#Import libraries
import pandas as pd
import numpy as np
from numpy import nan
pd.set_option('display.max_columns', None)

data=pd.read_excel('WK11Q2.xlsx')
data.head(20)

The data used in this analysis consists of the following fields:

ID: Patients' ID. There are 151 patients.
HCC: Hierarchical Condition Category. HCC field contains 3 different values for low, medium, and high severity as 1,2,3.
Dr: indicates patients was cared for by clinician or peer group.
DRG: Diagnostic Related Groups. The DRG field contains 10 different values.
LOS: Length of Stay. Outcomes of care are in column LOS.

data.dtypes

ID      int64
DRG     int64
HCC     int64
Dr     object
LOS     int64
dtype: object

###Create a binary column to convert clinician or peer group to 1/0
data['Clinician'] = np.where(data['Dr'] == 'Clinician',1,0)
data["Clinician"] = data["Clinician"].astype(float)
data['Peer'] = np.where(data['Dr']== 'Peer',1,0)
data["Peer"] = data["Peer"].astype(float)
data.dtypes

ID             int64
DRG            int64
HCC            int64
Dr            object
LOS            int64
Clinician    float64
Peer         float64
dtype: object

data1 = data[data['Clinician']== 1].reset_index()
data1

data2 = data[data['Peer']== 1].reset_index()
data2

#Calculate pattern of care for Clinician group which satisfy the 
#conditions of HCC & DRG at the same time
NumberC= data1.groupby(['HCC','DRG'],as_index = True).size().reset_index(name='Numb_C')
groupC1 = pd.pivot_table(NumberC,
                       index=['HCC','DRG'],
                       values='Numb_C',                            
                       fill_value = 0,
                       dropna=False,
                       aggfunc=np.sum).reset_index()
groupC1

LOSC=data1.groupby(['HCC','DRG'])['LOS'].max().reset_index(name='LOS_C')
groupC2 = pd.pivot_table(LOSC,
                       index=['HCC','DRG'],
                       values='LOS_C',                            
                       fill_value = 0,
                       dropna=False,
                       aggfunc=np.sum).reset_index()
groupC2

#Calculate pattern of care for Peer group which satisfy the conditions 
#of HCC & DRG at the same time
NumberP= data2.groupby(['HCC','DRG'],as_index = True).size().reset_index(name='Numb_P')
groupP1 = pd.pivot_table(NumberP,
                       index=['HCC','DRG'],
                       values='Numb_P',                            
                       fill_value = 0,
                       dropna=False,
                       aggfunc=np.sum).reset_index()
groupP1

LOSP=data2.groupby(['HCC','DRG'])['LOS'].max().reset_index(name='LOS_P')
groupP2 = pd.pivot_table(LOSP,
                       index=['HCC','DRG'],
                       values='LOS_P',                            
                       fill_value = 0,
                       dropna=False,
                       aggfunc=np.sum).reset_index()
groupP2

groupC_final = pd.merge(groupC1, groupC2, how='left',on=['HCC','DRG']).reset_index()
groupC_final = groupC_final.drop(columns = 'index', axis = 1)
groupC_final

groupP_final = pd.merge(groupP1, groupP2, how='left',on=['HCC','DRG']).reset_index()
groupP_final = groupP_final.drop(columns = 'index', axis = 1)
groupP_final

###Match clinicians and peer group on common strata
df = pd.merge(groupC_final, groupP_final, how='left',on=['HCC','DRG']).reset_index()
df = df.drop(columns = 'index', axis = 1)
df

#Calculate Total Number of patients for Clinician and Peer group 
df['Total_C'] = data1['Clinician'].count()
df['Total_P'] = data2['Peer'].count()
#Calculate Probability for Clinician and Peer group 
df['Prob_C']=df['Numb_C']/df['Total_C']
df['Prob_P']=df['Numb_P']/df['Total_P']
#Solve for 'Prob_C *CASE WHEN LOS_C is null 
df['ClinicianLOS'] = (df['Prob_C']) * df['LOS_C']
#Solve for 'Prob_C*LOS_P'
df['PeerLOS'] = (df['Prob_P']) * df['LOS_P']
df

###Does not matter if outcomes for clinician is null,
###but peer group's null values require synthetic control calculations.
Null_P=df[df['LOS_P'].isin(['0'])]
Null_P

###df['Total_C'] = data1['Clinician'].count() = 81
###Overlap between peer and clinician cases
Sum_Null=Null_P['Numb_C'].sum()
SumOverlap = (81 - Sum_Null)/df['Numb_C'].sum() *100
SumOverlap

81.48148148148148

df1 = pd.merge(df, data, on =['HCC'])
MergeonHCC = df1[df1['Peer']== 1]
MergeonHCC

df2 = pd.merge(df, data, on =['DRG'])
MergeonDRG = df2[df2['Peer']== 1]
MergeonDRG

df3=MergeonHCC[MergeonHCC['LOS_P'].isin(['0'])]
df3

df4=MergeonDRG[MergeonDRG['LOS_P'].isin(['0'])]
df4

###Average for a marginal
LOS1=df3['LOS'].mean()
LOS1

2.933333333333333

###Average for complement marginal
LOS2=df4['LOS'].mean()
LOS2

3.8484848484848486

LOS3=data2['LOS'].mean()
LOS3

3.3285714285714287

###Average for entire set
SUMLOSPAVG= LOS1 * LOS2 / LOS3
print(SUMLOSPAVG)

3.3915116833571766

### Select  is not null
df['LOS_P'] = df['LOS_P'].replace(0, nan)
df['LOS_P']=df['LOS_P'].fillna(value=SUMLOSPAVG)
df['LOS_P']

0     1.000000
1     1.000000
2     3.000000
3     2.000000
4     2.000000
5     3.000000
6     3.000000
7     3.000000
8     4.000000
9     4.000000
10    2.000000
11    3.391512
12    2.000000
13    3.000000
14    3.000000
15    3.391512
16    3.000000
17    4.000000
18    3.391512
19    3.391512
20    3.000000
21    3.000000
22    3.000000
23    3.000000
24    3.000000
25    4.000000
26    4.000000
27    4.000000
28    4.000000
29    5.000000
Name: LOS_P, dtype: float64

###Switch probabilities of peer group to clinician
df['Prob_P'] = df['Prob_C'].values
df['Prob_P']

0     0.024691
1     0.024691
2     0.037037
3     0.012346
4     0.024691
5     0.061728
6     0.024691
7     0.024691
8     0.012346
9     0.012346
10    0.024691
11    0.061728
12    0.086420
13    0.049383
14    0.024691
15    0.037037
16    0.037037
17    0.024691
18    0.037037
19    0.049383
20    0.037037
21    0.061728
22    0.037037
23    0.024691
24    0.000000
25    0.037037
26    0.074074
27    0.012346
28    0.000000
29    0.024691
Name: Prob_P, dtype: float64

###Second Overlap between peer and clinician cases
Null_P2=df[df['LOS_P'].isin(['0'])]
Sum_Null2=Null_P2['Numb_C'].sum()
SumOverlap2 = (81 - Sum_Null2)/df['Numb_C'].sum() *100
SumOverlap2

100.0

#Solve for 'Prob_C*LOS_C'
df['PeerLOS'] = (df['Prob_P']) * df['LOS_P']
df

#continue solving the code
SumClinicianLOS = df['ClinicianLOS'].sum()
print(SumClinicianLOS)
SumPeerLOS = df['PeerLOS'].sum()
print(SumPeerLOS)

2.9629629629629624
3.0478108055599704

PercentMoreEfficient = (SumPeerLOS-SumClinicianLOS)*100/SumPeerLOS
print(PercentMoreEfficient)

2.7838946709626575

Conclusion¶

According to the results, LOS is more in the peer group (3.05) as compared to the clinician (2.96).
Clinician results for LOS are 2.78% more efficent than peer group.

	ID	DRG	HCC	Dr	LOS
0	1	1	3	Peer	3
1	2	5	3	Peer	3
2	3	2	3	Clinician	3
3	4	1	1	Clinician	1
4	5	4	2	Peer	3
5	6	2	3	Clinician	3
6	7	8	3	Clinician	4
7	8	1	1	Peer	1
8	9	7	3	Clinician	4
9	10	6	3	Peer	4
10	11	10	1	Peer	4
11	12	2	3	Clinician	3
12	13	3	3	Peer	3
13	14	4	2	Peer	3
14	15	2	2	Clinician	2
15	16	5	1	Peer	2
16	17	4	3	Clinician	3
17	18	3	1	Clinician	2
18	19	5	2	Clinician	3
19	20	8	2	Clinician	4

	HCC	DRG	Numb_C
0	1	1	2
1	1	2	2
2	1	3	3
3	1	4	1
4	1	5	2
5	1	6	5
6	1	7	2
7	1	8	2
8	1	9	1
9	1	10	1
10	2	1	2
11	2	2	5
12	2	3	7
13	2	4	4
14	2	5	2
15	2	6	3
16	2	7	3
17	2	8	2
18	2	9	3
19	2	10	4
20	3	1	3
21	3	2	5
22	3	3	3
23	3	4	2
24	3	5	0
25	3	6	3
26	3	7	6
27	3	8	1
28	3	9	0
29	3	10	2

	HCC	DRG	LOS_C
0	1	1	1
1	1	2	1
2	1	3	2
3	1	4	2
4	1	5	2
5	1	6	3
6	1	7	3
7	1	8	3
8	1	9	4
9	1	10	4
10	2	1	2
11	2	2	2
12	2	3	2
13	2	4	3
14	2	5	3
15	2	6	3
16	2	7	3
17	2	8	4
18	2	9	4
19	2	10	4
20	3	1	3
21	3	2	3
22	3	3	3
23	3	4	3
24	3	5	0
25	3	6	4
26	3	7	4
27	3	8	4
28	3	9	0
29	3	10	5

	HCC	DRG	Numb_P
0	1	1	2
1	1	2	2
2	1	3	1
3	1	4	1
4	1	5	2
5	1	6	2
6	1	7	2
7	1	8	2
8	1	9	7
9	1	10	8
10	2	1	1
11	2	2	0
12	2	3	2
13	2	4	8
14	2	5	1
15	2	6	0
16	2	7	1
17	2	8	2
18	2	9	0
19	2	10	0
20	3	1	3
21	3	2	1
22	3	3	1
23	3	4	2
24	3	5	3
25	3	6	4
26	3	7	2
27	3	8	1
28	3	9	5
29	3	10	4

	HCC	DRG	LOS_P
0	1	1	1
1	1	2	1
2	1	3	3
3	1	4	2
4	1	5	2
5	1	6	3
6	1	7	3
7	1	8	3
8	1	9	4
9	1	10	4
10	2	1	2
11	2	2	0
12	2	3	2
13	2	4	3
14	2	5	3
15	2	6	0
16	2	7	3
17	2	8	4
18	2	9	0
19	2	10	0
20	3	1	3
21	3	2	3
22	3	3	3
23	3	4	3
24	3	5	3
25	3	6	4
26	3	7	4
27	3	8	4
28	3	9	4
29	3	10	5

	HCC	DRG	Numb_C	LOS_C
0	1	1	2	1
1	1	2	2	1
2	1	3	3	2
3	1	4	1	2
4	1	5	2	2
5	1	6	5	3
6	1	7	2	3
7	1	8	2	3
8	1	9	1	4
9	1	10	1	4
10	2	1	2	2
11	2	2	5	2
12	2	3	7	2
13	2	4	4	3
14	2	5	2	3
15	2	6	3	3
16	2	7	3	3
17	2	8	2	4
18	2	9	3	4
19	2	10	4	4
20	3	1	3	3
21	3	2	5	3
22	3	3	3	3
23	3	4	2	3
24	3	5	0	0
25	3	6	3	4
26	3	7	6	4
27	3	8	1	4
28	3	9	0	0
29	3	10	2	5

	HCC	DRG	Numb_P	LOS_P
0	1	1	2	1
1	1	2	2	1
2	1	3	1	3
3	1	4	1	2
4	1	5	2	2
5	1	6	2	3
6	1	7	2	3
7	1	8	2	3
8	1	9	7	4
9	1	10	8	4
10	2	1	1	2
11	2	2	0	0
12	2	3	2	2
13	2	4	8	3
14	2	5	1	3
15	2	6	0	0
16	2	7	1	3
17	2	8	2	4
18	2	9	0	0
19	2	10	0	0
20	3	1	3	3
21	3	2	1	3
22	3	3	1	3
23	3	4	2	3
24	3	5	3	3
25	3	6	4	4
26	3	7	2	4
27	3	8	1	4
28	3	9	5	4
29	3	10	4	5

	HCC	DRG	Numb_C	LOS_C	Numb_P	LOS_P
0	1	1	2	1	2	1
1	1	2	2	1	2	1
2	1	3	3	2	1	3
3	1	4	1	2	1	2
4	1	5	2	2	2	2
5	1	6	5	3	2	3
6	1	7	2	3	2	3
7	1	8	2	3	2	3
8	1	9	1	4	7	4
9	1	10	1	4	8	4
10	2	1	2	2	1	2
11	2	2	5	2	0	0
12	2	3	7	2	2	2
13	2	4	4	3	8	3
14	2	5	2	3	1	3
15	2	6	3	3	0	0
16	2	7	3	3	1	3
17	2	8	2	4	2	4
18	2	9	3	4	0	0
19	2	10	4	4	0	0
20	3	1	3	3	3	3
21	3	2	5	3	1	3
22	3	3	3	3	1	3
23	3	4	2	3	2	3
24	3	5	0	0	3	3
25	3	6	3	4	4	4
26	3	7	6	4	2	4
27	3	8	1	4	1	4
28	3	9	0	0	5	4
29	3	10	2	5	4	5

	HCC	DRG	Numb_C	LOS_C	Numb_P	LOS_P	Total_C	Total_P	Prob_C	Prob_P	ClinicianLOS	PeerLOS
0	1	1	2	1	2	1	81	70	0.024691	0.028571	0.024691	0.028571
1	1	2	2	1	2	1	81	70	0.024691	0.028571	0.024691	0.028571
2	1	3	3	2	1	3	81	70	0.037037	0.014286	0.074074	0.042857
3	1	4	1	2	1	2	81	70	0.012346	0.014286	0.024691	0.028571
4	1	5	2	2	2	2	81	70	0.024691	0.028571	0.049383	0.057143
5	1	6	5	3	2	3	81	70	0.061728	0.028571	0.185185	0.085714
6	1	7	2	3	2	3	81	70	0.024691	0.028571	0.074074	0.085714
7	1	8	2	3	2	3	81	70	0.024691	0.028571	0.074074	0.085714
8	1	9	1	4	7	4	81	70	0.012346	0.100000	0.049383	0.400000
9	1	10	1	4	8	4	81	70	0.012346	0.114286	0.049383	0.457143
10	2	1	2	2	1	2	81	70	0.024691	0.014286	0.049383	0.028571
11	2	2	5	2	0	0	81	70	0.061728	0.000000	0.123457	0.000000
12	2	3	7	2	2	2	81	70	0.086420	0.028571	0.172840	0.057143
13	2	4	4	3	8	3	81	70	0.049383	0.114286	0.148148	0.342857
14	2	5	2	3	1	3	81	70	0.024691	0.014286	0.074074	0.042857
15	2	6	3	3	0	0	81	70	0.037037	0.000000	0.111111	0.000000
16	2	7	3	3	1	3	81	70	0.037037	0.014286	0.111111	0.042857
17	2	8	2	4	2	4	81	70	0.024691	0.028571	0.098765	0.114286
18	2	9	3	4	0	0	81	70	0.037037	0.000000	0.148148	0.000000
19	2	10	4	4	0	0	81	70	0.049383	0.000000	0.197531	0.000000
20	3	1	3	3	3	3	81	70	0.037037	0.042857	0.111111	0.128571
21	3	2	5	3	1	3	81	70	0.061728	0.014286	0.185185	0.042857
22	3	3	3	3	1	3	81	70	0.037037	0.014286	0.111111	0.042857
23	3	4	2	3	2	3	81	70	0.024691	0.028571	0.074074	0.085714
24	3	5	0	0	3	3	81	70	0.000000	0.042857	0.000000	0.128571
25	3	6	3	4	4	4	81	70	0.037037	0.057143	0.148148	0.228571
26	3	7	6	4	2	4	81	70	0.074074	0.028571	0.296296	0.114286
27	3	8	1	4	1	4	81	70	0.012346	0.014286	0.049383	0.057143
28	3	9	0	0	5	4	81	70	0.000000	0.071429	0.000000	0.285714
29	3	10	2	5	4	5	81	70	0.024691	0.057143	0.123457	0.285714

	HCC	DRG	Numb_C	LOS_C	Numb_P	LOS_P	Total_C	Total_P	Prob_C	Prob_P	ClinicianLOS	PeerLOS
0	1	1	2	1	2	1.000000	81	70	0.024691	0.024691	0.024691	0.024691
1	1	2	2	1	2	1.000000	81	70	0.024691	0.024691	0.024691	0.024691
2	1	3	3	2	1	3.000000	81	70	0.037037	0.037037	0.074074	0.111111
3	1	4	1	2	1	2.000000	81	70	0.012346	0.012346	0.024691	0.024691
4	1	5	2	2	2	2.000000	81	70	0.024691	0.024691	0.049383	0.049383
5	1	6	5	3	2	3.000000	81	70	0.061728	0.061728	0.185185	0.185185
6	1	7	2	3	2	3.000000	81	70	0.024691	0.024691	0.074074	0.074074
7	1	8	2	3	2	3.000000	81	70	0.024691	0.024691	0.074074	0.074074
8	1	9	1	4	7	4.000000	81	70	0.012346	0.012346	0.049383	0.049383
9	1	10	1	4	8	4.000000	81	70	0.012346	0.012346	0.049383	0.049383
10	2	1	2	2	1	2.000000	81	70	0.024691	0.024691	0.049383	0.049383
11	2	2	5	2	0	3.391512	81	70	0.061728	0.061728	0.123457	0.209353
12	2	3	7	2	2	2.000000	81	70	0.086420	0.086420	0.172840	0.172840
13	2	4	4	3	8	3.000000	81	70	0.049383	0.049383	0.148148	0.148148
14	2	5	2	3	1	3.000000	81	70	0.024691	0.024691	0.074074	0.074074
15	2	6	3	3	0	3.391512	81	70	0.037037	0.037037	0.111111	0.125612
16	2	7	3	3	1	3.000000	81	70	0.037037	0.037037	0.111111	0.111111
17	2	8	2	4	2	4.000000	81	70	0.024691	0.024691	0.098765	0.098765
18	2	9	3	4	0	3.391512	81	70	0.037037	0.037037	0.148148	0.125612
19	2	10	4	4	0	3.391512	81	70	0.049383	0.049383	0.197531	0.167482
20	3	1	3	3	3	3.000000	81	70	0.037037	0.037037	0.111111	0.111111
21	3	2	5	3	1	3.000000	81	70	0.061728	0.061728	0.185185	0.185185
22	3	3	3	3	1	3.000000	81	70	0.037037	0.037037	0.111111	0.111111
23	3	4	2	3	2	3.000000	81	70	0.024691	0.024691	0.074074	0.074074
24	3	5	0	0	3	3.000000	81	70	0.000000	0.000000	0.000000	0.000000
25	3	6	3	4	4	4.000000	81	70	0.037037	0.037037	0.148148	0.148148
26	3	7	6	4	2	4.000000	81	70	0.074074	0.074074	0.296296	0.296296
27	3	8	1	4	1	4.000000	81	70	0.012346	0.012346	0.049383	0.049383
28	3	9	0	0	5	4.000000	81	70	0.000000	0.000000	0.000000	0.000000
29	3	10	2	5	4	5.000000	81	70	0.024691	0.024691	0.123457	0.123457

	index	ID	DRG	HCC	Dr	LOS	Clinician	Peer
0	2	3	2	3	Clinician	3	1.0	0.0
1	3	4	1	1	Clinician	1	1.0	0.0
2	5	6	2	3	Clinician	3	1.0	0.0
3	6	7	8	3	Clinician	4	1.0	0.0
4	8	9	7	3	Clinician	4	1.0	0.0
...	...	...	...	...	...	...	...	...
76	140	141	3	3	Clinician	3	1.0	0.0
77	141	142	10	1	Clinician	4	1.0	0.0
78	145	146	5	2	Clinician	3	1.0	0.0
79	146	147	7	3	Clinician	4	1.0	0.0
80	147	148	10	2	Clinician	4	1.0	0.0

	index	ID	DRG	HCC	Dr	LOS	Clinician	Peer
0	0	1	1	3	Peer	3	0.0	1.0
1	1	2	5	3	Peer	3	0.0	1.0
2	4	5	4	2	Peer	3	0.0	1.0
3	7	8	1	1	Peer	1	0.0	1.0
4	9	10	6	3	Peer	4	0.0	1.0
...	...	...	...	...	...	...	...	...
65	143	144	10	3	Peer	5	0.0	1.0
66	144	145	9	3	Peer	4	0.0	1.0
67	148	149	9	1	Peer	4	0.0	1.0
68	149	150	2	1	Peer	1	0.0	1.0
69	150	151	3	1	Peer	3	0.0	1.0

	HCC	DRG_x	Numb_C	LOS_C	Total_C	Total_P	Prob_C	ClinicianLOS	ID	DRG_y	Dr	LOS	Peer
550	2	2	5	2	81	70	0.061728	0.123457	5	4	Peer	3	1.0
551	2	2	5	2	81	70	0.061728	0.123457	14	4	Peer	3	1.0
556	2	2	5	2	81	70	0.061728	0.123457	24	4	Peer	3	1.0
559	2	2	5	2	81	70	0.061728	0.123457	33	1	Peer	2	1.0
560	2	2	5	2	81	70	0.061728	0.123457	37	8	Peer	4	1.0
565	2	2	5	2	81	70	0.061728	0.123457	45	4	Peer	3	1.0
566	2	2	5	2	81	70	0.061728	0.123457	46	4	Peer	3	1.0
567	2	2	5	2	81	70	0.061728	0.123457	50	4	Peer	3	1.0
571	2	2	5	2	81	70	0.061728	0.123457	61	5	Peer	3	1.0
573	2	2	5	2	81	70	0.061728	0.123457	69	3	Peer	2	1.0
583	2	2	5	2	81	70	0.061728	0.123457	106	3	Peer	2	1.0
584	2	2	5	2	81	70	0.061728	0.123457	111	7	Peer	3	1.0
585	2	2	5	2	81	70	0.061728	0.123457	117	8	Peer	4	1.0
596	2	2	5	2	81	70	0.061728	0.123457	138	4	Peer	3	1.0
597	2	2	5	2	81	70	0.061728	0.123457	143	4	Peer	3	1.0
750	2	6	3	3	81	70	0.037037	0.111111	5	4	Peer	3	1.0
751	2	6	3	3	81	70	0.037037	0.111111	14	4	Peer	3	1.0
756	2	6	3	3	81	70	0.037037	0.111111	24	4	Peer	3	1.0
759	2	6	3	3	81	70	0.037037	0.111111	33	1	Peer	2	1.0
760	2	6	3	3	81	70	0.037037	0.111111	37	8	Peer	4	1.0
765	2	6	3	3	81	70	0.037037	0.111111	45	4	Peer	3	1.0
766	2	6	3	3	81	70	0.037037	0.111111	46	4	Peer	3	1.0
767	2	6	3	3	81	70	0.037037	0.111111	50	4	Peer	3	1.0
771	2	6	3	3	81	70	0.037037	0.111111	61	5	Peer	3	1.0
773	2	6	3	3	81	70	0.037037	0.111111	69	3	Peer	2	1.0
783	2	6	3	3	81	70	0.037037	0.111111	106	3	Peer	2	1.0
784	2	6	3	3	81	70	0.037037	0.111111	111	7	Peer	3	1.0
785	2	6	3	3	81	70	0.037037	0.111111	117	8	Peer	4	1.0
796	2	6	3	3	81	70	0.037037	0.111111	138	4	Peer	3	1.0
797	2	6	3	3	81	70	0.037037	0.111111	143	4	Peer	3	1.0
900	2	9	3	4	81	70	0.037037	0.148148	5	4	Peer	3	1.0
901	2	9	3	4	81	70	0.037037	0.148148	14	4	Peer	3	1.0
906	2	9	3	4	81	70	0.037037	0.148148	24	4	Peer	3	1.0
909	2	9	3	4	81	70	0.037037	0.148148	33	1	Peer	2	1.0
910	2	9	3	4	81	70	0.037037	0.148148	37	8	Peer	4	1.0
915	2	9	3	4	81	70	0.037037	0.148148	45	4	Peer	3	1.0
916	2	9	3	4	81	70	0.037037	0.148148	46	4	Peer	3	1.0
917	2	9	3	4	81	70	0.037037	0.148148	50	4	Peer	3	1.0
921	2	9	3	4	81	70	0.037037	0.148148	61	5	Peer	3	1.0
923	2	9	3	4	81	70	0.037037	0.148148	69	3	Peer	2	1.0

	HCC_x	DRG	Numb_C	LOS_C	Total_C	Total_P	Prob_C	ClinicianLOS	ID	HCC_y	Dr	LOS	Peer
60	2	2	5	2	81	70	0.061728	0.123457	66	3	Peer	3	1.0
64	2	2	5	2	81	70	0.061728	0.123457	103	1	Peer	1	1.0
68	2	2	5	2	81	70	0.061728	0.123457	150	1	Peer	1	1.0
236	2	6	3	3	81	70	0.037037	0.111111	10	3	Peer	4	1.0
240	2	6	3	3	81	70	0.037037	0.111111	48	1	Peer	3	1.0
243	2	6	3	3	81	70	0.037037	0.111111	59	3	Peer	4	1.0
244	2	6	3	3	81	70	0.037037	0.111111	63	3	Peer	4	1.0
249	2	6	3	3	81	70	0.037037	0.111111	109	1	Peer	3	1.0
252	2	6	3	3	81	70	0.037037	0.111111	135	3	Peer	4	1.0
364	2	9	3	4	81	70	0.037037	0.148148	23	3	Peer	4	1.0
365	2	9	3	4	81	70	0.037037	0.148148	26	1	Peer	4	1.0
366	2	9	3	4	81	70	0.037037	0.148148	38	1	Peer	4	1.0
367	2	9	3	4	81	70	0.037037	0.148148	47	3	Peer	4	1.0
368	2	9	3	4	81	70	0.037037	0.148148	56	1	Peer	4	1.0
369	2	9	3	4	81	70	0.037037	0.148148	58	1	Peer	4	1.0
370	2	9	3	4	81	70	0.037037	0.148148	81	3	Peer	4	1.0
372	2	9	3	4	81	70	0.037037	0.148148	97	1	Peer	4	1.0
373	2	9	3	4	81	70	0.037037	0.148148	115	1	Peer	4	1.0
377	2	9	3	4	81	70	0.037037	0.148148	130	3	Peer	4	1.0
378	2	9	3	4	81	70	0.037037	0.148148	145	3	Peer	4	1.0
379	2	9	3	4	81	70	0.037037	0.148148	149	1	Peer	4	1.0
415	2	10	4	4	81	70	0.049383	0.197531	11	1	Peer	4	1.0
417	2	10	4	4	81	70	0.049383	0.197531	25	1	Peer	4	1.0
418	2	10	4	4	81	70	0.049383	0.197531	32	1	Peer	4	1.0
419	2	10	4	4	81	70	0.049383	0.197531	49	1	Peer	4	1.0
420	2	10	4	4	81	70	0.049383	0.197531	55	1	Peer	4	1.0
421	2	10	4	4	81	70	0.049383	0.197531	60	3	Peer	5	1.0
422	2	10	4	4	81	70	0.049383	0.197531	62	1	Peer	4	1.0
424	2	10	4	4	81	70	0.049383	0.197531	85	3	Peer	5	1.0
426	2	10	4	4	81	70	0.049383	0.197531	100	1	Peer	4	1.0
428	2	10	4	4	81	70	0.049383	0.197531	124	1	Peer	4	1.0
430	2	10	4	4	81	70	0.049383	0.197531	132	3	Peer	5	1.0
432	2	10	4	4	81	70	0.049383	0.197531	144	3	Peer	5	1.0

	HCC	DRG	Numb_C
0	1	1	2
1	1	2	2
2	1	3	3
3	1	4	1
4	1	5	2
5	1	6	5
6	1	7	2
7	1	8	2
8	1	9	1
9	1	10	1
10	2	1	2
11	2	2	5
12	2	3	7
13	2	4	4
14	2	5	2
15	2	6	3
16	2	7	3
17	2	8	2
18	2	9	3
19	2	10	4
20	3	1	3
21	3	2	5
22	3	3	3
23	3	4	2
24	3	5	0
25	3	6	3
26	3	7	6
27	3	8	1
28	3	9	0
29	3	10	2

	HCC	DRG	LOS_C
0	1	1	1
1	1	2	1
2	1	3	2
3	1	4	2
4	1	5	2
5	1	6	3
6	1	7	3
7	1	8	3
8	1	9	4
9	1	10	4
10	2	1	2
11	2	2	2
12	2	3	2
13	2	4	3
14	2	5	3
15	2	6	3
16	2	7	3
17	2	8	4
18	2	9	4
19	2	10	4
20	3	1	3
21	3	2	3
22	3	3	3
23	3	4	3
24	3	5	0
25	3	6	4
26	3	7	4
27	3	8	4
28	3	9	0
29	3	10	5

	HCC	DRG	Numb_P
0	1	1	2
1	1	2	2
2	1	3	1
3	1	4	1
4	1	5	2
5	1	6	2
6	1	7	2
7	1	8	2
8	1	9	7
9	1	10	8
10	2	1	1
11	2	2	0
12	2	3	2
13	2	4	8
14	2	5	1
15	2	6	0
16	2	7	1
17	2	8	2
18	2	9	0
19	2	10	0
20	3	1	3
21	3	2	1
22	3	3	1
23	3	4	2
24	3	5	3
25	3	6	4
26	3	7	2
27	3	8	1
28	3	9	5
29	3	10	4

	HCC	DRG	LOS_P
0	1	1	1
1	1	2	1
2	1	3	3
3	1	4	2
4	1	5	2
5	1	6	3
6	1	7	3
7	1	8	3
8	1	9	4
9	1	10	4
10	2	1	2
11	2	2	0
12	2	3	2
13	2	4	3
14	2	5	3
15	2	6	0
16	2	7	3
17	2	8	4
18	2	9	0
19	2	10	0
20	3	1	3
21	3	2	3
22	3	3	3
23	3	4	3
24	3	5	3
25	3	6	4
26	3	7	4
27	3	8	4
28	3	9	4
29	3	10	5

	HCC	DRG	Numb_C	LOS_C
0	1	1	2	1
1	1	2	2	1
2	1	3	3	2
3	1	4	1	2
4	1	5	2	2
5	1	6	5	3
6	1	7	2	3
7	1	8	2	3
8	1	9	1	4
9	1	10	1	4
10	2	1	2	2
11	2	2	5	2
12	2	3	7	2
13	2	4	4	3
14	2	5	2	3
15	2	6	3	3
16	2	7	3	3
17	2	8	2	4
18	2	9	3	4
19	2	10	4	4
20	3	1	3	3
21	3	2	5	3
22	3	3	3	3
23	3	4	2	3
24	3	5	0	0
25	3	6	3	4
26	3	7	6	4
27	3	8	1	4
28	3	9	0	0
29	3	10	2	5

	HCC	DRG	Numb_P	LOS_P
0	1	1	2	1
1	1	2	2	1
2	1	3	1	3
3	1	4	1	2
4	1	5	2	2
5	1	6	2	3
6	1	7	2	3
7	1	8	2	3
8	1	9	7	4
9	1	10	8	4
10	2	1	1	2
11	2	2	0	0
12	2	3	2	2
13	2	4	8	3
14	2	5	1	3
15	2	6	0	0
16	2	7	1	3
17	2	8	2	4
18	2	9	0	0
19	2	10	0	0
20	3	1	3	3
21	3	2	1	3
22	3	3	1	3
23	3	4	2	3
24	3	5	3	3
25	3	6	4	4
26	3	7	2	4
27	3	8	1	4
28	3	9	5	4
29	3	10	4	5

	HCC	DRG	Numb_C	LOS_C	Numb_P	LOS_P
0	1	1	2	1	2	1
1	1	2	2	1	2	1
2	1	3	3	2	1	3
3	1	4	1	2	1	2
4	1	5	2	2	2	2
5	1	6	5	3	2	3
6	1	7	2	3	2	3
7	1	8	2	3	2	3
8	1	9	1	4	7	4
9	1	10	1	4	8	4
10	2	1	2	2	1	2
11	2	2	5	2	0	0
12	2	3	7	2	2	2
13	2	4	4	3	8	3
14	2	5	2	3	1	3
15	2	6	3	3	0	0
16	2	7	3	3	1	3
17	2	8	2	4	2	4
18	2	9	3	4	0	0
19	2	10	4	4	0	0
20	3	1	3	3	3	3
21	3	2	5	3	1	3
22	3	3	3	3	1	3
23	3	4	2	3	2	3
24	3	5	0	0	3	3
25	3	6	3	4	4	4
26	3	7	6	4	2	4
27	3	8	1	4	1	4
28	3	9	0	0	5	4
29	3	10	2	5	4	5

	HCC	DRG	Numb_C
0	1	1	2
1	1	2	2
2	1	3	3
3	1	4	1
4	1	5	2
5	1	6	5
6	1	7	2
7	1	8	2
8	1	9	1
9	1	10	1
10	2	1	2
11	2	2	5
12	2	3	7
13	2	4	4
14	2	5	2
15	2	6	3
16	2	7	3
17	2	8	2
18	2	9	3
19	2	10	4
20	3	1	3
21	3	2	5
22	3	3	3
23	3	4	2
24	3	5	0
25	3	6	3
26	3	7	6
27	3	8	1
28	3	9	0
29	3	10	2

	HCC	DRG	LOS_C
0	1	1	1
1	1	2	1
2	1	3	2
3	1	4	2
4	1	5	2
5	1	6	3
6	1	7	3
7	1	8	3
8	1	9	4
9	1	10	4
10	2	1	2
11	2	2	2
12	2	3	2
13	2	4	3
14	2	5	3
15	2	6	3
16	2	7	3
17	2	8	4
18	2	9	4
19	2	10	4
20	3	1	3
21	3	2	3
22	3	3	3
23	3	4	3
24	3	5	0
25	3	6	4
26	3	7	4
27	3	8	4
28	3	9	0
29	3	10	5

	HCC	DRG	Numb_P
0	1	1	2
1	1	2	2
2	1	3	1
3	1	4	1
4	1	5	2
5	1	6	2
6	1	7	2
7	1	8	2
8	1	9	7
9	1	10	8
10	2	1	1
11	2	2	0
12	2	3	2
13	2	4	8
14	2	5	1
15	2	6	0
16	2	7	1
17	2	8	2
18	2	9	0
19	2	10	0
20	3	1	3
21	3	2	1
22	3	3	1
23	3	4	2
24	3	5	3
25	3	6	4
26	3	7	2
27	3	8	1
28	3	9	5
29	3	10	4

	HCC	DRG	LOS_P
0	1	1	1
1	1	2	1
2	1	3	3
3	1	4	2
4	1	5	2
5	1	6	3
6	1	7	3
7	1	8	3
8	1	9	4
9	1	10	4
10	2	1	2
11	2	2	0
12	2	3	2
13	2	4	3
14	2	5	3
15	2	6	0
16	2	7	3
17	2	8	4
18	2	9	0
19	2	10	0
20	3	1	3
21	3	2	3
22	3	3	3
23	3	4	3
24	3	5	3
25	3	6	4
26	3	7	4
27	3	8	4
28	3	9	4
29	3	10	5

	HCC	DRG	Numb_C	LOS_C
0	1	1	2	1
1	1	2	2	1
2	1	3	3	2
3	1	4	1	2
4	1	5	2	2
5	1	6	5	3
6	1	7	2	3
7	1	8	2	3
8	1	9	1	4
9	1	10	1	4
10	2	1	2	2
11	2	2	5	2
12	2	3	7	2
13	2	4	4	3
14	2	5	2	3
15	2	6	3	3
16	2	7	3	3
17	2	8	2	4
18	2	9	3	4
19	2	10	4	4
20	3	1	3	3
21	3	2	5	3
22	3	3	3	3
23	3	4	2	3
24	3	5	0	0
25	3	6	3	4
26	3	7	6	4
27	3	8	1	4
28	3	9	0	0
29	3	10	2	5

	HCC	DRG	Numb_P	LOS_P
0	1	1	2	1
1	1	2	2	1
2	1	3	1	3
3	1	4	1	2
4	1	5	2	2
5	1	6	2	3
6	1	7	2	3
7	1	8	2	3
8	1	9	7	4
9	1	10	8	4
10	2	1	1	2
11	2	2	0	0
12	2	3	2	2
13	2	4	8	3
14	2	5	1	3
15	2	6	0	0
16	2	7	1	3
17	2	8	2	4
18	2	9	0	0
19	2	10	0	0
20	3	1	3	3
21	3	2	1	3
22	3	3	1	3
23	3	4	2	3
24	3	5	3	3
25	3	6	4	4
26	3	7	2	4
27	3	8	1	4
28	3	9	5	4
29	3	10	4	5

	HCC	DRG	Numb_C	LOS_C	Numb_P	LOS_P
0	1	1	2	1	2	1
1	1	2	2	1	2	1
2	1	3	3	2	1	3
3	1	4	1	2	1	2
4	1	5	2	2	2	2
5	1	6	5	3	2	3
6	1	7	2	3	2	3
7	1	8	2	3	2	3
8	1	9	1	4	7	4
9	1	10	1	4	8	4
10	2	1	2	2	1	2
11	2	2	5	2	0	0
12	2	3	7	2	2	2
13	2	4	4	3	8	3
14	2	5	2	3	1	3
15	2	6	3	3	0	0
16	2	7	3	3	1	3
17	2	8	2	4	2	4
18	2	9	3	4	0	0
19	2	10	4	4	0	0
20	3	1	3	3	3	3
21	3	2	5	3	1	3
22	3	3	3	3	1	3
23	3	4	2	3	2	3
24	3	5	0	0	3	3
25	3	6	3	4	4	4
26	3	7	6	4	2	4
27	3	8	1	4	1	4
28	3	9	0	0	5	4
29	3	10	2	5	4	5