unique_claims_per_beneficiary.ipynb

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     "end_time": "2022-08-15T13:13:13.262794Z",
     "start_time": "2022-08-15T13:13:13.251794Z"
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   },
   "source": [
    "# Overview\n",
    "The purpose of this analysis is to compare counts of unique claims per beneficiary by service type.<br>\n",
    "Visit Data.gov to download the data used for this analysis."
   ]
  },
  {
   "cell_type": "raw",
   "id": "897b7044",
   "metadata": {
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    "\n",
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     "start_time": "2023-04-05T17:28:31.460786Z"
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   "outputs": [
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    "from IPython.display import HTML\n",
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  {
   "cell_type": "markdown",
   "id": "03595864",
   "metadata": {},
   "source": [
    "# Load libararies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2bf60b36",
   "metadata": {
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     "end_time": "2023-04-05T17:28:34.289015Z",
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    {
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       "<style>.container { width:90% !important; }</style>"
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       "<IPython.core.display.HTML object>"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "pio.renderers.default='iframe'\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "from matplotlib.pyplot import figure\n",
    "from matplotlib import rcParams\n",
    "matplotlib.rcParams['figure.figsize'] = (12,8)\n",
    "dpi=200\n",
    "matplotlib.rcParams['figure.dpi']=dpi\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4807f85f",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76ef1873",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T17:28:46.113040Z",
     "start_time": "2023-04-05T17:28:34.292015Z"
    }
   },
   "outputs": [],
   "source": [
    "# load data\n",
    "synthetic_bene_df = pd.read_csv('data/beneficiary_2022.csv', sep=\"|\", usecols=['BENE_ID'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'}, low_memory=False)\n",
    "synthetic_inpatient_df = pd.read_csv('data/inpatient.csv', sep=\"|\", usecols=['CLM_ID','BENE_ID', 'CLM_FROM_DT'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'}, low_memory=False)\n",
    "synthetic_outpatient_df = pd.read_csv('data/outpatient.csv', sep=\"|\", usecols=['CLM_ID','BENE_ID', 'CLM_FROM_DT'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'}, low_memory=False)\n",
    "synthetic_carrier_df = pd.read_csv('data/carrier.csv', sep=\"|\", usecols=['CLM_ID','BENE_ID', 'CLM_FROM_DT'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'}, low_memory=False)\n",
    "synthetic_dme_df = pd.read_csv('data/dme.csv', sep=\"|\", usecols=['CLM_ID','BENE_ID', 'CLM_FROM_DT'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'}, low_memory=False)\n",
    "synthetic_hha_df = pd.read_csv('data/hha.csv', sep=\"|\", usecols=['CLM_ID','BENE_ID', 'CLM_FROM_DT'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'},low_memory=False)\n",
    "synthetic_hospice_df = pd.read_csv('data/hospice.csv', sep=\"|\", usecols=['CLM_ID','BENE_ID','CLM_FROM_DT'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'}, low_memory=False)\n",
    "synthetic_snf_df = pd.read_csv('data/snf.csv', sep=\"|\", usecols=['CLM_ID','BENE_ID', 'CLM_FROM_DT'], dtype={'CLM_ID':'Int64','BENE_ID':'Int64','CLM_FROM_DT':'str'}, low_memory=False)\n",
    "synthetic_pde_df = pd.read_csv('data/pde.csv', sep=\"|\", usecols=['BENE_ID', 'PDE_ID','PD_DT'], dtype={'BENE_ID':'Int64'}, low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afdf8e5d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T17:28:46.128857Z",
     "start_time": "2023-04-05T17:28:46.117857Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8671 unique ids and 8671 total rows\n"
     ]
    }
   ],
   "source": [
    "# count number of unique BENE_IDs in real data\n",
    "unique_ids = synthetic_bene_df['BENE_ID'].unique()\n",
    "\n",
    "print(f\"{len(unique_ids)} unique ids and {synthetic_bene_df.shape[0]} total rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d9c89a",
   "metadata": {},
   "source": [
    "# Filter synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75af77f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T17:28:47.787918Z",
     "start_time": "2023-04-05T17:28:46.132858Z"
    }
   },
   "outputs": [],
   "source": [
    "# CARRIER\n",
    "# evaluate 2022 only\n",
    "values = [2022]\n",
    "# convert CLM_FROM_DT to datetime\n",
    "synthetic_carrier_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_carrier_df['CLM_FROM_DT'])\n",
    "# year from datetime\n",
    "synthetic_carrier_df['CLM_FROM_YR'] = synthetic_carrier_df['CLM_FROM_DT'].dt.year\n",
    "# limit year to 2021 only\n",
    "synthetic_carrier_df = synthetic_carrier_df[synthetic_carrier_df.CLM_FROM_YR.isin(values)]\n",
    "# merge on BENE_ID\n",
    "synthetic_carrier_df = pd.merge(synthetic_bene_df, synthetic_carrier_df, on ='BENE_ID', how =\"left\")\n",
    "# limit df to unique BENE_ID and CLM_ID pairs\n",
    "synthetic_carrier_df['tot_claims'] = synthetic_carrier_df.groupby(['BENE_ID'])['CLM_ID'].transform('nunique')\n",
    "# select first row in each groupby group to ensure bene_id and clm_id pairs are not duplicated\n",
    "synthetic_carrier_df = synthetic_carrier_df.groupby('BENE_ID').first()\n",
    "# reset index and save as a column\n",
    "synthetic_carrier_df.reset_index(inplace=True)\n",
    "# remove outliers\n",
    "# synthetic_carrier_df = synthetic_carrier_df[synthetic_carrier_df.tot_claims < synthetic_carrier_df.tot_claims.quantile(.98)]\n",
    "\n",
    "# INPATIENT\n",
    "values = [2022]\n",
    "synthetic_inpatient_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_inpatient_df['CLM_FROM_DT'])\n",
    "synthetic_inpatient_df['CLM_FROM_YR'] = synthetic_inpatient_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_inpatient_df = synthetic_inpatient_df[synthetic_inpatient_df.CLM_FROM_YR.isin(values)]\n",
    "synthetic_inpatient_df = pd.merge(synthetic_bene_df, synthetic_inpatient_df, on ='BENE_ID', how =\"left\")\n",
    "synthetic_inpatient_df['tot_claims'] = synthetic_inpatient_df.groupby(['BENE_ID'])['CLM_ID'].transform('nunique')\n",
    "synthetic_inpatient_df = synthetic_inpatient_df.groupby('BENE_ID').first()\n",
    "synthetic_inpatient_df.reset_index(inplace=True)\n",
    "# synthetic_inpatient_df = synthetic_inpatient_df[synthetic_inpatient_df.tot_claims < synthetic_inpatient_df.tot_claims.quantile(.99)]\n",
    "\n",
    "# OUTPATIENT\n",
    "values = [2022]\n",
    "synthetic_outpatient_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_outpatient_df['CLM_FROM_DT'])\n",
    "synthetic_outpatient_df['CLM_FROM_YR'] = synthetic_outpatient_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_outpatient_df = synthetic_outpatient_df[synthetic_outpatient_df.CLM_FROM_YR.isin(values)]\n",
    "synthetic_outpatient_df = pd.merge(synthetic_bene_df, synthetic_outpatient_df, on ='BENE_ID', how =\"left\")\n",
    "synthetic_outpatient_df['tot_claims'] = synthetic_outpatient_df.groupby(['BENE_ID'])['CLM_ID'].transform('nunique')\n",
    "synthetic_outpatient_df = synthetic_outpatient_df.groupby('BENE_ID').first()\n",
    "synthetic_outpatient_df.reset_index(inplace=True)\n",
    "# synthetic_outpatient_df = synthetic_outpatient_df[synthetic_outpatient_df.tot_claims < synthetic_outpatient_df.tot_claims.quantile(.98)]\n",
    "\n",
    "# DME\n",
    "values = [2022]\n",
    "synthetic_dme_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_dme_df['CLM_FROM_DT'])\n",
    "synthetic_dme_df['CLM_FROM_YR'] = synthetic_dme_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_dme_df = synthetic_dme_df[synthetic_dme_df.CLM_FROM_YR.isin(values)]\n",
    "synthetic_dme_df = pd.merge(synthetic_bene_df, synthetic_dme_df, on ='BENE_ID', how =\"left\")\n",
    "synthetic_dme_df['tot_claims'] = synthetic_dme_df.groupby(['BENE_ID'])['CLM_ID'].transform('nunique')\n",
    "synthetic_dme_df = synthetic_dme_df.groupby('BENE_ID').first()\n",
    "synthetic_dme_df.reset_index(inplace=True)\n",
    "# synthetic_dme_df = synthetic_dme_df[synthetic_dme_df.tot_claims < synthetic_dme_df.tot_claims.quantile(.99)]\n",
    "\n",
    "# SNF\n",
    "values = [2022]\n",
    "synthetic_snf_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_snf_df['CLM_FROM_DT'])\n",
    "synthetic_snf_df['CLM_FROM_YR'] = synthetic_snf_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_snf_df = synthetic_snf_df[synthetic_snf_df.CLM_FROM_YR.isin(values)]\n",
    "synthetic_snf_df = pd.merge(synthetic_bene_df, synthetic_snf_df, on ='BENE_ID', how =\"left\")\n",
    "synthetic_snf_df['tot_claims'] = synthetic_snf_df.groupby(['BENE_ID'])['CLM_ID'].transform('nunique')\n",
    "synthetic_snf_df = synthetic_snf_df.groupby('BENE_ID').first()\n",
    "synthetic_snf_df.reset_index(inplace=True)\n",
    "# synthetic_snf_df = synthetic_snf_df[synthetic_snf_df.tot_claims < synthetic_snf_df.tot_claims.quantile(.99)]\n",
    "\n",
    "# HOSPICE\n",
    "values = [2022]\n",
    "synthetic_hospice_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_hospice_df['CLM_FROM_DT'])\n",
    "synthetic_hospice_df['CLM_FROM_YR'] = synthetic_hospice_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_hospice_df = synthetic_hospice_df[synthetic_hospice_df.CLM_FROM_YR.isin(values)]\n",
    "synthetic_hospice_df = pd.merge(synthetic_bene_df, synthetic_hospice_df, on ='BENE_ID', how =\"left\")\n",
    "synthetic_hospice_df['tot_claims'] = synthetic_hospice_df.groupby(['BENE_ID'])['CLM_ID'].transform('nunique')\n",
    "synthetic_hospice_df = synthetic_hospice_df.groupby('BENE_ID').first()\n",
    "synthetic_hospice_df.reset_index(inplace=True)\n",
    "# synthetic_hospice_df = synthetic_hospice_df[synthetic_hospice_df.tot_claims < synthetic_hospice_df.tot_claims.quantile(.99)]\n",
    "\n",
    "# HHA\n",
    "values = [2022]\n",
    "synthetic_hha_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_hha_df['CLM_FROM_DT'])\n",
    "synthetic_hha_df['CLM_FROM_YR'] = synthetic_hha_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_hha_df = synthetic_hha_df[synthetic_hha_df.CLM_FROM_YR.isin(values)]\n",
    "synthetic_hha_df = pd.merge(synthetic_bene_df, synthetic_hha_df, on ='BENE_ID', how =\"left\")\n",
    "synthetic_hha_df['tot_claims'] = synthetic_hha_df.groupby(['BENE_ID'])['CLM_ID'].transform('nunique')\n",
    "synthetic_hha_df = synthetic_hha_df.groupby('BENE_ID').first()\n",
    "synthetic_hha_df.reset_index(inplace=True)\n",
    "# synthetic_hha_df = synthetic_hha_df[synthetic_hha_df.tot_claims < synthetic_hha_df.tot_claims.quantile(.995)]\n",
    "\n",
    "# PDE\n",
    "values = [2022]\n",
    "synthetic_pde_df['PD_DT'] = pd.to_datetime(synthetic_pde_df['PD_DT'])\n",
    "synthetic_pde_df['PD_YR'] = synthetic_pde_df['PD_DT'].dt.year\n",
    "synthetic_pde_df = synthetic_pde_df[synthetic_pde_df.PD_YR.isin(values)]\n",
    "synthetic_pde_df = pd.merge(synthetic_bene_df, synthetic_pde_df, on ='BENE_ID', how =\"left\")\n",
    "synthetic_pde_df['tot_claims'] = synthetic_pde_df.groupby(['BENE_ID'])['PDE_ID'].transform('nunique')\n",
    "synthetic_pde_df = synthetic_pde_df.groupby('BENE_ID').first()\n",
    "synthetic_pde_df.reset_index(inplace=True)\n",
    "# synthetic_pde_df = synthetic_pde_df[synthetic_pde_df.tot_claims < synthetic_pde_df.tot_claims.quantile(.99)]\n",
    "\n",
    "\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9357ea",
   "metadata": {},
   "source": [
    "# Synthetic number of claims per beneficiary distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18847e2e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T17:28:48.103859Z",
     "start_time": "2023-04-05T17:28:47.792864Z"
    }
   },
   "outputs": [
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       "    width=\"1220px\"\n",
       "    height=\"720\"\n",
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   ],
   "source": [
    "# # set figure size\n",
    "# figure(figsize=(5.5, 5), dpi=250)\n",
    "# establish subplot axes\n",
    "fig = make_subplots(rows=2, cols=4, horizontal_spacing = 0.1, vertical_spacing = 0.2, subplot_titles=(\"Carrier\",\n",
    "                                                    \"Inpatient\",\n",
    "                                                    \"Outpatient\",\n",
    "                                                    \"DME\",\n",
    "                                                    \"SNF\",\n",
    "                                                    \"Hospice\",\n",
    "                                                    \"HHA\",\n",
    "                                                    \"PDE\"))\n",
    "# CARRIER\n",
    "# plot histogram\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_carrier_df['tot_claims'],\n",
    "), row=1, col=1)\n",
    "\n",
    "# axes\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=1, col=1)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "# INPATIENT\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_inpatient_df['tot_claims'],\n",
    "), row=1, col=2)\n",
    "\n",
    "\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=1, col=2)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=1, col=2)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "# OUTPATIENT\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_outpatient_df['tot_claims'],\n",
    "), row=1, col=3)\n",
    "\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=1, col=3)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=1, col=3)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "# DME\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_dme_df['tot_claims'],\n",
    "), row=1, col=4)\n",
    "\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=1, col=4)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=1, col=4)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "# SNF\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_snf_df['tot_claims'],\n",
    "), row=2, col=1)\n",
    "\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=2, col=1)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=2, col=1)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "# HOSPICE\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_hospice_df['tot_claims'],\n",
    "), row=2, col=2)\n",
    "\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=2, col=2)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=2, col=2)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "# HHA\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_hha_df['tot_claims'],\n",
    "), row=2, col=3)\n",
    "\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=2, col=3)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=2, col=3)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "# PDE\n",
    "fig.append_trace(go.Histogram(\n",
    "    histnorm='percent',\n",
    "    hoverlabel = dict(namelength=0),\n",
    "    marker_color='mediumseagreen',\n",
    "    x=synthetic_pde_df['tot_claims'],\n",
    "), row=2, col=4)\n",
    "\n",
    "fig.update_xaxes(title_text=\"Total Claims\", row=2, col=4)\n",
    "fig.update_yaxes(title_text=\"Beneficiaries (%)\", row=2, col=4)\n",
    "fig.update_yaxes(range = [0,100])\n",
    "\n",
    "\n",
    "\n",
    "# update plot layout\n",
    "fig.update_layout(title_text=\"2022 Percentage of Synthetic Claims per Beneficiary\", bargap=0.30, width=1200, height=700, showlegend=False)"
   ]
  }
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