BEST PRACTICES IN EMR
CHRONIC DISEASE AND
PREVENTION
MANAGEMENT
OCFP ASA
NOV 29, 2013
AGENDA
Disclosure
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 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
Support
• 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 Bias
• InfoClin provides all workshops on a cost recovery basis
and as a service to the medical community
EMR Adoption
Dynamics –7 Years Later
N=112
Back to Paper
100%
25%
Implemented
EMR in 1999-2000
Efficient
Users
40%
Inefficient
Users
6
35%
NATURAL HISTORY OF
EHR USE
Struggle
With Use
Effective
Use
Start
Ineffective Use
7
Return to
Paper
TYPES OF BARRIERS
TO DATA DISCIPLINE
Struggling 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
OBJECTIVES
Principles 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 EMR
Patient 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 structured
Stamps 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 system
Reminders
• 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 PROBLEM
If 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 1
EMRs 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 3
As 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
Disease
True
Positives
Gold Standard Pilot
N= 5 physicians
With Dx in
Dx Missing
Prob List 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 for
accurate information about which patient has a condition.
ISSUE: FALSE POSITIVES
Gold Standard Pilot
N= 5 physicians
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%
Disease
KEY LESSON: Cannot depend solely on what is in the problem list for
accurate information about which patient does not have a condition!
ISSUE: POOR CAPTURE OF RISK
FACTORS
Site A
Site B
Site C
NON-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
DISCIPLINE
Data 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
DISCIPLINE
Data 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
Smears
Automatically 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
Smears
Exclusions (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
CESSATION
The task
• Separate smokers from non-smokers
Wrinkles
• Ex-smokers are previous smokers who could become smokers
at some point
• Second hand ‘smokers’ are not really smokers
Data 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
CARE
The Task
• Be able to find all patients with diabetes reliably
Wrinkles
• 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’d
Data 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
KESHAVJEE
Dr. 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 panCanadian 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.