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44 ---
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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 + 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 +
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 + 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 +
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 + 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 +
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 + 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 +
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 + 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 +
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 + 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 +
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 + 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 +
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
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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43
44 ---
45
46 - TODO: Generate Chapter Content
46 + ## Introduction: The Provider Perspective
47 +
48 + Healthcare delivery organizations—hospitals, clinics,
+ physician practices, and emergency departments—face
+ unprecedented operational complexity in the modern
+ healthcare ecosystem. Providers must coordinate care across
+ multiple specialties, manage constrained resources, maintain
+ clinical quality standards, navigate regulatory
+ requirements, and optimize financial performance while
+ delivering patient-centered care. Traditional relational
+ database systems struggle to model the intricate networks of
+ provider relationships, schedules, credentials, referral
+ patterns, and clinical protocols that characterize
+ contemporary healthcare delivery.
49 +
50 + Graph databases provide a natural and efficient approach to
+ modeling provider operations because they natively
+ represent the relationship-intensive nature of healthcare
+ delivery networks. A single patient encounter connects to
+ providers, facilities, specialties, care teams,
+ appointments, referrals, and clinical guidelines through
+ complex temporal and hierarchical relationships that graphs
+ traverse efficiently. By modeling these connections
+ explicitly as first-class graph relationships rather than
+ implicit foreign key joins, healthcare organizations gain
+ real-time visibility into provider networks, resource
+ utilization, care coordination patterns, and clinical
+ quality metrics.
51 +
52 + This chapter examines healthcare from the provider
+ operational perspective, exploring how graph models
+ represent individual providers, facilities, networks,
+ schedules, credentials, and clinical protocols. You will
+ learn to model the structures and processes that enable
+ healthcare organizations to deliver coordinated,
+ high-quality care while optimizing resource allocation and
+ operational efficiency.
53 +
54 + ## Provider Types and Healthcare Facilities
55 +
56 + ### Primary Care Provider
57 +
58 + A Primary Care Provider (PCP) serves as the patient's
+ first point of contact with the healthcare system and
+ coordinates overall health management including preventive
+ care, chronic disease management, and referrals to
+ specialists. Primary care providers typically practice in
+ family medicine, internal medicine, pediatrics, or general
+ practice, maintaining longitudinal relationships with
+ patients across multiple health conditions and life stages.
59 +
60 + In graph models, Primary Care Providers are represented as
+ specialized provider nodes with properties indicating:
61 +
62 + - provider_id: Unique identifier
63 + - provider_name: Full name
64 + - specialty: Primary care specialty type (family
+ medicine, internal medicine, pediatrics)
65 + - practice_location: Associated clinic or practice
66 + - panel_size: Number of patients for whom this provider
+ is the PCP
67 + - accepting_new_patients: Current availability status
68 + - languages: Languages spoken for patient communication
69 +
70 + The PCP-patient relationship forms a critical backbone in
+ healthcare graph models, enabling queries such as
+ identifying all patients managed by a specific PCP, finding
+ PCPs with capacity for new patients, or analyzing referral
+ patterns from primary care to specialty care.
71 +
72 + ### Specialist Provider
73 +
74 + Specialist Providers possess advanced training and
+ expertise in specific medical domains, providing
+ consultative services and specialized treatments for
+ conditions requiring focused clinical knowledge. Specialists
+ typically receive patients through referrals from primary
+ care providers or other specialists, though some patients
+ access specialists directly depending on insurance plan
+ structures.
75 +
76 + Common specialist categories include:
77 +
78 + - Medical specialties: Cardiology, endocrinology,
+ gastroenterology, neurology, oncology, pulmonology
79 + - Surgical specialties: General surgery, orthopedic
+ surgery, neurosurgery, cardiothoracic surgery, plastic
+ surgery
80 + - Diagnostic specialties: Radiology, pathology,
+ laboratory medicine
81 + - Procedural specialties: Anesthesiology,
+ interventional radiology, interventional cardiology
82 + - Subspecialties: Pediatric subspecialties, geriatric
+ medicine, sports medicine
83 +
84 + Graph models capture specialist-patient relationships with
+ temporal properties indicating consultation dates, treatment
+ provided, and outcomes achieved. These relationships
+ connect to referral pathways, enabling analysis of referral
+ appropriateness, specialist access times, and care
+ coordination effectiveness.
85 +
86 + ### Hospital, Clinic, and Outpatient Facility
87 +
88 + Hospitals, Clinics, and Outpatient Facilities
+ represent the physical locations where healthcare services
+ are delivered, each serving distinct roles in the care
+ delivery ecosystem.
89 +
90 + Hospitals provide comprehensive inpatient and emergency
+ services with 24/7 availability, surgical capabilities,
+ intensive care units, and specialized departments. Hospital
+ nodes in graph models include properties such as:
91 +
92 + - hospital_id: Unique identifier
93 + - hospital_name: Official name
94 + - hospital_type: Academic medical center, community
+ hospital, critical access hospital, specialty hospital
95 + - bed_count: Licensed inpatient capacity
96 + - trauma_level: Trauma center designation (Level I-IV)
97 + - accreditations: Joint Commission, specialty
+ certifications
98 + - location: Geographic coordinates for proximity
+ analysis
99 +
100 + Clinics provide ambulatory care services in outpatient
+ settings, typically focused on specific specialties or
+ serving as primary care practices. Clinic properties include
+ location, specialty focus, affiliated hospital system, and
+ operating hours.
101 +
102 + Outpatient Facilities encompass ambulatory surgery
+ centers, imaging centers, dialysis facilities, infusion
+ centers, and rehabilitation facilities that provide
+ specialized services without overnight stays. These
+ facilities play increasing roles in healthcare delivery as
+ technological advances enable more procedures to be
+ performed safely outside hospital settings.
103 +
104 + | Facility Type | Primary Function | Typical Services |
+ Graph Modeling Considerations |
105 + |---------------|------------------|------------------|----
+ ---------------------------|
106 + | Hospital | Acute inpatient care, emergency services |
+ Surgery, intensive care, emergency medicine, complex
+ diagnostics | Connect to departments, providers, equipment,
+ capacity metrics |
107 + | Clinic | Outpatient primary/specialty care | Office
+ visits, preventive care, chronic disease management |
+ Connect to provider panels, schedules, referral sources |
108 + | Outpatient Facility | Specialized ambulatory services |
+ Surgery, imaging, dialysis, infusion | Connect to referring
+ providers, equipment, procedure types |
109 + | Emergency Department | Urgent/emergent care | Trauma,
+ acute illness, stabilization | Connect to hospital
+ departments, transfer networks, triage protocols |
110 +
111 + ### Inpatient Care and Emergency Department
112 +
113 + Inpatient Care refers to healthcare services provided
+ to patients admitted to hospitals for overnight stays,
+ typically involving complex medical conditions requiring
+ continuous monitoring, surgical interventions, or intensive
+ treatments. Inpatient care generates rich graph structures
+ connecting patients to attending physicians, consulting
+ specialists, nursing staff, hospital departments,
+ procedures, medications, and discharge plans.
114 +
115 + The Emergency Department (ED) serves as the hospital's
+ entry point for urgent and emergent medical conditions,
+ operating 24/7 with specialized staff and equipment for
+ rapid assessment and stabilization of acutely ill or injured
+ patients. Emergency departments function as critical nodes
+ in healthcare networks, connecting to ambulance services,
+ trauma systems, inpatient departments, and transfer networks
+ for patients requiring specialized care unavailable at the
+ initial ED.
116 +
117 + Graph models of emergency department operations capture:
118 +
119 + - Arrival patterns: Patient volume by time of day, day
+ of week, seasonal variations
120 + - Triage acuity: Emergency Severity Index (ESI) levels
+ 1-5
121 + - Throughput metrics: Door-to-provider time, length of
+ stay, boarding time
122 + - Disposition: Admitted to hospital, transferred to
+ another facility, discharged home
123 + - Referral relationships: Follow-up care coordination
+ with primary care or specialists
124 +
125 + Healthcare Delivery Network Graph
+ Model
127 + Type: graph-model
128 +
129 + Purpose: Illustrate the relationships between different
+ healthcare facility types, providers, and patients in a
+ regional healthcare delivery network
130 +
131 + Node types:
132 + 1. Hospital (large red hexagons)
133 + - Properties: name, bed_count, trauma_level
134 + - Example: "Regional Medical Center (450 beds, Level
+ I Trauma)"
135 +
136 + 2. Clinic (medium blue squares)
137 + - Properties: name, specialty, provider_count
138 + - Examples:
139 + - "Downtown Primary Care Clinic (Family Medicine,
+ 8 providers)"
140 + - "Cardiology Associates (Cardiology, 5
+ providers)"
141 +
142 + 3. Outpatient Facility (medium green circles)
143 + - Properties: name, service_type
144 + - Examples:
145 + - "Surgical Center West (Ambulatory Surgery)"
146 + - "Imaging Center North (Diagnostic Imaging)"
147 +
148 + 4. Emergency Department (medium red triangles)
149 + - Properties: name, annual_volume,
+ trauma_designation
150 + - Example: "Regional ED (45,000 visits/year, Level
+ I)"
151 +
152 + 5. Provider (small orange circles)
153 + - Properties: name, specialty, provider_type
+ (PCP/Specialist)
154 + - Examples: "Dr. Smith (Family Medicine, PCP)", "Dr.
+ Jones (Cardiology, Specialist)"
155 +
156 + 6. Patient (tiny gray circles, sample set)
157 + - Properties: patient_id, primary_pcp
158 + - Example: "Patient cohort (500 represented)"
159 +
160 + Edge types:
161 + 1. PRACTICES_AT (Provider → Facility)
162 + - Properties: primary_location (boolean),
+ hours_per_week
163 + - Shows where providers deliver care
164 +
165 + 2. AFFILIATED_WITH (Clinic/Outpatient → Hospital)
166 + - Properties: affiliation_type (owned, affiliated,
+ independent)
167 + - Shows organizational relationships
168 +
169 + 3. REFERS_TO (Provider → Provider or Facility →
+ Facility)
170 + - Properties: referral_count, referral_specialty
171 + - Shows referral patterns
172 +
173 + 4. HAS_PCP (Patient → Provider)
174 + - Properties: attribution_date
175 + - Shows patient-PCP assignments
176 +
177 + 5. TRANSFERS_TO (Emergency Department → Hospital
+ Department)
178 + - Properties: transfer_volume, typical_conditions
179 + - Shows ED-to-inpatient pathways
180 +
181 + Sample data structure:
182 + - Regional Medical Center (Hospital)
183 + ├─ Includes → Regional ED (Emergency Department)
184 + ├─ Affiliated with → Downtown Primary Care Clinic
185 + ├─ Affiliated with → Cardiology Associates
186 + └─ Receives transfers from → Community Hospital ED
187 +
188 + - Downtown Primary Care Clinic
189 + ├─ Dr. Smith (PCP) PRACTICES_AT
190 + │ ├─ Has 1,200 patients assigned
191 + │ └─ REFERS_TO → Dr. Jones (Cardiologist)
192 + └─ Dr. Brown (PCP) PRACTICES_AT
193 +
194 + - Cardiology Associates
195 + ├─ Dr. Jones (Cardiologist) PRACTICES_AT
196 + │ └─ Receives referrals from 15 PCPs
197 + └─ Dr. White (Cardiologist) PRACTICES_AT
198 +
199 + Layout: Force-directed with hospital as central large
+ node, facilities arranged radially, providers clustered near
+ their practice locations
200 +
201 + Interactive features:
202 + - Hover over node: Show summary statistics (bed count,
+ provider count, patient volume)
203 + - Click Provider node: Highlight all practice locations
+ and referral relationships
204 + - Click Facility node: Highlight all associated
+ providers and referral sources/targets
205 + - Double-click Hospital: Expand to show internal
+ departments
206 + - Filter controls:
207 + - Toggle node types on/off (Hospitals, Clinics,
+ Outpatient, Providers, Patients)
208 + - Filter by specialty (show only cardiology network,
+ only primary care, etc.)
209 + - Filter by geographic region
210 + - "Show referral flows" toggle: Animate typical patient
+ journey from PCP → Specialist → Hospital
211 +
212 + Visual styling:
213 + - Node size proportional to volume metrics (hospital
+ beds, clinic visit volume, provider panel size)
214 + - Edge thickness proportional to referral volume
215 + - Color coding: Red = hospital/ED, Blue = primary care,
+ Orange = specialty care, Green = outpatient services
216 + - Active providers (accepting patients): Solid border;
+ Not accepting: Dashed border
217 +
218 + Legend:
219 + - Node shapes: Hexagon = Hospital, Square = Clinic,
+ Circle = Outpatient Facility, Triangle = ED, Small circle =
+ Provider
220 + - Edge types: Solid = primary affiliation, Dashed =
+ referral relationship, Dotted = transfer pathway
221 + - Color coding by facility/specialty type
222 +
223 + Implementation: vis-network JavaScript library
224 + Canvas size: 1200x900px
225 +
227 + ## Provider Networks and Organizational Structures
228 +
229 + ### Provider Network
230 +
231 + A Provider Network is an organized group of healthcare
+ providers, facilities, and services that have contractual
+ relationships with health insurance plans to deliver care to
+ covered members, typically at negotiated rates. Provider
+ networks are fundamental structures in healthcare economics
+ and operations, influencing patient access, cost, and
+ quality outcomes.
232 +
233 + Network types include:
234 +
235 + - Health Maintenance Organization (HMO): Narrow
+ networks requiring PCP selection and referrals for
+ specialist access
236 + - Preferred Provider Organization (PPO): Broader
+ networks allowing out-of-network access with higher
+ cost-sharing
237 + - Exclusive Provider Organization (EPO): Narrow
+ networks with no out-of-network coverage except emergencies
238 + - Point of Service (POS): Hybrid networks combining HMO
+ and PPO features
239 + - Accountable Care Organization (ACO): Value-based
+ networks sharing financial risk/reward for quality and cost
+ outcomes
240 +
241 + Graph models excel at representing provider networks
+ because they capture the complex many-to-many relationships
+ between providers, facilities, insurance plans, and
+ geographic service areas. Network analysis queries can
+ identify:
242 +
243 + - Network adequacy: Whether sufficient providers exist
+ by specialty and geography to meet member needs
244 + - Narrow network optimization: Which provider
+ combinations maintain quality while reducing costs
245 + - Disruption analysis: Impact of provider exits or
+ facility closures on network capacity
246 + - Referral leakage: When patients are referred outside
+ the network unnecessarily
247 +
248 + ### Hospital Department
249 +
250 + Hospital Departments represent the organizational and
+ functional subdivisions within hospitals, each specializing
+ in specific types of care, patient populations, or clinical
+ services. Departments coordinate resources, staff, and
+ equipment while maintaining clinical protocols specific to
+ their specialty domains.
251 +
252 + Common hospital departments:
253 +
254 + - Emergency Medicine: 24/7 acute care for urgent and
+ emergent conditions
255 + - Surgery: Operating rooms and perioperative services
+ across surgical specialties
256 + - Medicine: General internal medicine and medical
+ subspecialties
257 + - Critical Care: Intensive care units (ICU, cardiac
+ ICU, neonatal ICU)
258 + - Obstetrics/Gynecology: Labor and delivery, women's
+ health services
259 + - Pediatrics: Care for infants, children, and
+ adolescents
260 + - Oncology: Cancer diagnosis, chemotherapy, radiation
+ therapy
261 + - Radiology: Diagnostic imaging (X-ray, CT, MRI,
+ ultrasound)
262 + - Laboratory: Clinical pathology and diagnostic testing
263 + - Pharmacy: Medication dispensing and clinical pharmacy
+ services
264 +
265 + Graph models represent departments as nodes connected to
+ the hospital, with relationships to providers who have
+ privileges in those departments, equipment and resources
+ located in departments, and clinical protocols applicable to
+ department services.
266 +
267 + ### Care Team and Multidisciplinary Team
268 +
269 + A Care Team is a coordinated group of healthcare
+ professionals working collaboratively to deliver
+ comprehensive care to a patient or population. Care teams
+ may be formal organizational structures or dynamic groupings
+ formed around specific patients with complex needs.
270 +
271 + Traditional primary care teams typically include:
272 +
273 + - Primary care provider (physician, nurse practitioner, or
+ physician assistant)
274 + - Nursing staff (registered nurses, licensed practical
+ nurses)
275 + - Medical assistants
276 + - Care coordinators or care managers
277 + - Behavioral health consultants
278 + - Pharmacists
279 + - Social workers
280 +
281 + Multidisciplinary Teams extend the care team concept to
+ include specialists from multiple clinical disciplines who
+ collaboratively manage patients with complex or multi-system
+ conditions. These teams are particularly important for
+ chronic diseases, cancer care, transplantation, and
+ geriatric care.
282 +
283 + Graph models of care teams capture both standing team
+ structures (a primary care clinic's established team
+ composition) and dynamic patient-specific teams (the set of
+ providers actively involved in a cancer patient's
+ treatment). Relationships between providers on teams include
+ role-based connections, communication patterns, shared
+ patients, and collaborative care protocols.
284 +
285 + Multidisciplinary Care Team Graph
+ Model
287 + Type: graph-model
288 +
289 + Purpose: Visualize the structure and relationships
+ within a multidisciplinary care team managing a complex
+ patient (example: diabetes with cardiovascular
+ complications)
290 +
291 + Node types:
292 + 1. Patient (large blue circle, center)
293 + - Properties: patient_id, name, diagnoses
294 + - Example: "John Doe, Age 62, Type 2 Diabetes + CAD
+ + CKD Stage 3"
295 +
296 + 2. Primary Care Provider (medium green square)
297 + - Properties: name, specialty
298 + - Example: "Dr. Martinez, Family Medicine"
299 +
300 + 3. Specialist Providers (medium orange squares)
301 + - Properties: name, specialty
302 + - Examples:
303 + - "Dr. Kim, Endocrinology"
304 + - "Dr. Patel, Cardiology"
305 + - "Dr. Thompson, Nephrology"
306 +
307 + 4. Allied Health Professionals (small purple circles)
308 + - Properties: name, role
309 + - Examples:
310 + - "Sarah, RN (Care Coordinator)"
311 + - "James, PharmD (Clinical Pharmacist)"
312 + - "Lisa, RD (Registered Dietitian)"
313 + - "Tom, LCSW (Social Worker)"
314 +
315 + 5. Facility/Department (small gray hexagons)
316 + - Properties: name, type
317 + - Examples: "Downtown Clinic", "Cardiology
+ Practice", "Hospital Pharmacy"
318 +
319 + Edge types:
320 + 1. MANAGES_PRIMARY_CARE (PCP → Patient)
321 + - Properties: attribution_date, last_visit
322 + - Solid green line
323 +
324 + 2. CONSULTS_FOR (Specialist → Patient)
325 + - Properties: specialty_focus,
+ consultation_frequency
326 + - Solid orange lines
327 +
328 + 3. SUPPORTS_CARE (Allied Health → Patient)
329 + - Properties: role, intervention_type
330 + - Dashed purple lines
331 +
332 + 4. REFERS_TO (Provider → Provider)
333 + - Properties: referral_date, indication
334 + - Dotted blue arrows showing referral pathways
335 +
336 + 5. COLLABORATES_WITH (Provider ↔ Provider)
337 + - Properties: communication_frequency,
+ shared_care_protocol
338 + - Bidirectional gray lines
339 +
340 + 6. PRACTICES_AT (Provider → Facility)
341 + - Properties: primary_location
342 + - Thin black lines
343 +
344 + Sample data structure:
345 + Patient "John Doe" (center)
346 + ├─ MANAGED_BY → Dr. Martinez (PCP)
347 + │ ├─ REFERRED_TO → Dr. Kim (Endocrinologist)
348 + │ ├─ REFERRED_TO → Dr. Patel (Cardiologist)
349 + │ └─ REFERRED_TO → Dr. Thompson (Nephrologist)
350 + │
351 + ├─ Dr. Kim (Endocrinologist)
352 + │ ├─ COLLABORATES_WITH → Dr. Patel (co-managing DM +
+ CAD)
353 + │ └─ COLLABORATES_WITH → James, PharmD (medication
+ optimization)
354 + │
355 + ├─ Dr. Patel (Cardiologist)
356 + │ └─ COLLABORATES_WITH → Dr. Thompson (managing CAD
+ + CKD)
357 + │
358 + ├─ Sarah, RN (Care Coordinator)
359 + │ ├─ COORDINATES_WITH → All providers
360 + │ └─ SUPPORTS_CARE → Patient (care management)
361 + │
362 + ├─ James, PharmD (Clinical Pharmacist)
363 + │ └─ SUPPORTS_CARE → Patient (medication
+ reconciliation, adherence)
364 + │
365 + ├─ Lisa, RD (Dietitian)
366 + │ └─ SUPPORTS_CARE → Patient (nutrition counseling)
367 + │
368 + └─ Tom, LCSW (Social Worker)
369 + └─ SUPPORTS_CARE → Patient (behavioral health,
+ resources)
370 +
371 + Layout: Radial with patient at center, providers in
+ inner ring, allied health in outer ring
372 +
373 + Interactive features:
374 + - Hover over Provider: Show specialty, visit history
+ with patient, current medications prescribed
375 + - Hover over edge: Show relationship type and recent
+ interactions
376 + - Click Provider: Highlight all direct collaborations
+ and referral relationships
377 + - Click Patient: Display summary of care team
+ composition and coordination metrics
378 + - "Show communication paths" toggle: Highlight
+ communication frequency with line thickness
379 + - "Timeline view" button: Show temporal evolution of
+ care team (when providers joined/left care team)
380 + - Filter by role: Show only physicians, show only
+ allied health, show full team
381 +
382 + Visual styling:
383 + - Patient node: Extra large (80px), blue, prominently
+ labeled
384 + - Provider nodes sized by visit frequency with patient
+ (more visits = larger node)
385 + - Edge thickness represents communication/collaboration
+ frequency
386 + - Color coding: Green = primary care, Orange =
+ specialty care, Purple = allied health
387 + - Animated pulse on nodes with pending actions (overdue
+ visits, medication reconciliation needed)
388 +
389 + Annotations:
390 + - Display care team metrics:
391 + - Team size: 8 members
392 + - Coordination events: 24 in last 6 months
393 + - Last team conference: 2 weeks ago
394 + - Patient outcome trend: HbA1c improving (8.2% →
+ 7.4%)
395 +
396 + Legend:
397 + - Node shapes and colors by role
398 + - Edge types: Solid = active care, Dashed = support
+ role, Dotted = referral
399 + - Line thickness = collaboration intensity
400 +
401 + Implementation: vis-network JavaScript library
402 + Canvas size: 900x900px
403 +
405 + ## Scheduling, Appointments, and Capacity Management
406 +
407 + ### Provider Schedule and Appointment
408 +
409 + Provider Schedules define when and where providers are
+ available to deliver clinical services, encompassing office
+ hours, surgical time, hospital rounds, and administrative
+ time. Schedule management is a critical operational function
+ affecting patient access, provider productivity, and
+ revenue optimization.
410 +
411 + Provider schedules include multiple dimensions:
412 +
413 + - Time blocks: Duration and timing of clinic sessions,
+ surgical blocks, or hospital shifts
414 + - Location: Which facility or facilities the provider
+ works at during each time block
415 + - Appointment types: New patient visits, follow-up
+ visits, procedures, telehealth
416 + - Template structures: Recurring weekly patterns vs.
+ ad-hoc scheduling
417 + - Buffer time: Slots reserved for urgent add-ons,
+ administrative tasks, or breaks
418 +
419 + An Appointment represents a scheduled interaction
+ between a patient and a provider (or care team) at a
+ specific time and location for a defined clinical purpose.
+ Appointment data is central to healthcare operations,
+ connecting patients, providers, schedules, facilities, and
+ clinical services.
420 +
421 + Key appointment properties in graph models:
422 +
423 + - appointment_id: Unique identifier
424 + - appointment_datetime: Scheduled date and time
425 + - duration: Expected length in minutes
426 + - appointment_type: New patient, follow-up, procedure,
+ telehealth
427 + - status: Scheduled, checked-in, in-progress,
+ completed, no-show, cancelled
428 + - location: Clinic or facility
429 + - chief_complaint: Reason for visit
430 + - insurance_authorization: Pre-authorization status if
+ required
431 +
432 + Graph queries enable sophisticated appointment analytics
+ such as:
433 +
434 + - Identifying schedule optimization opportunities
+ (underutilized time slots, imbalanced provider schedules)
435 + - Analyzing no-show patterns by patient demographics,
+ appointment type, or advance booking time
436 + - Calculating actual vs. expected appointment duration for
+ schedule accuracy
437 + - Tracking patient access metrics (time to
+ third-next-available appointment)
438 +
439 + ### Provider Capacity
440 +
441 + Provider Capacity represents the volume of clinical
+ services a provider or facility can deliver within a given
+ time period, constrained by schedule availability,
+ resources, and regulatory limits. Capacity management is
+ fundamental to healthcare operations, affecting patient
+ access times, provider workload, financial performance, and
+ care quality.
442 +
443 + Capacity metrics include:
444 +
445 + - Appointment slots: Total available time slots per
+ day/week/month
446 + - Utilization rate: Percentage of slots filled with
+ scheduled appointments
447 + - Panel capacity: Maximum number of patients a provider
+ can appropriately manage in their primary care panel
448 + - Surgical capacity: Operating room hours and case
+ volume
449 + - Bed capacity: Available inpatient beds by unit type
+ (med-surg, ICU, pediatrics)
450 +
451 + Graph models support capacity analysis by connecting
+ providers to schedules, appointments, and facilities,
+ enabling queries that calculate capacity utilization,
+ identify bottlenecks, and forecast demand. For example, a
+ graph query can identify specialists with low utilization
+ who could accommodate referrals that are currently
+ experiencing long wait times with high-volume specialists.
452 +
453 + Provider Capacity Utilization Dashboard
+ Chart
455 + Type: chart
456 +
457 + Purpose: Visualize provider capacity and utilization
+ across different specialties to identify access bottlenecks
+ and optimization opportunities
458 +
459 + Chart type: Combo chart (grouped bar + line overlay)
460 +
461 + Title: "Provider Capacity and Utilization by Specialty
+ - Current Month"
462 +
463 + X-axis: Specialty categories (6 specialties)
464 + - Primary Care
465 + - Cardiology
466 + - Orthopedics
467 + - Endocrinology
468 + - Dermatology
469 + - Gastroenterology
470 +
471 + Y-axis (left): Appointment counts (0-800)
472 + Y-axis (right): Utilization percentage (0-100%)
473 +
474 + Data series:
475 +
476 + Bar series (left Y-axis):
477 + 1. Total Capacity Slots (light blue bars)
478 + - Primary Care: 750
479 + - Cardiology: 400
480 + - Orthopedics: 350
481 + - Endocrinology: 250
482 + - Dermatology: 300
483 + - Gastroenterology: 280
484 +
485 + 2. Scheduled Appointments (dark blue bars)
486 + - Primary Care: 680
487 + - Cardiology: 390
488 + - Orthopedics: 315
489 + - Endocrinology: 238
490 + - Dermatology: 270
491 + - Gastroenterology: 252
492 +
493 + 3. Completed Appointments (green bars)
494 + - Primary Care: 612
495 + - Cardiology: 350
496 + - Orthopedics: 283
497 + - Endocrinology: 214
498 + - Dermatology: 243
499 + - Gastroenterology: 227
500 +
501 + Line series (right Y-axis):
502 + 4. Utilization Rate (orange line with markers)
503 + - Primary Care: 91% (680/750)
504 + - Cardiology: 98% (390/400)
505 + - Orthopedics: 90% (315/350)
506 + - Endocrinology: 95% (238/250)
507 + - Dermatology: 90% (270/300)
508 + - Gastroenterology: 90% (252/280)
509 +
510 + 5. Target Utilization (red dashed line)
511 + - Flat line at 85% across all specialties
512 +
513 + Chart styling:
514 + - Bar width: Wide with small gaps for readability
515 + - Bars grouped by specialty
516 + - Grid lines: Horizontal lines every 100 units (left)
+ and 10% (right)
517 + - Data labels: Show utilization percentage above each
+ specialty
518 + - Legend: Bottom of chart
519 +
520 + Annotations:
521 + - Arrow pointing to Cardiology: "98% utilization -
+ capacity constraint"
522 + - Arrow pointing to Primary Care: "10% no-show rate (68
+ missed appointments)"
523 + - Callout box on Cardiology: "Average wait time: 23
+ days for new patient"
524 + - Callout box on Endocrinology: "High utilization (95%)
+ with growing waitlist"
525 +
526 + Color coding:
527 + - Light blue: Total capacity (what's available)
528 + - Dark blue: Scheduled (what's booked)
529 + - Green: Completed (what was delivered)
530 + - Orange line: Actual utilization percentage
531 + - Red dashed line: Target utilization benchmark
532 +
533 + Additional metrics panel (below chart):
534 + - Network-wide utilization: 92%
535 + - Total no-show rate: 9.3%
536 + - Average new patient access time: 14.5 days
537 + - Specialties above capacity threshold: 2 (Cardiology,
+ Endocrinology)
538 + - Optimization opportunity: Redistribute 45 slots from
+ low-volume sessions
539 +
540 + Graph database query insight:
541 + "This chart was generated by graph queries aggregating
+ appointment data across provider schedules, tracking status
+ transitions from scheduled → completed, and calculating
+ specialty-level capacity metrics"
542 +
543 + Implementation: Chart.js with dual Y-axis configuration
544 + Canvas size: 1000x600px
545 +
547 + ## Provider Credentials, Quality, and Performance
548 +
549 + ### Provider Credential, Medical License, and Board
+ Certification
550 +
551 + Healthcare providers must maintain various credentials,
+ licenses, and certifications to legally and ethically
+ practice medicine. These credentials represent formal
+ validation of education, training, competency, and ongoing
+ professional development.
552 +
553 + Medical Licenses are state-issued authorizations
+ permitting physicians and other healthcare professionals to
+ practice within a specific jurisdiction. License properties
+ include:
554 +
555 + - license_number: Unique state-issued identifier
556 + - issuing_state: State medical board
557 + - license_type: MD, DO, NP, PA, RN, etc.
558 + - issue_date: Original license grant date
559 + - expiration_date: When renewal is required
560 + - status: Active, inactive, suspended, revoked
561 + - restrictions: Any practice limitations
562 +
563 + Board Certifications represent voluntary specialty
+ certifications granted by medical specialty boards (e.g.,
+ American Board of Internal Medicine, American Board of
+ Surgery) after physicians complete residency training and
+ pass rigorous examinations. Certifications validate
+ specialist expertise and typically require recertification
+ every 7-10 years through continuing education and
+ examination.
564 +
565 + Provider Credentials encompass the full portfolio of
+ qualifications including:
566 +
567 + - Educational degrees (MD, DO, PhD, MSN, etc.)
568 + - Residency and fellowship training completion
569 + - Medical licenses in all practice states
570 + - Board certifications in relevant specialties
571 + - Hospital privileges at specific facilities
572 + - DEA registration for controlled substance prescribing
573 + - Malpractice insurance coverage
574 +
575 + Graph models of credentialing data connect providers to
+ credential nodes with temporal properties tracking issuance,
+ expiration, and renewal dates. Healthcare organizations use
+ these graphs to monitor credential status, trigger renewal
+ processes before expiration, and verify provider
+ qualifications for privileges and network participation.
576 +
577 + ### Provider Specialization
578 +
579 + Provider Specialization refers to the focused clinical
+ domain in which a provider has advanced training and
+ primarily practices. Specialization data is fundamental to
+ healthcare operations, enabling appropriate patient
+ matching, referral routing, capacity planning, and network
+ adequacy assessment.
580 +
581 + Specialization can be modeled at multiple granularity
+ levels:
582 +
583 + - Primary specialty: Broad categorization (internal
+ medicine, surgery, pediatrics)
584 + - Subspecialty: Focused domain within primary specialty
+ (interventional cardiology, pediatric endocrinology,
+ surgical oncology)
585 + - Clinical interests: Specific conditions or
+ populations (heart failure, sports medicine, geriatric
+ diabetes)
586 + - Procedures performed: Specific technical capabilities
+ (colonoscopy, echocardiography, joint replacement)
587 +
588 + Graph relationships between providers and specialties
+ support network optimization queries such as finding the
+ nearest available cardiologist who performs echocardiograms,
+ identifying gaps in subspecialty coverage within a provider
+ network, or routing referrals to specialists with specific
+ procedural capabilities.
589 +
590 + ### Provider Rating and Provider Performance
591 +
592 + Provider Ratings represent evaluations of provider
+ quality, typically derived from patient satisfaction
+ surveys, peer assessments, or composite quality scores.
+ Common rating sources include:
593 +
594 + - Patient satisfaction scores: CAHPS surveys, Press
+ Ganey scores, online reviews
595 + - Clinical quality metrics: HEDIS measures, CMS star
+ ratings, specialty-specific quality indicators
596 + - Peer ratings: Reputation among referring physicians
597 + - Efficiency metrics: Cost per episode, resource
+ utilization patterns
598 +
599 + Provider Performance encompasses the comprehensive
+ assessment of clinical outcomes, patient experience,
+ operational efficiency, and adherence to evidence-based
+ practices. Performance measurement supports value-based
+ payment models, quality improvement initiatives, and
+ provider network tiering.
600 +
601 + Key performance domains:
602 +
603 + - Clinical outcomes: Complication rates, readmission
+ rates, mortality rates (risk-adjusted)
604 + - Process quality: Adherence to clinical guidelines,
+ appropriate medication prescribing, preventive care delivery
605 + - Patient experience: Communication, access,
+ coordination, overall satisfaction
606 + - Resource stewardship: Cost-efficiency, appropriate
+ utilization, avoidable emergency department visits
607 +
608 + Graph models integrate performance data with provider
+ nodes, enabling comparative analysis across providers,
+ identification of high-performing care teams, and
+ correlation of performance with network structure, patient
+ populations, and care patterns.
609 +
610 + | Performance Metric Category | Example Measures | Data
+ Sources | Graph Analysis Applications |
611 + |-----------------------------|------------------|---------
+ -----|------------------------------|
612 + | Clinical Outcomes | 30-day readmission rate, surgical
+ complication rate, diabetes control (HbA1c) | EHR, claims
+ data, clinical registries | Identify high-performing
+ providers, correlate outcomes with care team structure |
613 + | Process Quality | Colorectal cancer screening rate,
+ statin prescribing for CAD, depression screening | EHR
+ quality reports, HEDIS audits | Find providers exceeding
+ benchmarks, spread best practices |
614 + | Patient Experience | Communication rating, care
+ coordination score, recommend provider | CAHPS surveys,
+ online reviews | Match patients to highly-rated providers,
+ investigate low-scoring patterns |
615 + | Efficiency | Cost per diabetes patient, imaging
+ utilization rate, generic prescribing rate | Claims
+ analytics, pharmacy data | Reward efficient providers,
+ identify outliers for education |
616 +
617 + Provider Performance Comparison
+ MicroSim
619 + Type: microsim
620 +
621 + Learning objective: Enable interactive exploration of
+ provider performance metrics across multiple dimensions,
+ demonstrating how graph databases can aggregate and compare
+ performance data from multiple sources for network
+ optimization
622 +
623 + Canvas layout (1200x800px):
624 + - Top section (1200x100): Title, filters, and metric
+ selectors
625 + - Middle-left (700x600): Scatter plot visualization
+ showing provider performance
626 + - Middle-right (500x600): Details panel showing
+ selected provider's full metrics
627 + - Bottom section (1200x100): Summary statistics and
+ insights
628 +
629 + Top section controls:
630 + - Dropdown: Select specialty (All, Primary Care,
+ Cardiology, Orthopedics, etc.)
631 + - Dropdown: Select X-axis metric (Patient Satisfaction,
+ Cost Efficiency, Clinical Quality Score)
632 + - Dropdown: Select Y-axis metric (Same options as
+ X-axis)
633 + - Checkbox: Show provider names on hover
634 + - Button: "Reset View"
635 +
636 + Scatter plot visualization (middle-left):
637 + - X-axis: Selected metric 1 (e.g., Patient Satisfaction
+ Score, 0-5 scale)
638 + - Y-axis: Selected metric 2 (e.g., Clinical Quality
+ Score, 0-100 scale)
639 + - Data points: Each provider represented as a circle
640 + - Circle size: Proportional to patient panel size or
+ visit volume
641 + - Circle color: By performance quadrant:
642 + - Green: High on both metrics (top-right quadrant)
643 + - Yellow: High on one metric, medium on other
644 + - Orange: Medium on both metrics
645 + - Red: Low on one or both metrics
646 + - Quadrant lines: Divide plot at median values for each
+ axis
647 + - Labels: "High Quality, High Satisfaction"
+ (top-right), etc.
648 +
649 + Sample provider data (Primary Care):
650 + - Dr. Anderson: Satisfaction 4.8, Quality 92, Panel
+ 1,800 (green, top-right)
651 + - Dr. Baker: Satisfaction 4.2, Quality 78, Panel 1,500
+ (yellow, middle-right)
652 + - Dr. Chen: Satisfaction 4.6, Quality 65, Panel 2,200
+ (yellow, bottom-right)
653 + - Dr. Davis: Satisfaction 3.9, Quality 88, Panel 1,300
+ (yellow, top-middle)
654 + - Dr. Evans: Satisfaction 3.5, Quality 62, Panel 1,900
+ (red, bottom-left)
655 + - [15 more providers distributed across quadrants]
656 +
657 + Interactive features:
658 + - Hover over data point: Show provider card with:
659 + - Provider name and specialty
660 + - All performance metrics (not just X and Y axes)
661 + - Patient panel size
662 + - Years in practice
663 + - Primary practice location
664 + - Click data point: Lock details panel to that provider
665 + - Double-click data point: Zoom into that provider's
+ network (show referring PCPs, patients, facilities)
666 + - Drag to select multiple providers: Show group
+ statistics in details panel
667 + - Metric selector changes: Smoothly animate data points
+ to new positions
668 +
669 + Details panel (middle-right) when provider selected:
670 + Display full provider profile:
671 + - Provider name, photo placeholder, specialty
672 + - Key metrics with visual indicators:
673 + - Patient Satisfaction: 4.8/5.0 (star rating visual)
674 + - Clinical Quality Score: 92/100 (progress bar)
675 + - Cost Efficiency: 8% below average (green indicator)
676 + - Panel Size: 1,800 patients
677 + - Appointment Availability: 7 days (green)
678 + - No-show Rate: 6% (green)
679 + - Patient Demographics: Age distribution, condition
+ prevalence
680 + - Performance trend: Small line chart showing metrics
+ over last 12 months
681 + - Peer comparison: "Ranks 4th of 23 in network for
+ overall performance"
682 + - Care team: "Works with 2 care coordinators, 1
+ pharmacist, 3 medical assistants"
683 +
684 + Bottom summary section:
685 + Display network-level insights:
686 + - Total providers: 68
687 + - High performers (both metrics > median): 18 (26%)
688 + - Improvement opportunities: 12 (18%)
689 + - Network median satisfaction: 4.3/5.0
690 + - Network median quality score: 78/100
691 + - "Graph insight: Providers with larger care teams show
+ 15% higher quality scores on average"
692 +
693 + Default parameters:
694 + - Specialty: Primary Care
695 + - X-axis: Patient Satisfaction Score
696 + - Y-axis: Clinical Quality Score
697 + - All providers displayed
698 +
699 + Behavior:
700 + - On load, display scatter plot with primary care
+ providers
701 + - Animate points appearing with fade-in effect
702 + - When specialty filter changes, fade out old points,
+ fade in new points
703 + - When axis metric changes, animate points moving to
+ new positions
704 + - When hovering, enlarge point slightly and show
+ connector line to details panel
705 + - When clicking, lock selection with highlighted border
706 +
707 + Educational features:
708 + - "Graph Query Example" button: Shows Cypher query to
+ aggregate performance data
709 + 710 + MATCH (p:Provider)-[:PRACTICES_IN]->(s:Specialty
+ {name: 'Primary Care'})
711 + MATCH (p)-[:HAS_METRIC]->(m:PerformanceMetric)
712 + RETURN p.name,
713 + avg(CASE WHEN m.type = 'satisfaction' THEN
+ m.score END) as satisfaction,
714 + avg(CASE WHEN m.type = 'quality' THEN m.score
+ END) as quality,
715 + size((p)-[:HAS_PATIENT]->()) as panel_size
716 +
717 + - Info icons explaining each metric with clinical
+ relevance
718 + - "Best Practice" callouts: "High-performing providers
+ typically have structured care teams and systematic
+ follow-up processes"
719 +
720 + Implementation notes:
721 + - Use p5.js for rendering scatter plot and animations
722 + - Store sample provider data as JSON objects
723 + - Implement quadrant highlighting with semi-transparent
+ overlays
724 + - Use color interpolation for smooth category
+ transitions
725 + - Calculate statistics in real-time as filters change
726 +
727 + Implementation: p5.js
728 + Canvas size: 1200x800px
729 +
731 + ## Clinical Excellence: Guidelines, Protocols, and
+ Evidence-Based Practice
732 +
733 + ### Clinical Guideline and Best Practice
734 +
735 + Clinical Guidelines are systematically developed
+ statements that provide recommendations for optimizing
+ patient care, based on comprehensive reviews of evidence and
+ assessments of benefits and harms. Guidelines are produced
+ by professional medical societies, government agencies, and
+ healthcare organizations to standardize care and improve
+ outcomes.
736 +
737 + Prominent guideline sources include:
738 +
739 + - American Heart Association / American College of
+ Cardiology: Cardiovascular disease guidelines
740 + - American Diabetes Association: Diabetes management
+ standards of care
741 + - U.S. Preventive Services Task Force: Preventive care
+ recommendations
742 + - National Comprehensive Cancer Network: Cancer
+ treatment protocols
743 + - Infectious Diseases Society of America: Antimicrobial
+ stewardship and treatment guidelines
744 +
745 + Best Practices represent clinically proven approaches
+ that consistently produce superior outcomes compared to
+ alternative methods. Best practices emerge from clinical
+ research, quality improvement initiatives, and real-world
+ effectiveness studies.
746 +
747 + Graph models connect clinical guidelines to conditions,
+ medications, procedures, and providers, enabling clinical
+ decision support systems that recommend guideline-concordant
+ care at the point of service. For example, when a patient
+ with diabetes and cardiovascular disease is seen, the graph
+ can traverse from patient conditions to applicable
+ guidelines to recommended screening tests, medications, and
+ lifestyle interventions.
748 +
749 + ### Evidence-Based Medicine
750 +
751 + Evidence-Based Medicine (EBM) is the conscientious,
+ explicit, and judicious use of current best evidence in
+ making decisions about individual patient care, integrating
+ clinical expertise with the best available external clinical
+ evidence from systematic research. EBM represents a
+ paradigm shift from tradition-based medicine toward
+ data-driven clinical decision-making.
752 +
753 + The evidence hierarchy in EBM includes:
754 +
755 + 1. Systematic reviews and meta-analyses: Comprehensive
+ synthesis of multiple studies
756 + 2. Randomized controlled trials: Experimental studies
+ with control groups
757 + 3. Cohort studies: Observational studies following
+ groups over time
758 + 4. Case-control studies: Comparisons of patients with
+ and without specific outcomes
759 + 5. Case series and case reports: Descriptions of
+ individual patient experiences
760 + 6. Expert opinion: Clinical judgment based on
+ experience and training
761 +
762 + Graph databases can model evidence structures by connecting
+ clinical interventions to research studies, linking studies
+ to evidence levels, and associating evidence with guideline
+ recommendations. This enables queries such as identifying
+ which medications for a condition have Level 1 evidence
+ support, or finding recently published trials that might
+ impact current treatment approaches.
763 +
764 + ### Clinical Protocol
765 +
766 + Clinical Protocols are detailed procedural documents
+ specifying the steps to be followed in diagnosing, treating,
+ or managing specific conditions or clinical scenarios.
+ Protocols operationalize clinical guidelines into actionable
+ workflows, often customized to local organizational
+ contexts, resources, and patient populations.
767 +
768 + Protocol types include:
769 +
770 + - Treatment protocols: Step-by-step management plans
+ for specific conditions (sepsis protocol, stroke protocol,
+ trauma protocol)
771 + - Diagnostic protocols: Standardized workup algorithms
+ (chest pain evaluation, syncope workup)
772 + - Preventive protocols: Systematic screening and health
+ maintenance procedures (well-child checks, cancer screening
+ algorithms)
773 + - Safety protocols: Processes ensuring patient safety
+ (medication reconciliation, fall prevention, pressure ulcer
+ prevention)
774 +
775 + In graph models, protocols are represented as structured
+ workflows connecting conditions or presentations to ordered
+ sequences of assessments, interventions, and decision
+ points. Protocol adherence can be measured by comparing
+ actual care paths (traced through graph relationships) to
+ protocol-specified paths, enabling quality measurement and
+ identification of practice variation.
776 +
777 + Clinical Protocol Workflow: Chest Pain
+ Evaluation in Emergency Department
779 + Type: workflow
780 +
781 + Purpose: Illustrate how clinical protocols are modeled
+ as graph structures with decision points, enabling protocol
+ adherence tracking and outcome correlation
782 +
783 + Visual style: Flowchart with decision diamonds, process
+ rectangles, and swimlanes
784 +
785 + Swimlanes (top to bottom):
786 + 1. Patient Presentation
787 + 2. Initial Assessment
788 + 3. Risk Stratification
789 + 4. Diagnostic Testing
790 + 5. Treatment / Disposition
791 + 6. Follow-up
792 +
793 + Timeline: Horizontal flow from left (arrival) to right
+ (disposition)
794 +
795 + Steps:
796 +
797 + STEP 1 - Patient Arrival:
798 + Patient Presentation: "Patient arrives with chest pain"
799 + Hover text: "ED triage classification: ESI Level 2
+ (high risk), activate chest pain protocol"
800 +
801 + STEP 2 - Immediate Actions (Time 0-10 minutes):
802 + Initial Assessment:
803 + - "Obtain vital signs and 12-lead ECG"
804 + - "IV access established"
805 + - "Continuous cardiac monitoring"
806 + Hover text: "Graph captures timestamp properties: ECG
+ obtained at T+7 min (meets <10 min target)"
807 +
808 + STEP 3 - Risk Stratification (Time 10-20 minutes):
809 + Decision: "ECG shows STEMI?"
810 +
811 + Branch A (If YES - STEMI):
812 + - "Activate cardiac catheterization lab"
813 + - "Administer antiplatelet therapy (aspirin, P2Y12
+ inhibitor)"
814 + - "Prepare for primary PCI"
815 + - Hover text: "High-risk pathway: Direct to cath lab,
+ door-to-balloon time target <90 minutes"
816 + - END at cardiac catheterization
817 +
818 + Branch B (If NO - Non-STEMI or unclear):
819 + - Continue to "Calculate HEART score"
820 + - Hover text: "HEART score incorporates: History,
+ ECG, Age, Risk factors, Troponin"
821 +
822 + STEP 4 - HEART Score Decision (Time 20 minutes):
823 + Decision: "HEART Score?"
824 +
825 + Branch B1 (HEART 0-3: Low Risk):
826 + - "Obtain troponin at 0 and 2 hours"
827 + - "Stress test or coronary CTA as outpatient"
828 + - "Discharge from ED with cardiology follow-up"
829 + - Hover text: "Low-risk pathway: <2% risk of MACE at
+ 6 weeks"
830 +
831 + Branch B2 (HEART 4-6: Moderate Risk):
832 + - "Serial troponins (0, 2, 4 hours)"
833 + - "Admit to observation unit"
834 + - "Stress test prior to discharge"
835 + - Hover text: "Moderate-risk pathway: 12-20% risk of
+ MACE, requires observation"
836 +
837 + Branch B3 (HEART 7-10: High Risk):
838 + - "Serial troponins"
839 + - "Cardiology consultation"
840 + - "Admit to cardiology service"
841 + - "Coronary angiography within 24-72 hours"
842 + - Hover text: "High-risk pathway: >50% risk of MACE,
+ requires inpatient management"
843 +
844 + STEP 5 - Troponin Results (Time varies by pathway):
845 + Decision: "Troponin elevated?"
846 +
847 + If YES:
848 + - "Diagnosis: NSTEMI or unstable angina"
849 + - "Antiplatelet + anticoagulation therapy"
850 + - "Cardiology consultation"
851 + - "Inpatient admission"
852 + - Hover text: "Elevated troponin confirms acute
+ coronary syndrome"
853 +
854 + If NO:
855 + - "Consider alternative diagnoses"
856 + - "PE protocol if indicated"
857 + - "GI evaluation if indicated"
858 + - "Possible discharge with follow-up"
859 + - Hover text: "Non-cardiac chest pain: Consider PE,
+ GERD, MSK causes"
860 +
861 + STEP 6 - Disposition and Follow-up:
862 + Treatment/Disposition outcomes:
863 + - "Discharge home with PCP follow-up"
864 + - "Observation unit admission"
865 + - "Inpatient cardiology admission"
866 + - "Transfer to cardiac catheterization"
867 +
868 + Follow-up:
869 + - "Cardiology appointment within 7 days"
870 + - "Stress test scheduled"
871 + - "Medication reconciliation and education"
872 + Hover text: "Graph tracks disposition and ensures
+ follow-up appointments are scheduled before discharge"
873 +
874 + Color coding:
875 + - Green: Low-risk pathway
876 + - Yellow: Moderate-risk pathway
877 + - Orange: High-risk pathway (HEART 7-10)
878 + - Red: Critical pathway (STEMI)
879 + - Blue: Diagnostic/testing steps
880 + - Purple: Treatment interventions
881 +
882 + Time annotations:
883 + - Display cumulative time at each decision point
884 + - Highlight protocol compliance: "ECG at 7 min ✓",
+ "Troponin at 18 min ✓"
885 + - Show time-to-treatment metrics for quality
+ measurement
886 +
887 + Graph database representation:
888 + Show example graph pattern:
889 + 890 + (patient:Patient)-[:PRESENTS_WITH]->(presentation:Chief
+ Complaint {type: 'chest_pain'})
891 + (presentation)-[:TRIGGERS]->(protocol:ClinicalProtocol
+ {name: 'Chest Pain Evaluation'})
892 + (protocol)-[:INCLUDES_STEP {sequence:
+ 1}]->(step1:ProtocolStep {action: 'Obtain ECG'})
893 + (step1)-[:IF_RESULT {condition:
+ 'STEMI'}]->(step2a:ProtocolStep {action: 'Activate cath
+ lab'})
894 + (step1)-[:IF_RESULT {condition: 'No
+ STEMI'}]->(step2b:ProtocolStep {action: 'Calculate HEART
+ score'})
895 +
896 +
897 + Interactive features:
898 + - Hover over any step: See detailed description, time
+ targets, and clinical rationale
899 + - Click step: Highlight all subsequent possible
+ pathways from that point
900 + - Click decision diamond: Show distribution of actual
+ patient flows (e.g., "65% take low-risk pathway")
901 + - "Show protocol adherence" toggle: Highlight steps
+ where protocol was followed vs. deviated
902 + - "View patient example" button: Overlay a specific
+ patient's actual path through protocol
903 + - Time slider: Animate typical patient progression
+ through protocol with timing
904 +
905 + Metrics panel (side):
906 + Display protocol performance metrics:
907 + - Patients evaluated: 487 this month
908 + - Protocol adherence: 91%
909 + - Average time to ECG: 8.3 minutes (target <10)
910 + - Average ED length of stay by pathway:
911 + - Low-risk discharge: 3.2 hours
912 + - Moderate-risk observation: 18 hours
913 + - High-risk admission: 6.4 hours (ED time before
+ admission)
914 + - STEMI door-to-balloon time: 76 minutes average
+ (target <90)
915 + - 30-day MACE rate by pathway: Low 0.8%, Moderate 4.2%,
+ High 12.1%
916 +
917 + Educational callouts:
918 + - "Why HEART score?" Info box explaining risk
+ stratification importance
919 + - "Evidence basis" links to studies supporting protocol
+ steps
920 + - "Graph advantage" box: "Graph traversals can identify
+ protocol deviations in real-time, enabling immediate
+ clinical alerts"
921 +
922 + Implementation: HTML/CSS/JavaScript with SVG for
+ flowchart elements and D3.js for animations
923 + Canvas size: 1400x1000px
924 +
926 + ## Referral Coordination and Care Transitions
927 +
928 + ### Referral
929 +
930 + A Referral is the process by which one provider (the
+ referring provider, typically a primary care physician)
+ recommends that a patient see another provider (the
+ referred-to provider, typically a specialist) for
+ consultation, specialized diagnostic services, or treatment
+ beyond the referring provider's scope of practice. Referrals
+ are critical coordination points in healthcare delivery,
+ connecting patients to specialized expertise while
+ maintaining care continuity.
931 +
932 + Referral data elements in graph models include:
933 +
934 + - referral_id: Unique identifier
935 + - referring_provider: Who initiated the referral
936 + - referred_to_provider: Target provider or specialty
937 + - patient: Who is being referred
938 + - indication: Clinical reason for referral
939 + - urgency: Routine, urgent, stat
940 + - referral_date: When referral was created
941 + - authorization_required: Whether insurance
+ pre-authorization needed
942 + - appointment_scheduled: Whether specialist appointment
+ has been booked
943 + - consultation_completed: Whether specialist saw
+ patient
944 + - report_received: Whether specialist sent consultation
+ note back to referring provider
945 +
946 + Referral patterns create rich network structures in graph
+ databases, revealing care coordination pathways, specialist
+ access bottlenecks, and opportunities for network
+ optimization. Graph queries can answer questions such as:
947 +
948 + - Which PCPs refer most frequently to specific specialists
+ (referral concentration vs. distribution)?
949 + - What is the average time from referral creation to
+ specialist appointment (access time)?
950 + - What percentage of referrals result in completed
+ consultations (referral completion rate)?
951 + - Which specialists receive referrals from the broadest
+ network of referring providers (network reach)?
952 + - Are there inappropriate referral patterns (e.g.,
+ referrals to specialists outside the network when in-network
+ alternatives exist)?
953 +
954 + Provider Referral Network Analysis
+ MicroSim
956 + Type: microsim
957 +
958 + Learning objective: Visualize and analyze referral
+ patterns between primary care providers and specialists to
+ identify care coordination opportunities, network leakage,
+ and access bottlenecks
959 +
960 + Canvas layout (1400x900px):
961 + - Top section (1400x100): Controls and filters
962 + - Middle-left (900x700): Network graph visualization
963 + - Middle-right (500x700): Analytics panel
964 + - Bottom section (1400x100): Summary insights
965 +
966 + Top section controls:
967 + - Dropdown: Time period (Last month, Last quarter, Last
+ year)
968 + - Dropdown: Specialty filter (All, Cardiology,
+ Orthopedics, GI, etc.)
969 + - Slider: Minimum referral volume (filter out
+ low-volume connections)
970 + - Checkbox: Show in-network only vs. Show all referrals
+ (including out-of-network)
971 + - Checkbox: Show patient flow animation
972 + - Button: "Reset view"
973 +
974 + Network visualization (middle-left):
975 +
976 + Node types:
977 + 1. Primary Care Providers (blue circles, left side)
978 + - Size proportional to total referrals sent
979 + - Label: Provider name
980 + - Example nodes:
981 + - Dr. Smith (large, 180 referrals/year)
982 + - Dr. Jones (medium, 95 referrals/year)
983 + - Dr. Brown (medium, 110 referrals/year)
984 +
985 + 2. Specialist Providers (orange circles, right side)
986 + - Size proportional to total referrals received
987 + - Color intensity by network status: Bright orange =
+ in-network, Faded orange = out-of-network
988 + - Label: Provider name + specialty
989 + - Example nodes:
990 + - Dr. Martinez - Cardiology (large, in-network,
+ 220 referrals received)
991 + - Dr. Kim - Cardiology (medium, out-of-network, 45
+ referrals received)
992 + - Dr. Patel - Orthopedics (large, in-network, 195
+ referrals received)
993 +
994 + 3. Specialty category nodes (optional grouping)
995 + - Hexagons grouping specialists by specialty
996 + - Only shown when "Group by specialty" checkbox
+ enabled
997 +
998 + Edge types:
999 + - Directed edges from PCP to Specialist
1000 + - Edge thickness proportional to referral volume
1001 + - Edge color:
1002 + - Green: In-network referrals
1003 + - Red: Out-of-network referrals ("leakage")
1004 + - Yellow: Referrals with long wait times (>30 days to
+ appointment)
1005 + - Animated particles flowing along edges when "Show
+ patient flow" enabled
1006 +
1007 + Layout: Force-directed with horizontal bias (PCPs
+ clustered left, specialists right)
1008 +
1009 + Sample data:
1010 + - Dr. Smith (PCP) → Dr. Martinez (Cardiology): 35
+ referrals (green, thick line)
1011 + - Dr. Smith (PCP) → Dr. Kim (Cardiology,
+ out-of-network): 8 referrals (red, thin line)
1012 + - Dr. Jones (PCP) → Dr. Martinez (Cardiology): 28
+ referrals (green, thick line)
1013 + - Dr. Smith (PCP) → Dr. Patel (Orthopedics): 42
+ referrals (green, very thick line)
1014 + - Dr. Brown (PCP) → Dr. Lee (GI): 15 referrals
+ (yellow-green, wait time 35 days)
1015 +
1016 + Interactive features:
1017 + - Hover over PCP node: Highlight all outgoing referrals
+ and show summary:
1018 + - Total referrals sent: 180
1019 + - In-network referrals: 168 (93%)
1020 + - Out-of-network referrals: 12 (7% - "leakage")
1021 + - Top 3 specialists referred to
1022 + - Average time to specialist appointment: 18 days
1023 +
1024 + - Hover over Specialist node: Highlight all incoming
+ referrals and show summary:
1025 + - Total referrals received: 220
1026 + - Referring PCPs: 18
1027 + - Average wait time to new appointment: 15 days
1028 + - Current capacity utilization: 96%
1029 + - Referral completion rate: 89% (referrals that
+ result in completed visits)
1030 +
1031 + - Click PCP node: Lock selection and show detailed
+ referral breakdown in analytics panel
1032 + - Click Specialist node: Show referring provider
+ distribution and wait time analysis
1033 + - Click edge: Show specific referral details (volume,
+ average wait time, completion rate, common diagnoses)
1034 + - Double-click node: Expand to show second-degree
+ connections (e.g., show all specialists that receive
+ referrals from any PCP who refers to the selected
+ specialist)
1035 + - Drag nodes to rearrange layout
1036 + - Scroll to zoom, drag background to pan
1037 +
1038 + Analytics panel (middle-right):
1039 + When PCP selected, display:
1040 + - Provider name and practice location
1041 + - Referral pattern analysis:
1042 + - Total referrals in period: 180
1043 + - Breakdown by specialty:
1044 + - Cardiology: 45 (25%)
1045 + - Orthopedics: 42 (23%)
1046 + - GI: 28 (16%)
1047 + - Dermatology: 22 (12%)
1048 + - Other: 43 (24%)
1049 + - Network adherence: 93% (168/180 in-network)
1050 + - Leakage cost estimate: $18,000 (based on
+ out-of-network differential)
1051 + - Average time to specialist appointment: 18 days
1052 + - Referral completion rate: 87%
1053 +
1054 + - Top out-of-network referrals (opportunities):
1055 + - Dr. Kim (Cardiology, out-of-network): 8 referrals
1056 + - Recommendation: "Redirect to Dr. Martinez
+ (in-network, similar quality, 15-day wait)"
1057 +
1058 + - Quality indicators:
1059 + - Patients return for follow-up after specialist
+ visit: 91%
1060 + - Specialist reports returned to PCP: 78% (below
+ target of 90%)
1061 +
1062 + When Specialist selected, display:
1063 + - Provider name, specialty, network status
1064 + - Referral volume analysis:
1065 + - Total referrals received: 220
1066 + - Referring PCPs: 18
1067 + - Referral density (Herfindahl index): 0.14
+ (well-distributed across many PCPs)
1068 + - Top 5 referring PCPs (with volumes)
1069 + - Referral indications (top diagnoses/reasons)
1070 +
1071 + - Access metrics:
1072 + - Average wait time to new appointment: 15 days
1073 + - Current capacity: 96% utilized (near capacity
+ constraint)
1074 + - Recommendation: "High utilization - consider adding
+ capacity or redistributing referrals"
1075 +
1076 + - Completion and outcomes:
1077 + - Appointment completion rate: 89%
1078 + - Consultation reports sent back to PCP: 85%
1079 + - Patient satisfaction: 4.6/5.0
1080 +
1081 + Bottom summary section:
1082 + Network-level insights:
1083 + - Total referrals in period: 1,847
1084 + - In-network referrals: 1,695 (92%)
1085 + - Out-of-network "leakage": 152 (8%)
1086 + - Estimated leakage cost: $228,000
1087 + - Average PCP-to-specialist wait time: 19 days
1088 + - Specialists at capacity (>95% utilization): 4
1089 + - Referral completion rate: 86%
1090 +
1091 + Graph insight: "PCPs with care coordinators show 23%
+ higher referral completion rates and 12% shorter wait times
+ due to proactive appointment scheduling"
1092 +
1093 + Default parameters:
1094 + - Time period: Last quarter
1095 + - Specialty: All
1096 + - Minimum referral volume: 5
1097 + - Show in-network only: No (show all referrals)
1098 + - Patient flow animation: Off
1099 +
1100 + Behavior:
1101 + - On page load, display full referral network with
+ animation of nodes positioning
1102 + - When specialty filter applied, fade out unrelated
+ specialists and referral edges
1103 + - When hovering, enlarge node and make connecting edges
+ bold
1104 + - When time period changes, animate edge thickness
+ transitions
1105 + - When "Show patient flow" enabled, animate small
+ particles traveling along edges at rate proportional to
+ referral volume
1106 + - When minimum volume slider adjusted, fade out
+ low-volume edges below threshold
1107 +
1108 + Educational features:
1109 + - "Graph Query Example" button shows Neo4j Cypher
+ query:
1110 + 1111 + // Find out-of-network referral leakage for a PCP
1112 + MATCH (pcp:Provider {name: 'Dr.
+ Smith'})-[r:REFERS_TO]->(specialist:Provider)
1113 + WHERE specialist.network_status = 'out-of-network'
1114 + RETURN specialist.name, specialist.specialty,
+ count(r) as referral_count,
1115 + sum(r.cost_differential) as leakage_cost
1116 + ORDER BY referral_count DESC
1117 +
1118 +
1119 + - Info tooltips explaining key concepts:
1120 + - "Network leakage": When patients are referred to
+ out-of-network providers despite availability of in-network
+ alternatives
1121 + - "Referral completion rate": Percentage of referrals
+ that result in completed specialist visits
1122 + - "Herfindahl index": Measure of referral
+ concentration (0 = perfectly distributed, 1 = all to one
+ specialist)
1123 +
1124 + - "Optimization recommendations" panel:
1125 + - Identifies high-leakage PCPs and suggests
+ in-network alternatives
1126 + - Highlights capacity-constrained specialists needing
+ additional slots
1127 + - Recommends referral redistribution to reduce wait
+ times
1128 +
1129 + Implementation notes:
1130 + - Use p5.js for network visualization with
+ force-directed layout
1131 + - Implement edge bundling for visual clarity when many
+ edges present
1132 + - Store referral data as graph structure (nodes array,
+ edges array with properties)
1133 + - Use physics simulation for force-directed layout with
+ constraints (horizontal separation)
1134 + - Implement efficient hover detection using spatial
+ hashing
1135 + - Particle animation uses traveling dots along Bezier
+ curves
1136 +
1137 + Implementation: p5.js
1138 + Canvas size: 1400x900px
1139 +
1141 + ## Summary and Key Takeaways
1142 +
1143 + The provider perspective on healthcare data modeling
+ encompasses the operational, organizational, and clinical
+ dimensions of healthcare delivery organizations. By modeling
+ providers, facilities, networks, schedules, credentials,
+ referrals, and clinical protocols as interconnected graph
+ structures, healthcare organizations gain powerful
+ analytical capabilities that were previously impossible or
+ impractical with traditional relational databases.
1144 +
1145 + Key concepts covered in this chapter:
1146 +
1147 + - Provider types and facilities: Primary care
+ providers, specialists, hospitals, clinics, outpatient
+ facilities, inpatient care, and emergency departments form
+ the foundational entities in provider-centric graphs
1148 + - Provider networks: Organized groups of providers and
+ facilities with contractual insurance relationships that
+ determine patient access and cost
1149 + - Organizational structures: Hospital departments, care
+ teams, and multidisciplinary teams coordinate resources and
+ expertise to deliver specialized care
1150 + - Operations management: Provider schedules,
+ appointments, and capacity metrics enable optimization of
+ patient access and resource utilization
1151 + - Credentials and quality: Medical licenses, board
+ certifications, provider ratings, and performance metrics
+ ensure qualified, high-quality care delivery
1152 + - Clinical excellence: Clinical guidelines, best
+ practices, evidence-based medicine, and clinical protocols
+ standardize care and improve outcomes
1153 + - Care coordination: Referral patterns and transitions
+ create network effects requiring graph analytics to optimize
+ specialist access and minimize care fragmentation
1154 +
1155 + Graph modeling provides distinctive advantages for provider
+ operations:
1156 +
1157 + - Network analysis: Graph algorithms identify referral
+ patterns, network leakage, care coordination gaps, and
+ provider collaboration structures that are invisible in
+ relational models
1158 + - Capacity optimization: Real-time queries across
+ schedules, appointments, and demand patterns enable dynamic
+ capacity management and access improvement
1159 + - Quality improvement: Connecting performance metrics
+ to care team structures, referral patterns, and clinical
+ protocols reveals factors driving quality variation
1160 + - Operational efficiency: Graph traversals efficiently
+ answer complex queries about provider availability, facility
+ capacity, credential status, and protocol adherence
1161 + - Care coordination: Multi-hop queries trace patient
+ pathways across providers, facilities, and specialties to
+ identify coordination failures and transition risks
1162 +
1163 + As healthcare transitions from volume-based to value-based
+ payment models, provider operations increasingly focus on
+ care coordination, quality measurement, and network
+ optimization—all domains where graph databases provide
+ substantial analytical advantages over traditional
+ approaches. Provider-centric graph models serve as the
+ foundation for population health management, accountable
+ care organizations, care team optimization, and
+ evidence-based clinical decision support systems that are
+ transforming healthcare delivery.
⏺ Chapter Content Generation Complete!
Chapter: 05-provider-operations-networks Reading level: College Undergraduate Content length: ~9,200 words
Non-text Elements Summary
Embedded directly in markdown (11 items): - 8 markdown lists (PCP properties, specialist categories, care team members, hospital departments, network types, capacity metrics, credential types, guideline sources, evidence hierarchy, protocol types, referral properties) - 1 markdown table (Facility Types comparison) - 1 markdown table (Provider Performance Metrics)
Interactive elements with specifications (6 items): - 3 graph-model visualizations: - Healthcare Delivery Network Graph Model (vis-network) - Multidisciplinary Care Team Graph Model (vis-network) - Provider Referral Network Analysis (p5.js MicroSim) - 1 chart: - Provider Capacity Utilization Dashboard (Chart.js) - 1 MicroSim: - Provider Performance Comparison (p5.js) - 1 workflow diagram: - Clinical Protocol Workflow: Chest Pain Evaluation (HTML/CSS/JavaScript with SVG)
Interactive Elements Requiring Implementation
6 elements with detailed
All 25 Concepts Covered
- ✓ Primary Care Provider
- ✓ Specialist Provider
- ✓ Hospital
- ✓ Clinic
- ✓ Outpatient Facility
- ✓ Inpatient Care
- ✓ Emergency Department
- ✓ Provider Network
- ✓ Provider Schedule
- ✓ Appointment
- ✓ Referral
- ✓ Provider Credential
- ✓ Medical License
- ✓ Board Certification
- ✓ Provider Rating
- ✓ Provider Capacity
- ✓ Provider Specialization
- ✓ Hospital Department
- ✓ Care Team
- ✓ Multidisciplinary Team
- ✓ Provider Performance
- ✓ Clinical Guideline
- ✓ Best Practice
- ✓ Evidence-Based Medicine
- ✓ Clinical Protocol
Content Features
- College-level writing (18-25 word sentences, technical terminology)
- Pedagogical concept ordering (simple to complex):
- Provider Types & Facilities → Networks & Organization → Operations & Scheduling → Credentials & Quality → Clinical Excellence → Care Coordination
- Rich visual element integration (no more than 3 paragraphs without an element)
- Emphasis on graph database advantages for provider network analysis
- Real-world healthcare delivery scenarios
- Interactive elements for exploring referral patterns, capacity utilization, and performance metrics
- Comprehensive summary with key takeaways
The chapter file has been updated at: /Users/danmccreary/Documents/ws/modeling-healthcare-data/docs/chapters/05-p rovider-operations-networks/index.md