Transcript The Characteristics of DRG Outliers at

Recommendations on Minimum Data Recording
Requirements in Hospitals from the Directorate of
Health in Iceland:
Is it possible to use Hospital Patient Registry data to
decrease the cost of outliers
Arnar Berþórsson BA
Kristlaug H. Jónasdóttir BS, MSc
Landspítali University Hospital (LSH)
Key statistics 2008
Population in Iceland
319.326
Number of individuals receiving hospital care
106.699
Outpatient units - visits
Day units - visits
Emergency department - visits
Hospital at home service - visits
367.540
93.422
94.650
14.798
Admissions
Patient days
Average length of stay (LOS)
Patient acuity
28.607
232.570
8,1
5,2
1,18
Deliveries
Surgical procedures
Diagnostic imaging
3.376
14.583
123.956
Average LOS Excluding Division of Rehabilitation and Division of Geriatrics
Number of employees (at the end of the year)
Full-time equivalents (mean)
5.022
3.872
Outliers as persetages of total admissions
Outliers cost as a percentage of total operational cost
3.0%
21,5%
Prospective Payment Systems (PPS) and Diagnosis
Related Groups (DRG)
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Fixed payment per discharge.
Payment is the same for all patients within each DRG group.
Patients within each DRG group should show homogeneity in clinical
conditions as well as in cost.
Payment for DRG groups is based on average costs for patient within the
group.
Patients grouped based on:
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Principle diagnosis ICD-10
Secondary diagnosis ICD-10
Procedures and imaging examination NCSP+
Length of stay
Age
Gender
Type of discharge
DRG weight: mean cost in each DRG divided by total mean cost in all DRGs.
Outliers
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An observation that is numerically distant from the rest of the
data.
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In most large samples of data, some data points will be further away
from the sample mean than what is deemed reasonable
They can occur by chance, but they can also be an indicator of either
measurement- or coding errors or that the data has a heavy-tailed
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distribution.
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In health care reimbursement, especially in PPS, outliers are those
patients that require an unusually long hospital stay or whose stay
generates unusually high costs.
Hypothesis
T0 :Following model, based on Guidelines from the Directorate of Health
for minimal registration requirements for patient information, can be
used as an indicator for a patient’s probability of becoming an outlier.
Log (p/1-p) =c+β1*gender+ β2*age (+70) +β3*age (0-18)+
β4 * ln(Number of IDC-10 diagnosis)+β5* ln( Number of NCPS+ theraphutic procedures)+
β6* Types of admissions+β7* Types of discharges_MORS+β8*Types of Discharges_Other+β9*Ln(LOS)+e
p measures the probability that a patient will become an outlier.
Calculation of outliers
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Outliers are admissions that exceed a certain cost limits
calculated within each DRG group, see formula below.
Outlieri = Q3i + k *(Q3i – Q1i)
k = (P95 – Q3) / (Q3 – Q1)
Where Q1 is 25th percentile, Q3 is 75th percentile and k is
a constant that set the outlier limit to 5 percent. P95 is
95th percentile.
Methodology
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Research design: Non-experimental analytic analysis.
Sample: Discharges from all wards within LSH except:
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Sample criteria:
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Long term Geriatric wards
Long term Psychiatric wards
Rehabilitation wards
Palliative care ward
Healthy newborns
Discharges in the period 1. Jan – 31. Des 2008 (n=21.912)
Cases classified into DRG groups
DRG groups ≥ 30 cases (196 DRG groups)
Data analysis: Logistic regression (stepwise method)
Methodology
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Dependent variable: Outlier=1, Non Outlier=0
Independent variables :
 Gender, 1=male, 2=female
 Age, children ≤ 18, adults 19 to 69, elderly ≥ 70
 Number of ICD-10, (International Classification of Deceases)
codes, (Transformed to ln(x) to correct skewness)
 Number of NCSP+ codes, (Nordic Classification of Surgical
Procedures), (Transformed to ln(x) to correct skewness)
 Types of admissions, acute =1, non acute =0
 Types of discharges, home=1, died=2, other=3
 Length of stay, (LOS) (Transformed to ln(x) to correct
skewness)
Methodology: Sample
Total number
Gender
Male
Female
Number
21.912
9.194
12.718
percent
/
42%
58%
17.494
4.418
80%
20%
Number of outliers
703
3%
322
3,5%
381
3,0%
Types of adamission
Acute
Non acute
Discharge
Home
19.895
91%
406
2%
Mors
341
2%
49
14%
Other*
1.676
8%
248
15%
*Nursing-homes, other hospitals and other institutes
598
105
3,4%
2,4%
Sample
Methodology
Logistic regression
predict the probability of Y occorrung given known values of
predicting variables
Result
Acute admission*
Length of stay*
Number of ICD-10*
Number of NCSP*
Mors*
Transferd*
17 years and yonger*
70 years and older**
Constant
Change in
risk
1,77
1,94
-0,36
1,20
1,32
0,46
0,78
-0,30
-8,41
p<
0,01
0,01
0,01
0,01
0,01
0,01
0,01
0,001
0,01
Discussions
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Why is it that with increasing number of registered diagnosis
the probability of a patient becoming an outlier decreases??
Children (0-17) are more likely to become outliers than 18-69
years old
But older patients (70+) are less likely to become a outlier than
18-69 years old.
Death, mortality and length of stay provide strong evidence of
who become an outliers.
Patient that are discharged to nursing homes, other hospitals
and institutes are more likely to become an outlier.
Limitation
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DRG groups with fewer than 30 discharges were ignored.
Cost is partly distributed by Length of stay, does this cause
problem for the assumption to the model?
We could not use Marital Status
Distinguish between Discharges to other specialitis and to other
institutions.
Use of the result
The purpose is not to decrease outliers
The purpose is to influence the factors that cause the patient to be a
outlier.
According to this study, outliers are 7 times more expensive than
average patient in the same DRG group.
Further studies and ideas
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Effect of marital status and discharge mode
Connection between number of registered diagnosis and
outliers within DRG group
Add other relevant variables to the model such as Acuity, readmission, waiting list, chronic diseases, test results….
Limit the sample to smaller groups such as single DRG groups
or MDC groups or speciality.
Effect of quality of coding and homogeneity of DRG groups.
Result I