Transcript Handling patient complexity in casemix classifications

Handling Patient Complexity in
Casemix Classifications
Associate Professor Janette Green
Australian Health Services Research Institute (AHRSI)
Sydney Business School,
University of Wollongong, Australia
ABF 2013 Sydney
Acknowledgement
This presentation is based on findings from a series of projects
undertaken with AHSRI colleagues working in our subcentres
– NCCC
– AROC
– PCOC
– CHSD
– CASiH
Overview
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Patient complexity seen as complications and
comorbidities (CCs)
Are CCs important for ABF?
How do we deal with them in AR-DRGs?
Let’s do something about our PCCL!!
Internationally?
What about other classifications?
Are they important in subacute care?
Are they important in mental health?
Patient complexity
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An episode of care is assigned a principal diagnosis
Additional diagnoses that affect the patient’s care are
also coded (plus…)
Comorbidity - pre-existing condition, not the main
reason for the admission
Complication - a condition acquired during a hospital
stay
Identified with ICD codes in the patient’s record
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Still the best way
to describe today’s admitted patients?
MDC based on principal diagnosis based on standards
In reality, for patients who are more complex, it is harder to
isolate a single diagnosis as the PDx
Good care for these patients is more than just treating the
PDx.... Cluster of diagnoses? Precedent with procs
With more choices for care, greater concentration of complex
patients admitted to hospitals
How precise is the PDx? MH PDx – more than 20% do not see
a MH team.
Care type changes to create a new episode - CCs relevant for
the first episode?
But I digress...
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Why is patient complexity important
in an ABF world?
More complex patients = more complex care required
More complex care = higher cost care
Complexity needs to be incorporated accurately in our
casemix classifications
It’s no secret that our population is ageing!!
Result –
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Increase in numbers of patients who are complex
Increase in degree of complexity
Sounds logical but what is the evidence?
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Using PCCL, an imperfect measure of CCs used in acute care,
levels 0 to 4
Patient Clinical Complexity Level (PCCL)
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Assume we can isolate a PDx and be clear about CCs
PCCL is used in acute admitted care and partly defines
some DRGs
Each additional diagnosis in a record is assigned a
complication and comorbidity level (CCL) value from a
matrix, if it is not found to be an exclusion (except
neonates)
Combination of CCLs produces a PCCL for each record
0 – 4 scores, would expect costs to increase
Is PCCL doing its job?
A surgical ADRG
A medical ADRG
When this methodology was developed...
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A computer was a huge contraption living alone in an air
conditioned room. You submitted a job, it would join a
queue and you waited....
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Computer power and speed have increased
Individual access to computers has improved
Statistical methodology has advanced
More data are available
Is the PCCL methodology outdated?
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Certainly it is past time to review and update
CCLs in the matrix may be inaccurate BUT
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Who says the choice of levels is correct?
Who says a matrix is the way to go?
Who says the exclusions are right?
Who says there are no interactions between CCs?
Who says the way they're combined to create the PCCL is
correct?
Who says the impact is the same on everyone?... Different
for paediatric patients, the elderly, other subgroups?
What about incorporating procedures?
What about function or severity measures?
Is just updating existing CCL values in the matrix like
applying a band aid to a broken leg?
Internationally, some differences include...
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Number of severity levels varies
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Limited eg to 2 in Nordic DRGs
Unlimited eg Austria and Germany
Type of CC from a limited list rather than using exclusions in
some systems
To determine the severity level
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The highest ranked CC determines the severity level in many systems
...combined with age, LOS and death during admission in France
No secondary diagnosis is used in the Netherlands. If a patient is
treated for an additional diagnosis they go to a new class.
Other classifications - subacute
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A recent review of AN-SNAP found CCs act as a cost driver
A cost driver is not necessarily a good classification variable
Principal diagnosis vs function
AROC has been collecting data on complications and
comorbidities separately, using lists of options
The effect of CCs and of function on LOS are confounded
More complex patients arrive with poorer function and
– Have more potential to improve
– Take longer to do so
OR
 Can tolerate less therapy, so have shorter LOS
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Effect of Co-morbidities on
ALOS and FIM change in 2006
Other (n=5196)
Other (n=5196)
Osteoarthritis (n=3511)
Osteoarthritis (n=3511)
Ischaemic heart disease (n=2288)
Ischaemic heart disease (n=2288)
Osteoporosis (n=1517)
Osteoporosis (n=1517)
Atrial fibrillation (n=1357)
Atrial fibrillation (n=1357)
Depression (n=773)
Depression (n=773)
CVA (n=758)
Primary comorbidity reported
Primary comorbidity reported
CVA (n=758)
Cardiac failure (n=639)
Asthma (n=529)
CAL/COPD (n=438)
Visual impairment (n=424)
Dementia (n=388)
Cardiac failure (n=639)
Asthma (n=529)
CAL/COPD (n=438)
Visual impairment (n=424)
Dementia (n=388)
Parkinson (n=296)
Parkinson (n=296)
Spinal cord injury /disease (n=280)
Spinal cord injury /disease (n=280)
Renal failure (n=236)
Renal failure (n=236)
Hearing impairment (n=190)
Hearing impairment (n=190)
Epilepsy (n=135)
Epilepsy (n=135)
Drug and alcohol abuse (n=104)
Drug and alcohol abuse (n=104)
Low er limb amputation (n=101)
Low er limb amputation (n=101)
Schizophrenia (n=41)
Schizophrenia (n=41)
Upper limb amputation (n=20)
Upper limb amputation (n=20)
-4.00
-2.00
0.00
2.00
Lower than the national average
ALOS
4.00
6.00
8.00
10.00
Higher than the national average
-3.00
-2.00
-1.00
Lower than the national average
0.00
1.00
2.00
3.00
4.00
Higher than the national average
FIM change
The question is...
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Do additional diagnoses provide greater explanatory
power after function has been taken into account?
In AROC data set with roughly 94,000 records, CCs
increased the average LOS from 17 to 23 days.
Impact stronger for comps than for comorbs
Number of CCs
Average LOS
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17
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23
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28
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31
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37
LOS in some stroke classes
Stroke class
High function
Moderate
function
Low function,
older
Low function,
younger
0 CCs
1 CC
3 CCs
5 CCs
14.1
15.2
17.2
18.9
22.0
23.6
25.5
25.0
34.2
33.5
40.8
40.1
46.6
47.8
52.2
55.0
Other classifications – mental health
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New mental health classification(s) will be built
PCCL would not be good as a first split, though there is
some evidence that it explains some of the variability in
cost of admitted episodes
Splitting all inpatient mental health cases, regardless of
diagnosis, into
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PCCL mild and moderate – average cost $9,500
PCCL severe or catastrophic – average cost $21,500
Should be considered as a potential splitting variable
In summary
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A comprehensive review of PCCL is required in AR-DRGs
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Work on subacute classification should consider CCs as an
additional classification variable
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Perhaps more than just PCCL...
But be aware of confounding
CCs should be tested along with other variables for
inclusion in the future mental health classification
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Something has got to work!!
References
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NCCC (2013). Australian Refined Diagnostic Related
Groups AR-DRG. Version 7.0: Definitions manual. Volume
3. National Casemix & Classification Centre, Australian
Health Services Research Institute, University of
Wollongong
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AROC publications http://ahsri.uow.edu.au/aroc/index.html
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Busse R, Geissler A, Quentin W, Wiley M (eds) (2011)
Diagnosis-Related Groups in Europe. McGraw-Hill
Thank you
Janette Green
Australian Health Services Research Institute
University of Wollongong
[email protected]
02 4221 5734