Scoring Methodology

TransPlan's scoring engine computes a weighted composite score (0 to 100) for each of the 248 SRTR transplant centers, based on a patient's profile. (Earlier versions scored 22 cities; that limit was retired in #293 and centers are now identified by SRTR center code.)

Overview

Eight categories are scored independently and combined with fixed weights:

#	Category	Weight	Primary Data Sources
1	Medical Compatibility	25%	Blood type, age, sex, BMI
2	Wait Time & Competition	20%	SRTR Table B10 wait time distributions
3	Donor Availability	18%	Donor registration rates, living donor programs
4	Hospital Quality	15%	CMS volume data, SRTR center reports
5	Geographic & Logistical	10%	BLS cost of living, EPA air quality, climate scores
6	Health Demographics	7%	CDC disease prevalence (diabetes, obesity, hypertension)
7	Policy & Legal	3%	State donation law tiers
8	Socioeconomic	2%	Support system rubric

Total: 100%

Category Details

1. Medical Compatibility (25%)

Scores the biological match between patient profile and the donor pool at each city.

Inputs: organ, blood_type, age, sex, weight_lbs, height_inches (for BMI)

Blood type compatibility is the primary factor. O- scores highest universally while AB+ is most restricted, and a compatibility matrix is applied per organ. Age also affects scoring because pediatric and elderly patients face different wait time patterns relative to the expected donor age distribution. Organ-specific clinical scores (cPRA for kidney, MELD for liver, LAS for lung) directly influence the compatibility assessment. BMI is also considered because extreme values can disqualify candidates at some centers.

2. Wait Time & Competition (20%)

Estimates relative wait time advantage based on city-level SRTR data and patient urgency.

Inputs: urgency (1 to 4), organ-specific clinical scores

City wait time factors are derived from SRTR Table B10 log-normal distribution parameters. An urgency multiplier applies for Status 1A/1B patients (heart/liver), significantly reducing expected wait. The competition index reflects the ratio of candidates to donors at each center.

3. Donor Availability (18%)

Scores each city's supply of organs relative to demand.

The donor registration rate (state-level, from health demographics data) is the primary input, supplemented by living donor program strength from SRTR center volume data. Population size relative to transplant center capacity and OPO (Organ Procurement Organization) effectiveness scores round out this category.

4. Hospital Quality (15%)

Ranks centers by volume and quality indicators.

Annual transplant volume per organ type comes from SRTR biannual reports. Volume alone is a weak quality proxy, so it is supplemented by two directly observed SRTR measures: the official 5-tier program ratings (transplant rate, waitlist survival and graft survival, each risk-adjusted by SRTR's own model) and the risk-adjusted Offer Acceptance Rate Ratio, which captures how readily a program accepts offers for its candidates. Centers are also assessed by specialization (pediatric programs, multi-organ capability) and CMS outcome ratings where available.

5. Geographic & Logistical (10%)

Practical livability during transplant workup and recovery.

The cost of living index (BLS, normalized dynamically) is combined with the air quality index (EPA AQS annual average), a climate recovery score (manually curated for temperature extremes, humidity, and UV), and distance from major transplant centers.

6. Health Demographics (7%)

Disease prevalence in the local population affects donor pool quality.

This category aggregates CDC SODA data on diabetes prevalence, obesity prevalence, CKD prevalence (relevant to kidney donor viability), hypertension prevalence, and smoking rates. Lower prevalence of disqualifying conditions yields a higher score.

7. Policy & Legal (3%)

State-level policies affecting donation rates and patient rights.

Inputs include first-person consent law strength (opt-in vs. opt-out framing), hospital donor conversion rates (HRSA OPO data), and any state-level mandated choice or presumed consent laws.

8. Socioeconomic (2%)

Support systems that improve transplant success rates.

This category scores transplant financial assistance programs, housing assistance near medical centers, community support networks, and transportation access to transplant centers.

Note: This category deliberately avoids wealth-correlated metrics to prevent penalizing patients by income.

Score Combination

The final score is a weighted sum:

score = Σ(category_score_i × weight_i)  for i in [1..8]

Each category is internally normalized to [0, 100] before weighting. The composite score also falls in [0, 100].

Interactive: Category Weight Impact

The heatmap below illustrates how each scoring category contributes to the final composite score across sample cities, using default weights.

Category Scores by City (Kidney, O+ Blood Type)

	Med. Compat.	Wait Time	Donor Avail.	Hosp. Quality	Geo/Logistic	Health Demo.	Policy	Socioeconomic
Houston	72	45	68	85	62	58	70	65
Pittsburgh	78	62	72	92	55	64	72	60
Cleveland	75	58	70	95	50	60	68	58
Minneapolis	74	70	75	88	48	72	74	62
Nashville	70	52	65	80	65	55	66	63
Chicago	76	55	71	90	45	56	70	60

Adjusting Weights

Users can customize category weights via the methodology accordion in the app. Weights must sum to 100%. The algorithm automatically re-normalizes if they don't.

Limitations

Scores are population-level estimates, not individual predictions. SRTR data is updated biannually, so current data may be 6 to 18 months old. Some data points are manually curated and may lag real-world changes. OPO boundary effects are not modeled; see docs/limitations.md L-009 for details.

See Phase 2: Monte Carlo Simulation for individual-level probabilistic estimates.