analytics/bigquery_bcda_exporation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BCDA Synthetic Data Exploration",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# BCDA Synthetic Data Example Exploration\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/medical_claims_tools/blob/main/analytics/bigquery_bcda_exporation.ipynb)",
        "\n",
        "\n",
        "This notebook contains basic examples of exploring Medicare BCDA FHIR data\n",
        "in BigQuery. The results saved below are from the synthetic BCDA example data,\n",
        "but this notebook can be run against any BCDA data imported into FHIR Store and\n",
        "BigQuery using the `bcda_fetch` program.\n",
        "\n"
      ],
      "metadata": {
        "id": "WbzoLoTRRwZ5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup\n"
      ],
      "metadata": {
        "id": "iJYYbOqqMoFI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6w3wUQYgHNGt",
        "outputId": "c3322c43-ceb7-4bee-b77c-c681db6bc49c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authenticated\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"
          ]
        }
      ],
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "print('Authenticated')\n",
        "\n",
        "from google.cloud import bigquery\n",
        "bqclient = bigquery.Client(project=\"betazed\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Parameters\n",
        "bigquery_table_prefix = \"my_project.my_dataset\" #@param {type:\"string\"}\n"
      ],
      "metadata": {
        "id": "b-Ul7P9nZkqv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Some basic analytics"
      ],
      "metadata": {
        "id": "mq7SKGSVMq-l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Claims broken down by type"
      ],
      "metadata": {
        "id": "Jm3gHlgyRxVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_str = \"\"\"\n",
        "SELECT\n",
        "  (\n",
        "    SELECT m.code\n",
        "    FROM UNNEST(type.coding) m\n",
        "    WHERE m.system = 'https://bluebutton.cms.gov/resources/codesystem/eob-type'\n",
        "  ) AS eob_code,\n",
        "  COUNT(*) AS cnt\n",
        "FROM `{bigquery_table_prefix}.ExplanationOfBenefit`\n",
        "GROUP BY eob_code\n",
        "LIMIT 10;\n",
        "\"\"\".format(bigquery_table_prefix=bigquery_table_prefix)\n",
        "dataframe = bqclient.query(query_str).result().to_dataframe()\n",
        "print(dataframe)\n",
        "dataframe.plot.bar(rot=0, x=\"eob_code\", title=\"Claims by EOB Type\", ylabel=\"Count\", xlabel=\"EOB Type Code\",figsize=(8,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "sIOTQt5dH4S8",
        "outputId": "98dd9fc3-76a3-4d7f-999c-fc8542467299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     eob_code   cnt\n",
            "0         PDE  2532\n",
            "1  OUTPATIENT  3658\n",
            "2   INPATIENT   372\n",
            "3         SNF    35\n",
            "4     CARRIER  2947\n",
            "5         HHA   231\n",
            "6     HOSPICE    13\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61dbeb9210>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's look at PDE (Pharmacy) Claims"
      ],
      "metadata": {
        "id": "BZ9U-brVR117"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, note that in our example data there's only 1 item per EOB (again, needs to be verified if this is true in general). We will use this to simplify our\n",
        "queries."
      ],
      "metadata": {
        "id": "T2VuxvBjayGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_str =\"\"\"\n",
        "SELECT ARRAY_LENGTH(item) AS itm_len, COUNT(*) AS cnt\n",
        "FROM `{bigquery_table_prefix}.ExplanationOfBenefit`\n",
        "GROUP BY itm_len\n",
        "\"\"\".format(bigquery_table_prefix=bigquery_table_prefix)\n",
        "print(bqclient.query(query_str).result().to_dataframe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldSuM3rUa7Gd",
        "outputId": "e5d1f2f0-28ee-4392-faec-8e3d9b764881"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   itm_len   cnt\n",
            "0        1  9788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Note we assume all EOB have an item length of 1, and we only look at the \n",
        "# first coding inside the codeable concept with the NDC coding system. \n",
        "query_str = \"\"\"\n",
        "WITH\n",
        "  PDEs AS (\n",
        "    SELECT *\n",
        "    FROM `{bigquery_table_prefix}.ExplanationOfBenefit`\n",
        "    WHERE\n",
        "      (\n",
        "        SELECT m.code\n",
        "        FROM UNNEST(type.coding) m\n",
        "        WHERE\n",
        "          m.system = 'https://bluebutton.cms.gov/resources/codesystem/eob-type'\n",
        "      )\n",
        "      = 'PDE'\n",
        "  )\n",
        "SELECT\n",
        "  ARRAY(\n",
        "    SELECT m.code FROM UNNEST(item[OFFSET(0)].productOrService.coding) AS m\n",
        "  )[OFFSET(0)] AS ndc,\n",
        "  COUNT(*) AS cnt\n",
        "FROM PDEs\n",
        "WHERE\n",
        "  (SELECT m.system FROM UNNEST(item[OFFSET(0)].productOrService.coding) AS m)\n",
        "  = 'http://hl7.org/fhir/sid/ndc'\n",
        "GROUP BY ndc\n",
        "ORDER BY cnt DESC\n",
        "\"\"\".format(bigquery_table_prefix=bigquery_table_prefix)\n",
        "dataframe = bqclient.query(query_str).result().to_dataframe()\n",
        "dataframe.head().plot.bar(rot=0, x=\"ndc\", xlabel=\"NDC Code\", ylabel=\"Count\", title=\"Most frequent PDE drugs by NDC\", width=0.5, figsize=(10,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "FsauZ2JLBihO",
        "outputId": "abf87fd3-cbf7-44b9-c8af-58fbea0f38aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61db960790>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_str = \"\"\"\n",
        "WITH\n",
        "  PDEs AS (\n",
        "    SELECT *\n",
        "    FROM `{bigquery_table_prefix}.ExplanationOfBenefit`\n",
        "    WHERE\n",
        "      (\n",
        "        SELECT m.code\n",
        "        FROM UNNEST(type.coding) m\n",
        "        WHERE\n",
        "          m.system = 'https://bluebutton.cms.gov/resources/codesystem/eob-type'\n",
        "      )\n",
        "      = 'PDE'\n",
        "  )\n",
        "SELECT\n",
        "  ARRAY(\n",
        "    SELECT m.display FROM UNNEST(item[OFFSET(0)].productOrService.coding) AS m\n",
        "  )[OFFSET(0)] AS display,\n",
        "  COUNT(*) AS cnt\n",
        "FROM PDEs\n",
        "WHERE\n",
        "  (SELECT m.system FROM UNNEST(item[OFFSET(0)].productOrService.coding) AS m)\n",
        "  = 'http://hl7.org/fhir/sid/ndc'\n",
        "GROUP BY display\n",
        "ORDER BY cnt DESC\n",
        "\"\"\".format(bigquery_table_prefix=bigquery_table_prefix)\n",
        "dataframe = bqclient.query(query_str).result().to_dataframe()\n",
        "top_df = dataframe.head()\n",
        "# Most frequent display value is none, so let's clip that\n",
        "top_df_without_none = top_df[1:]\n",
        "top_df_without_none.plot.bar(rot=0, x=\"display\", xlabel=\"Display\", ylabel=\"Count\", title=\"Most frequent PDE drugs by display value\", width=0.5, figsize=(18,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "rUJnUVdCSTEi",
        "outputId": "1576d13b-b712-46fc-ad0f-0bfe087a2930"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61db7f2810>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kind of interesting--seems like NDCs and display values in the FHIR probably don't line up exactly. Could be an artifact of syntehtic data, or could be similar in real data. If appropirate, we can always use a sideloaded NDC dictionary. "
      ],
      "metadata": {
        "id": "O-6PCF_6Qnc9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Outpatient Claims\n"
      ],
      "metadata": {
        "id": "8njre3YhSCGM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take a look at the most common primary outpatient procedures. By this, I mean we only look at the 0th procedure entry per ExplanationOfBenefit. In the synthetic data, we only have one per EOB anyway--if used on non-synthetic data, this must be considered and taken into account. We also only look at procedures\n",
        "coded with ICD10 to simplify."
      ],
      "metadata": {
        "id": "Yp83VI0SVUU1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_str = \"\"\"\n",
        "WITH\n",
        "  outpatient_claims AS (\n",
        "    SELECT *\n",
        "    FROM `{bigquery_table_prefix}.ExplanationOfBenefit`\n",
        "    WHERE\n",
        "      (\n",
        "        SELECT m.code\n",
        "        FROM UNNEST(type.coding) m\n",
        "        WHERE\n",
        "          m.system = 'https://bluebutton.cms.gov/resources/codesystem/eob-type'\n",
        "      )\n",
        "      = 'OUTPATIENT'\n",
        "  )\n",
        "SELECT\n",
        "  ARRAY(\n",
        "    SELECT m.code\n",
        "    FROM UNNEST(procedure[OFFSET(0)].procedure.codeableConcept.coding) AS m\n",
        "  )[OFFSET(0)] AS icd10,\n",
        "  COUNT(*) AS cnt\n",
        "FROM outpatient_claims\n",
        "WHERE\n",
        "  ARRAY_LENGTH(procedure) > 0\n",
        "  AND (\n",
        "    SELECT m.system\n",
        "    FROM UNNEST(procedure[OFFSET(0)].procedure.codeableConcept.coding) AS m\n",
        "  ) = 'http://hl7.org/fhir/sid/icd-10'\n",
        "GROUP BY icd10\n",
        "ORDER BY cnt DESC\n",
        "\"\"\".format(bigquery_table_prefix=bigquery_table_prefix)\n",
        "dataframe = bqclient.query(query_str).result().to_dataframe()\n",
        "dataframe.head().plot.bar(rot=0, x=\"icd10\", xlabel=\"ICD10 Code\", ylabel=\"Count\", title=\"Most Frequent Primary Outpatient Procedures\", width=0.5, figsize=(10,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "BfbflOW-SEhu",
        "outputId": "a38ae83f-1adf-4281-ae76-3157b670f76d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61db7ffa50>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_str = \"\"\"\n",
        "WITH\n",
        "  outpatient_claims AS (\n",
        "    SELECT *\n",
        "    FROM `{bigquery_table_prefix}.ExplanationOfBenefit`\n",
        "    WHERE\n",
        "      (\n",
        "        SELECT m.code\n",
        "        FROM UNNEST(type.coding) m\n",
        "        WHERE\n",
        "          m.system = 'https://bluebutton.cms.gov/resources/codesystem/eob-type'\n",
        "      )\n",
        "      = 'OUTPATIENT'\n",
        "  )\n",
        "SELECT\n",
        "  ARRAY(\n",
        "    SELECT m.display\n",
        "    FROM UNNEST(procedure[OFFSET(0)].procedure.codeableConcept.coding) AS m\n",
        "  )[OFFSET(0)] AS procedure_display,\n",
        "  COUNT(*)\n",
        "FROM outpatient_claims\n",
        "WHERE\n",
        "  ARRAY_LENGTH(procedure) > 0\n",
        "  AND (\n",
        "    SELECT m.system\n",
        "    FROM UNNEST(procedure[OFFSET(0)].procedure.codeableConcept.coding) AS m\n",
        "  ) = 'http://hl7.org/fhir/sid/icd-10'\n",
        "GROUP BY procedure_display\n",
        "ORDER BY 2 DESC\n",
        "\"\"\".format(bigquery_table_prefix=bigquery_table_prefix)\n",
        "dataframe = bqclient.query(query_str).result().to_dataframe()\n",
        "dataframe.head().plot.bar(rot=0, x=\"procedure_display\", xlabel=\"Procedure Display\", ylabel=\"Count\", title=\"Most Frequent Primary Outpatient Procedures (group by display)\", width=0.5, figsize=(30,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "KxGxDToOXnvl",
        "outputId": "e194c9d3-8eb9-41c9-cc10-2cc2f789c82c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61db928490>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}