Electronic Medical Records: From Clinical Decision Support to Precision Medicine

From Clinical Decision SupportTo Precision Medicine ELECTRONIC MEDICAL RECORDS

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

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?

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

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• Social media? providers, patients

Clinical Workflow Workflow

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

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

Diagnostic Cockpit

CDS Example: Order Sets

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

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

Clinical Decision SupportExamples New diagnosis of Rheumatoid Arthritis, prompted to refer to preventive cardiology

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

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

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

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

EMRs and Quality of Care

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

Meaningful Use

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

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

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

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

Pediatric Surgical SiteInfection 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 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 Outcomes Quality of life Activities of daily living Recording weight, diet, exercise using apps Quantified Self

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

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

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

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

Algorithmsclevelandclinicmeded.com/medicalpubs/micu/

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

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

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.

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/

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

Artificial Intelligence inMedicine 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 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 Humans Convergence of:  Genomics  Social media  mHealth  Rebooting Clinical Trials

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

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

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

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

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

Contact 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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Electronic Medical Records: From Clinical Decision Support to Precision Medicine

by John Sharp

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