This video describes Intellimessage, a system for generating tailored medical messages to patients through the creation of individualized health profiles. The system matches patient data and preferences with clinical records to create personalized messages that improve health outcomes. It aims to reduce preventable hospital readmissions through evidence-based, tailored post-care messaging. The company was founded by a multidisciplinary team from the University of Kentucky, including a principal investigator, research assistants, an engineering student, and a successful entrepreneur.

1. http://www.youtube.com/watch?v=FBZZxjVhUpE

2. Intellimessage
TEAM 7
Team 7
Total Contacts: 95
Tailored medical messaging system through patient
profile generation and tailored post-care messaging
Meaningful information about YOUR
health will lead (RA) a better life.
Dan O’Hair
(PI)
to Wes(EL)
Derek Lane Brooks Randall Stevens
(IM)
We match your patient data and preferences with your clinical records
to create personalized messages that improve your health outcomes

3. Current Information is too Generic and Excessive
“Should I be worried “I can take care of
about this?” myself.”
Age: 55 Age: 51
Sex: Female Sex: Male
Occupation: Librarian Occupation: Retired football coach
Need for information: Give me DETAILS! Need for information: Just the FACTS!
Both Patients Had Congestive Heart Failure

4. 30-Day Readmissions Will No
Longer Be Reimbursed
$17B Spent Annually
$12.5B Preventable
1 in 5 admitted patients
75% of those are
are re-hospitalized
preventable.
within 30 days.

5. Physician Physician
Communication is
DIFFICULT
Patient

6. Federally Mandated Meaningful Use
Set up Electronic Interoperability + Improved
Health Records Data Standards Outcomes

7. Clinical Patient
Record Data

8. Your Health Profile
Clinical Patient
Record Data

9. Your Health Profile
Clinical Patient
Record Data
Tailored
Messages

10. Our Journey

11. Where We Started
Create Patient
Communication Profile
Pa
tie
nt
Re
c ei Give Doctors/Nurses
ve Tips on Patient Comm.
s Ca
re
Patient Receives Tailored
Message about Self-Care

12. Model v1.0 - Post-Care Message Platform

13. Who We Talked With
Hospital Staff
Discharge Centers
Physicians
Health Leaders
Insurance Providers
KY Dept. of
Channel Partners
Public Health

14. What We Learned
Hospital Staff
Discharge Centers
“We love this idea! This would be really helpful!”
Physicians
“But this problem is not painful enough for us
yet that we’d spend a bunch of money to fix it.”
Health Leaders
Insurance Providers
KY Dept. of
Channel Partners
Public Health
Potential Partners =

15. What We Learned
Hospital Staff
Discharge Centers
“We love this idea! This would be really helpful!”
Physicians
“But this problem is not painful enough for us
yet that we’d spend a bunch of money to fix it.”
Health Leaders
Insurance Providers
KY Dept. of
Channel Partners
Public Health
Potential Partners =
Found University of KY Healthcare was generating
patient discharge summaries that could be tailored

16. Week 5 - v2.0 - Discharge Communication Platform

17. What We Did
Discharge Focused Model
@UK Healthcare Discharge Unit
- Learned that high risk patients receive discharge summaries
- Information lacked usability and feedback
- Researched how to improve discharge communication
- Find drivers for wider adoption of discharge summaries

18. What We Did
Discharge Focused Model
@UK Healthcare Discharge Unit
- Learned that high risk patients receive discharge summaries
- Information lacked usability and feedback
- Researched how to improve discharge communication
- Find drivers for wider adoption of discharge summaries

19. What We Did
Discharge Focused Model
@UK Healthcare Discharge Unit
- Learned that high risk patients receive discharge summaries
- Information lacked usability and feedback
- Researched how to improve discharge communication
- Find drivers for wider adoption of discharge summaries

20. Here’s What We Found
Meaningful Use
Set up Electronic Interoperability + Improved
Health Records Data Standards Outcomes

21. What We Found
Before Regulations
Doctor A Doctor B
Electronic Electronic
Health Record Health Record
Patient

22. What We Found
After Regulations
Doctor A Doctor B
Continuity of
Care Documents
Electronic Electronic
Health Record Health Record
Patient Visit
Summaries
Patient Portal Patient Portal
Patient

23. Competitive Landscape
Employer-Based
Patient-Based
Clinician-Based
Nobody connects
clinical and patient data
Nobody uses tailored messages

24. Distribution Channels

25. Revenue Model
Health
Healthcare Patient Data
Information
Providers Exchanges
Tailored Messaging
Portal $$$
for + Patient Outcomes
Patient Data
Patient
Analytics $$$
Resources Health
Patient
/Tools Insights
Profile
Patient

26. User Market Size
TAM = 128M
People in the US
between 25 and 55
SAM = 63M
uses online banking
or shops online
TM = 20.2M
segment using health
information exchanges

27. Week 10 - v3.0 - Health Profiles for Tailored Messages

28. Dan O’Hair (PI) Jennifer Dupuis (RS)
UK CIS Dean UK CIS Faculty
Tailored Comm Health Literacy Researcher
Researcher Sales Experience
Derek Lane (RS) Randall Stevens (IM)
CIS Dist. Faculty Successful Entrepreneur
Tailored Comm 2 Past Companies, 1
Researcher New startup (Punndit)
Wes Brooks (EL)
EGR Student
3 Startups, 1@Stanford
QS Enthusiast

29. Previous Slides

30. Intellimessage Team 7
Total Contacts: 13
3/21 Contacts: 5
Tailored medical messaging system through patient
profile healthcare providers to improve self care messaging
Helping generation and tailored post-care and reduce
re-admittance through evidence-based tailored messages
Dan O’Hair Derek Lane Wes Brooks Randall Stevens
(PI) S
(RA) (EL) (IM)

31. Key Partners Key Activities Value Proposition Customer Customer
Relationships Segments
Existing Comm Algorithm for Tailored Risk Assessment Diabetes Associations Health Care
Providers Message Creation Based on Individual Partnerships Organizations
Differences
Continuous Evidence- Insurance
Dev Team Based Profile Building Decision Support Tool Companies
M.D. Opinion Multimedia Message Clear Customized Pharmaceutical
Leaders Delivery System Health Messages Organizations
Improved Patient High Risk
Medical Social Key Resources Channels
Outcomes Diabetic Patients
Media Content State-of-the-Art Lab Direct Sales Existing Com
Providers and Proof of Concept Reduced Health
Center Care Costs Platforms
Existing Com
Data Entry Mechanism Platforms
Finding Early Adopting Partner Sales
Experimental HCO’s
Health Care
Reform Act
Cost Structure Revenue Streams
Algorithm and Interface Design Cost-Saving Sharing Federal Grants
Continuous Data Collection Recurring Subscription Service
Integration with Partner Platform Software Licensing
Per Patient Fee