{
  "format": "dataset-manifest/1",
  "slug": "doctor-appointment-scheduler",
  "version": 3,
  "seed": "doctor-appointment-scheduler:1",
  "spec": {
    "format": "dataset/1",
    "slug": "doctor-appointment-scheduler",
    "name": "Healthcare Appointment Insights",
    "tagline": "Patient demographics and appointment details from a healthcare environment, including routine check-ups and emergencies.",
    "description": "Healthcare providers manage a variety of patient appointments tailored to diverse medical needs. This dataset models the intricate dynamics of scheduling, capturing essential details such as patient demographics, appointment types, insurance information, and medical prescriptions. The data reveals patterns in healthcare utilization, showcasing a range of appointment types from routine check-ups to emergency consultations. By analyzing this dataset, users can identify trends in patient visits, understand the distribution of appointment types, and assess the effectiveness of clinics in accommodating patient needs. It is particularly beneficial for those looking to practice SQL skills or seed ORM implementations, offering a rich set of columns that reflect real-world healthcare scenarios. The dataset is synthetic, ensuring a diverse and realistic set of entries for analysis.",
    "domain": "healthcare",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "appointments",
      "patients",
      "healthcare",
      "insurance",
      "demographics",
      "medical prescriptions",
      "consultations",
      "check-ups"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "appointments": {
        "description": "Each row represents a unique patient appointment, linking to various patient demographics and appointment details. It captures critical information needed for healthcare scheduling and analysis.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each appointment."
          },
          "patient_name": {
            "kind": "fullName",
            "description": "Full name of the patient."
          },
          "patient_age": {
            "kind": "int",
            "min": 0,
            "max": 120,
            "description": "Age of the patient.",
            "distribution": "normal"
          },
          "appointment_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date and time of the appointment.",
            "dayProfile": "waking"
          },
          "appointment_type": {
            "kind": "enum",
            "values": [
              "Routine Check-up",
              "Emergency Consultation",
              "Vaccination",
              "Follow-up",
              "Specialist Referral"
            ],
            "weights": [
              0.5,
              0.1,
              0.2,
              0.1,
              0.1
            ],
            "description": "Type of the appointment scheduled."
          },
          "insurance_provider": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Name of the patient's insurance provider.",
            "pool": [
              "Aetna",
              "Cigna",
              "BlueCross",
              "UnitedHealth",
              "Humana",
              "Medicare",
              "Medicaid",
              "Anthem",
              "Kaiser",
              "WellCare",
              "Tricare",
              "HealthNet",
              "Guardian",
              "MetLife",
              "Fidelis",
              "Ambetter",
              "Oscar",
              "Horizon",
              "Nippon",
              "Bupa",
              "Allianz"
            ]
          },
          "medical_prescription": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Details of any medical prescription given during the appointment.",
            "pool": [
              "Ibuprofen",
              "Amoxicillin",
              "Metformin",
              "Lisinopril",
              "Amlodipine",
              "Simvastatin",
              "Levothyroxine",
              "Omeprazole",
              "Sertraline",
              "Atorvastatin",
              "Albuterol",
              "Clonazepam",
              "Gabapentin",
              "Hydrochlorothiazide",
              "Citalopram",
              "Fluoxetine",
              "Trazodone",
              "Warfarin",
              "Furosemide",
              "Prednisone",
              "Insulin"
            ]
          },
          "allergies": {
            "kind": "words",
            "nullable": true,
            "description": "Any known allergies of the patient.",
            "pool": [
              "Peanuts",
              "Shellfish",
              "Penicillin",
              "Lactose",
              "Gluten",
              "Soy",
              "Eggs",
              "TreeNut",
              "Latex",
              "Aspirin",
              "Fish",
              "Milk",
              "Wheat",
              "Pollen",
              "Dust",
              "Mold",
              "BeeStings",
              "Coconut",
              "Sesame",
              "Strawberry",
              "Citrus"
            ],
            "min": 1,
            "max": 3
          },
          "clinic_location": {
            "kind": "city",
            "description": "Location of the clinic where the appointment is held."
          },
          "status": {
            "kind": "enum",
            "values": [
              "Scheduled",
              "Completed",
              "Cancelled"
            ],
            "weights": [
              0.7,
              0.2,
              0.1
            ],
            "description": "Current status of the appointment."
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "nullable",
      "categorical",
      "timestamped",
      "free-text"
    ],
    "analysisQuestions": [
      "What are the most common types of appointments scheduled?",
      "How does patient age correlate with the frequency of emergency consultations?",
      "What is the average time between scheduling and completion of appointments?",
      "How do different insurance providers impact appointment types?",
      "What clinic locations have the highest patient visit rates?"
    ],
    "suggestedQueries": [
      {
        "title": "Retrieve all scheduled appointments",
        "sql": "SELECT * FROM appointments WHERE status = 'Scheduled';",
        "dialect": "postgres"
      },
      {
        "title": "Count appointments by type",
        "sql": "SELECT appointment_type, COUNT(*) FROM appointments GROUP BY appointment_type;",
        "dialect": "postgres"
      },
      {
        "title": "Find average age of patients by appointment type",
        "sql": "SELECT appointment_type, AVG(patient_age) FROM appointments GROUP BY appointment_type;",
        "dialect": "postgres"
      },
      {
        "title": "List completed appointments within the last month",
        "sql": "SELECT * FROM appointments WHERE status = 'Completed' AND appointment_date >= NOW() - INTERVAL '30 days';",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Healthcare Data Analyst",
        "body": "A healthcare data analyst uses this dataset to uncover trends in patient appointment types, aiming to improve scheduling efficiency and patient satisfaction. By analyzing the data, they can present insights to clinic management on how to optimize staffing during peak appointment times."
      },
      {
        "title": "Medical Researcher",
        "body": "A medical researcher leverages the dataset to study the correlation between patient demographics and the types of healthcare services utilized. The findings can contribute to understanding healthcare disparities and inform policy changes within the healthcare system."
      },
      {
        "title": "Software Developer",
        "body": "A software developer working on a healthcare application can seed their database with this synthetic dataset to test appointment scheduling features. By simulating real-world scenarios, the developer ensures the application handles various edge cases and user inputs effectively."
      }
    ]
  },
  "counts": {
    "appointments": 500
  },
  "stats": {
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      "columns": {
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          "nulls": 0,
          "distinct": 500,
          "min": 1,
          "max": 500
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        "patient_name": {
          "nulls": 0,
          "distinct": 479,
          "min": "Alice Carter",
          "max": "Zoe Torres"
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        "patient_age": {
          "nulls": 0,
          "distinct": 80,
          "min": 7,
          "max": 97
        },
        "appointment_date": {
          "nulls": 0,
          "distinct": 500,
          "min": "2023-01-01 17:08:51",
          "max": "2023-12-30 23:35:23"
        },
        "appointment_type": {
          "nulls": 0,
          "distinct": 5,
          "min": "Emergency Consultation",
          "max": "Vaccination"
        },
        "insurance_provider": {
          "nulls": 0,
          "distinct": 311
        },
        "medical_prescription": {
          "nulls": 0,
          "distinct": 331
        },
        "allergies": {
          "nulls": 54,
          "distinct": 299
        },
        "clinic_location": {
          "nulls": 0,
          "distinct": 63,
          "min": "Amsterdam",
          "max": "Zurich"
        },
        "status": {
          "nulls": 0,
          "distinct": 3,
          "min": "Cancelled",
          "max": "Scheduled"
        }
      }
    }
  },
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}