Medical Record Review - Center for Research on Applied Gerontology

9/9/2011
A Medical Record Review of the Adaptive Driving Program
a project of the NSF project of the NSF QoLT
QoLT ERC
Dawn of Safe Driving
Years of Experience
Age
5
0
16
Fall of Driver Capability
21
10
26
20
36
40
##
56
##
N
M B
1
2
Medical Record Review
Adaptive Driving Program
Amy Lane, OTR/L, CDRS
132 clients were served in 2009
served in 2009
School of Health
and Rehabilitation
Sciences
Center for Assistive
Technology
gy
Pittsburgh, PA
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3
Analysis
Suggest applications for Intelligent Transportation Systems (ITS) in Driver Rehab and Explore “The Experience Age” in four categories of age groups (N=132)
Experience Age
in four categories of age groups (N=132)
Explore Age
16‐24
N=29
Excel Age
25‐54
N=27
Endure Age
55‐74
N=41
Exist Age
75 and up
N=25
At Times, I Exclude Cases if no On‐The‐Road Evaluation or Training is Completed Excluded N = 10 •
•
•
•
Code Data and Compare
p
Demographics & Intake Responses Referral Pathways
Errors During On‐the‐Road (OTR) Evaluation
Driver Cues & Assistance VS. Outcome
4
Medical Record Review
Adaptive Driving Program in 2009
101
38
5
21
6
12
2
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5
Demographics & Intake Responses
Total
6
Demographics & Intake Responses
Total
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Demographics & Intake Responses
Total
8
Demographics & Intake Responses
Total
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Demographics & Intake Responses
y=mx+b
Exp=Age‐16
10
Demographics & Intake Responses
• 17.8% have visited an Adaptive Driving Ad ti D i i
Program before
• 24.6% have been involved in a prior accident
• ~7% have had prior tickets or citations
tickets or citations
• Clients are 2:1 ratio of Male:Female
except in “Exist Age” Group [23:1]
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Medical Reporting Fact Sheet
PennDOT Report Resolutions by Percent
14
36
22
28
13
21
29
37
17
22
28
16
21
29
50
16
October 2005
March 2008
August 2010
February 2011
20,000 reported
2800 revoked
51% Under 45
27,000 reported
5,940 revoked
45% Under 65
27,000 reported
5,940 revoked
50% Under 65
22,000 reported
11,000 revoked
50% Under 65
Revoke License
Place Restrictions **
Request More Info
*
Take No Action
*
* No Data Reported
** No Data Reported in 2011
Number of Reports in a Year
16
20
45
40
65
60
Age
80
100
12
Referral Pathways
PCP Referrals Span Age Groups
PM&R Referrals Declined for Age 75 and up
Neurologists Referrals Focus on Ages 35‐74
Vocational Rehab not involved with older drivers
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Referral Pathways
Billing Address…
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ITS Applications for Driver Rehab?
Triage of Referrals
• 48% (58 of 121) Pass Rate of OTR Evaluation without T i i I t
Training Intervention
ti
Follow‐up Monitoring
• Supervised Driving of Novice Drivers under Graduating Drivers License
• Clients with Progressive Medical Conditions or an Expected Decline of Health Status
• Driving Performance using Adaptive Equipment for Di i P f
i Ad i E i
f
Evidence‐Based Practice
OVERALL, we need an Intelligent Referral System
OVERALL, we need an Intelligent Referral System
So let’s consider Intelligent Transportation Systems
So let’s consider Intelligent Transportation Systems
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Errors During OTR Evaluation
1. Di
1
Digitize Narrative Report
ii N
i R
2. List and Tally Unique Errors Raw Error Data
a) by Age Group b) by OTR Evaluation Outcome
3. Develop Classifications for Error Types
4 Apply Error Classification Codes
4.
Apply Error Classification Codes
E
Error 5. Tally Errors within Classification
Classification
a) By Age Group
b) By Marginal or Failing OTR Evaluation Outcome
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Errors During OTR Evaluation
My Coding Structure:
Scan, Identify Predict, Decide, Execute
APPROACH
Scan
Identify
Predict
MANEUVER
RESPONSE
REACTION
Decide
Execute
Attitude
Awareness
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Errors During OTR Evaluation
Specific to Marginal or Specific
to Marginal or
Failing OTR Evaluation?
1. Normalize Error Count by Number of Clients in Outcome Group
2. Sum Error/Person Ratios across Outcome Groups
3. Compare Sums Across Age Groups
G1
G2
G3
G4
4. Compare Sums to Sum for only Marginal and Failing Outcomes
Common or Unique to Older Drivers?
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Errors During OTR Evaluation
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Errors During OTR Evaluation
Positive Outcomes
Dawn of Safe Driving
Negative Outcomes
Negative Outcomes
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Ages at Failure
Fall of Driver Capability
Evaluation
Training
Cessation
ID
Age
Sex
Drv . Med. Cond.
Exp.
# of Meds
# of Past Errors ADP
Past Legal
16
57
F
5
Stroke
5
9
N
N/A
32
58
F
10
Cognitive
2
5
N
N
87
71
M
55
Cognitive
14
6
N
Accident
50
23
F
1
N/A
0
10
Y
Accident
56
81
M
65
Stroke
7
8
N
N
73
21
M
0
N/A
1
12
N
N
128 21
F
0
Stroke
1
3
Y
N
63
57
M
39
Stroke
3
3
N
N/A
66
86
M
60
Neurological
0
5
N
N
12 discontinued, dropped out, or will retry later
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Errors During OTR Evaluation
APPROACH
1.4
Wider spectrum of errors
1.2
SSpecific to ifi
Marginal/ Failing Outcome
1
0.8
0.6
0.4
0.2
0
Lane Selection
Interpreting Signs
Using Head Checks
Using Mirrors
Speed Matching
Gauging Traffic
Lane Selection Knowing Signs Head Checks Mirrors Speed Match Traffic Watch
22
Errors During OTR Evaluation
MANEUVER
8
7
Frequent but Common to All
eque bu o
o o
6
5
Rare but Specific to Marginal/ F ili
Failing Outcome
4
3
2
1
0
Entry
Lane Keep
Merge
Lane Chng
Turn
Intersection
Entry Lane Keep Merge Lane Change Turn Intersection
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Errors During OTR Evaluation
RESPONSE
Specific to Marginal/ Failing Outcome for Older Drivers
SPEED STEERING OTHER
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The “Experience Age”
Length of The Experience Age
The Experience Age: ‐5, ‐5, ‐4, 0, 5, 34, 50, 55, 60
5 5 4 0 5 34 50 55 60
Subtracted 5yrs from Years of Driving Experience to approx. time to Safe Driving
A = 21.11 or 49.75
Avg
21 11 49 75 Yrs
Y Long
L
• Novice drivers and drivers needing adaptive equipment will have more errors and should be viewed as a separate training group
• Drivers age 75 and up were at no greater risk of failing evaluations
failing evaluations • Referral sources and funding outlook is variable by age group 12
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Driver Cues & Assistance VS. Outcome
H
How about linking b
li ki
errors to crash risk?
Cues could reflect decision support, while assistance could reflect loss of independent operation of a vehicle.
Can this be considered a surrogate measure for crash risk?
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Driver Cues & Assistance VS. Outcome
Consider the potential of documenting cues and assistance as flags to inspect naturalistic driving li i d i i
data with ITS…that is my next task and presentation at RSS2011
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Limitations
• Some missing or inaccurate data due to self report on intake forms
t i t k f
• Referral source not documented in relation to physician reporting or PennDOT pathway
• Data collection only for clients of low‐tech adaptive equipment (No electronic control no
adaptive equipment (No electronic control, no conversion vans)
• Error analysis uses a custom coding scheme
28
Questions for You!
• access for Driver Rehabilitation Specialists to p
obtain traffic records?
• variations of AMA ADReS Guidelines for physicians based on PCP vs Specialist referral?
• failures vs disability results as validations for Driver Fitness Medical Guidelines publication?
Driver Fitness, Medical Guidelines publication?
• prior visit to ADP + Progressive Condition + variability of error rate as need for longitudinal data source in screening & prognosis of drivers?
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Acknowledgements
• Amy Lane and Rosemarie Cooper
Co‐PI’s on the study
Adaptive Driving Adaptive Driving
Program
• NSF Quality of Life Technology Engineering Research Center
• D
Dr. Rory Cooper:
R
C
Human Engineering Research Laboratories
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Questions for Me?
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APPROACH
Detailed Coding
CODE
Errors
Lane Selection
help with appropriate lane selection
Knowing Signs
Knowing Signs
hesitant and slow @ intersection
hesitant
and slow @ intersection
hesitant and stopped in middle of intersection
missed stop sign
trouble deciding right of way
insufficient opportunity to observe
Head Checks
questionable check for traffic during lane changes
lane change by mirrors only
Mirrors
required cues to check mirrors
no mirror checks or unsatisfactory
lane change without checking for traffic
questionable check for traffic at intersections
cued to make more mirror checks
Speed Match
poor speed matching to flow of traffic on hwy
need to work on entry and exit for hwy
Traffic Watch
overyielding for merge of lane change
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Disability Classification
Disability Type
Self‐Reported Terms
Brain Injury
TBI, brain injury, head injury, closed skull base fracture, post‐concussion b
f
i
syndrome, brain aneurism, intracerebral
hemorrhage, subarrachnoid hemorrhage, subdural hematoma
Stroke
Stroke, CVA, late eff/CV Dis
Neurological
Cerebellar ataxia, CP, spastic paraparesis, any neuropathy (ex. Diabetes)
Spinal Cord Injury
Spinal Cord Injury
SCI spina bifida
SCI, spina
Age
Age – an employer‐mandated review every two years
Other
Encephalitis, deconditioning post surgery, COPD
Cognitive
Memory loss, reading disab., Parkinson’s, cognitive disorder
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Review of Driver Rehabilitation Programs
O’Connor et al, (2008)
Boston, MA, USA
DriveWise
Diagnosed Groups
Overall
Summary of practice – 12 yrs
Client volume & outcomes
380 clients
Marshal et al, (2005)
Ottawa, Ontario, CA
CanDRIVE
Retrospective cohort ‐ 8 yrs
Logistic regression
Predictive elements of passing
Predictive elements of passing 628 clients
Fox et al, (1992)
Sydney, Australia
Coorabel Programme
Summary of practice – 3 yrs
Client volume & outcomes
129 clients
•Similarity in passing rate results across programs
•Most predictive elements
•Observes pedestrians
•Anticipates hazards
•Proper stopping position
•Scanning
•Other studies considered: clinical tests, CDRS assistance, & following signs
Review of Driver Rehabilitation Programs
French & Hanson, (1999)
Gainesville, FL, USA
Southeast DRSs
Klavora et al, (2000)
Toronto, Ontario, CA
Bloorview MacMillan Centre
Prasad et al, (2006)
Edinburgh, UK
Scottish Driving A.S.
Self‐Report Questionnaire
Summary of practice
31 Driver Rehab Programs
Medical Record Review
Client demographics and outcomes
364 clients out of 7300 clients
Self‐Report Questionnaire
Duration of Driving and Accidents
589 clients over 3 year period
61% conducted pre‐drivers evaluations. Clinical tests used varied widely. All surveyed programs recommended behind‐the‐wheel evaluation upon passing pre‐drivers (clinical) evaluation. Better to assess than to predict
Using Electronic Database…
Allowed tracking of changes in client demographics Allowed
tracking of changes in client demographics
Helped support decision making: resource acquisition vs. referral to other program
Basic data collection is good for business
Found higher crash rate among hand control users than left foot accelerator users Contrary to clinicians opinion
Building evidence can support more opportunities
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Referral Pathways & Referral Hit Ratios
Hit Ratio =
Nclients‐>Neg.Outcome | Referral Source
Nclients|Referral Source
36
Referral Pathways & Referral Hit Ratios
0.2
02
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Referral Pathways & Referral Hit Ratios
0.2
02
38
Referral Pathways & Referral Hit Ratios
0.1
01
19
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Referral Pathways & Referral Hit Ratios
0.0
00
20