Notebook #2: Digging into participation, utilization, cost, and quality¶

This notebook...

looks at which CMS programs the cardiologist participates in
brings in data from CMS Aggregate Utilization and Cost data for CY2014
starts to compare the 8 given cardiologists to the population of 5,480 CA cardiologists we have Medicare data on

import pandas as pd
import numpy as np

pd.set_option('display.max_columns', None)

# Let's load the cardiologist file again
given_cardiologists = pd.read_csv("data/given_cardiologists_plus_national_downloadable_file.csv")

cms_program_cols = ['Participating in eRx',
                    'Participating in PQRS',
                    'Participating in EHR',
                    'Received PQRS Maintenance of Certification Program Incentive',
                    'Participated in Million Hearts']

given_cardiologists.replace(np.nan, "NULL", inplace=True)
given_cardiologists.groupby(cms_program_cols).size()

Participating in eRx  Participating in PQRS  Participating in EHR  Received PQRS Maintenance of Certification Program Incentive  Participated in Million Hearts
NULL                  NULL                   NULL                  NULL                                                          NULL                              2
                      Y                      Y                     NULL                                                          NULL                              6
dtype: int64

Analysis of CMS program participation¶

We can see that none of the cardiologists are participating in eRX, Million Hearts, or have received the PQRS incentive.
6 of the 8 physicians are participating in both PQRS and EHR incentive program -- we may want to prioritize these six

# Let's now look at the aggregate provider utilizationd data from CMS (source #3 in README.md)
all_provider_agg_util = pd.read_csv("data/data.cms.gov_4a3h-46r6_CY2015-Aggregate-Utilization.csv", low_memory=False)

# and join in the all_provider_agg_util data with given_cardiologist data into a new dataframe
given_cardiologists_plus_agg_util = pd.merge(given_cardiologists, all_provider_agg_util, how='left', left_on='npi', right_on='National Provider Identifier', suffixes=('_ndf','_agg'))
# write this merge to the file system for safe keeping
given_cardiologists_plus_agg_util.to_csv("data/given_cardiologists_plus_agg_util.csv")


# we may want to look at all cardiologists' aggregate information so let's bring in the original source #1 dataset and join it with this one as well
all_providers = pd.read_csv("data/data.medicare.gov_s63f-csi6_National-Downloadable_File.csv", low_memory=False)
all_cardiologists = all_providers[all_providers['Primary specialty'] == 'CARDIOVASCULAR DISEASE (CARDIOLOGY)']
all_cardiologists_in_ca = all_cardiologists[all_cardiologists['State'] == 'CA']
all_cardiologists_in_ca_plus_agg_util = pd.merge(all_cardiologists_in_ca, all_provider_agg_util, how='left', left_on='npi', right_on='National Provider Identifier', suffixes=('_ndf','_agg'))

# So what data do we have to work with now?
given_cardiologists_plus_agg_util

Analysis of given cardiologists and all CA cardiologists¶

# How many CA cardiologists can we compare these 8 with?
print "There are {0} cardiologists in CA that we will compare these {1} with".format(len(all_cardiologists_in_ca_plus_agg_util), len(given_cardiologists_plus_agg_util))

There are 5480 cardiologists in CA that we will compare these 8 with

# We will use plotly for interactive charting and visual analytics
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools

# helper method for creating hover over tooltips/labels
def list_of_provider_labels(df):
    labels = []
    for n, provider in df.iterrows():
        labels.append("NPI: {0}<br />Name: {1}, {2}".format(provider['npi'], provider['Last Name'], provider['First Name']))
    return labels

# given one df, this produces a two-column layout: [boxplot | scatterplot]
## Example usage:
## fig = plotly_box_and_scatter_plot(given_cardiologists_plus_agg_util, 'Graduation year')
## py.iplot(fig)
def plotly_box_and_scatter_plot(df, column_to_graph):
    data = [
        go.Box(
            name = column_to_graph,
            y = df[column_to_graph],
            boxmean = True
        ),
        go.Scatter(
            x = np.full(len(df), 0),
            y = df[column_to_graph].tolist(),
            mode = 'markers',
            text = list_of_provider_labels(df) #df['npi'].tolist(),
        )
    ]
    layout = go.Layout(
        showlegend=False,
        hovermode='closest',
        title="{0}".format(column_to_graph)
    )
    fig = go.Figure(data=data, layout=layout)
    return fig

# given two dfs, this produces a two-column layout: [given boxplot | given scatterplot | all boxplot | all scatterplot]
## Example usage:
## fig = plotly_compare_distribution_of_column(
##     column_to_graph = 'Percent (%) of Beneficiaries Identified With Ischemic Heart Disease ',
##     given_name = 'Nearby Cardios', given_df = given_cardiologists_plus_agg_util,
##     all_name = 'All CA Cardios', all_df = all_cardiologists_in_ca_plus_agg_util
##     y_min = 0, y_min = 100 # these are optional!
##     )
## py.iplot(fig)
def plotly_compare_distribution_of_column(column_to_graph, given_df, given_name, all_df, all_name, y_min=None, y_max=None):
    given_box = go.Box(
        name = given_name,
        y = given_df[column_to_graph],
        boxmean = True
    )
    given_scatter = go.Scatter(
        name = given_name,
        x = np.full(len(given_df), 0),
        y = given_df[column_to_graph].tolist(),
        mode = 'markers',
        text = list_of_provider_labels(given_df)
    )

    all_box = go.Box(
        name = all_name,
        y = all_df[column_to_graph],
        boxmean = True
    )
    all_scatter = go.Scatter(
        name = all_name,
        x = np.full(len(all_df), 0),
        y = all_df[column_to_graph].tolist(),
        mode = 'markers',
        text = list_of_provider_labels(all_df)
    )

    fig = tools.make_subplots(rows=1, cols=4)

    fig.append_trace(given_box, 1, 1)
    fig.append_trace(given_scatter, 1, 2)
    fig.append_trace(all_box, 1, 3)
    fig.append_trace(all_scatter, 1, 4)

    fig['layout'].update(title=column_to_graph, showlegend=True, hovermode='closest')
    fig['layout']['xaxis2'].update(title=given_name, type='category',  ticks='', showticklabels=False)
    fig['layout']['xaxis4'].update(title=all_name, type='category', ticks='', showticklabels=False)

    # if y_min and y_max are non-None, set all axes to the specified min and max
    if y_min != None and y_max != None:
        y_axis_layout = dict(range = [y_min, y_max], autorange = False)
        fig['layout']['yaxis1'].update(y_axis_layout)
        fig['layout']['yaxis2'].update(y_axis_layout)
        fig['layout']['yaxis3'].update(y_axis_layout)
        fig['layout']['yaxis4'].update(y_axis_layout)
    
    return fig

fig = plotly_box_and_scatter_plot(given_cardiologists_plus_agg_util, 'Percent (%) of Beneficiaries Identified With Heart Failure ')
py.iplot(fig)

fig = plotly_compare_distribution_of_column(
    column_to_graph = 'Percent (%) of Beneficiaries Identified With Heart Failure ',
    given_name = 'Nearby Cardios', given_df = given_cardiologists_plus_agg_util,
    all_name = 'All CA Cardios', all_df = all_cardiologists_in_ca_plus_agg_util)
py.iplot(fig)

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

fig1 = plotly_compare_distribution_of_column(
    column_to_graph = 'Percent (%) of Beneficiaries Identified With Heart Failure ',
    given_name = 'Nearby Cardios', given_df = given_cardiologists_plus_agg_util,
    all_name = 'All CA Cardios', all_df = all_cardiologists_in_ca_plus_agg_util,
    y_min = 0, y_max = 100)

fig2 = plotly_compare_distribution_of_column(
    column_to_graph = 'Percent (%) of Beneficiaries Identified With Ischemic Heart Disease ',
    given_name = 'Nearby Cardios', given_df = given_cardiologists_plus_agg_util,
    all_name = 'All CA Cardios', all_df = all_cardiologists_in_ca_plus_agg_util,
    y_min = 0, y_max = 100)

display(py.iplot(fig1))
display(py.iplot(fig2))

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

from IPython.display import display

# let's chart a bunch more interesting columns
columns_to_chart = ['Number of HCPCS', 
                    'Number of Services',
                    'Number of Medicare Beneficiaries',
                    'Number of Medical Services',
                    'Average Age of Beneficiaries',
                    'Average HCC Risk Score of Beneficiaries ',
                    'Number of Beneficiaries With Medicare & Medicaid Entitlement']

for column in columns_to_chart:
    fig = plotly_compare_distribution_of_column(
        column_to_graph = column,
        given_name = 'Nearby Cardios', given_df = given_cardiologists_plus_agg_util,
        all_name = 'All CA Cardios', all_df = all_cardiologists_in_ca_plus_agg_util)
    display(py.iplot(fig))

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

This is the format of your plot grid:
[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]  [ (1,4) x4,y4 ]

	npi	PAC ID	Professional Enrollment ID	Last Name	First Name	Middle Name	Suffix	Gender	Credential	Medical school name	Graduation year	Primary specialty	Secondary specialty 1	Secondary specialty 2	Secondary specialty 3	Secondary specialty 4	All secondary specialties	Organization legal name	Organization DBA name	Group Practice PAC ID	Number of Group Practice members	Line 1 Street Address	Line 2 Street Address	Marker of address line 2 suppression	City	State	Zip Code	Claims based hospital affiliation CCN 1	Claims based hospital affiliation LBN 1	Claims based hospital affiliation CCN 2	Claims based hospital affiliation LBN 2	Claims based hospital affiliation CCN 3	Claims based hospital affiliation LBN 3	Claims based hospital affiliation CCN 4	Claims based hospital affiliation LBN 4	Claims based hospital affiliation CCN 5	Claims based hospital affiliation LBN 5	Professional accepts Medicare Assignment	Participating in eRx	Participating in PQRS	Participating in EHR	Received PQRS Maintenance of Certification Program Incentive	Participated in Million Hearts	National Provider Identifier	Last Name/Organization Name of the Provider	First Name of the Provider	Middle Initial of the Provider	Credentials of the Provider	Gender of the Provider	Entity Type of the Provider	Street Address 1 of the Provider	Street Address 2 of the Provider	City of the Provider	Zip Code of the Provider	State Code of the Provider	Country Code of the Provider	Provider Type of the Provider	Medicare Participation Indicator	Number of HCPCS	Number of Services	Number of Medicare Beneficiaries	Total Submitted Charge Amount	Total Medicare Allowed Amount	Total Medicare Payment Amount	Total Medicare Standardized Payment Amount	Drug Suppress Indicator	Number of HCPCS Associated With Drug Services	Number of Drug Services	Number of Medicare Beneficiaries With Drug Services	Total Drug Submitted Charge Amount	Total Drug Medicare Allowed Amount	Total Drug Medicare Payment Amount	Total Drug Medicare Standardized Payment Amount	Medical Suppress Indicator	Number of HCPCS Associated With Medical Services	Number of Medical Services	Number of Medicare Beneficiaries With Medical Services	Total Medical Submitted Charge Amount	Total Medical Medicare Allowed Amount	Total Medical Medicare Payment Amount	Total Medical Medicare Standardized Payment Amount	Average Age of Beneficiaries	Number of Beneficiaries Age Less 65	Number of Beneficiaries Age 65 to 74	Number of Beneficiaries Age 75 to 84	Number of Beneficiaries Age Greater 84	Number of Female Beneficiaries	Number of Male Beneficiaries	Number of Non-Hispanic White Beneficiaries	Number of Black or African American Beneficiaries	Number of Asian Pacific Islander Beneficiaries	Number of Hispanic Beneficiaries	Number of American Indian/Alaska Native Beneficiaries	Number of Beneficiaries With Race Not Elsewhere Classified	Number of Beneficiaries With Medicare Only Entitlement	Number of Beneficiaries With Medicare & Medicaid Entitlement	Percent (%) of Beneficiaries Identified With Atrial Fibrillation	Percent (%) of Beneficiaries Identified With Alzheimer’s Disease or Dementia	Percent (%) of Beneficiaries Identified With Asthma	Percent (%) of Beneficiaries Identified With Cancer	Percent (%) of Beneficiaries Identified With Heart Failure	Percent (%) of Beneficiaries Identified With Chronic Kidney Disease	Percent (%) of Beneficiaries Identified With Chronic Obstructive Pulmonary Disease	Percent (%) of Beneficiaries Identified With Depression	Percent (%) of Beneficiaries Identified With Diabetes	Percent (%) of Beneficiaries Identified With Hyperlipidemia	Percent (%) of Beneficiaries Identified With Hypertension	Percent (%) of Beneficiaries Identified With Ischemic Heart Disease	Percent (%) of Beneficiaries Identified With Osteoporosis	Percent (%) of Beneficiaries Identified With Rheumatoid Arthritis / Osteoarthritis	Percent (%) of Beneficiaries Identified With Schizophrenia / Other Psychotic Disorders	Percent (%) of Beneficiaries Identified With Stroke	Average HCC Risk Score of Beneficiaries
0	1184787020	6.002875e+09	I20041006000639	KUMAR	ANIL	NULL	NULL	M	MD	OTHER	1977.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	NULL	NULL	NULL	NULL	NULL	KUMAR MEDICAL CORPORATION	NULL	1.456389e+09	2.0	44215 15TH W ST	SUITE 215	N	LANCASTER	CA	93534.0	50056.0	ANTELOPE VALLEY HOSPITAL DISTRICT	50204	LANCASTER HOSPITAL CORPORATION	51333	RIDGECREST REGIONAL HOSPITAL	NULL	NULL	NULL	NULL	Y	NULL	Y	Y	NULL	NULL	1184787020	KUMAR	ANIL	NaN	NaN	M	I	44215 15TH ST W	SUITE 215	LANCASTER	935344014	CA	US	Cardiology	Y	88	12593.0	2274	2124265.16	1083048.40	819039.53	755562.09	NaN	3.0	497.0	235.0	4299.08	604.52	471.91	471.91	NaN	85.0	12096.0	2274.0	2119966.08	1082443.88	818567.62	755090.18	72	473.0	787.0	711.0	303.0	1269.0	1005.0	1263.0	432.0	86.0	450.0	NaN	NaN	1228.0	1046.0	17.0	16.0	14.0	11.0	41.0	41.0	39.0	24.0	53.0	66.0	75.0	65.0	11.0	49.0	7.0	12.0	2.1222
1	1477612752	5.193784e+09	I20041006000364	GILL	KANWALJIT	S	NULL	M	MD	OTHER	1990.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	INTERNAL MEDICINE	PERIPHERAL VASCULAR DISEASE	NULL	NULL	INTERNAL MEDICINE, PERIPHERAL VASCULAR DISEASE	KANWALJIT S. GILL MD INC	NULL	8.729247e+09	1.0	38656 MEDICAL CTR DR A	NULL	N	PALMDALE	CA	935514483.0	50204.0	LANCASTER HOSPITAL CORPORATION	50056	ANTELOPE VALLEY HOSPITAL DISTRICT	51333	RIDGECREST REGIONAL HOSPITAL	NULL	NULL	NULL	NULL	Y	NULL	Y	Y	NULL	NULL	1477612752	GILL	KANWALJIT	S	MD	M	I	38656 MEDICAL CENTER DRIVE	SUITE A	PALMDALE	93551	CA	US	Cardiology	Y	96	7498.0	1855	1072273.00	645088.80	492869.32	471072.83	NaN	6.0	1284.0	102.0	33340.00	11253.55	8825.95	8825.95	NaN	90.0	6214.0	1853.0	1038933.00	633835.25	484043.37	462246.88	72	405.0	644.0	548.0	258.0	1028.0	827.0	1142.0	300.0	73.0	314.0	NaN	NaN	1058.0	797.0	17.0	17.0	14.0	10.0	45.0	44.0	36.0	24.0	52.0	69.0	75.0	63.0	10.0	51.0	7.0	12.0	2.2935
2	1952460248	6.483407e+08	I20080603000419	KHANAL	SANJAYA	NULL	NULL	M	NULL	OTHER	1991.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	CARDIAC SURGERY	NULL	NULL	NULL	CARDIAC SURGERY	ANTELOPE VALLEY CARDIOLOGY ASSOCIATES	NULL	7.416055e+09	4.0	43723 20TH ST W	NULL	N	LANCASTER	CA	935344763.0	50056.0	ANTELOPE VALLEY HOSPITAL DISTRICT	50204	LANCASTER HOSPITAL CORPORATION	51333	RIDGECREST REGIONAL HOSPITAL	NULL	NULL	NULL	NULL	Y	NULL	Y	Y	NULL	NULL	1952460248	KHANAL	SANJAYA	NaN	MD	M	I	43807 N 10TH ST WEST	SUITE F	LANCASTER	93534	CA	US	Cardiology	Y	91	6167.0	1347	1194343.00	491828.50	371113.86	349615.01	NaN	3.0	2468.0	144.0	42311.00	19027.85	14919.55	14919.55	NaN	88.0	3699.0	1347.0	1152032.00	472800.65	356194.31	334695.46	72	280.0	491.0	404.0	172.0	722.0	625.0	832.0	207.0	49.0	239.0	NaN	NaN	759.0	588.0	18.0	19.0	16.0	11.0	46.0	46.0	43.0	29.0	57.0	73.0	75.0	74.0	11.0	58.0	8.0	16.0	2.3065
3	1114980703	3.779470e+09	I20040301001181	ALTURJUMAN	AHMAD	MOUTAZ	NULL	M	MD	OTHER	1989.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	CRITICAL CARE (INTENSIVISTS)	INTERNAL MEDICINE	NULL	NULL	CRITICAL CARE (INTENSIVISTS), INTERNAL MEDICINE	BLACKSTONE MEDICAL CORP.	NULL	5.991726e+09	2.0	4500 BROCKTON AVE	SUITE 305	N	RIVERSIDE	CA	925014027.0	50022.0	RIVERSIDE HEALTHCARE SYSTEM, L.P.	50770	LOMA LINDA UNIVERSITY MEDICAL CENTER MURRIETA	50775	TEMECULA VALLEY HOSPITAL, INC.	50102	PARKVIEW COMMUNITY HOSPITAL MEDICAL CENTER	50327	SEVENTH DAY ADVENTISTS LOMA LINDA UNIVERSITY M...	Y	NULL	Y	Y	NULL	NULL	1114980703	ALTURJUMAN	AHMAD	NaN	M.D.	M	I	4500 BROCKTON AVE	NaN	RIVERSIDE	925014090	CA	US	Cardiology	Y	94	6458.0	1030	1732247.00	530223.99	400437.80	388564.23	NaN	2.0	2562.0	78.0	62680.00	12303.84	9646.20	9646.20	NaN	92.0	3896.0	1030.0	1669567.00	517920.15	390791.60	378918.03	74	141.0	397.0	321.0	171.0	591.0	439.0	587.0	100.0	44.0	270.0	NaN	NaN	583.0	447.0	22.0	18.0	11.0	11.0	55.0	41.0	28.0	23.0	50.0	70.0	75.0	75.0	9.0	41.0	5.0	13.0	2.0265
4	1215918800	9.234126e+09	I20070823000730	SHENKMAN	HEATHER	J	NULL	F	MD	ALBANY MEDICAL COLLEGE OF UNION UNIVERSITY	1999.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	INTERNAL MEDICINE	NULL	NULL	NULL	INTERNAL MEDICINE	LAKESIDE MEDICAL ASSOCIATES, A MEDICAL GROUP, ...	NULL	1.951202e+09	2.0	7345 MEDICAL CTR DR	SUITE 500	N	WEST HILLS	CA	913071964.0	50235.0	PROVIDENCE HEALTH SYSTEM-SOUTHERN CALIFORNIA	50761	PROVIDENCE HEALTH SYSTEM-SOUTHERN CALIFORNIA	50278	PROVIDENCE HEALTH SYSTEM - SOUTHERN CALIFORNIA	NULL	NULL	NULL	NULL	M	NULL	NULL	NULL	NULL	NULL	1215918800	SHENKMAN	HEATHER	J	M.D.	F	I	7325 MEDICAL CENTER DR	SUITE 300	WEST HILLS	913071925	CA	US	Cardiology	Y	30	499.0	255	126213.86	50570.65	37976.14	35403.57	*	NaN	NaN	NaN	NaN	NaN	NaN	NaN	#	NaN	NaN	NaN	NaN	NaN	NaN	NaN	77	18.0	94.0	68.0	75.0	116.0	139.0	201.0	NaN	11.0	30.0	NaN	NaN	166.0	89.0	38.0	24.0	13.0	14.0	58.0	47.0	30.0	33.0	47.0	74.0	75.0	75.0	11.0	46.0	7.0	16.0	2.2654
5	1255334769	3.678470e+09	I20080307000417	ON	ROGER	C	NULL	M	NULL	TUFTS UNIVERSITY SCHOOL OF MEDICINE	1978.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	NULL	NULL	NULL	NULL	NULL	LAKESIDE MEDICAL ORGANIZATION, A MEDICAL GROUP...	NULL	7.618005e+09	100.0	191 S BUENA VISTA	SUITE 440	N	BURBANK	CA	915054554.0	50235.0	PROVIDENCE HEALTH SYSTEM-SOUTHERN CALIFORNIA	50278	PROVIDENCE HEALTH SYSTEM - SOUTHERN CALIFORNIA	50761	PROVIDENCE HEALTH SYSTEM-SOUTHERN CALIFORNIA	NULL	NULL	NULL	NULL	Y	NULL	Y	Y	NULL	NULL	1255334769	ON	ROGER	C	M.D.	M	I	191 S BUENA VISTA ST	STE 400	BURBANK	915054554	CA	US	Cardiology	Y	35	908.0	333	156434.52	89911.22	66399.56	60943.32	*	NaN	NaN	NaN	NaN	NaN	NaN	NaN	#	NaN	NaN	NaN	NaN	NaN	NaN	NaN	78	15.0	115.0	107.0	96.0	166.0	167.0	277.0	NaN	15.0	24.0	0.0	NaN	262.0	71.0	27.0	16.0	10.0	14.0	35.0	44.0	20.0	21.0	31.0	75.0	75.0	57.0	10.0	46.0	4.0	9.0	1.6168
6	1891765574	3.274586e+09	I20050211000931	GUPTA	VINOD	K	NULL	M	MD	UNIVERSITY OF CALIFORNIA, SAN FRANCISCO SCHOOL...	1966.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	NULL	NULL	NULL	NULL	NULL	HEART CENTER OF SOUTHERN CALIFORNIA INC	NULL	8.820040e+09	1.0	2876 N SYCAMORE DR	NULL	N	SIMI VALLEY	CA	930651550.0	50236.0	SIMI VALLEY HOSPITAL & HEALTH CARE SERVICES	NULL	NULL	NULL	NULL	NULL	NULL	NULL	NULL	M	NULL	Y	Y	NULL	NULL	1891765574	GUPTA	VINOD	K	M.D.	M	I	2876 SYCAMORE DR	NaN	SIMI VALLEY	930651550	CA	US	Cardiology	Y	18	4723.0	205	511145.88	249300.54	188086.11	176705.68	NaN	2.0	3239.0	73.0	23557.88	8768.47	6877.35	6877.35	NaN	16.0	1484.0	205.0	487588.00	240532.07	181208.76	169828.33	74	22.0	83.0	62.0	38.0	98.0	107.0	129.0	NaN	20.0	45.0	0.0	NaN	124.0	81.0	19.0	11.0	10.0	5.0	27.0	22.0	8.0	15.0	37.0	75.0	75.0	75.0	11.0	34.0	NaN	7.0	1.1640
7	1851401822	6.800077e+09	I20110218000225	SILVA	AUGUSTO	NULL	NULL	M	NULL	OTHER	1967.0	CARDIOVASCULAR DISEASE (CARDIOLOGY)	NULL	NULL	NULL	NULL	NULL	LAKESIDE MEDICAL ORGANIZATION, A MEDICAL GROUP...	NULL	7.618005e+09	100.0	191 S BUENA VISTA ST	SUITE 400	N	BURBANK	CA	915054543.0	50235.0	PROVIDENCE HEALTH SYSTEM-SOUTHERN CALIFORNIA	NULL	NULL	NULL	NULL	NULL	NULL	NULL	NULL	Y	NULL	NULL	NULL	NULL	NULL	1851401822	SILVA	AUGUSTO	NaN	M.D.	M	I	2601 W ALAMEDA AVE	NaN	BURBANK	915054800	CA	US	Cardiology	Y	35	1802.0	509	331223.34	155159.63	115930.25	106481.89	NaN	3.0	205.0	40.0	9440.00	3947.54	3094.76	3094.76	NaN	32.0	1597.0	509.0	321783.34	151212.09	112835.49	103387.13	77	30.0	173.0	178.0	128.0	240.0	269.0	389.0	NaN	22.0	78.0	0.0	NaN	374.0	135.0	29.0	21.0	13.0	16.0	35.0	33.0	20.0	21.0	37.0	69.0	75.0	65.0	14.0	50.0	5.0	14.0	1.8477