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
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.
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
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
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
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
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
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/
38.
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