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 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 providers, • Social media? patients
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_clinicalDecision.asp
Like a GPS, CDS suppliesinformation tailored to the current situation, and organized for maximum value.
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 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, drugtreatment 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
Clinical Decision Support Examples New diagnosis of Rheumatoid Arthritis, prompted to refer to preventive cardiology
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
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 usage, blood Dose Range Checking 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 CDS http://www2.eerp.usp.br/Nepien/DisponibilizarArquivos/tomada_de_decis%C3%A3o.pdf
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
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_Slides.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 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
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
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 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 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