This document discusses the transition from traditional clinical decision support using electronic medical records to precision medicine. It provides examples of how Cleveland Clinic has used electronic medical records to create registries for conditions like chronic kidney disease, develop predictive models, and power algorithms for precision treatment recommendations. The document envisions precision medicine relying on vast amounts of molecular, genomic, and patient-reported data integrated into clinical decision support.

1. From Clinical Decision Support
To Precision Medicine
ELECTRONIC
MEDICAL
RECORDS

2. Cleveland Clinic
 1300 bed main hospital
 9 Regional Hospitals
 54,000 admissions, 2 million visits
 Group practice of 2700 salaried physicians
and scientists
 3000+ research projects
 Innovative Medical School
 30 spin off companies
 Office of Patient Experience

3. 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?

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

5. 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
• Social media? providers, patients

6. Clinical Workflow
Workflow

7. 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_clinicalDec
ision.asp

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

9. Diagnostic Cockpit

10. CDS Example: Order Sets

11. CDS as a Strategic Tool
• CDS should be used as a strategic tool for
achieving an organization’s priority care delivery
objectives.
• These objectives are driven by external forces
such as
• payment models
• regulations related to improving care quality and safety
• internal needs for improving quality and patient safety
• reducing medical errors
• increasing efficiency

12. 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

13. Clinical Decision Support
Examples
 New diagnosis of Rheumatoid
Arthritis, prompted to refer to preventive
cardiology

14. Clinical Decision Support
Examples
 Age > 50 and a fragile fracture diagnosis –
order set for bone density scan and
appropriate medication regimen
 Go to Smart Set

15. Clinical Decision Support
Examples
 Solid organ transplant – chemoprevention for
skin cancer

16. The CDS Toolbox
(more examples)
 Drug-Drug Interactions  Rules to meet strategic
 Drug-Allergy interactions objectives (core
measures, antibiotic
 Dose Range Checking
usage, blood management)
 Standardized evidence based
 Documentation templates
ordersets
 Relevant data displays
 Links to knowledge references
 Point of care reference
 Links to local policies
information (i.e. InfoButtons)
 Web based reference
information
 Diagnostic decision support
tools

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

19. EMRs and Quality of Care

20. 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
 Across all insurance types, EHR sites were
associated with significantly higher achievement
of care and outcome standards and greater
improvement in diabetes care
 Better Health Greater Cleveland

21. Meaningful
Use

22. 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

23. 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_12022011_Slid
es.pdf

24. 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

25. Registry Results
 2011
 5 out of 5 abstracts accepted to
American Society of Nephrology annual
meeting
 Three papers accepted to nephrology
journals
 NIH grant
 Partnerships with other research centers

26. Pediatric Surgical Site
Infection
 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

27. 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/

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

29. Population Health
 New tools to enable the study of disease
trends and epidemics
 PopHealth - submission of quality measures
to public health organizations
http://projectpophealth.org
 Query Health – standards to enable
Distributed Health Queries
http://wiki.siframework.org/Query+Health

30. 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

31. Postoperative nomogram based on 996 patients treated at The Methodist
Hospital, Houston, TX, for predicting PSA recurrence after radical prostatectomy.
Nomograms bring into visual perspective the effect
exerted by continuous variables
against measured end points
Kattan M W et al. JCO 1999;17:1499-1499
©1999 by American Society of Clinical Oncology

32. BiCaucasi.an
Nouanide (e.g
gFemale
No
Risk
Calculators
Type 2
Diabetes
Predicting
6-Year
Mortality
Risk

33. Algorithms
clevelandclinicmeded.com/
medicalpubs/micu/

34. Against Diagnosis
 The act of diagnosis requires that patients be placed
in a binary category of either having or not having a
certain disease.
 These cut-points do not adequately reflect disease
biology, may inappropriately treat patients
 Risk prediction as an alternative to diagnosis
 Patient risk factors (blood pressure, age) are
combined into a single statistical model (risk for a
cardiovascular event within 10 years) and the results
are used in shared decision making about possible
treatments.
 Annals of Internal Medicine, August 5, 2008vol. 149 no. 3 200-203

35. Information Overload
 New information in the  Information about an
medical literature individual patient
 PubMed adding over  Lab results
670,000 new entries per  Vitals
year  Imaging
 Genomics

36. 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.

37. Example–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/

39. 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

40. 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/2012
/02/10/the-national-library-of-
medicine-explores-a-i/

41. 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

42. Digital Humans
Convergence of:
 Genomics
 Social media
 mHealth
 Rebooting Clinical Trials

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

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

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

46. Conclusion - 4
 Predictive models inform care
 How do we integrate these into practice in
the EMR?

47. Conclusion - 5
 How can we reduce the lethal lag time?
 Getting medical findings into practice more
rapidly
 How can we engage patients?
 Real time data on populations
 New technology for Big Data in health care

48. Contact me
 @JohnSharp
 Ehealth.johnwsharp.com
 Linkedin.com/in/johnsharp
 Slideshare.net/johnsharp
______________________
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 @ClevelandClinic
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 youtube.com/clevelandclinic