Medication Adherence Record
The tracked pattern of medication fills and refills including proportion of days covered, gaps in therapy, and intervention history.
Why This Object Matters for AI
AI adherence prediction requires fill history to identify at-risk patients; without adherence data, AI cannot target interventions effectively.
Pharmacy Operations Capacity Profile
Typical CMC levels for pharmacy operations in Healthcare organizations.
CMC Dimension Scenarios
What each CMC level looks like specifically for Medication Adherence Record. Baseline level is highlighted.
Medication adherence patterns are not formally documented. Whether patients are actually taking their medications as prescribed is unknown. Clinicians ask patients if they are taking their medications and accept the answer at face value. There is no systematic record of fill patterns, refill timing, or gaps in therapy that would reveal actual medication-taking behavior.
None — AI cannot predict medication non-adherence, identify at-risk patients, or recommend targeted interventions because no formal medication adherence records exist.
Create formal medication adherence records — document prescription fill and refill patterns including fill dates, days supplied, proportion of days covered (PDC), gap duration, and intervention history for each patient's chronic medications.
Basic medication fill records exist in the pharmacy dispensing system showing when prescriptions were filled. But fills are tracked as dispensing events rather than adherence indicators. There is no calculation of proportion of days covered, no identification of fill gaps, and no documentation of adherence interventions. A pharmacist can see that a prescription was filled on a date but cannot easily determine whether the patient has been consistently filling their medication over time.
AI can count prescription fills for a medication, but cannot assess adherence patterns, calculate PDC metrics, or identify at-risk patients because fill records are not organized as adherence intelligence — they are isolated dispensing events without longitudinal adherence context.
Standardize medication adherence documentation — implement structured records calculating proportion of days covered for each chronic medication, documenting fill gaps with duration and reason codes, recording adherence intervention contacts, and tracking patient-reported adherence barriers.
Medication adherence records follow a standardized format: PDC calculations for each chronic medication, documented fill gaps with duration, coded gap reason categories (cost, side effects, access, forgetfulness, intentional discontinuation), adherence intervention history, and patient-reported barrier documentation. Every patient's chronic medications have consistently calculated adherence metrics. But adherence records are standalone metrics — not linked to clinical outcomes, social determinant records, or health literacy assessments that contextualize adherence patterns.
AI can identify patients below PDC thresholds, rank non-adherence by severity and medication criticality, and track intervention effectiveness from standardized records. Cannot predict adherence risk from social, economic, or health literacy factors because adherence records are not connected to patient contextual data.
Link adherence records to patient context — connect each adherence pattern to clinical outcome measurements, social determinant of health records, health literacy assessments, and prescription benefit coverage details to understand the root causes of non-adherence.
Medication adherence records connect to patient context. Each adherence pattern links to clinical outcomes (whether non-adherence correlates with disease progression or acute events), social determinant records (transportation barriers, food insecurity, housing instability), health literacy assessments, and prescription benefit coverage (copay amounts, prior authorization barriers). A pharmacist can query 'show me patients with PDC below 80% on antihypertensives where high copay burden is documented as the primary barrier.'
AI can perform root-cause adherence analysis — predicting which patients are at risk of non-adherence based on social, economic, and health literacy factors, recommending targeted interventions matched to documented barriers, and correlating adherence patterns with clinical outcomes.
Implement formal adherence entity schemas — model each adherence record as a structured entity with typed relationships to medication profiles, clinical outcome measurements, social determinant records, benefit coverage, and intervention effectiveness tracking.
Medication adherence records are schema-driven entities with full relational modeling. Each record links to the patient's complete medication profile, clinical outcome trajectory, social determinant assessments, health literacy scores, benefit coverage details, pharmacy access patterns, and intervention history with effectiveness measurement. An AI agent can navigate from any adherence gap to the complete clinical, social, and economic context.
AI can autonomously manage medication adherence — predicting non-adherence from multi-factorial risk models, recommending personalized interventions matched to root cause barriers, monitoring intervention effectiveness, and optimizing adherence program resource allocation.
Implement real-time adherence intelligence streaming — publish every fill event, gap detection, intervention contact, and outcome measurement as it occurs for continuous adherence management intelligence.
Medication adherence records are real-time intelligence streams. Every prescription fill, expected fill date passing, intervention contact, patient engagement event, and clinical outcome updates the adherence profile continuously. The adherence record is a living patient engagement dashboard, not a retrospective PDC calculation reviewed periodically.
Fully autonomous adherence intelligence — continuously monitoring every medication-taking signal in real-time, predicting gaps before they occur, and orchestrating proactive engagement as a comprehensive medication adherence management engine.
Ceiling of the CMC framework for this dimension.
Capabilities That Depend on Medication Adherence Record
Other Objects in Pharmacy Operations
Related business objects in the same function area.
Medication Record
EntityThe patient's comprehensive medication list including active prescriptions, historical medications, allergies, adverse reactions, and adherence patterns.
Pharmacy Formulary
EntityThe approved list of medications available for prescribing including formulary status, restrictions, therapeutic alternatives, and prior authorization requirements.
Controlled Substance Dispensing Record
EntityThe detailed transaction record of controlled substance withdrawals from automated dispensing cabinets including user, patient, quantity, waste, and witness.
IV Compounding Order
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Drug Shortage Record
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Antimicrobial Stewardship Record
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Pharmacogenomic Profile
EntityThe patient's genetic test results relevant to drug metabolism including gene variants, metabolizer phenotypes, and actionable drug-gene interactions.
Antibiogram
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