Problem Lists Across Organizations: Implications for...
Transcript of Problem Lists Across Organizations: Implications for...
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Problem Lists Across Organizations:
Implications for Design and Implementation
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Background Information
• Can we all agree on a definition?
– Ongoing management issues Ex: HTN, DM
– Undiagnosed Symptoms / Results Ex: SOB, high LFTs
• Why is a problem list content so debated?
– Different approaches between EHRs
– Who owns and manages the problem list? IP vs Amb.?
• What do we know about problem list content?
– Filled with non-problems Ex: Physical Exam
– Missing key problems Ex: DM on 60%2
Very
Controversial
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DM DM
eC
W Automatic
& Manual
Automatic
& Manual
DM
EHRs Approach Problem Lists Different
Encounter Dx Problem List PMHx
DM DM
Ep
ic
Manual ManualDM
DM DM
Ce
rne
r
Manual Automatic, only
when resolved
DM
Encounter = Problem List
DMGE
• Most of the EHRs prevent exact duplicate problems on lists• Only Cerner forces past medical history (PMHx) to be resolved
3
Free text
DM
• Do active problems belong on the on the problem list AND
the Past Medical History?
Does PMHx mean problems which were in the past (i.e.
everything) or problems which are passed (i.e. resolved)?
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Data and Analysis
• Users add problems; do not resolve or delete
• Systems were similar in active problems
– 89-92% active: Epic and Cerner
– 74-77% active: GE
Cedars-Sinai
Amb.
Cleveland Clinic
Amb. & IP
Memorial HermannCerner(IP) GE(Amb)
Number Patients 160,000 1 million 290,000 315,000
Number of problems 1 million 5 million 1 million 2.7 million
Providers 250 2400 5500 ~200
Years of data 3 years 10 years 1 year 8 years
Problem Active
Status Resolved
Deleted
75%
23%
2%
(GE)
91%
4%
5%
(Epic)
92% 74%
8%
(Cerner IP) GE(Amb)
89%
2%
9%
(Epic)
1. Auto-stop 2. Changed EHRs
Doctors Doctors Nurses
Across different institutions, different EMRs, different positions,
problems are added more than deleted or resolved.
4
24%
2%
Problem List Clean-Up (GE to Cerner)- Removed 30-40% of problems
- Physicals, Acute problems (URIs), Family Hx
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Problem Lists Changes
EpicCentricity
Added, not resolved or
deleted; lists get longer
Issue crosses EMR:
reflects MD behavior
Cedars-Sinai Outpatient Problem List Changes
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0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
'01 '02 '03 '04 '05 '06 '07 '08 '09 '10
Cle
vela
nd
Clin
ic
Number of Patients with Problem Lists Of Different Size
Problem List Size Over Time
CSLink
Dashed lines
6
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
'01 '02 '03 '04 '05 '06 '07 '08 '09 '10
Ce
dar
s-Si
nai
Cle
vela
nd
Clin
ic
Number of Patients with Problem Lists Of Different Size
Cleveland Clinic
Solid Lines
More pts had problem lists;
PL size grew over time
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Issues Affecting PLs
Cedars-Sinai:
• 23% of all active
problems (150,000)
Cleveland Clinic:
• 20% of all active
problems (1 million)
MH:
• 30% IP (300,000)
• 30% Amb (300,000)
• Non Problem V-Codes
• Acute Problems
• Duplicate ICD-9s
• Expired ICD-9s
• Two Active, Same Meaning
• Symptoms to Diagnosis
• NOS/NEC on List
• Similar ICDs
Cedars-Sinai:
- 160,000 Patient Lists
Longer lists have more issues
Longer lists are poorer maintained
ICD-10 will make this worse7
Physical Exam
Sinusitis, UTIs
HTN twice
BPH (expired)
HIV & h/o HIV
Colon CA
Screen for Colon CA
HTN, NOS & NEC
Basic ICD-9: First 3 numbers Ex: - 477 is all allergies
- 477.2, 477.8, 477.9 on list
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Measles Outbreak at Memorial Hermann
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(In the EHR only)
• Noticed a spike in Measles at one practice location
– 10% of the population (350 pts);
– MA used Measles instead of Vaccine for Measles
– Also Mumps (400 pts) & Chick Pox (3500 pts)
– Provider said he could recall who had it for real, but quit
• Analysis: Prevalence of problems per office
– Average & std dev. calculated compared by specialty
– Measles at the office: 4 standard dev. above the mean
– Over 65 “Outbreaks” throughout the system
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Measles (4)
Parentheses are
the number of standard
deviations above the mean
Mumps (4)Chickenpox (4)ADHD (3)
Measles (4)Mumps (4)Chickenpox (4)Hashimoto’s (4)
B12 Def. (4)
B12 Def. (4)
B12 Def. (4)Hypogonadism(4)
High Risk Meds (6)
Hypothryoidism (4)
Hypothryoidism (6)
Mood D/O (6)ADHD (3)
ADHD (4)
Neuropathy (6)
ADHD (4)
CRF III (6)
CRF III (6)
CRF III (6)
Prediabetes (6)Prediabetes (6)
Hypothryoidism(4)
ADHD (3)
Erectile Dysfx (6)
Erectile Dysfx (6)
Opiod Dep. (6)
SVT (6) Positive ANA (3)
Positive ANA (4)Chest Pain (6)
Mood D/O (3)
High Risk Sex Beh. (6)
Prostatitis (6)
Hemorroids (6)
Strep. Throat (6)
Strep. Throat (6)
Hyponatremia (6)Is this
practice
good or bad?
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Conclusions and Questions
• Variable EHR designs & providers use is corrupting PLs
• Results in long, increasing useless lists (& outbreaks)
So how are we going to clean up problem lists? What
are the implications for EHR design?
• To make EHR problem lists more clinically useful:
–Agree on a definition & teach etiquette (limited usefulness)
–Implement CDS to make sure the PL are complete
–Create a technological method to clean up PL
–Develop a method to use PL to tell a patient story10
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Questions
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Thanks to the Cleveland Clinic,
Cedars-Sinai and Memorial Hermann
for allowing me to share their data
Thanks to Dr. Michael Shabot, Dr.
David James and David Bradshaw
for their leadership and mentorship
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Additional Slides if Needed
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Solutions: Better Approach
Problem List- Longer
- More dups
- Less useful
Past Med. History
Is past really
resolved?
Technical &
manual
solutions
Manual
Ideas
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Add• ICD-10 / HCC Look-Up
• CDS for completeness
• Meaning Use / CMS
Don’t Add Policies• Nonspecific
• Redundancies
Delete
• Policy & procedures
• Etiquette documents
• More training
3. Create an automatic, periodic
clean up process
2. Change
Problem List
Paradigm
1.
Block
some
adds
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Incomplete Problem Lists
• What percent of problem lists capture issues?
– 78.5% Breast Cancer
– 61.9% Diabetes
– 50.7% Hypertension
• Memorial Hermann ambulatory problem lists
- 9% of pts are obese (>50% in Texas)
- 50% of pts taking Phentermine are not Obese on PL
- 50% of pts taking insulin are missing Diabetes
- 60% of pts taking Adderall are not listed as having ADHD
A Wright et al.,
2012
CDS can assist
to add missing
problems
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0
1000
2000
3000
4000
5000
6000
7000
8000
Number of Patients
Total Duplicates
Epic Error – allowed dups
Effect of ICD-10 on Problem Lists
ICD-9 to IMO: 13,000 to 60,000 diagnoses
ICD-9 to ICD-10: 13,000 to 68,000 diagnoses
Expect ICD-10 (and IMO) to cause problem lists to
greatly increase in size.
Examples: 250.00 on list 2x or HTN NOS & NEC
IMO implemented
Cleveland Clinic Duplicates or Similar Codes Placed on Problem Lists
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