Presentation in Barcelona, June 12, 2012

1. Electronic Medical Records:
From Clinical Decision Support
To Precision Medicine
June 12, 2012
John Sharp, MSSA, PMP, FHIMSS
Research Informatics

3. Opportunities for
Collaboration
• Cleveland Clinic
Leadership Academy
• Affiliate Program
• Innovations

4. Changing Medicine

5. Changing Medicine

6. Changing Medicine

7. Changing Medicine

8. Changing Healthcare

9. Themes
1. EMR as the platform for clinical
decision support
2. Impact on quality of care
3. Role of disease registries
4. Personalized and Precision
Medicine
5. Reducing the Lethal Lag Time

10. Lethal Lag Time
• It takes an average of 17 years to
implement clinical research results
into daily practice
• Unacceptable to patients
• Can Electronic Medical Records and
Clinical Decision Support Systems
change this?

11. Electronic Medical Records
• Comprehensive
medical information
• Images
• Communication with
other physicians,
medical professionals
• Communication with
patients
• 3 million active
patients, 10 years

12. EMR Inputs and Outputs
Inputs EMR Tools Outputs
• Clinical • Alerts Secondary Use
• Labs • Best practices • Data sets
• Devices • Smart sets • Registries
• Remote monitoring • Workflow • Quality reports
• Pt outcomes • Communication to
• Omics other providers,
• Social media? patients

13. Clinical Decision Support
• Process for enhancing health-related
decisions and actions with pertinent,
organized clinical knowledge and
patient information
• to improve health and healthcare
delivery.
• Information recipients can include
patients, clinicians and others involved
in patient care delivery
http://www.himss.org/ASP/topics_clinic
alDecision.asp

14. Like a GPS, CDS supplies
information tailored to the current
situation, and organized for
maximum value.

15. Diagnostic Cockpit

16. Clinical Workflow
Clinical Decision Support
Needs to be integrated into
EMR Workflow

17. EMR Alert Types
Clinical Decision Support
Target Area of Care Example
Preventive care Immunization, screening, disease management
guidelines for secondary prevention
Diagnosis Suggestions for possible diagnoses that match a
patient’s signs and symptoms
Planning or implementing Treatment guidelines for specific diagnoses, drug
treatment dosage recommendations, alerts for drug-drug
interactions
Followup management Corollary orders, reminders for drug adverse event
monitoring
Hospital, provider efficiency Care plans to minimize length of stay, order sets
Cost reductions and improved Duplicate testing alerts, drug formulary guidelines
patient convenience

18. The CDS Toolbox
(more examples)
• Drug-Drug Interactions • Rules to meet strategic
• Drug-Allergy interactions objectives (core measures,
• Dose Range Checking antibiotic usage, blood
management)
• Standardized evidence
based ordersets • Diagnostic decision
support tools
• Links to knowledge
references
• Links to local policies

19. Clinical Decision Support
Examples
• New diagnosis of Rheumatoid
Arthritis
• Prompted to refer to preventive
cardiology

20. Clinical Decision Support
Examples
• Age > 50 and a fragile fracture
diagnosis
• order set for bone density scan and
appropriate medication regimen

21. Clinical Decision Support
Examples
• Solid organ transplant –
chemoprevention for skin cancer

22. Virtuous Cycle of
Clinical Decision Support
Registry Measure
Practice Guideline
CDS
http://www2.eerp.usp.br/Nepien/DisponibilizarArquivos/tomada_de_decis%C3%A3o.pdf

23. EMRs and Quality of Care

24. EMR and Quality of Care
• Diabetes care was 35.1 percentage points
higher at EHR sites than at paper-based
sites
• Standards for outcomes was 15.2
percentage points higher
• Better Health Greater Cleveland Project

25. The Role of Registries
• EMR data available to create a
registry for any condition
• Study the condition
– progression, treatments
• Comparative effectiveness of
treatments
• Recruit for clinical trials
• Develop clinical decision support

26. Chronic Kidney
Disease Registry
• Chronic Kidney Disease Registry
• Established 2009
• 60,000 patients from the health
system
• Cohort – Adults with two eGFRs less
than 60 within 3 months, outpatient
results only, or diagnosis of CKD
• http://www.chrp.org/pdf/HSR_120220
11_Slides.pdf

27. Validation Results
• Our dataset’s agreement with EHR-
extracted data for documentation of the
presence and absence of comorbid
conditions, ranged from substantial to
near perfect agreement.
• Hypertension and coronary artery
disease were exceptions
• EMR data accurate for research use

28. Pediatric Surgical Site
Infection Registry
• Data from the EMR and the operative
record
• When did antibiotics start?
• Was pre-op skin prep done?
• Was the time-out and checklist
observed in the OR
• Post-op care quality

29. Patient Reported Outcomes
• Understanding the outcomes of
treatment incomplete without
• Patient Reported Outcomes
Measurement Information System
http://www.nihpromis.org/
• Patient-Centered Outcomes
Research Institute
http://www.pcori.org/

30. Patient Reported Outcomes
• Quality of life
• Activities of daily living
• Recording weight, diet, exercise
using apps
• Quantified Self

31. Mining of electronic health records (EHRs)
has the potential for establishing new
patient stratification principles and
for revealing unknown disease correlations.
- Nature Reviews | Genetics, June 2012

32. Evidence Generating
Medicine
• The next step beyond
evidence-based medicine
• The systematic incorporation of
research and quality improvement
considerations into the organization
and practice of healthcare
• to advance biomedical science and
thereby improve the health of
individuals and populations.

33. Predictive Models
• Predicting 6-Year Mortality Risk in Patients
With Type 2 Diabetes
• Cohort of 33,067 patients with type 2
diabetes identified in the Cleveland EMR
• Prediction tool created in this study was
accurate in predicting 6-year mortality risk
among patients with type 2 diabetes
• Diabetes Care December 2008, vol. 31 no. 12: 2301-2306

34. Diabetes Outcomes by Drug Class

35. BiCaucasian
NoFemale
guanide (e.g.
No
Risk
Calculators
Type 2
Diabetes
Predicting
6-Year
Mortality
Risk
Rcalc.ccf.org

36. Treatment
Algorithms
clevelandclinicmeded.com/
medicalpubs/micu/

37. Information Overload
• New information in • Information about
the medical an individual
literature patient
- PubMed adding - Medical history
over 670,000 new - Lab results
entries per year - Vitals
- Imaging
- Genomics

38. Pardigm Shift
to algorithm medicine

39. New Paradigm for CDS
Family History | Whole Genome | Clinical Data | Patient Reported |Monitoring
Algorithms
Clinical Decision Support
Personalized Medicine

40. Personalized Medicine
• The boundaries are fading between
basic research and the clinical
applications of systems biology and
proteomics
• New therapeutic models
• Journal of Proteome Research Vol. 3, No. 2, 2004, 179-196.

41. Personalized Medicine
Parkinson’s Disease
• New Cleveland Clinic partnership
with 23andMe to collect DNA from
Parkinson’s patients
• Looking for Genome Wide
Associations (GWAS)
• 23andme.com/pd/

42. Precision Medicine
• ”state-of-the-art molecular profiling to
create diagnostic, prognostic, and
therapeutic strategies precisely tailored
to each patient's requirements.”
• ”The success of precision medicine
will depend on establishing frameworks
for …interpreting the influx of
information that can keep pace with
rapid scientific developments.”
• N Engl J Med 2012; 366:489-491, 2/ 9/2012

43. Artificial Intelligence in
Medicine
• Developing a search engine that
will scan thousands of medical
records to turn up documents
related to patient queries.
• Learn based on how it is used
• “We are not contemplating ―
unless this were an
unbelievably fantastic success
― letting a machine practice
medicine.”
• http://www.health2news.com/20
12/02/10/the-national-library-of-
medicine-explores-a-i/

44. IBM Watson
• Medical records, texts, journals and
research documents are all written in
natural language – a language that
computers traditionally struggle to
understand. A system that instantly
delivers a single, precise answer
from these documents could
transform the healthcare industry.
• “This is no longer a game”
• http://tinyurl.com/3b8y8os

45. Digital Humans
Convergence of:
• Genomics
• Social media
• mHealth
• Rebooting Clinical
Trials

46. Conclusion - 1
• EMR as the platform for the future of
medicine
• Data incoming
- Clinical
- Patient Reported
- Genomic
- Proteomic
- Home monitoring

47. Conclusion - 2
• Exploit all uses of the EMR
- Improve practice efficiency
- Ensure patient safety
- Learn about your patients
(registries)
- Compare treatments
- Engage with patients

48. Conclusion - 3
• Understand Personalized
and Precision medicine
• How will we integrate
genomic data in clinical
practice in the future?

49. Conclusion - 4
• Predictive models inform care
• Diagnostic & treatment
algorithms
• How do we integrate
these into practice
in the EMR?

50. Conclusion - 5
• How can we reduce
the lethal lag time?
• Getting medical findings into practice
more rapidly
• How can we engage patients?
• New technology for Big Data in
health care

51. Contact me
• @JohnSharp
• Ehealth.johnwsharp.com
• Linkedin.com/in/johnsharp
• Slideshare.net/johnsharp
______________________
• ClevelandClinic.org
• @ClevelandClinic
• Facebook.com/ClevelandClinic
• youtube.com/clevelandclinic