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