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Presentation in Barcelona, June 12, 2012
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Electronic Medical Records:From Clinical Decision Support
To Precision Medicine
Electronic Medical Records:From Clinical Decision Support
To Precision Medicine
June 12, 2012John Sharp, MSSA, PMP, FHIMSS
Research Informatics
Cle
vela
nd C
linic
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
Opportunities for Collaboration
Opportunities for Collaboration
• Cleveland Clinic Leadership Academy
• Affiliate Program• Innovations
Changing MedicineChanging Medicine
• Changing the Conversation• Social Media
Changing MedicineChanging Medicine
• Changing How we work• Electronic Medical Records
Changing MedicineChanging Medicine
Changing MedicineChanging Medicine
Changing HealthcareChanging Healthcare
• Changing Precision• Personalized healthcare
ThemesThemes
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
Lethal Lag TimeLethal 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?
Electronic Medical RecordsElectronic Medical Records
• Comprehensive medical information
• Images• Communication with
other physicians, medical professionals
• Communication with patients
• 3 million active patients, 10 years
EMR Inputs and OutputsEMR Inputs and Outputs
Inputs• Clinical• Labs• Devices• Remote monitoring• Pt outcomes• Omics• Social media?
EMR Tools• Alerts• Best practices• Smart sets• Workflow• Communication to
other providers, patients
OutputsSecondary Use• Data sets• Registries• Quality reports
Clinical Decision SupportClinical 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_clinicalDecision.asp
Like a GPS, CDS supplies information tailored to the current
situation, and organized for maximum value.
Diagnostic CockpitDiagnostic Cockpit
Clinical WorkflowClinical Workflow
Clinical Decision Support Needs to be integrated intoEMR Workflow
EMR Alert TypesClinical Decision Support
EMR Alert TypesClinical 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
Treatment guidelines for specific diagnoses, drug 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 patient convenience
Duplicate testing alerts, drug formulary guidelines
The CDS Toolbox (more examples)
The CDS Toolbox (more examples)
• Drug-Drug Interactions • Drug-Allergy interactions • Dose Range Checking • Standardized evidence
based ordersets • Links to knowledge
references • Links to local policies
• Rules to meet strategic objectives (core measures, antibiotic usage, blood management)
• Diagnostic decision support tools
Clinical Decision SupportExamples
Clinical Decision SupportExamples
• New diagnosis of Rheumatoid Arthritis
• Prompted to refer to preventive cardiology
Clinical Decision SupportExamples
Clinical Decision SupportExamples
• Age > 50 and a fragile fracture diagnosis
• order set for bone density scan and appropriate medication regimen
Clinical Decision SupportExamples
Clinical Decision SupportExamples
• Solid organ transplant – chemoprevention for skin cancer
Virtuous Cycle of Clinical Decision Support
Virtuous Cycle of Clinical Decision Support
Measure
Guideline
CDS
Practice
Registry
http://www2.eerp.usp.br/Nepien/DisponibilizarArquivos/tomada_de_decis%C3%A3o.pdf
EMRs and Quality of CareEMRs and Quality of Care
EMR and Quality of CareEMR 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
The Role of RegistriesThe 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
Chronic Kidney Disease RegistryChronic 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_Slides.pdf
Validation ResultsValidation 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
Pediatric Surgical Site Infection Registry
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
Patient Reported OutcomesPatient 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/
Patient Reported OutcomesPatient Reported Outcomes
• Quality of life• Activities of daily living• Recording weight, diet, exercise
using apps• Quantified Self
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
Evidence Generating Medicine
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.
Predictive ModelsPredictive 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
Diabetes Outcomes by Drug Class
Risk Calculators
Type 2 Diabetes
Predicting 6-Year
Mortality Risk
Risk Calculators
Type 2 Diabetes
Predicting 6-Year
Mortality Risk
FemaleCaucasianNoNoNoBiguanide (e.g.NoNoNoNoNoNo
Rcalc.ccf.org
TreatmentAlgorithmsTreatmentAlgorithms
clevelandclinicmeded.com/medicalpubs/micu/
Information OverloadInformation Overload
• New information in the medical literature- PubMed adding
over 670,000 new entries per year
• Information about an individual patient- Medical history- Lab results- Vitals- Imaging- Genomics
Pardigm Shift to algorithm medicine
Pardigm Shift to algorithm medicine
New Paradigm for CDSNew Paradigm for CDS
Family History | Whole Genome | Clinical Data | Patient Reported |Monitoring
Algorithms
Clinical Decision SupportPersonalized Medicine
Personalized MedicinePersonalized 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.
Personalized MedicineParkinson’s Disease
Personalized MedicineParkinson’s Disease
• New Cleveland Clinic partnership with 23andMe to collect DNA from Parkinson’s patients
• Looking for Genome Wide Associations (GWAS)
• 23andme.com/pd/
Precision MedicinePrecision 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
Artificial Intelligence in Medicine
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/
IBM WatsonIBM 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
Digital HumansDigital Humans
Convergence of:• Genomics• Social media• mHealth• Rebooting Clinical
Trials
Conclusion - 1Conclusion - 1
• EMR as the platform for the future of medicine
• Data incoming- Clinical- Patient Reported- Genomic- Proteomic- Home monitoring
Conclusion - 2Conclusion - 2
• Exploit all uses of the EMR - Improve practice efficiency- Ensure patient safety- Learn about your patients
(registries)- Compare treatments- Engage with patients
Conclusion - 3Conclusion - 3
• Understand Personalized and Precision medicine
• How will we integrate genomic data in clinicalpractice in the future?
Conclusion - 4Conclusion - 4• Predictive models inform care• Diagnostic & treatment
algorithms
• How do we integrate these into practice in the EMR?
Conclusion - 5Conclusion - 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
Contact meContact me• @JohnSharp• Ehealth.johnwsharp.com• Linkedin.com/in/johnsharp• Slideshare.net/johnsharp
______________________• ClevelandClinic.org• @ClevelandClinic• Facebook.com/ClevelandClinic• youtube.com/clevelandclinic
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