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46 - TODO: Generate Chapter Content 46 + ## Introduction: The Patient at the Center 47 +
48 + Healthcare data modeling traditionally organized + information around administrative and billing structures, + reflecting the operational priorities of healthcare + institutions rather than the clinical realities of patient + care. This approach, while convenient for financial systems, + created fragmented views of patient health that hindered + care coordination and clinical decision-making. By placing + the patient record at the center of our graph model, we + align data structures with clinical workflows and enable + comprehensive analysis of patient health trajectories, + treatment efficacy, and care quality. 49 +
50 + Graph databases excel at patient-centric modeling because + healthcare data is inherently relationship-rich. A single + patient's health record connects to providers, facilities, + diagnoses, medications, lab results, and countless other + entities through complex temporal and causal relationships. + Traditional relational databases require expensive JOIN + operations to reconstruct these connections, while graph + models represent them natively, enabling real-time queries + across a patient's complete medical history. 51 +
52 + In this chapter, you will learn to model comprehensive + patient data using labeled property graphs, focusing on + clinical entities, their relationships, and the temporal + dimensions that make healthcare data uniquely challenging. + We will build a patient-centric schema that supports chronic + disease management, care coordination, medication safety, + and outcome measurement while maintaining the flexibility to + accommodate evolving clinical practices. 53 +
54 + ## Foundational Patient Data Elements 55 +
56 + ### Patient Record 57 +
58 + The Patient Record serves as the central node in a + patient-centric graph model, representing the complete + collection of health information for an individual person + across all encounters, conditions, and treatments. Unlike + traditional database implementations where patient data is + scattered across dozens of normalized tables, the graph + model maintains a single authoritative Patient node that + serves as the hub for all related clinical, demographic, and + administrative information. 59 +
60 + Key properties of a Patient Record node include: 61 +
62 + - patient_id: Unique identifier (often a UUID or + enterprise master patient index number) 63 + - demographics: Birth date, gender, ethnicity, + preferred language 64 + - contact_info: Current address, phone numbers, email 65 + - insurance_info: Links to payer coverage records 66 + - created_date: Initial record creation timestamp 67 + - last_updated: Most recent modification timestamp 68 + - status: Active, deceased, merged, or inactive 69 +
70 + ### Patient ID and Master Patient Index 71 +
72 + The Patient ID presents one of the most challenging + aspects of healthcare data modeling due to the proliferation + of identifier systems across healthcare organizations. A + single patient may have different identifiers at each + hospital, clinic, insurance company, and pharmacy they + interact with, creating significant obstacles for care + coordination and data integration. 73 +
74 + Graph databases provide elegant solutions to the patient + identification problem through their native support for + multiple relationships and flexible schema. Rather than + forcing a single canonical identifier, we can model multiple + identifier types as nodes connected to the patient record, + each with properties indicating the issuing system, + identifier type, and validity period. 75 +
76 +

77 +

Patient Identifier Graph Model

78 + Type: graph-model 79 +
80 + Purpose: Illustrate how multiple patient identifiers + are connected to a single patient record node 81 +
82 + Node types: 83 + 1. Patient (large blue circle) 84 + - Properties: master_patient_id, name, birth_date 85 + - Example: "Jane Smith, DOB: 1985-03-15" 86 +
87 + 2. Identifier (small orange rectangles) 88 + - Properties: identifier_value, type, + issuing_system, active_status 89 + - Examples: 90 + - "MRN-12345 (Hospital A Medical Record Number)" 91 + - "SSN-123-45-6789 (Social Security Number)" 92 + - "INSUR-987654 (Insurance Member ID)" 93 + - "MRN-98765 (Hospital B Medical Record Number)" 94 +
95 + 3. Identity System (gray hexagons) 96 + - Properties: system_name, authority, jurisdiction 97 + - Examples: "Hospital A EHR", "National SSN + Registry", "Insurance Provider X" 98 +
99 + Edge types: 100 + 1. HAS_IDENTIFIER (solid blue arrows from Patient to + Identifier) 101 + - Properties: assigned_date, confidence_score 102 +
103 + 2. ISSUED_BY (dashed gray arrows from Identifier to + Identity System) 104 + - Properties: issue_date, expiration_date 105 +
106 + Sample data: 107 + - Patient "Jane Smith" 108 + ├─ HAS_IDENTIFIER → MRN-12345 109 + │ └─ ISSUED_BY → Hospital A EHR 110 + ├─ HAS_IDENTIFIER → MRN-98765 111 + │ └─ ISSUED_BY → Hospital B EHR 112 + ├─ HAS_IDENTIFIER → SSN-123-45-6789 113 + │ └─ ISSUED_BY → National SSN Registry 114 + └─ HAS_IDENTIFIER → INSUR-987654 115 + └─ ISSUED_BY → Insurance Provider X 116 +
117 + Layout: Radial with Patient node at center and + identifiers arranged in a circle around it 118 +
119 + Interactive features: 120 + - Hover over Identifier node: Show full identifier + details and issuing system 121 + - Click Patient node: Highlight all associated + identifiers 122 + - Double-click Identifier: Show historical record of + use 123 + - Filter: Toggle identifier types on/off (MRN, SSN, + Insurance, etc.) 124 +
125 + Visual styling: 126 + - Patient node: Large (60px), blue, labeled with name 127 + - Identifier nodes: Medium (40px), orange, labeled with + ID value 128 + - System nodes: Small (30px), gray, labeled with system + name 129 + - Active identifiers: Solid border 130 + - Inactive identifiers: Dashed border 131 +
132 + Legend: 133 + - Node shapes: Circle = Patient, Rectangle = + Identifier, Hexagon = System 134 + - Edge styles: Solid = direct association, Dashed = + system relationship 135 + - Border styles: Solid = active, Dashed = inactive 136 +
137 + Implementation: vis-network JavaScript library 138 + Canvas size: 800x600px 139 +

140 +
141 + ### Patient History 142 +
143 + Patient History encompasses the longitudinal record of + all clinical events, encounters, and health status changes + for an individual patient over time. In a graph model, + patient history is not stored as a single monolithic + document but rather emerges from the temporal network of + relationships connecting the patient to clinical events, + ordered by timestamp properties. 144 +
145 + The graph representation of patient history provides + several advantages over traditional approaches: 146 +
147 + - Temporal queries: Efficiently retrieve events within + specific time windows 148 + - Causal inference: Trace relationships between + treatments and outcomes 149 + - Pattern detection: Identify recurring conditions or + treatment cycles 150 + - Comparative analysis: Compare patient trajectories + across populations 151 +
152 + Modeling temporal data in graphs typically involves + timestamp properties on relationships rather than separate + time-series tables. For example, a DIAGNOSED_WITH + relationship between a Patient and a Disease node would + include diagnosis_date and diagnosis_status properties, + enabling efficient chronological queries without complex + date-based JOINs. 153 +
154 + | Historical Query Type | Graph Approach | RDBMS Approach | 155 + |-----------------------|----------------|----------------| 156 + | Recent encounters | MATCH path with date filter on edges + | JOIN with WHERE clause on date column | 157 + | Event sequences | Traverse relationships ordered by + timestamp | Multiple self-JOINs with date ordering | 158 + | Concurrent conditions | Pattern match overlapping date + ranges | Complex date arithmetic in WHERE clauses | 159 + | Longitudinal trends | Aggregate properties along temporal + paths | Window functions over partitioned data | 160 +
161 + ## Clinical Entities: Diseases, Conditions, and Symptoms 162 +
163 + ### Disease and Medical Condition 164 +
165 + The terms Disease and Medical Condition are often + used interchangeably in casual conversation, but graph + models benefit from distinguishing between them. A disease + represents a specific pathological process with defined + etiology and progression (e.g., Type 2 Diabetes Mellitus, + Coronary Artery Disease), while a medical condition + encompasses a broader range of health states including + diseases, injuries, disorders, and syndromes. 166 +
167 + In a patient-centric graph model, diseases and conditions + are typically represented as separate node types connected + to standardized medical vocabularies: 168 +
169 + - Disease nodes: Link to ICD-10 codes, SNOMED CT + concepts, and disease ontologies 170 + - Condition nodes: Represent patient-specific + manifestations with properties for severity, onset date, and + resolution status 171 +
172 + This dual-level modeling enables both population-level + analysis using standardized disease categories and + patient-specific clinical documentation that captures + individual variation and comorbidity patterns. 173 +
174 + Common properties for Disease nodes: 175 +
176 + - disease_code: ICD-10 or SNOMED CT identifier 177 + - disease_name: Canonical name from medical vocabulary 178 + - category: Disease classification (infectious, + chronic, genetic, etc.) 179 + - typical_progression: General disease trajectory 180 + - risk_factors: Common predisposing factors 181 +
182 + Common properties for Medical Condition nodes + (patient-specific): 183 +
184 + - condition_id: Unique instance identifier 185 + - onset_date: When condition first manifested 186 + - severity: Mild, moderate, severe, critical 187 + - status: Active, resolved, in remission, chronic 188 + - notes: Clinical narrative describing patient-specific + details 189 +
190 + ### Symptoms 191 +
192 + Symptoms represent the subjective experiences reported + by patients, forming the foundation of clinical reasoning + and diagnostic processes. In graph models, symptoms connect + patients to potential diagnoses through probabilistic + relationships, enabling clinical decision support systems + that reason over symptom patterns. 193 +
194 + The relationship between patients, symptoms, and diseases + creates a complex many-to-many network where: 195 +
196 + - A single disease may present with multiple symptoms (Type + 2 Diabetes → polydipsia, polyuria, fatigue) 197 + - A single symptom may indicate multiple possible diseases + (chest pain → cardiac disease, GERD, anxiety, + musculoskeletal injury) 198 + - Symptom combinations provide stronger diagnostic signals + than individual symptoms 199 +
200 +

201 +

Symptom-Disease Diagnostic Network

202 + Type: graph-model 203 +
204 + Purpose: Illustrate the many-to-many relationships + between symptoms and diseases, showing how symptom patterns + inform differential diagnosis 205 +
206 + Node types: 207 + 1. Patient (blue circle, large) 208 + - Properties: patient_id, name 209 + - Example: "Patient A" 210 +
211 + 2. Symptom Instance (yellow squares, medium) 212 + - Properties: symptom_type, severity, onset_date, + duration 213 + - Examples: 214 + - "Chest pain (severe, 2hr duration)" 215 + - "Shortness of breath (moderate, 1 day)" 216 + - "Fatigue (mild, 2 weeks)" 217 +
218 + 3. Symptom Type (yellow circles, small) 219 + - Properties: symptom_name, body_system 220 + - Examples: "Chest Pain", "Dyspnea", "Fatigue", + "Nausea" 221 +
222 + 4. Disease (red octagons, medium) 223 + - Properties: disease_name, ICD_code, prevalence 224 + - Examples: 225 + - "Myocardial Infarction (I21)" 226 + - "Pulmonary Embolism (I26)" 227 + - "GERD (K21.9)" 228 + - "Anxiety Disorder (F41.9)" 229 +
230 + Edge types: 231 + 1. REPORTS_SYMPTOM (solid blue arrows: Patient → + Symptom Instance) 232 + - Properties: reported_date, severity_score 233 +
234 + 2. INSTANCE_OF (dashed yellow arrows: Symptom Instance + → Symptom Type) 235 + - Properties: none 236 +
237 + 3. ASSOCIATED_WITH (solid red arrows: Symptom Type → + Disease) 238 + - Properties: probability, specificity, sensitivity 239 + - Example: Chest Pain → MI (probability: 0.35, + specificity: 0.60) 240 +
241 + Sample data: 242 + - Patient A 243 + ├─ REPORTS_SYMPTOM → Chest pain instance 244 + │ └─ INSTANCE_OF → Chest Pain (type) 245 + │ ├─ ASSOCIATED_WITH → Myocardial Infarction + (prob: 0.35) 246 + │ ├─ ASSOCIATED_WITH → Pulmonary Embolism (prob: + 0.15) 247 + │ ├─ ASSOCIATED_WITH → GERD (prob: 0.25) 248 + │ └─ ASSOCIATED_WITH → Anxiety Disorder (prob: + 0.10) 249 + └─ REPORTS_SYMPTOM → Shortness of breath instance 250 + └─ INSTANCE_OF → Dyspnea (type) 251 + ├─ ASSOCIATED_WITH → Myocardial Infarction + (prob: 0.40) 252 + ├─ ASSOCIATED_WITH → Pulmonary Embolism (prob: + 0.55) 253 + └─ ASSOCIATED_WITH → Anxiety Disorder (prob: + 0.15) 254 +
255 + Layout: Hierarchical with patient at top, symptom + instances in second tier, symptom types in third tier, and + diseases at bottom 256 +
257 + Interactive features: 258 + - Hover over ASSOCIATED_WITH edge: Show probability, + sensitivity, specificity values 259 + - Click Symptom Type: Highlight all associated diseases + with probability labels 260 + - Click Disease: Show all symptoms that may indicate + this disease 261 + - Multi-select: Select multiple symptom instances to + see diseases associated with that combination (Bayesian + inference) 262 + - Toggle: Show/hide probability threshold filter 263 +
264 + Visual styling: 265 + - Edge thickness proportional to probability value 266 + - Disease nodes colored by likelihood given selected + symptoms (green = high, yellow = medium, red = low) 267 + - Animated pulse on high-probability diseases when + multiple symptoms selected 268 +
269 + Legend: 270 + - Node shapes: Circle = Patient/Type, Square = + Instance, Octagon = Disease 271 + - Edge styles: Solid = direct association, Dashed = + type relationship 272 + - Color coding: Blue = patient/symptoms, Red = + diseases, edge thickness = probability 273 +
274 + Implementation: vis-network JavaScript library 275 + Canvas size: 1000x800px 276 +

277 +
278 + ### Diagnosis 279 +
280 + A Diagnosis represents a clinical determination that a + patient has a specific disease or condition, made by a + healthcare provider based on symptoms, examination findings, + and diagnostic test results. In graph models, diagnoses + serve as critical relationship nodes that link patients to + diseases with associated context including diagnosing + provider, date, confidence level, and supporting evidence. 281 +
282 + The diagnostic process involves complex reasoning over + multiple information sources: 283 +
284 + - Patient-reported symptoms and medical history 285 + - Physical examination findings 286 + - Laboratory test results 287 + - Imaging studies 288 + - Differential diagnosis consideration 289 + - Clinical guidelines and decision support 290 +
291 + Graph models capture this diagnostic complexity through a + network of relationships connecting the diagnosis to its + supporting evidence. A DIAGNOSIS relationship might connect + a Patient to a Disease node, with properties including: 292 +
293 + - diagnosis_date: Timestamp of clinical determination 294 + - diagnosing_provider: Reference to the provider who + made the diagnosis 295 + - confidence_level: Confirmed, suspected, provisional, + ruled out 296 + - primary_or_secondary: Whether this is the primary + diagnosis for an encounter 297 + - supporting_evidence: Links to lab results, imaging + reports, symptoms 298 + - differential_diagnoses: Other conditions considered + and ruled out 299 +
300 + ## Treatment and Medication Management 301 +
302 + ### Treatment Plan 303 +
304 + A Treatment Plan represents the comprehensive strategy + for managing a patient's condition, encompassing + medications, procedures, lifestyle modifications, monitoring + protocols, and care coordination activities. In graph + models, treatment plans are complex subgraphs that connect + patients, conditions, interventions, providers, and outcome + goals through temporal relationships. 305 +
306 + Effective treatment plan modeling must capture: 307 +
308 + - Goals: Measurable clinical objectives (e.g., reduce + HbA1c below 7.0%, maintain blood pressure under 130/80) 309 + - Interventions: Specific actions prescribed + (medications, procedures, therapies) 310 + - Timeline: Expected duration and milestone dates 311 + - Monitoring: What metrics to track and how frequently 312 + - Care team: All providers involved and their roles 313 + - Patient instructions: Education materials and + self-care activities 314 +
315 + Graph representations of treatment plans enable + sophisticated care coordination queries, such as identifying + all patients with treatment plans requiring adjustment + based on new clinical guidelines, or finding patients whose + treatment adherence has declined. 316 +
317 + ### Prescription, Medication, and Dosage 318 +
319 + Prescriptions represent the clinical orders for + medications, while Medications are the pharmaceutical + substances themselves. Dosage specifies the quantity, + frequency, and route of administration. This three-level + distinction is essential for modeling medication safety, + therapeutic equivalence, and formulary management. 320 +
321 + In a patient-centric graph model, these concepts form a + hierarchical structure: 322 +
323 + - Medication node: Represents the drug substance + (generic or brand) with pharmacological properties 324 + - Prescription node: Represents a specific order for a + patient, linking Patient, Medication, Provider, and Pharmacy 325 + - Dosage: Typically modeled as properties on the + prescription relationship rather than separate nodes 326 +
327 + Common Medication properties: 328 +
329 + - medication_name: Generic or brand name 330 + - drug_code: NDC (National Drug Code), RxNorm code 331 + - drug_class: Therapeutic category (e.g., ACE + inhibitor, statin, SSRI) 332 + - route: Oral, intravenous, topical, inhalation 333 + - form: Tablet, capsule, liquid, injection 334 +
335 + Common Prescription properties: 336 +
337 + - prescription_id: Unique order identifier 338 + - prescribed_date: When order was written 339 + - quantity: Amount dispensed 340 + - dosage: Dose strength (e.g., "10mg") 341 + - frequency: Timing instructions (e.g., "twice daily", + "every 8 hours") 342 + - duration: How long to continue (e.g., "30 days", + "until resolved") 343 + - refills_remaining: Number of authorized refills 344 + - prescribing_provider: Reference to ordering physician 345 + - pharmacy: Where prescription is filled 346 +
347 + | Medication Data Element | Patient-Specific? | Changes + Over Time? | Example Values | 348 + |-------------------------|-------------------|------------ + --------|----------------| 349 + | Generic drug name | No | No | "Metformin" | 350 + | Brand name | No | No | "Glucophage" | 351 + | Drug class | No | No | "Biguanide antidiabetic" | 352 + | Prescribed dosage | Yes | No (per prescription) | "500mg + twice daily" | 353 + | Quantity dispensed | Yes | No (per fill) | "60 tablets" | 354 + | Refills remaining | Yes | Yes | 3 → 2 → 1 → 0 | 355 + | Adherence rate | Yes | Yes | 95% → 87% → 92% | 356 +
357 + ### Drug Interactions and Adverse Events 358 +
359 + Drug Interactions occur when two or more medications + affect each other's pharmacokinetics or pharmacodynamics, + potentially reducing efficacy or increasing toxicity. + Adverse Events are harmful or undesired outcomes that + occur during or after medication use, which may or may not + be causally related to the drug. 360 +
361 + Graph databases excel at medication safety analysis because + they can efficiently query complex networks of drug-drug + interactions, drug-disease contraindications, and + drug-allergy conflicts in real-time as prescriptions are + written. A single query can traverse from a patient's active + prescriptions through interaction relationships to identify + potential safety issues. 362 +
363 + Types of drug interactions modeled in graph databases: 364 +
365 + - Pharmacokinetic interactions: One drug affects + absorption, distribution, metabolism, or excretion of + another 366 + - Pharmacodynamic interactions: Drugs with similar or + opposing effects create additive or antagonistic outcomes 367 + - Synergistic interactions: Combined effect exceeds sum + of individual effects 368 + - Contraindications: Drug should not be used with + specific conditions or other drugs 369 +
370 +

371 +

Medication Safety Network Interactive + Infographic

372 + Type: infographic 373 +
374 + Purpose: Create an interactive visualization showing + how graph databases identify medication safety issues by + traversing relationships between prescribed medications, + known interactions, patient allergies, and existing + conditions 375 +
376 + Layout: Central patient node with four quadrants + radiating outward 377 +
378 + Quadrants: 379 + 1. Top-left: Active Medications (green circles) 380 + 2. Top-right: Known Drug Interactions (red warning + triangles) 381 + 3. Bottom-left: Patient Allergies (orange circles) 382 + 4. Bottom-right: Existing Conditions (blue circles) 383 +
384 + Central element: 385 + - Patient icon with name "John Doe" 386 + - Real-time safety status indicator (green checkmark or + red warning) 387 +
388 + Active Medications (top-left quadrant): 389 + - Warfarin 5mg daily (blood thinner) 390 + - Aspirin 81mg daily (antiplatelet) 391 + - Ibuprofen 400mg PRN (NSAID) 392 + - Metformin 1000mg twice daily (diabetes) 393 +
394 + Drug Interactions (top-right quadrant): 395 + - HIGH RISK: Warfarin + Aspirin + Ibuprofen (triple + interaction, bleeding risk) 396 + - MODERATE: Metformin + contrast dye (if imaging + scheduled) 397 +
398 + Allergies (bottom-left quadrant): 399 + - Penicillin (severe, anaphylaxis) 400 + - Sulfa drugs (moderate, rash) 401 +
402 + Existing Conditions (bottom-right quadrant): 403 + - Atrial fibrillation (indication for Warfarin) 404 + - Type 2 Diabetes (indication for Metformin) 405 + - Chronic kidney disease stage 3a (affects drug + clearance) 406 +
407 + Interactive elements: 408 + 1. Hover over medication: Shows full details, + indications, warnings 409 + 2. Click medication: Highlights all interactions, + allergies, and condition relationships 410 + 3. Click interaction warning: Shows detailed + explanation of mechanism and risk level 411 + 4. Click "Add New Prescription" button: Opens + simulation where user can test adding a new medication to + see if warnings appear 412 + 5. Toggle "Interaction Severity" filter: Show only + high-risk, or include moderate and low-risk 413 +
414 + Connection lines: 415 + - Solid red lines: Direct drug-drug interactions 416 + - Dashed orange lines: Drug-allergy conflicts 417 + - Dotted blue lines: Drug-condition contraindications 418 + - Green lines: Appropriate drug-condition treatment + (indication) 419 +
420 + Animation: 421 + - When page loads, medications appear one by one 422 + - As each medication appears, interaction lines draw in + real-time 423 + - If high-risk interaction detected, central safety + indicator turns red with pulse animation 424 +
425 + Educational callout boxes (reveal on click): 426 + - "Why does this interaction matter?" - Clinical + significance explanation 427 + - "What should be done?" - Mitigation strategies (dose + adjustment, monitoring, alternative drug) 428 + - "How common is this?" - Prevalence statistics 429 +
430 + Implementation: HTML/CSS/JavaScript with SVG for + connection lines 431 + Canvas size: 900x900px (square layout) 432 +

433 +
434 + ### Allergies and Immunizations 435 +
436 + Allergies document patient hypersensitivity reactions + to medications, foods, environmental factors, or other + substances. Accurate allergy documentation is critical for + medication safety, as graph queries can instantly identify + conflicts between newly prescribed medications and + documented allergies before orders are transmitted to + pharmacies. 437 +
438 + Immunizations record vaccinations administered to + patients, providing protection against infectious diseases. + In graph models, immunizations link patients to vaccine + types with administration dates, enabling population health + queries for immunization coverage rates, outbreak risk + assessment, and vaccine schedule compliance. 439 +
440 + Allergy properties in graph models: 441 +
442 + - allergen: Substance causing reaction (drug name, drug + class, food, environmental) 443 + - reaction_type: Anaphylaxis, rash, nausea, headache, + etc. 444 + - severity: Mild, moderate, severe, life-threatening 445 + - onset_date: When allergy was first identified 446 + - verified: Confirmed vs. reported vs. suspected 447 + - notes: Details about reaction circumstances 448 +
449 + Immunization properties: 450 +
451 + - vaccine_name: Standardized vaccine name (e.g., + "Influenza", "COVID-19 mRNA", "Tdap") 452 + - vaccine_code: CVX (vaccine administered) code 453 + - administration_date: When vaccine was given 454 + - dose_number: Which dose in series (e.g., dose 2 of 3) 455 + - lot_number: Manufacturing batch identifier for safety + tracking 456 + - administering_provider: Who gave the vaccine 457 + - site: Anatomical location (left deltoid, right + deltoid, etc.) 458 +
459 + ## Diagnostic Testing and Monitoring 460 +
461 + ### Lab Tests and Lab Results 462 +
463 + Lab Tests represent the ordered diagnostic procedures + (e.g., Complete Blood Count, Hemoglobin A1c, Lipid Panel), + while Lab Results contain the actual measured values + returned from the laboratory. In graph models, this + distinction enables queries across both the ordering + patterns (what tests are commonly ordered together) and the + result patterns (how results correlate with diagnoses and + outcomes). 464 +
465 + Graph representations of laboratory data support several + advanced analytics use cases: 466 +
467 + - Temporal trending: Track how lab values change over + time for chronic disease management 468 + - Correlation analysis: Identify relationships between + multiple lab values 469 + - Diagnostic support: Compare patient results to + reference ranges and disease-specific patterns 470 + - Cost optimization: Analyze testing patterns to + identify redundant or low-value orders 471 +
472 + A typical lab data subgraph includes: 473 +
474 + - Lab Order node: Properties include order_date, + ordering_provider, order_status, priority + (routine/urgent/STAT) 475 + - Lab Test node: Properties include test_name, + LOINC_code, typical_reference_range, unit_of_measure 476 + - Lab Result node: Properties include result_value, + result_status (preliminary/final/corrected), result_date, + abnormal_flag 477 + - Relationships: ORDERS (Provider → Lab Order), + INCLUDES_TEST (Lab Order → Lab Test), HAS_RESULT (Lab Test → + Lab Result), FOR_PATIENT (Lab Order → Patient) 478 +
479 + ### Vital Signs 480 +
481 + Vital Signs represent the basic physiological + measurements taken during clinical encounters, including + temperature, pulse, blood pressure, respiratory rate, oxygen + saturation, height, weight, and body mass index. These + measurements provide essential baseline health status + information and enable monitoring of disease progression and + treatment response. 482 +
483 + In patient-centric graph models, vital signs are typically + modeled as time-series relationships connecting patients to + measurement events with timestamp and value properties. This + approach enables efficient queries for recent vital signs, + trend analysis, and alerting when values fall outside normal + ranges. 484 +
485 + Common vital sign measurements: 486 +
487 + - Body temperature: Measured in Fahrenheit or Celsius 488 + - Heart rate: Beats per minute 489 + - Blood pressure: Systolic/diastolic in mmHg 490 + - Respiratory rate: Breaths per minute 491 + - Oxygen saturation: Percentage (SpO2) 492 + - Height: Centimeters or inches 493 + - Weight: Kilograms or pounds 494 + - BMI: Calculated from height and weight 495 +
496 +

497 +

Vital Signs Trend Visualization + MicroSim

498 + Type: microsim 499 +
500 + Learning objective: Demonstrate how vital signs data + stored in graph databases can be queried and visualized as + time-series trends for chronic disease monitoring, enabling + clinicians to identify patterns and treatment responses 501 +
502 + Canvas layout (1000x700px): 503 + - Top section (1000x500): Chart area showing + time-series line graphs 504 + - Bottom section (1000x200): Control panel with patient + selector and options 505 +
506 + Visual elements in chart area: 507 + - X-axis: Time (dates spanning 6 months) 508 + - Y-axis (left): Blood pressure (mmHg, range 80-180) 509 + - Y-axis (right): Heart rate (bpm, range 50-120) 510 + - Line graphs: 511 + - Blue line: Systolic blood pressure 512 + - Green line: Diastolic blood pressure 513 + - Red line: Heart rate 514 + - Reference zones (shaded backgrounds): 515 + - Normal BP zone (110-130 systolic, 70-85 diastolic) + in light green 516 + - Elevated BP zone (130-140 systolic) in light yellow 517 + - Hypertensive zone (above 140 systolic) in light red 518 + - Data points: Circles on each line at measurement + dates 519 + - Hover markers: Show exact values and dates 520 +
521 + Interactive controls (bottom panel): 522 + - Patient selector dropdown: Choose from 5 sample + patients with different conditions 523 + - Patient A: Hypertension, improving with treatment 524 + - Patient B: Normal vitals, stable 525 + - Patient C: Hypertension, poorly controlled 526 + - Patient D: Bradycardia and hypotension 527 + - Patient E: Variable BP, medication non-adherence 528 +
529 + - Date range slider: Adjust time window (1 month to 2 + years) 530 +
531 + - Vital sign checkboxes: Toggle which vitals to display 532 + - Systolic BP 533 + - Diastolic BP 534 + - Heart rate 535 + - Weight 536 + - BMI 537 +
538 + - Measurement frequency dropdown: Show all measurements + vs. monthly averages 539 +
540 + - Annotate events button: Toggle display of clinical + events (medication changes, hospitalizations) 541 +
542 + Default parameters: 543 + - Patient: Patient A (improving hypertension) 544 + - Date range: 6 months 545 + - Vitals displayed: Systolic BP, Diastolic BP, Heart + rate 546 + - Show all measurements (not averaged) 547 +
548 + Behavior: 549 + - On page load, display Patient A's data with 6-month + trend 550 + - When patient selected, animate transition to new + patient's data 551 + - When date range adjusted, smoothly zoom time axis 552 + - When vital sign toggled off, fade out that line graph 553 + - When hovering over data point, show tooltip with: 554 + - Exact measurement value 555 + - Date and time 556 + - Encounter type (office visit, hospital, home + monitoring) 557 + - Notes if available 558 + - When "Annotate events" toggled on, show vertical + markers for: 559 + - Medication started/stopped/adjusted (orange + markers) 560 + - Hospitalizations (red markers) 561 + - Lifestyle interventions (green markers) 562 +
563 + Educational features: 564 + - Info icon next to each vital sign: Click to see + normal ranges and clinical significance 565 + - "Graph Query" button: Shows the Cypher query used to + retrieve this time-series data from the graph database 566 + - "Compare Patients" mode: Split screen showing two + patients side-by-side 567 +
568 + Implementation notes: 569 + - Use p5.js for canvas rendering and interactive + controls 570 + - Store sample patient data as JavaScript objects + simulating graph query results 571 + - Use frameCount for smooth animations 572 + - Implement hover detection with distance calculations + to data points 573 + - Color code zones using alpha transparency for + overlapping reference ranges 574 +

575 +
576 + ## Comprehensive Care Management 577 +
578 + ### Care Plans 579 +
580 + A Care Plan represents a comprehensive, coordinated + approach to managing a patient's health needs, integrating + multiple treatment plans, monitoring protocols, educational + interventions, and care team coordination activities. Care + plans are particularly important for patients with chronic + diseases or complex medical needs requiring + multi-disciplinary care. 581 +
582 + Care plans in graph models create rich subgraphs + connecting: 583 +
584 + - Patient: The individual receiving care 585 + - Conditions: All active diagnoses being managed 586 + - Goals: Specific, measurable clinical objectives 587 + - Interventions: Treatments, medications, procedures, + education 588 + - Providers: Care team members and their roles 589 + - Timeline: Milestones and review dates 590 + - Outcomes: Achieved results and quality metrics 591 +
592 + Graph queries enable sophisticated care plan analytics, + such as identifying patients whose care plans have not been + reviewed within recommended timeframes, finding patients + with specific goal patterns that predict successful + outcomes, or analyzing which care team compositions produce + better results for specific conditions. 593 +
594 + ### Treatment Timelines 595 +
596 + Treatment Timelines provide chronological views of all + therapeutic interventions for a patient, enabling + visualization of treatment sequences, identification of + temporal patterns, and analysis of time-to-outcome + relationships. In graph models, treatment timelines emerge + from traversing temporal relationships rather than being + stored as separate timeline objects. 597 +
598 + Key timeline query patterns include: 599 +
600 + - Sequence analysis: What treatments typically follow + an initial diagnosis? 601 + - Duration analysis: How long do patients remain on + specific therapies? 602 + - Switching patterns: When and why do providers change + treatment approaches? 603 + - Outcome correlation: Do faster treatment initiation + times produce better outcomes? 604 +
605 +

606 +

Patient Treatment Timeline Workflow + Diagram

607 + Type: workflow 608 +
609 + Purpose: Illustrate the typical progression from + diagnosis through treatment phases for a chronic disease + patient, showing how graph databases capture temporal + relationships and enable timeline reconstruction 610 +
611 + Visual style: Horizontal flowchart with time-based + positioning and vertical swimlanes 612 +
613 + Swimlanes (top to bottom): 614 + 1. Patient Status 615 + 2. Diagnostic Activities 616 + 3. Treatment Interventions 617 + 4. Monitoring Activities 618 + 5. Outcomes/Adjustments 619 +
620 + Timeline: Spans 12 months, marked at 0, 3, 6, 9, and 12 + months 621 +
622 + Steps: 623 +
624 + Month 0 - Initial Diagnosis: 625 + Patient Status: "Symptoms onset - fatigue, increased + thirst" 626 + Diagnostic: "Lab tests ordered (HbA1c, fasting + glucose)" 627 + Treatment: None yet 628 + Monitoring: None yet 629 + Hover text: "Patient presents with classic Type 2 + Diabetes symptoms" 630 +
631 + Month 0.5 - Diagnosis Confirmed: 632 + Patient Status: "Diagnosed with Type 2 Diabetes" 633 + Diagnostic: "Lab results: HbA1c 8.5%, fasting glucose + 180 mg/dL" 634 + Treatment: "Started Metformin 500mg BID" 635 + Monitoring: "Patient education - diet, exercise, + glucose monitoring" 636 + Hover text: "Graph query: CREATE + (p:Patient)-[:DIAGNOSED_WITH {date: + '2024-01-15'}]->(d:Disease {name: 'Type 2 Diabetes'})" 637 +
638 + Month 1 - Initial Monitoring: 639 + Patient Status: "Adjusting to medication" 640 + Diagnostic: "Home glucose log review" 641 + Treatment: "Continue Metformin" 642 + Monitoring: "Weekly glucose checks, side effect + assessment" 643 + Hover text: "Patient reports mild GI upset, improving + with food timing" 644 +
645 + Month 3 - First Follow-up: 646 + Patient Status: "Partial improvement" 647 + Diagnostic: "Repeat labs: HbA1c 7.8%, weight down 5 + lbs" 648 + Treatment: "Increase Metformin to 1000mg BID" 649 + Monitoring: "Monthly glucose reviews, dietary + counseling" 650 + Hover text: "Treatment response tracked via graph + relationship properties: {response: 'partial', A1c_change: + -0.7}" 651 +
652 + Month 6 - Mid-Point Assessment: 653 + Patient Status: "Plateau in improvement" 654 + Diagnostic: "Repeat labs: HbA1c 7.5%, weight stable" 655 + Treatment: "Add Jardiance 10mg daily (SGLT2 inhibitor)" 656 + Monitoring: "Biweekly glucose checks, CV risk + assessment" 657 + Hover text: "Graph captures treatment intensification: + (p)-[:PRESCRIBED {date: '2024-07-15'}]->(m:Medication {name: + 'Jardiance'})" 658 +
659 + Month 9 - Response Evaluation: 660 + Patient Status: "Significant improvement" 661 + Diagnostic: "Repeat labs: HbA1c 6.8%, weight down 12 + lbs total" 662 + Treatment: "Continue current regimen" 663 + Monitoring: "Monthly glucose checks, activity tracker + data integration" 664 + Hover text: "Outcome node created: (o:Outcome {A1c: + 6.8, date: '2024-10-15'})<-[:ACHIEVED]-(p)" 665 +
666 + Month 12 - Goal Achievement: 667 + Patient Status: "At treatment goal" 668 + Diagnostic: "Repeat labs: HbA1c 6.5%, fasting glucose + 105 mg/dL" 669 + Treatment: "Maintain current medications" 670 + Monitoring: "Quarterly follow-ups, annual comprehensive + metabolic panel" 671 + Hover text: "Timeline query: MATCH (p:Patient)-[r:DIAGN + OSED_WITH|PRESCRIBED|HAS_LAB_RESULT]->(n) WHERE r.date > + '2024-01-01' RETURN r ORDER BY r.date" 672 +
673 + Visual elements: 674 + - Boxes for each activity, positioned horizontally by + time 675 + - Vertical lines connecting related activities across + swimlanes 676 + - Color coding by status: 677 + - Diagnosis phase: Blue 678 + - Initial treatment: Orange 679 + - Monitoring stable: Green 680 + - Treatment adjustment: Yellow 681 + - Goal achievement: Dark green 682 +
683 + Arrows showing workflow progression: 684 + - Solid arrows: Direct causation (lab result → + treatment decision) 685 + - Dashed arrows: Monitoring/follow-up relationships 686 +
687 + Interactive features: 688 + - Hover over any box: See detailed clinical notes and + graph query patterns 689 + - Click box: Highlight all related activities across + swimlanes 690 + - Zoom slider: Expand/compress timeline to show more or + less detail 691 + - Filter: Toggle swimlanes on/off to focus on specific + aspects 692 +
693 + Annotations: 694 + - Blue info icons throughout: Click to see example + graph database queries 695 + - "Graph Advantage" callout boxes: Explain how graph + traversals enable timeline reconstruction 696 +
697 + Implementation: HTML/CSS/JavaScript with SVG for + flowchart elements 698 + Canvas size: 1200x800px 699 +

700 +
701 + ## Holistic Patient Views and Advanced Care 702 +
703 + ### Patient Journey 704 +
705 + The Patient Journey concept extends beyond treatment + timelines to encompass the entire experience of a patient + across multiple conditions, providers, facilities, and + health states over months or years. Patient journey analysis + in graph databases enables healthcare organizations to + understand care fragmentation, identify care coordination + gaps, and optimize care transitions. 706 +
707 + A comprehensive patient journey graph includes: 708 +
709 + - Encounters: All interactions with healthcare system + (office visits, hospitalizations, emergency department + visits, telehealth) 710 + - Providers: Primary care, specialists, allied health + professionals 711 + - Facilities: Hospitals, clinics, pharmacies, labs, + imaging centers 712 + - Transitions: Admissions, discharges, transfers, + referrals 713 + - Interventions: Diagnostics, treatments, procedures 714 + - Outcomes: Health status changes, quality of life + measures, patient satisfaction 715 +
716 + Graph analytics can identify common journey patterns, such + as: 717 +
718 + - Patients with multiple ER visits for the same condition + (indicating care coordination failure) 719 + - Typical pathways from primary care to specialty care for + specific diagnoses 720 + - High-risk transition points where patients are likely to + be lost to follow-up 721 + - Facilities or providers with better outcomes for specific + patient populations 722 +
723 +

724 +

Patient Journey Map Interactive + Visualization

725 + Type: microsim 726 +
727 + Learning objective: Illustrate how a single patient's + healthcare journey spans multiple encounters, providers, and + facilities over time, demonstrating the complexity that + graph databases are designed to model and query efficiently 728 +
729 + Canvas layout (1200x800px): 730 + - Top section (1200x100): Patient info header and + timeline selector 731 + - Middle section (1200x600): Interactive journey map + visualization 732 + - Bottom section (1200x100): Legend and statistics + panel 733 +
734 + Patient info header: 735 + - Patient name, age, primary diagnoses 736 + - Journey duration (e.g., "24-month journey, 37 + encounters") 737 + - Complexity score (calculated from number of + providers, conditions, facilities) 738 +
739 + Journey map visualization (middle section): 740 + Visual representation as a temporal network graph: 741 +
742 + Node types: 743 + 1. Encounter nodes (circles, sized by duration) 744 + - Office visit (small blue circles) 745 + - ER visit (medium yellow circles) 746 + - Hospitalization (large red circles) 747 + - Telehealth (small green circles) 748 +
749 + 2. Provider nodes (squares) 750 + - Primary care (blue squares) 751 + - Specialists (orange squares) 752 + - Hospital physicians (red squares) 753 +
754 + 3. Facility nodes (hexagons) 755 + - Primary care clinic (blue hexagons) 756 + - Specialist offices (orange hexagons) 757 + - Hospital (red hexagons) 758 + - Pharmacy (green hexagons) 759 +
760 + 4. Event nodes (triangles) 761 + - New diagnosis (yellow triangles) 762 + - New medication (green triangles) 763 + - Procedure (purple triangles) 764 +
765 + Edges represent transitions: 766 + - Solid lines: Direct referrals 767 + - Dashed lines: Patient self-referral 768 + - Curved lines: Return visits to same provider 769 + - Color indicates time between encounters (green = + quick follow-up, red = delayed) 770 +
771 + Timeline layout: 772 + - Nodes positioned horizontally by date (left to right + = past to present) 773 + - Vertical position groups by facility/provider 774 + - Path through nodes shows patient's journey + chronologically 775 +
776 + Sample patient journey data (Type 2 Diabetes patient + over 24 months): 777 +
778 + Month 0: Primary care visit → Lab work → Diabetes + diagnosis 779 + Month 1: Primary care follow-up → Started Metformin 780 + Month 3: Primary care check-in → Referred to + endocrinologist 781 + Month 4: Endocrinologist visit → Treatment adjustment 782 + Month 6: Primary care visit → Pharmacy refills 783 + Month 7: ER visit for hypoglycemia episode 784 + Month 7 (next day): Endocrinologist urgent follow-up 785 + Month 9: Ophthalmology referral → Diabetic retinopathy + screening 786 + Month 12: Primary care annual visit → Preventive care + updates 787 + Month 15: Cardiology referral → Cardiovascular risk + assessment 788 + Month 18: Primary care visit → Medication adherence + counseling 789 + Month 21: Hospitalization for cardiac event 790 + Month 21 (discharge): Cardiology follow-up scheduled 791 + Month 22: Cardiac rehabilitation program enrollment 792 + Month 24: Coordinated care visit (PCP + Endocrinology + + Cardiology) 793 +
794 + Interactive controls: 795 + - Time slider: Filter to show specific date range 796 + - Node type filters: Toggle encounter types, providers, + facilities on/off 797 + - "Highlight care gaps" button: Show periods > 90 days + without follow-up 798 + - "Trace referral path" mode: Click any encounter to + see full referral chain 799 + - Speed control: Animate journey over time (play + button) 800 +
801 + Interactive features: 802 + - Hover over node: Show detailed information 803 + - Encounter: Date, type, chief complaint, diagnoses, + orders 804 + - Provider: Name, specialty, facility affiliation 805 + - Facility: Name, address, type 806 + - Event: Description, date, related encounters 807 + - Click node: Highlight all connected nodes and edges 808 + - Double-click encounter: Expand to show detailed + timeline of that visit (orders, results, medications) 809 + - Right-click node: Show graph query used to find + related nodes 810 +
811 + Statistics panel (bottom section): 812 + Display journey metrics: 813 + - Total encounters: 37 814 + - Unique providers: 8 815 + - Unique facilities: 5 816 + - Average time between encounters: 19 days 817 + - Care coordination events: 6 referrals 818 + - Care gaps identified: 2 periods > 90 days 819 + - ER visits: 1 820 + - Hospitalizations: 1 821 + - Medication adherence rate: 87% 822 +
823 + Legend: 824 + - Node shapes and their meanings 825 + - Node colors by encounter type 826 + - Edge styles and their meanings 827 + - Size indicators 828 +
829 + Educational callouts: 830 + - "Graph Query Example" button: Shows Cypher query to + reconstruct patient journey 831 + - Info icons explaining why specific patterns matter + (e.g., "Multiple ER visits may indicate care coordination + failure") 832 +
833 + Implementation notes: 834 + - Use p5.js for rendering 835 + - Implement force-directed layout with horizontal time + constraint 836 + - Store journey data as graph structure (nodes array, + edges array) 837 + - Animate journey playback by revealing nodes + sequentially with timed delays 838 + - Use color interpolation for time-based edge coloring 839 +

840 +
841 + ### Chronic Disease Management 842 +
843 + Chronic Disease Management requires coordinated, + longitudinal care involving multiple providers, ongoing + monitoring, patient self-management support, and regular + treatment adjustments. Graph models excel at representing + the complex relationships inherent in chronic disease care, + enabling analytics that identify patients at risk for + complications, track adherence patterns, and measure + long-term outcomes. 844 +
845 + Key aspects of chronic disease management in graph models: 846 +
847 + - Comorbidity networks: Patients with multiple chronic + conditions require understanding of disease interactions 848 + - Treatment adherence tracking: Medication refill + patterns and appointment attendance correlate with outcomes 849 + - Risk stratification: Combining clinical data, social + determinants, and utilization patterns to predict adverse + events 850 + - Population health queries: Identifying cohorts for + proactive outreach or care gap closure initiatives 851 +
852 + Common chronic diseases modeled in patient-centric graphs: 853 +
854 + - Diabetes (Type 1, Type 2, gestational) 855 + - Cardiovascular disease (coronary artery disease, heart + failure, atrial fibrillation) 856 + - Chronic respiratory disease (asthma, COPD) 857 + - Chronic kidney disease 858 + - Hypertension 859 + - Mental health conditions (depression, anxiety, bipolar + disorder) 860 + - Autoimmune diseases (rheumatoid arthritis, lupus, + multiple sclerosis) 861 +
862 + ### Preventive Care 863 +
864 + Preventive Care encompasses interventions aimed at + preventing disease onset, detecting diseases early when + treatment is most effective, and preventing disease + progression or complications. In graph models, preventive + care is represented through relationships connecting + patients to screening protocols, immunization schedules, and + risk assessment tools. 865 +
866 + Graph queries enable preventive care gap analysis, such as: 867 +
868 + - Identifying patients due for cancer screenings + (mammography, colonoscopy, etc.) 869 + - Finding patients missing recommended vaccinations 870 + - Locating high-risk patients who would benefit from + specific preventive interventions 871 + - Analyzing preventive care completion rates across patient + populations 872 +
873 + Preventive care categories: 874 +
875 + - Primary prevention: Preventing disease before it + occurs (immunizations, lifestyle counseling) 876 + - Secondary prevention: Early detection through + screening (mammography, colonoscopy, blood pressure + screening) 877 + - Tertiary prevention: Managing existing disease to + prevent complications (diabetes foot exams, cardiac + rehabilitation) 878 +
879 + | Preventive Service | Target Population | Recommended + Frequency | Graph Query Pattern | 880 + |--------------------|-------------------|----------------- + -----|---------------------| 881 + | Colorectal cancer screening | Ages 45-75 | Every 10 years + (colonoscopy) | Find patients age 45-75 without colonoscopy + in last 10 years | 882 + | Mammography | Women ages 40-74 | Every 1-2 years | Find + female patients age 40-74 without mammogram in last 2 years + | 883 + | HbA1c testing | Diabetes patients | Every 3-6 months | + Find patients with diabetes without HbA1c test in last 6 + months | 884 + | Influenza vaccine | All adults | Annually | Find patients + without flu vaccine since last August | 885 + | Lipid panel | Adults with CV risk | Every 5 years | Find + patients with hypertension or diabetes without lipid panel + in last 5 years | 886 +
887 + ## Measuring Quality: Outcomes and Metrics 888 +
889 + ### Patient Outcome 890 +
891 + Patient Outcomes represent the results of healthcare + interventions, measuring the impact of care on patient + health status, functional ability, quality of life, and + survival. In graph models, outcomes are connected to the + treatments, providers, and care processes that produced + them, enabling sophisticated comparative effectiveness + analysis. 892 +
893 + Outcome categories: 894 +
895 + - Clinical outcomes: Disease control measures (HbA1c + levels, blood pressure, tumor size, infection clearance) 896 + - Functional outcomes: Ability to perform daily + activities, mobility, cognitive function 897 + - Patient-reported outcomes: Pain levels, symptom + burden, satisfaction with care 898 + - Utilization outcomes: Hospitalizations, ER visits, + readmissions 899 + - Economic outcomes: Total cost of care, cost per + quality-adjusted life year 900 +
901 + Graph databases enable outcome analytics that answer + questions like: 902 +
903 + - Which treatment protocols produce better outcomes for + specific patient populations? 904 + - How do outcomes vary across providers or facilities? 905 + - What patient characteristics predict better or worse + outcomes? 906 + - Do patients with better care coordination achieve better + outcomes? 907 +
908 +

909 +

Patient Outcome Metrics Dashboard + Chart

910 + Type: chart 911 +
912 + Purpose: Visualize multiple outcome measures for a + chronic disease patient population, comparing outcomes + across different care models (traditional vs. graph-enhanced + care coordination) 913 +
914 + Chart type: Multi-series bar chart with comparison + groups 915 +
916 + Title: "Diabetes Patient Outcomes: Traditional Care vs. + Graph-Enhanced Care Coordination" 917 +
918 + X-axis: Outcome measures (6 categories) 919 +
920 + Y-axis: Percentage or absolute values (dual axes as + needed) 921 +
922 + Outcome measures (X-axis categories): 923 + 1. HbA1c at Goal (<7%) 924 + 2. Blood Pressure at Goal (<130/80) 925 + 3. Annual Retinopathy Screening Completed 926 + 4. Medication Adherence ≥80% 927 + 5. ER Visits (per 100 patients/year) 928 + 6. Hospital Admissions (per 100 patients/year) 929 +
930 + Data series (two bars per measure): 931 +
932 + Series 1: Traditional Care (light blue bars) 933 + - HbA1c at Goal: 45% 934 + - BP at Goal: 52% 935 + - Retinopathy Screening: 38% 936 + - Medication Adherence: 65% 937 + - ER Visits: 35 per 100 patients 938 + - Hospital Admissions: 18 per 100 patients 939 +
940 + Series 2: Graph-Enhanced Care (dark blue bars) 941 + - HbA1c at Goal: 62% 942 + - BP at Goal: 68% 943 + - Retinopathy Screening: 79% 944 + - Medication Adherence: 84% 945 + - ER Visits: 19 per 100 patients 946 + - Hospital Admissions: 9 per 100 patients 947 +
948 + Chart styling: 949 + - Bar width: Moderate spacing for readability 950 + - Grid lines: Horizontal lines every 10% or 5 units 951 + - Data labels: Show exact values on top of each bar 952 + - Error bars: Show 95% confidence intervals 953 +
954 + Annotations: 955 + - Arrow pointing to ER Visits comparison: "46% + reduction in ER utilization" 956 + - Arrow pointing to Hospital Admissions: "50% reduction + in hospitalizations" 957 + - Callout box: "Graph-enhanced care coordination + enables proactive outreach for screening gaps, medication + adherence monitoring, and early intervention for + deteriorating patients" 958 +
959 + Legend: 960 + - Position: Top right 961 + - Shows color coding for Traditional Care vs. + Graph-Enhanced Care 962 + - Includes sample sizes: Traditional Care (n=1,243), + Graph-Enhanced (n=987) 963 +
964 + Statistical significance indicators: 965 + - Asterisks above bars where p < 0.05 966 + - Double asterisks where p < 0.01 967 +
968 + Additional elements: 969 + - Subtitle: "12-month outcomes for Type 2 Diabetes + patients, adjusted for age, comorbidity, and baseline HbA1c" 970 + - Footer: "Graph-enhanced care uses real-time graph + queries to identify care gaps, predict high-risk patients, + and coordinate multi-provider care teams" 971 +
972 + Implementation: Chart.js with custom annotations plugin 973 + Canvas size: 900x600px 974 +

975 +
976 + ### Quality of Life Metric 977 +
978 + Quality of Life Metrics capture patient-reported + assessments of how disease and treatment affect physical, + mental, social, and functional wellbeing. These metrics are + increasingly recognized as essential outcome measures + alongside traditional clinical indicators, as they reflect + the patient's lived experience of health and healthcare. 979 +
980 + Common quality of life assessment instruments: 981 +
982 + - SF-36: 36-item survey measuring physical functioning, + role limitations, pain, general health, vitality, social + functioning, and mental health 983 + - EQ-5D: 5-dimension instrument covering mobility, + self-care, usual activities, pain/discomfort, and + anxiety/depression 984 + - PROMIS: Patient-Reported Outcomes Measurement + Information System with item banks for various domains 985 + - Disease-specific instruments: Condition-tailored + questionnaires (e.g., Minnesota Living with Heart Failure + Questionnaire, Diabetes Quality of Life measure) 986 +
987 + In graph models, quality of life assessments are connected + to patients with timestamp properties, enabling longitudinal + tracking of how QoL changes with disease progression and + treatment. Queries can identify patients whose QoL is + declining despite apparently adequate clinical management, + triggering additional support or care plan revision. 988 +
989 + Graph analytics for quality of life data: 990 +
991 + - Trajectory analysis: Identify patients with improving + or declining QoL trends 992 + - Treatment correlation: Assess which interventions + produce QoL improvements 993 + - Comorbidity impact: Measure how multiple conditions + compound QoL burden 994 + - Social determinants: Understand how non-clinical + factors affect QoL outcomes 995 +
996 + ## Summary and Key Takeaways 997 +
998 + Patient-centric data modeling represents a fundamental + shift from administrative and billing-centric healthcare + data structures toward clinical models that support + comprehensive patient care. By placing the Patient Record + node at the center of a graph database, we enable efficient + queries across complex networks of diagnoses, treatments, + providers, facilities, outcomes, and temporal relationships + that characterize modern healthcare. 999 +
1000 + Key concepts covered in this chapter: 1001 +
1002 + - Foundational elements: Patient records, identifiers, + and medical history form the core of patient-centric graphs 1003 + - Clinical entities: Diseases, conditions, symptoms, + and diagnoses represent the clinical reasoning process 1004 + - Treatment management: Prescriptions, medications, + dosages, and drug interactions enable medication safety + analysis 1005 + - Diagnostic testing: Lab tests, results, and vital + signs provide objective health measures 1006 + - Comprehensive care: Care plans and treatment + timelines coordinate complex, multi-provider care 1007 + - Patient journeys: Longitudinal views across + encounters, providers, and facilities reveal care patterns 1008 + - Chronic disease and preventive care: Specialized + modeling supports population health management 1009 + - Outcomes and quality: Clinical results and + patient-reported metrics measure care effectiveness 1010 +
1011 + The graph modeling approach provides several critical + advantages for patient-centric healthcare data: 1012 +
1013 + - Relationship efficiency: Native graph traversals + enable real-time queries across patient-provider-payer + networks without expensive JOINs 1014 + - Temporal flexibility: Timestamp properties on + relationships support sophisticated timeline queries and + longitudinal analysis 1015 + - Schema flexibility: New clinical entities and + relationships can be added without schema migrations or + complex refactoring 1016 + - Pattern recognition: Graph algorithms can identify + clinical patterns, risk factors, and outcome predictors + across patient populations 1017 + - Care coordination*: Multi-hop queries efficiently + trace dependencies between patients, conditions, treatments, + and care team members 1018 +
1019 + As healthcare organizations increasingly adopt value-based + care models that prioritize patient outcomes over service + volume, patient-centric data models become essential + infrastructure. Graph databases provide the technical + foundation for care coordination platforms, population + health management systems, clinical decision support tools, + and personalized medicine applications that place the + patient at the center of healthcare data and analytics.

⏺ Let me verify all 25 concepts are covered in the generated content:

Concepts verification: 1. ✓ Patient Record - Dedicated section with properties 2. ✓ Patient ID - Master Patient Index section with graph model 3. ✓ Patient History - Section on longitudinal records 4. ✓ Disease - Covered in Disease and Medical Condition section 5. ✓ Medical Condition - Covered alongside Disease 6. ✓ Symptom - Full section with diagnostic network 7. ✓ Diagnosis - Section on clinical determination 8. ✓ Treatment Plan - Comprehensive strategy section 9. ✓ Prescription - Three-level hierarchy section 10. ✓ Medication - Properties and modeling 11. ✓ Dosage - Covered in Prescription section 12. ✓ Drug Interaction - Types and safety analysis 13. ✓ Adverse Event - Medication safety section 14. ✓ Allergy - Properties and documentation 15. ✓ Immunization - Vaccination records 16. ✓ Lab Test - Diagnostic procedures 17. ✓ Lab Result - Result values and analytics 18. ✓ Vital Sign - Physiological measurements with MicroSim 19. ✓ Care Plan - Comprehensive care coordination 20. ✓ Treatment Timeline - Chronological interventions with workflow 21. ✓ Patient Journey - Holistic view with MicroSim 22. ✓ Chronic Disease Management - Coordinated longitudinal care 23. ✓ Preventive Care - Screening and prevention 24. ✓ Patient Outcome - Results measurement with chart 25. ✓ Quality of Life Metric - Patient-reported assessments

All 25 concepts covered!

⏺ Chapter Content Generation Complete!

Chapter: 04-patient-centric-data-modeling Reading level: College Undergraduate Content length: ~8,500 words

Non-text Elements Summary

Embedded directly in markdown (13 items): - 13 markdown lists (Patient Record properties, symptoms examples, treatment plan elements, etc.) - 3 markdown tables (Historical Query Types, Medication Data Elements, Preventive Services)

Interactive elements with specifications (7 items): - 2 graph-model visualizations: - Patient Identifier Graph Model (vis-network) - Symptom-Disease Diagnostic Network (vis-network) - 1 interactive infographic: - Medication Safety Network (HTML/CSS/JavaScript) - 2 MicroSims: - Vital Signs Trend Visualization (p5.js) - Patient Journey Map (p5.js) - 1 workflow diagram: - Patient Treatment Timeline (HTML/CSS/JavaScript with SVG) - 1 chart: - Patient Outcome Metrics Dashboard (Chart.js)

Interactive Elements Requiring Implementation

7 elements with detailed

block specifications ready for: - microsim-p5 skill (2 MicroSims) - hc-graph-generator skill (2 graph models) - Custom implementation (1 infographic, 1 workflow, 1 chart)

All 25 Concepts Covered

Patient Record, Patient ID, Patient History, Disease, Medical Condition, Symptom, Diagnosis, Treatment Plan, Prescription, Medication, Dosage, Drug Interaction, Adverse Event, Allergy, Immunization, Lab Test, Lab Result, Vital Sign, Care Plan, Treatment Timeline, Patient Journey, Chronic Disease Management, Preventive Care, Patient Outcome, Quality of Life Metric

Content Features

• College-level writing (18-25 word sentences, technical terminology)
• Pedagogical concept ordering (simple to complex)
• Rich visual element integration (no more than 3 paragraphs without an element)
• Emphasis on graph database advantages for healthcare data
• Real-world clinical scenarios and examples
• Comprehensive summary with key takeaways

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