database

UM Nursing Sandbox

Data

Explore

Research Views

• Unified (claims/EHR)

• Death Index

• SES (social/economic)

Team

• Project Mgt

• Domain Ex

• Machine Learn

• Data Dic. Analyst

Data Warehouse

• Project Mgr

• Data Engineer

• Data Dic. Analyst

AHC

• Medicine

• Pharmacy

• Public Health

• Joint academic/corporate partnership to conduct research

• Academic/Industry National Research Collaborative.

• Facilitate ongoing big data research between faculty, industry and grantees

Page 11: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Partners Academic/ Industry Partnership

Sample of Research Studies

Funding

Source

Otolaryngology

Prediction model: causal factors in patients presenting

with dizziness

NIH

Nursing Prediction model: Patients experiencing adverse

effects of statin therapy

UM Internal

Prediction model: Cardiovascular disease risk

prediction using EHR/claims data

UM Internal

AHC Seed

Symptom management of liver transplant patients NIH

Prevention of urinary tract infections in young women NIH

Public Health Prediction model: Diffusion of knowledge from

clinical trials to practice.

NIH

Comparative effectiveness of extended oral

anticoagulant use

PCORI

Contemporary Venous Thromboembolism Treatment -

NIH

NIH

Neurosurgery Comparative effectiveness between surgical and non-

surgical intervention of low back pain.

NIH

Page 13: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Product/Service Development

• Collaborate with an industry partner to develop a new product or service. • Faculty/student/industry

collaboration on reducing infections from IV tubing in pediatric oncology patients.

• Validation through research

• Publications and presentations

• Office of Technology Commercialization

• New patent

Page 14: Creative Partnerships between PhD Programs and Industry · NIH Research Evaluation and Commercialization Hubs (REACH) •Nine million in funding to develop best practices in translating

Questions?

Thomas R. Clancy, PhD, MBA, RN, FAAN

[email protected]