ELECTRONICMEDICAL RECORDS

From Clinical Decision SupportTo Precision Medicine

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• 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 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_clinicalDecision.asp

Like a GPS, CDS supplies information 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 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

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 SupportExamples

Solid organ transplant – chemoprevention for skin cancer

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)

Documentation templates Relevant data displays Point of care reference

information (i.e. InfoButtons)

Web based reference information

Diagnostic decision support tools

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 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_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

Postoperative nomogram based on 996 patients treated at The Methodist Hospital, Houston, TX, for predicting PSA recurrence after radical prostatectomy.

Kattan M W et al. JCO 1999;17:1499-1499

©1999 by American Society of Clinical Oncology

Nomograms bring into visual perspective the effect exerted by continuous variables against measured end points

Risk CalculatorsType 2 DiabetesPredicting 6-Year Mortality Risk

FemaleCaucasianNoNoNoBiguanide (e.g.NoNoNoNoNoNo

Algorithms

clevelandclinicmeded.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 medical literature PubMed  adding

over 670,000 new entries per year

Information about an individual patient Lab results Vitals 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 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 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 clinicalpractice 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