{ "format": "dataset-manifest/1", "slug": "chronic-illness-management-app", "version": 2, "seed": "chronic-illness-management-app:1", "spec": { "format": "dataset/1", "slug": "chronic-illness-management-app", "name": "Chronic Illness Tracking Dataset", "tagline": "Patient data on chronic illnesses including prescriptions, symptoms, device readings, and clinician interactions.", "description": "Patients receive tailored care plans and ongoing support from their healthcare providers. Clinicians closely monitor their patients' health by tracking medication prescriptions, device readings, symptoms, and lab tests. Each patient record in the 'patients' table represents a unique individual with chronic illness, detailing their demographics, treatment history, and contact information. The 'prescriptions' table holds information about the medications prescribed to each patient, linked back to the patient ID, allowing for a clear view of each individual's treatment regimen. Device readings, recorded in the 'device_readings' table, offer vital statistics such as blood pressure or glucose levels, capturing real-time data that can significantly impact treatment decisions. Symptoms reported by patients are cataloged in the 'symptoms' table, serving as an essential measure of their health status and progression of their illness.\n\nThe 'clinician_interactions' table documents each encounter between patients and their healthcare providers, ensuring continuity of care and enabling adjustments to treatment plans based on patient feedback and clinical assessments. Finally, the 'lab_tests' table contains results from various tests performed on patients, providing invaluable insights into their health conditions and helping clinicians make informed medical decisions. Each table interrelates through patient IDs, allowing for robust analysis of patient health trajectories.\n\nThis dataset is ideal for practice because it answers questions about patient compliance with medication, the relationship between symptoms and device readings, and the effectiveness of various treatment plans. It allows developers to explore trends in chronic illness management, build predictive models, and analyze clinician-patient interactions. As a synthetic dataset, it provides a safe environment to hone analytical skills without compromising real patient information.", "domain": "saas", "shape": "time-series", "useCases": [ "analytics-demo", "bi-dashboards" ], "keywords": [ "healthcare", "chronic illness", "patient management", "prescription tracking", "health monitoring", "data analysis" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "patients": { "description": "Each row represents a unique patient, linking to their demographic details and health conditions.", "rows": 100, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each patient." }, "full_name": { "kind": "fullName", "description": "Full name of the patient." }, "date_of_birth": { "kind": "date", "from": "1950-01-01", "to": "2005-12-31", "description": "Patient's date of birth." }, "gender": { "kind": "enum", "values": [ "male", "female", "other" ], "description": "Gender of the patient." }, "chronic_condition": { "kind": "enum", "values": [ "diabetes", "hypertension", "asthma", "heart disease" ], "description": "Chronic condition diagnosed for the patient." } } }, "prescriptions": { "description": "This table records the prescriptions assigned to each patient, linking back to the 'patients' table.", "rows": { "per": "patients", "via": "patient_id", "min": 1, "max": 5, "skew": "long-tail" }, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each prescription." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient's unique identifier." }, "medication_name": { "kind": "words", "min": 1, "max": 3, "description": "Name of the prescribed medication.", "pool": [ "aspirin", "metformin", "simvastatin", "lisinopril", "levothyroxine", "atorvastatin", "omeprazole", "sertraline", "gabapentin", "amoxicillin", "hydrochlorothiazide", "fluoxetine", "prednisone", "albuterol", "furosemide", "clonazepam", "duloxetine", "venlafaxine", "carvedilol", "tamsulosin", "bupropion", "losartan", "citalopram", "tramadol", "nitroglycerin", "mirtazapine", "metoprolol", "dexamethasone", "celecoxib", "tizanidine", "topiramate" ] }, "dosage": { "kind": "sentence", "min": 10, "max": 50, "description": "Dosage instructions for the medication." }, "start_date": { "kind": "date", "from": "2020-01-01", "to": "2023-12-31", "description": "Date when the prescription was started." }, "end_date": { "kind": "date", "from": "2020-01-01", "to": "2023-12-31", "nullable": true, "description": "Date when the prescription ended, if applicable." } } }, "device_readings": { "description": "Records health metrics from devices used by patients, linking to the 'patients' table.", "rows": { "per": "patients", "via": "patient_id", "min": 1, "max": 10, "skew": "long-tail" }, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each reading." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient's unique identifier." }, "reading_type": { "kind": "enum", "values": [ "blood pressure", "glucose", "heart rate" ], "description": "Type of health reading recorded." }, "value": { "kind": "decimal", "min": 0, "max": 300, "scale": 2, "description": "Recorded value of the health metric.", "distribution": "normal" }, "recorded_at": { "kind": "datetime", "from": "2020-01-01", "to": "2023-12-31", "dayProfile": "business", "description": "Timestamp when the reading was taken." } } }, "symptoms": { "description": "Captures symptoms reported by patients, linking back to the 'patients' table.", "rows": { "per": "patients", "via": "patient_id", "min": 1, "max": 5, "skew": "long-tail" }, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each symptom record." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient's unique identifier." }, "symptom_description": { "kind": "sentence", "min": 5, "max": 100, "description": "Description of the symptom experienced by the patient." }, "severity": { "kind": "int", "min": 1, "max": 10, "description": "Severity of the symptom on a scale from 1 to 10.", "distribution": "high" }, "reported_at": { "kind": "datetime", "from": "2020-01-01", "to": "2023-12-31", "dayProfile": "waking", "description": "Timestamp when the symptom was reported." } } }, "clinician_interactions": { "description": "Tracks interactions between patients and clinicians, providing insights into care continuity.", "rows": { "per": "patients", "via": "patient_id", "min": 1, "max": 3, "skew": "long-tail" }, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each interaction." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient's unique identifier." }, "interaction_type": { "kind": "enum", "values": [ "consultation", "follow-up", "emergency" ], "description": "Type of interaction with the clinician." }, "interaction_date": { "kind": "date", "from": "2020-01-01", "to": "2023-12-31", "description": "Date of the patient-clinician interaction." }, "notes": { "kind": "paragraph", "min": 10, "max": 500, "description": "Notes taken during the interaction." } } }, "lab_tests": { "description": "Contains records of lab tests performed for patients, linking to the 'patients' table.", "rows": { "per": "patients", "via": "patient_id", "min": 0, "max": 2, "skew": "long-tail" }, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each lab test record." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient's unique identifier." }, "test_name": { "kind": "words", "min": 1, "max": 3, "description": "Name of the lab test performed.", "pool": [ "blood_glucose", "lipid_panel", "complete_blood_count", "thyroid_stimulating_hormone", "hemoglobin_a1c", "basic_metabolic_panel", "liver_function_tests", "urinalysis", "creatinine", "electrolytes", "prothrombin_time", "thyroid_panel", "c-reactive_protein", "vitamin_d", "insulin", "hba1c", "blood_culture", "serum_electrolytes", "calcium", "magnesium", "phosphorus", "cholesterol", "triglycerides", "urine_culture", "sedimentation_rate", "autoimmune_panel", "coagulation_panel", "serum_albumin", "b-type_natriuretic_peptide", "cortisol", "testosterone" ] }, "test_date": { "kind": "date", "from": "2020-01-01", "to": "2023-12-31", "description": "Date when the lab test was conducted." }, "results": { "kind": "paragraph", "min": 10, "max": 500, "description": "Results of the lab test." } } } }, "structure": "multi-table", "traits": [ "timestamped", "categorical", "nullable" ], "analysisQuestions": [ "What patterns can be observed in patients' symptoms over time?", "How do medication changes impact patients' health readings?", "Which medications are most commonly prescribed for specific chronic conditions?", "How frequently do patients interact with their clinicians?", "What correlations exist between lab test results and reported symptoms?" ], "suggestedQueries": [ { "title": "Join Patients and Prescriptions", "sql": "SELECT p.full_name, pr.medication_name FROM patients p JOIN prescriptions pr ON p.id = pr.patient_id;", "dialect": "postgres" }, { "title": "Aggregate Device Readings by Patient", "sql": "SELECT patient_id, AVG(value) as avg_reading FROM device_readings GROUP BY patient_id;", "dialect": "postgres" }, { "title": "Count Symptoms by Severity", "sql": "SELECT severity, COUNT(*) as symptom_count FROM symptoms GROUP BY severity;", "dialect": "postgres" }, { "title": "Find Patients with Multiple Clinician Interactions", "sql": "SELECT patient_id, COUNT(*) as interaction_count FROM clinician_interactions GROUP BY patient_id HAVING COUNT(*) > 1;", "dialect": "postgres" }, { "title": "Lab Test Results with Patient Details", "sql": "SELECT p.full_name, lt.test_name, lt.results FROM lab_tests lt JOIN patients p ON lt.patient_id = p.id;", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Healthcare Analytics Dashboard", "body": "Healthcare providers can utilize this dataset to create analytics dashboards that visualize patient health trends over time. By identifying correlations between medication adherence and health outcomes, they can improve care strategies and patient engagement." }, { "title": "Research on Chronic Condition Management", "body": "Researchers studying chronic illness management can leverage this data to analyze how different treatment plans impact patient health. Statistical analysis can reveal insights into effective interventions and help shape future healthcare policies." }, { "title": "Medical Training and Simulation", "body": "Medical educators can use this dataset in training simulations, allowing students to practice real-world scenarios involving chronic illness management. This hands-on experience enhances learning and prepares future clinicians for patient interactions." }, { "title": "Application Development for Patient Monitoring", "body": "Developers can use this dataset to build applications that monitor chronic illness patients. By integrating the data into their apps, they can create features that alert healthcare providers about critical changes in patient health metrics." } ] }, "counts": { "patients": 100, "clinician_interactions": 151, "device_readings": 410, "lab_tests": 69, "prescriptions": 212, "symptoms": 224 }, "stats": { "patients": { "rows": 100, "columns": { "id": { "nulls": 0, "distinct": 100, "min": 1, "max": 100 }, "full_name": { "nulls": 0, "distinct": 100, "min": "Alice Silva", "max": "Zoe Patel" }, "date_of_birth": { "nulls": 0, "distinct": 100, "min": "1951-09-30", "max": "2004-12-31" }, "gender": { "nulls": 0, "distinct": 3, "min": "female", "max": "other" }, "chronic_condition": { "nulls": 0, "distinct": 4, "min": "asthma", "max": "hypertension" } } }, "prescriptions": { "rows": 212, "columns": { "id": { "nulls": 0, "distinct": 212, "min": 1, "max": 212 }, "patient_id": { "nulls": 0, "distinct": 100, "min": 1, "max": 100 }, "medication_name": { "nulls": 0, "distinct": 174 }, "dosage": { "nulls": 0, "distinct": 212 }, "start_date": { "nulls": 0, "distinct": 200, "min": "2020-01-07", "max": "2023-12-12" }, "end_date": { "nulls": 15, "distinct": 182, "min": "2020-02-06", "max": "2023-12-24" } } }, "device_readings": { "rows": 410, "columns": { "id": { "nulls": 0, "distinct": 410, "min": 1, "max": 410 }, "patient_id": { "nulls": 0, "distinct": 100, "min": 1, "max": 100 }, "reading_type": { "nulls": 0, "distinct": 3, "min": "blood pressure", "max": "heart rate" }, "value": { "nulls": 0, "distinct": 401, "min": 54.94, "max": 270.45 }, "recorded_at": { "nulls": 0, "distinct": 410, "min": "2020-01-04 10:27:00", "max": "2023-12-14 11:42:32" } } }, "symptoms": { "rows": 224, "columns": { "id": { "nulls": 0, "distinct": 224, "min": 1, "max": 224 }, "patient_id": { "nulls": 0, "distinct": 100, "min": 1, "max": 100 }, "symptom_description": { "nulls": 0, "distinct": 223 }, "severity": { "nulls": 0, "distinct": 10, "min": 1, "max": 10 }, "reported_at": { "nulls": 0, "distinct": 224, "min": "2020-01-03 17:07:25", "max": "2023-12-31 12:52:49" } } }, "clinician_interactions": { "rows": 151, "columns": { "id": { "nulls": 0, "distinct": 151, "min": 1, "max": 151 }, "patient_id": { "nulls": 0, "distinct": 100, "min": 1, "max": 100 }, "interaction_type": { "nulls": 0, "distinct": 3, "min": "consultation", "max": "follow-up" }, "interaction_date": { "nulls": 0, "distinct": 142, "min": "2020-01-15", "max": "2023-12-30" }, "notes": { "nulls": 0, "distinct": 151 } } }, "lab_tests": { "rows": 69, "columns": { "id": { "nulls": 0, "distinct": 69, "min": 1, "max": 69 }, "patient_id": { "nulls": 0, "distinct": 46, "min": 1, "max": 100 }, "test_name": { "nulls": 0, "distinct": 63 }, "test_date": { "nulls": 0, "distinct": 69, "min": "2020-01-16", "max": "2023-12-30" }, "results": { "nulls": 0, "distinct": 69 } } } }, "artifacts": [ { "file": "csv/patients.csv", "format": "csv", "table": "patients", "key": "datasets/chronic-illness-management-app/v2/csv/patients.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/chronic-illness-management-app/v2/csv/patients.csv", "bytes": 4429, "sha256": "8857ec045ec13a4f3606c3d286c7c1a17748ad191a8e088b4d19dd87b2a1284c", "rows": 100, "contentType": "text/csv; 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