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From Clinical Decision Support to Precision Medicine

Presentation in Barcelona, June 12, 2012

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From Clinical Decision Support to Precision Medicine

  1. 1. Electronic Medical Records:From Clinical Decision Support To Precision Medicine June 12, 2012 John Sharp, MSSA, PMP, FHIMSS Research Informatics
  2. 2. Opportunities for Collaboration• Cleveland Clinic Leadership Academy• Affiliate Program• Innovations
  3. 3. Changing Medicine
  4. 4. Changing Medicine
  5. 5. Changing Medicine
  6. 6. Changing Medicine
  7. 7. Changing Healthcare
  8. 8. Themes1. EMR as the platform for clinical decision support2. Impact on quality of care3. Role of disease registries4. Personalized and Precision Medicine5. Reducing the Lethal Lag Time
  9. 9. 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?
  10. 10. Electronic Medical Records• Comprehensive medical information• Images• Communication with other physicians, medical professionals• Communication with patients• 3 million active patients, 10 years
  11. 11. EMR Inputs and OutputsInputs 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
  12. 12. 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
  13. 13. Like a GPS, CDS suppliesinformation tailored to the current situation, and organized for maximum value.
  14. 14. Diagnostic Cockpit
  15. 15. Clinical WorkflowClinical Decision SupportNeeds to be integrated intoEMR Workflow
  16. 16. EMR Alert Types Clinical Decision Support Target Area of Care ExamplePreventive care Immunization, screening, disease management guidelines for secondary preventionDiagnosis Suggestions for possible diagnoses that match a patient’s signs and symptomsPlanning or implementing Treatment guidelines for specific diagnoses, drugtreatment dosage recommendations, alerts for drug-drug interactionsFollowup management Corollary orders, reminders for drug adverse event monitoringHospital, provider efficiency Care plans to minimize length of stay, order setsCost reductions and improved Duplicate testing alerts, drug formulary guidelinespatient convenience
  17. 17. 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
  18. 18. Clinical Decision Support Examples• New diagnosis of Rheumatoid Arthritis• Prompted to refer to preventive cardiology
  19. 19. Clinical Decision Support Examples• Age > 50 and a fragile fracture diagnosis• order set for bone density scan and appropriate medication regimen
  20. 20. Clinical Decision Support Examples• Solid organ transplant – chemoprevention for skin cancer
  21. 21. Virtuous Cycle of Clinical Decision Support Registry Measure Practice Guideline CDShttp://www2.eerp.usp.br/Nepien/DisponibilizarArquivos/tomada_de_decis%C3%A3o.pdf
  22. 22. EMRs and Quality of Care
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. 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
  28. 28. 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/
  29. 29. Patient Reported Outcomes• Quality of life• Activities of daily living• Recording weight, diet, exercise using apps• Quantified Self
  30. 30. Mining of electronic health records (EHRs)has the potential for establishing newpatient stratification principles andfor revealing unknown disease correlations.- Nature Reviews | Genetics, June 2012
  31. 31. 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.
  32. 32. 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
  33. 33. Diabetes Outcomes by Drug Class
  34. 34. BiCaucasianNoFemale guanide (e.g. No Risk Calculators Type 2 Diabetes Predicting 6-Year Mortality Risk Rcalc.ccf.org
  35. 35. TreatmentAlgorithmsclevelandclinicmeded.com/medicalpubs/micu/
  36. 36. 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
  37. 37. Pardigm Shiftto algorithm medicine
  38. 38. New Paradigm for CDSFamily History | Whole Genome | Clinical Data | Patient Reported |Monitoring Algorithms Clinical Decision Support Personalized Medicine
  39. 39. 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.
  40. 40. 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/
  41. 41. Precision Medicine• ”state-of-the-art molecular profiling to create diagnostic, prognostic, and therapeutic strategies precisely tailored to each patients 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
  42. 42. 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/
  43. 43. 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
  44. 44. Digital Humans Convergence of: • Genomics • Social media • mHealth • Rebooting Clinical Trials
  45. 45. Conclusion - 1• EMR as the platform for the future of medicine• Data incoming - Clinical - Patient Reported - Genomic - Proteomic - Home monitoring
  46. 46. Conclusion - 2• Exploit all uses of the EMR - Improve practice efficiency - Ensure patient safety - Learn about your patients (registries) - Compare treatments - Engage with patients
  47. 47. Conclusion - 3• Understand Personalized and Precision medicine• How will we integrate genomic data in clinical practice in the future?
  48. 48. Conclusion - 4• Predictive models inform care• Diagnostic & treatment algorithms• How do we integrate these into practice in the EMR?
  49. 49. 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
  50. 50. Contact me• @JohnSharp• Ehealth.johnwsharp.com• Linkedin.com/in/johnsharp• Slideshare.net/johnsharp______________________• ClevelandClinic.org• @ClevelandClinic• Facebook.com/ClevelandClinic• youtube.com/clevelandclinic