Best Practices in EMR Chronic Disease and Prevention Management

OCFP ASA

Nov 29, 2013

AgendaDisclosure

Welcome and Introductions

Natural history of EMR Adoption

Principles of Data Discipline

Preventive Care –streamlining information, preventing mishaps

Smoking Cessation –problems and solutions

Diabetes Management –problems and solutions

Hands-On component

-Cleaning up data

-Extracting data

Questions

Wrap up and Evaluation

Faculty/Presenter DisclosureFaculty/Presenter Disclosure

• Faculty: KARIM KESHAVJEE• Program: 51st Annual Scientific Assembly

• Relationships with commercial interests:– Grants/Research Support: NONE– Speakers Bureau/Honoraria: ASTRA-ZENECA, GSK– Consulting Fees: AEREUS TECHNOLOGIES– Other: Employee of INFOCLIN

Disclosure of Commercial Disclosure of Commercial SupportSupport

• This program has received financial support from NONE in the form of NONE.

• This program has received in-kind support from NONE in the form of NONE.

• Potential for conflict(s) of interest:– KARIM KESHAVJEE has received CONSULTING FEES from INFOCLIN, whose

product(s) are being discussed in this program].– INFOCLIN delivers a product that will be discussed in this program: Workshops

similar to the one you are attending

Mitigating Potential BiasMitigating Potential Bias

• InfoClin provides all workshops on a cost recovery basis and as a service to the medical community

EMR Adoption Dynamics –7 Years Later

6

N=112

100%Implemented

EMR in 1999-2000

40%Inefficient

Users

25%EfficientUsers

Natural History of EHR Use

7

StartReturn toPaper

Struggle With Use

Ineffective Use

Effective Use

Types of Barriers to Data DisciplineStruggling with EHR use

• Poor clinical workflows• Duplicate paper and EHR processes• Lack of data standards and data cleaning processes

Ineffective EHR users

• Good clinical workflows• Paper documents are scanned or eliminated through integration with

HIEs.• Haven’t worked out data standards and data cleaning processes

Efficient EHR users

• Good clinical workflows• Efficient handling of documents from outside clinic• Use data standards and data cleaning, even if only locally applicable

Learning ObjectivesPrinciples of data discipline for chronic disease and prevention management in EMR

Preventive care

• Data standards for pap smears, mammograms, fecal occult blood results and immunizations

• Cleaning data in the EMR• Structuring data from pathology, radiology and laboratory• Running queries and constructing reminders• Maintaining clean data in the EMR for effective preventive care

Smoking cessation

• How to set data standards• Cleaning data in the EMR• Running queries and constructing reminders• Maintaining clean data in the EMR

Learning Objectives

Diabetes management

• How to set data standards• Cleaning data in the EMR• Running queries and constructing reminders• Maintaining clean data in the EMR• Generating data for population management

• % patients with uncontrolled diabetes, HTN and LDL

Orientation to THE EMRPatient profile

• Cumulative patient summary information at top of screen• Data is entered into this section manually• Data is not automatically structured or coded –need for structuring and/or coding• Structured data does exist in some fields

• Medications, allergies, risks, Labs

• Some data comes in electronically –it is structuredStamps and Custom Forms

• Templates for data entry• Mostly text-based, but Custom Forms allow structured data entry

• Can pull in structured or coded information from the systemReminders

• Queries that identify patients and flag them for some action• Reminder messages are posted in the patient profile section

Searches

• Queries that identify patients and can extract data from the system• Data can be exported to other software

The ProblemIf you can’t tell which of your patients has had a pap smear, how can you do proper cervical cancer screening?

If you can’t tell which of your patients has bowel disease, how can you do proper colon cancer screening?

If you can’t reliably identify patients with chronic disease in your EMR, how can you have a chronic disease management program?

If you don’t know which patients are on warfarin, how can you make sure that you are prescribing safely?

Why Data in EMR is Poor 1EMRs are optimized for individual patient care

• Documentation within a patient is quite good• Allows you to view data from a variety of sources in one place

Current EMRs are not designed for population-based care

• Data capture is not standardized• Standard terminology is poorly enforced in most EMRs• Meta-data is poorly captured (i.e., can put data in the ‘wrong

place’)

• Data inconsistency is rampant• Many patients with HBA1c > 7 or on Insulin are not labelled as

diabetic in the EMR

• Data inaccuracies abound• Many patients with diagnosis code of 250 in the billing system are

not diabetic

Why Data in EMR is Poor 2• Current EMRs are not designed for population-based care cont’d

• There are few standardized data feeds into the EMR• Medications, consult notes, hospital discharges and diagnostic imaging do not

come in a consistent and standard way

• Lab is starting to be standardized, but not in every province

• Data good enough for individual care are too complex for population care• To manage a colon cancer screening program, you need to enter information in

4 different places

• Lab data: Stool Occult Blood Test result –if lab doesn’t send it, add it manually!

• Procedures: Colonoscopy

• Past medical history: Colon cancer or inflammatory bowel disease

• Problem list: current cancer or inflammatory bowel disease

• Any error in where you put the data, will put the patient in the wrong category

• Diabetes patient management is even more complex –requires data in 7 places, not all of which are structured in most EMRs

Why Data in EMRs is Poor 3As humans, we

• Are chronically inconsistent• We continue to prescribe glyburide and forget to label the patient as being

diabetic• Deviate from standard terms

• CAD, Atherosclerosis, CHD, ASHD all mean the same thing –but computers don’t know that!

• Forget to change the status of information in the EMR• We tell the patient to stop taking a medication, but don’t actively stop it in the

EMR• Use terms that denote a class, when we really mean an instance

• We say ACE inhibitors or statins, when we really prescribe ramipril and atorvastatin –but computers only know instances, not classes!

Medical Knowledge and Terminology evolves over time

• Juvenile vs. Adult onset• IDDM vs. NIDDM• Type 1 vs. Type 2

Current EMRs don’t make up for the foibles of humans or the vagaries of human progress

Issue: Missing Data

DiseaseTrue

PositivesWith Dx in Prob List

Dx Missing from Prob List

Percent Missing

COPD 58 43 15 26%

Depression 418 304 114 27%

Diabetes Mellitus 153 141 12 8%

Hypertension 475 393 82 17%

Osteoarthritis 208 193 15 7%

KEY LESSON: Cannot depend solely on what is in the problem list foraccurate information about which patient has a condition.

Gold Standard PilotN= 5 physicians

Issue: False Positives

Disease

True Positive With Dx in Prob List

Count by Problem List % Incorrect

COPD 43 69 38%

Depression 304 377 19%

Diabetes Mellitus 141 142 1%

Hypertension 393 440 11%

Osteoarthritis 193 202 4%

KEY LESSON: Cannot depend solely on what is in the problem list foraccurate information about which patient does not have a condition!

Gold Standard PilotN= 5 physicians

Issue: Poor Capture of Risk Factors

Site A Site B Site CNON-SMOKER TOBACCO NON-SMOKER NON SMOKER

T TOBACCO NEVER SMOKER

EX-SMOKER TOBACCO EX SMOKER QUIT > 1 YEAR

SMOKER: QUITTING TOBACCO NON-SMOKER QUIT < 1 YEAR

SMOKER: NO PLAN TO QUIT TOBACCO SMOKER

SMOKER: ACTIVELY QUITING NEVER SMOKED

TOBACCO USE (305.1) TOBACCO NON SMOKER

SMOKER: ACTIVELY QUITTING

SMOKING

NON SMOKER

NICOTINE ADDICTION

NONSMOKER

EX SMOKER

Principles of Data DisciplineData Standardization

• Coding• Diagnoses, Medications, Labs, History

Data Cleaning

• Goal: Right patients in, wrong patients out• Coded –all relevant data is coded or in a single format

Data Discipline

• Systems thinking• Using templates, reminders and searches to ensure clean data is

captured on an on-going basis

• Pay attention to environmental cues• A reminder that stays on when you think it should not be there is

a sure sign of dirty data

Principles of Data DisciplineData discipline should be maintained using ‘systems’

• Using a template for a particular aspect of care should automatically provide the data to turn a reminder off

• E.g., smoking cessation counselling, any form of in-office procedure that is not lab related

• Using lab results to turn off a reminder• E.g., pap smear, HbA1c, LDL, FOBT etc

• Using a scanned report to turn off a reminder• E.g., mammography, optometry/ophthalmology

Importance of Reminders

• They are a good signal of ‘dirty data’Three issues that keep cropping up

• Patients have inconsistent labelling –DM, Diabetes• Patients not labelled –on Insulin or HbA1c >7, but no DM in Profile• Data is entered in the ‘wrong’ place

Benefits of Data Discipline

Increased confidence that right patients are being identified

Queries become ‘plug-and-play’

Queries are now shareable

Queries are more reliable

Repeatability of queries over time

New queries are easier to construct

Prevention Screening

Preventive Care Summary Report

• Automatically gives you reports on paps, mamms, immunizations and FOBTs

• # of eligible patients

• # excluded

• # done

• # not done

• % complete

• But…the data has to be entered accurately• You will still need reminders during the encounter to remind

you what services the patient needs

Prevention –Pap SmearsAutomatically adds labs received electronically to the patient’s chart

• Different labs may change the name of a pap smear or not mention pap at all

• looks for the following text to find paps• PAP• Cytopathology• pap smear• cervical smear• Cytotechnologist

• If you receive a pap report on paper, enter it electronically by clicking on Apple-R, select Diagnostic Tests and click on Pap Test Report

• This will ensure the data is available to the system for its calculations

Prevention –Pap SmearsExclusions (in HPH)

• hysterectomy• hysterosal• The ICD-9 code 68 (hysterectomy)• Contains hyst but does not contain hystero• if Q140A was billed.

Things to watch out for

• ICD-9 code 68 is also used for fibroid removals –can confuse the system –don’t use this code for fibroid removal

• Do not use the term hysterosalpingoscopy in the History of Past Health, as it will be misinterpreted as hysterosalpingectomy

Prevention –Mammograms Mammograms don’t come in electronically

Scan them in and train your front staff to label them as a mammogram report

• (Apple-R diagnostic imaging mammogram)This will allow the system to know that the patient received a mammogram

For exclusions

• Code your patients with breast cancer with the ICD code of 174• However, PSS checks for all the following

• ca breast• fibrocystic breast• breast ca• cancer breast• mastectomy• The ICD-9 code – 174• Checks if Q141A was ever billed

Prevention –Child Immunization

Looks for

• 4 DPTPs and • 2 MMRs before age 2• Enter these using Apple-J

Speed up entry:

• If the patient had a DPTP or MMR before • Double-click on it and select “Perform Immunization Again”

There are no exclusions for this category

Prevention –Flu Shots

Looks for flu vaccine given at the right time

• Use Apple-J to record the flu shot• Vaxigrip, Fluzone, etc

• If the patient had a flu shot before, double-click and select “Perform Immunization Again”

There are no exclusions for this category

Don’t turn off reminders by double-clicking

• If you have to resort to that, it means the system will be counting things incorrectly

Prevention –FOBT

PSS looks for the word ‘occult’ in electronic labs

• If FOBT reports come in on paper, record them during the scanning process

Exclusions

• crohn• colitis• checks if Q142A was billed• colonoscopy done in the last 60 months

Managing Smoking CessationThe task

• Separate smokers from non-smokersWrinkles

• Ex-smokers are previous smokers who could become smokers at some point

• Second hand ‘smokers’ are not really smokersData issues

• Many different terms are used by individual physicians• There are literally 10’s of ways to say a patient doesn’t smoke

• Never smoked, non-smoker, non smoker, smoker: no, quit smoking, ex-smoker, smoking = 0, not a smoker, doesn’t smoke, etc

• Smoking status can be in many different places• Past history, Problem List, Risk Factors, Personal History• Makes it difficult to find it routinely

Smoking Cessation

Data Standards

• If patient is smoker, record “Current Smoker”• If patient never smoked, record “Never smoked”• If patient quit smoking, record “Ex-smoker”

Data cleaning

• Import and run the Smokers Clean-up Search• Keep the list to the right of the screen and move the EMR

screen to the left• Click through the list of patients one at a time and clean up

the data by changing smoking status to the correct one in the Risk Factors

Managing Diabetes CareThe Task

• Be able to find all patients with diabetes reliablyWrinkles

• There are different types of diabetes that can confuse the issue –gestational diabetes, diabetes insipidus

• Patients may not have a diagnosis listed, but may have other signs of diabetes: on insulin, high blood sugar

Data Issues

• Different terms are used for diabetes: DM and diabetes mellitus are the most prevalent

• The diagnosis could be in 2 different places –History of Past Health and Problem List

• Some signs of diabetes are also a sign of other things: metformin (a drug for diabetes) is also a drug for another unrelated disease (polycystic ovarian syndrome)

Managing Diabetes Care cont’dData Standards

• ICD9 code of 250 should be used• Double-click on a diagnosis and ‘associate’ it with a code of 250

Data Cleaning

• First run Data Cleanup #1• Associate all relevant diagnoses of diabetes in the Problem List

with the code of 250• If the patient is diabetic, but doesn’t have the diagnosis in the

Problem List, add it and associate it with 250

• Next run Data Cleanup #2• Create a list and give it to the physician –they will need to decide

whether any of those patients are truly diabetic• Depending on the site, there will be a small or large number of

patients on that list

Managing Diabetes

Reminders

• Install the reminders for quarterly and annual visits into the Reminder system

• These will provide reminders for the physician about diabetes care

Attendee Requested Condition

Attendees can select a condition they would like to manage

We will work through the following:

• Set standards for how that condition will be labelled• Develop a Search for finding patients with that condition, so

we can label them properly• Develop Reminders for providing care to those patients• Develop a Search for finding those patients reliably in the

future

Conclusion

Chronic Disease Management and Prevention are here to stay

• Requires new ways of working• Requires data discipline and data systems

Data discipline is not easy, but is manageable with

• Some prior thought• Good use of standards• Using a systematic approach• Regular review of data quality in the record

Dr. Karim KeshavjeeDr. Karim Keshavjee is a Family Physician with a part-time practice in Mississauga. He spent five years in the pharmaceutical industry managing clinical trials and managing an electronic drug utilization project. He is currently an Associate Member of the Centre for Evaluation of Medicines and an Assistant Adjunct Professor at the University of Victoria.

Karim was the Clinician-Project Director for the COMPETE (Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness) series of research studies.

Karim was also the physician consultant to Canada Health Infoway for the pan-Canadian electronic prescribing project (CeRx), the inter-operable electronic health record (iEHR) project and the consumer health architecture project (PAQC).

Karim is currently the Research Data Systems Architect and EMR consultant to the College of Family Physicians of Canada’s National Chronic Disease Surveillance Network, CPCSSN. You can find out more at www.cpcssn.ca.

Karim completed his MBA at the Rotman School of Business in 2004 in technology commercialization. He now specializes in helping all stakeholders build and use EMRs more effectively. You can find out more about InfoClin at www.infoclin.ca. You can contact Karim at karim@infoclin.ca.