{ "format": "dataset-manifest/1", "slug": "patient-education-and-information-platform", "version": 1, "seed": "patient-education-and-information-platform:1", "spec": { "format": "dataset/1", "slug": "patient-education-and-information-platform", "name": "Patient Engagement Insights", "tagline": "Patient engagement metrics including support tickets, webinar participation, and educational resources.", "description": "Support tickets streamline the communication between patients and healthcare providers, addressing queries and issues related to their conditions. Educational webinars empower patients by providing them with valuable knowledge directly related to their health needs. Health articles serve as a repository of information, while quiz results gauge patient understanding and engagement, allowing for tailored recommendations. Personalized doctor recommendations are derived from these interactions, ensuring that patients receive the most relevant support based on their individual circumstances.\n\nIn the patient engagement table, each row represents a unique patient interaction, capturing details such as the type of support ticket submitted, webinar participation, and quiz results. This allows for a comprehensive view of how each patient engages with the platform's resources. The connections between support tickets, engagement in webinars, and quiz outcomes illustrate how different elements influence patient knowledge and involvement in their healthcare journey. By analyzing these relationships, one can uncover trends in patient behavior, identify knowledge gaps, and evaluate the effectiveness of educational initiatives.\n\nThe data answers vital questions regarding patient engagement, such as which resources are most utilized by patients, how participation in educational efforts correlates with health outcomes, and what common themes emerge in support requests. Developers working with this synthetic dataset can practice various data analysis techniques, from basic aggregations and joins to more advanced machine learning models aimed at predicting patient engagement levels. This dataset is an excellent resource for honing skills in relational database management, data visualization, and deriving insights from patient interactions.", "domain": "customer-support", "shape": "slowly-changing", "useCases": [ "etl-pipelines", "teaching" ], "keywords": [ "patient engagement", "healthcare support", "education resources", "webinars", "support tickets", "health articles" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "patient_engagement": { "description": "Each row represents a unique patient engagement instance, detailing interactions with various educational and support resources. The table captures the patient ID, engagement type, resource type, and engagement metrics, linking directly to the corresponding healthcare resources.", "rows": 500, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each engagement instance." }, "patient_id": { "kind": "uuid", "description": "Unique identifier for the patient associated with the engagement." }, "engagement_type": { "kind": "enum", "values": [ "support_ticket", "webinar", "health_article", "quiz", "doctor_recommendation" ], "description": "Type of engagement the patient had with the platform." }, "resource_type": { "kind": "enum", "values": [ "article", "video", "live_session", "quiz" ], "description": "The format of the educational resource accessed by the patient." }, "engagement_date": { "kind": "datetime", "from": "2022-01-01", "to": "2023-12-31", "description": "Date and time the engagement occurred.", "dayProfile": "waking" }, "engagement_score": { "kind": "int", "min": 0, "max": 100, "description": "Score representing the level of engagement, where 0 is no engagement and 100 is high engagement.", "nullable": true }, "condition_category": { "kind": "enum", "values": [ "diabetes", "hypertension", "mental_health", "cardiovascular", "general" ], "description": "Medical condition category relevant to the patient's engagement.", "nullable": true, "when": { "column": "resource_type", "in": [ "article", "video" ] } } } } }, "structure": "single-table", "traits": [ "categorical", "nullable" ], "analysisQuestions": [ "What are the most common engagement types among patients?", "Which conditions have the highest number of support tickets submitted?", "How does engagement score correlate with the types of resources accessed?", "Are there specific conditions that lead to higher webinar attendance?" ], "suggestedQueries": [ { "title": "Count of Engagement Types", "sql": "SELECT engagement_type, COUNT(*) FROM patient_engagement GROUP BY engagement_type;", "dialect": "postgres" }, { "title": "Average Engagement Score by Condition", "sql": "SELECT condition_category, AVG(engagement_score) FROM patient_engagement GROUP BY condition_category;", "dialect": "postgres" }, { "title": "Webinar Participation by Patient", "sql": "SELECT patient_id, COUNT(*) FROM patient_engagement WHERE engagement_type = 'webinar' GROUP BY patient_id;", "dialect": "postgres" }, { "title": "Engagement Trends Over Time", "sql": "SELECT DATE(engagement_date) AS engagement_day, COUNT(*) FROM patient_engagement GROUP BY engagement_day ORDER BY engagement_day;", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Analytics for Healthcare Providers", "body": "Healthcare providers can utilize this dataset to analyze patient engagement trends over time, identifying which educational resources yield the most interaction. By understanding these patterns, they can optimize their support offerings and tailor resources to meet the specific needs of patients, ultimately improving health literacy and outcomes." }, { "title": "Teaching SQL and Data Analysis", "body": "Educators can employ this dataset in classroom settings to teach students SQL and data analysis skills. With various engagement types and metrics to explore, students can practice writing queries, performing aggregations, and deriving insights from the data, making learning more practical and relevant." }, { "title": "Patient Engagement Strategy Development", "body": "Health administrators can use insights from the dataset to design better patient engagement strategies. By assessing which resources are most accessed and which conditions are associated with higher engagement, they can create targeted interventions that enhance the patient experience on the platform." } ] }, "counts": { "patient_engagement": 500 }, "stats": { "patient_engagement": { "rows": 500, "columns": { "id": { "nulls": 0, "distinct": 500, "min": 1, "max": 500 }, "patient_id": { "nulls": 0, "distinct": 500 }, "engagement_type": { "nulls": 0, "distinct": 5, "min": "doctor_recommendation", "max": "webinar" }, "resource_type": { "nulls": 0, "distinct": 4, "min": "article", "max": "video" }, "engagement_date": { "nulls": 0, "distinct": 500, "min": "2022-01-03 14:57:34", "max": "2023-12-31 17:30:53" }, "engagement_score": { "nulls": 65, "distinct": 100, "min": 0, "max": 100 }, "condition_category": { "nulls": 243, "distinct": 5, "min": "cardiovascular", "max": "mental_health" } } } }, "artifacts": [ { "file": "csv/patient_engagement.csv", "format": "csv", "table": "patient_engagement", "key": "datasets/patient-education-and-information-platform/v1/csv/patient_engagement.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/csv/patient_engagement.csv", "bytes": 46191, "sha256": "3647df7f9aac54154c07947d0a8d525a132c9ee7721b15137a5200e6b240a5b7", "rows": 500, "contentType": "text/csv; charset=utf-8" }, { "file": "patient-education-and-information-platform.json", "format": "json", "table": null, "key": "datasets/patient-education-and-information-platform/v1/patient-education-and-information-platform.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/patient-education-and-information-platform.json", "bytes": 140373, "sha256": "fd9e15e5070d3706213eb222b7f3e6dfd223164447120f5912c05b1383057521", "rows": null, "contentType": "application/json; charset=utf-8" }, { "file": "sql/postgres.sql", "format": "sql-postgres", "table": null, "key": "datasets/patient-education-and-information-platform/v1/sql/postgres.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/sql/postgres.sql", "bytes": 59096, "sha256": "90c3d0e7d22a8fb39963ac51e2630a006f2b3e87074e6618b205b1edb5783e26", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mysql.sql", "format": "sql-mysql", "table": null, "key": "datasets/patient-education-and-information-platform/v1/sql/mysql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/sql/mysql.sql", "bytes": 58976, "sha256": "429f280335b6c965858789a9fcb90a45f674f4bc0db2420c42db44658d56b7f6", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mssql.sql", "format": "sql-mssql", "table": null, "key": "datasets/patient-education-and-information-platform/v1/sql/mssql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/sql/mssql.sql", "bytes": 61326, "sha256": "940ed89cca8f9e6f7d4e49a6e77b55b04faddd6c5af1471f31030488ef780248", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/sqlite.sql", "format": "sql-sqlite", "table": null, "key": "datasets/patient-education-and-information-platform/v1/sql/sqlite.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/sql/sqlite.sql", "bytes": 58934, "sha256": "fd9049c2ae8ab79d0377a01131a584381ab752eccac7944bf8918e844843d6a1", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "prisma/seed.ts", "format": "prisma-seed", "table": null, "key": "datasets/patient-education-and-information-platform/v1/prisma/seed.ts", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/prisma/seed.ts", "bytes": 120621, "sha256": "f30a8047c805b58329a1db075a587c416cfa212e73c27905ffa6afa42dab3094", "rows": null, "contentType": "application/typescript; charset=utf-8" }, { "file": "django/fixtures.json", "format": "django-fixture", "table": null, "key": "datasets/patient-education-and-information-platform/v1/django/fixtures.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/patient-education-and-information-platform/v1/django/fixtures.json", "bytes": 167843, "sha256": "86a6825d1775d9dc971dd54a79afbaf04776fb5165535753294358df4bf1d760", "rows": null, "contentType": "application/json; charset=utf-8" } ], "publishedAt": 1788766725702, "updatedAt": 1788766725702 }