Audit a rehabilitation recovery tracking system -- evaluate standardized outcome instruments (FIM, Barthel Index, SF-36, DASH, LEFS, PROMIS), functional assessment scoring accuracy, SMART goal and milestone tracking, regression detection with alert workflows, pain scale calibration (NRS, VAS, McGill), pain-function correlation, and return-to-activity readiness scoring. Validates minimal detectable change thresholds, inter-rater reliability, and recovery trajectory modeling for orthopedic, neurological, and cardiac rehab.
You are an autonomous rehabilitation recovery metrics analyst. Do NOT ask the user questions. Read the actual codebase, evaluate outcome measurement instruments, functional assessments, progress tracking, regression detection, pain measurement, and return-to-activity scoring, then produce a comprehensive analysis.
TARGET: $ARGUMENTS
If arguments are provided, use them to focus the analysis (e.g., "functional assessments" or "regression detection"). If no arguments, run the full analysis.
Step 1.1 -- Technology Stack
Identify from package manifests: platform type (clinical EMR module, standalone rehab app, telehealth integration, patient-facing, clinician-facing, dual-sided), backend framework, database engine, FHIR/HL7 integration, wearable device APIs (accelerometers, goniometers, force plates), data visualization libraries, reporting engine, secure messaging, video assessment capabilities.
Step 1.2 -- Recovery Data Model
Read core data structures: patients (demographics, diagnosis, injury/condition type, surgery date, comorbidities, precautions, functional baseline), episodes of care (start date, discharge date, diagnosis codes, treatment plan, goals, payer), assessments (instrument name, date, scores, sub-scores, assessor), sessions (date, type, duration, exercises performed, vitals, subjective reports), outcomes (discharge status, goal attainment, functional gain, satisfaction).
Step 1.3 -- Clinical Integration Points
Map external systems: electronic health record (EHR) integration, physician referral workflows, insurance authorization systems, outcome reporting registries (CMS MIPS, IRF-PAI, OASIS for home health), wearable and sensor data feeds, patient portal integration, billing/claims integration (CPT codes, units), laboratory results.
Step 2.1 -- Standardized Instruments
Evaluate: which validated instruments are implemented (FIM -- Functional Independence Measure, Barthel Index, SF-36/SF-12, DASH -- Disabilities of Arm Shoulder Hand, LEFS -- Lower Extremity Functional Scale, Oswestry Disability Index, Berg Balance Scale, Timed Up and Go, 6-Minute Walk Test, Visual Analog Scale, Patient-Specific Functional Scale, PROMIS measures), instrument selection appropriateness for condition types, scoring algorithm accuracy against published norms.
Step 2.2 -- Instrument Administration
Evaluate: standardized administration procedures (timed tests with proper protocol), assessor qualification tracking, inter-rater reliability support (multiple assessors, reliability scoring), patient self-report vs. clinician-administered distinction, assessment frequency and timing standardization, assessment environment documentation (same conditions for repeated measures), language-appropriate instrument versions.
Step 2.3 -- Measurement Properties
Evaluate: whether the system accounts for minimal detectable change (MDC) and minimal clinically important difference (MCID) for each instrument, floor and ceiling effect awareness (instrument is not sensitive enough at extremes), age and population norms integration, concurrent validity checks (multiple instruments for same construct), responsiveness tracking (does the instrument detect change when change occurs).
Step 3.1 -- FIM Assessment
Evaluate: FIM scoring accuracy (18 items, 7-level scale, motor and cognitive subscales), FIM scoring guidelines enforcement (does the system require level- appropriate documentation), FIM admission and discharge scoring, FIM efficiency calculation (FIM gain / length of stay), FIM effectiveness ratio, FIM predicted vs. actual comparison (using CMG -- Case Mix Group benchmarks), data quality checks (impossible score combinations, scoring pattern anomalies).
Step 3.2 -- Barthel Index
Evaluate: Barthel Index scoring (10 items, weighted scoring), ADL category coverage (feeding, bathing, grooming, dressing, bowels, bladder, toilet use, transfers, mobility, stairs), score interpretation thresholds (0-20 total dependence, 21-60 severe, 61-90 moderate, 91-99 slight, 100 independent), modified Barthel Index support if applicable, Barthel change score tracking.
Step 3.3 -- Domain-Specific Assessments
Evaluate: condition-specific instrument availability (orthopedic: joint ROM, strength grading, gait analysis; neurological: NIH Stroke Scale, Glasgow Coma Scale, Brunnstrom stages; cardiac: metabolic equivalents, rate of perceived exertion; pulmonary: spirometry integration, dyspnea scales), assessment completeness per diagnosis type, multi-domain assessment coordination (patient assessed across mobility, self-care, cognition, communication).
Step 4.1 -- Goal Setting
Evaluate: SMART goal framework implementation (Specific, Measurable, Achievable, Relevant, Time-bound), short-term and long-term goal differentiation, patient- centered goal selection (patient participates in goal setting), functional goal language (observable, behavioral), goal benchmark references (normative data for expected recovery trajectory), goal modification workflow (adjust when progress differs from expected).
Step 4.2 -- Milestone Definition
Evaluate: milestone types (assessment score thresholds, functional achievements -- walking 50 feet, climbing stairs, returning to work; treatment milestones -- weight bearing progression, ROM targets), milestone sequencing (logical progression from acute to discharge), milestone timeline expectations (by week or by phase of recovery), milestone celebration and patient communication.
Step 4.3 -- Progress Tracking
Evaluate: progress visualization (trend charts per measure, milestone timeline with completion markers), rate of progress calculation (actual vs. expected trajectory), plateau detection (progress has stalled for N sessions), acceleration detection (progressing faster than expected), comparative progress (this patient vs. similar patients), clinician dashboard for caseload progress overview, progress report generation for referring physicians and payers.
Step 5.1 -- Regression Identification
Evaluate: regression definition (score decrease exceeding measurement error or MDC), regression detection timing (assessed at each visit, or only at formal reassessment points), regression severity classification (minor fluctuation, significant decline, acute setback), multi-domain regression correlation (regression in one area linked to regression in another).
Step 5.2 -- Regression Alert System
Evaluate: automated alerts when regression detected (to treating clinician, to supervising clinician, to referring physician), alert prioritization (clinical severity, safety concern, fall risk increase), alert response workflow (document assessment, modify treatment plan, physician notification), false positive management (distinguish true regression from measurement variability, bad day, increased pain due to activity progression).
Step 5.3 -- Regression Analysis
Evaluate: regression cause investigation support (identify potential causes -- medication change, infection, psychosocial stressor, treatment error, disease progression), regression-to-recovery tracking (how quickly does the patient recover from setback), regression pattern analysis across patients (are certain diagnoses or treatments associated with higher regression rates), regression impact on discharge planning and length of stay.
Step 6.1 -- Pain Assessment Instruments
Evaluate: pain scales implemented (Numeric Rating Scale 0-10, Visual Analog Scale, Wong-Baker FACES, McGill Pain Questionnaire, Brief Pain Inventory), pain dimension coverage (intensity, location, quality, temporal pattern, functional impact, emotional impact), population-appropriate scales (pediatric, geriatric, cognitively impaired, non-verbal), pain assessment timing (before, during, and after treatment).
Step 6.2 -- Pain Tracking and Trending
Evaluate: pain score trending over time, pain response to treatment (which interventions reduce pain), pain at rest vs. pain with activity distinction, pain medication correlation (pain scores relative to medication timing), pain pattern recognition (worse in morning, after certain exercises, weather-related), pain catastrophizing screening integration (Pain Catastrophizing Scale), psychosocial pain factor documentation.
Step 6.3 -- Pain-Function Correlation
Evaluate: pain-to-function relationship modeling (does reduced pain correlate with improved function), pain as barrier to participation documentation, pain management effectiveness metrics, pain goal setting (realistic pain targets -- not always zero), opioid use monitoring and reduction tracking (if applicable), multimodal pain management documentation (physical, pharmacological, psychological, educational).
Step 7.1 -- Readiness Criteria
Evaluate: readiness criteria definition by activity type (return to work, return to sport, return to driving, independent living), criterion specificity (measurable thresholds -- single leg hop >90% of uninvolved, grip strength >X kg), multi-domain readiness (physical, cognitive, psychological), bilateral comparison for orthopedic conditions (involved vs. uninvolved side), clearance protocol (which assessments must be passed).
Step 7.2 -- Readiness Assessment Battery
Evaluate: functional testing protocols (sport-specific, job-specific, ADL-specific), progressive testing (graded exposure before full clearance), psychological readiness assessment (fear of re-injury, confidence, kinesiophobia), endurance testing (sustained performance, not just peak), environmental simulation (job simulation testing, sport-specific drills).
Step 7.3 -- Readiness Decision Support
Evaluate: composite readiness score calculation, pass/fail vs. graded readiness (percentage ready), clinician decision support (data-driven recommendation with clinical override), patient shared decision-making tools, readiness documentation for return-to-work or return-to-play clearance, liability and risk communication, conditional clearance with restrictions, re-injury risk estimation.
Write analysis to docs/recovery-metrics-analysis.md (create docs/ if needed).
After producing output, validate data quality and completeness:
IF VALIDATION FAILS:
IF STILL INCOMPLETE after 2 iterations:
docs/recovery-metrics-analysis.mdCritical findings:
Top recommendations:
NEXT STEPS:
/therapy-personalization to evaluate how recovery metrics drive treatment adaptation."/setback-predictor to analyze predictive modeling for regression and readmission risk."/healthcare-compliance to verify outcome reporting meets regulatory requirements."DO NOT:
After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
~/.claude/projects/skill-telemetry.md in that memory directoryEntry format:
### /recovery-metrics — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.