Online Tool

Dysphagia Intervention Cost Calculator

Estimate per-patient and annual savings from functional swallowing improvement in neurological inpatients, based on Bayesian posterior predictions stratified by geriatric status.

Disclaimer. This tool is a simulation and comes with important caveats. First, all estimated FOIS improvements are simulated on a dataset reflecting an already high-performing dysphagia team — any additional gains would need to come on top of that baseline. Your mileage may vary. Second, a foundational assumption is that every simulated intervention takes effect well within the normal length of stay (ideally early during admission) in order to produce the LOS reduction that serves as the basis for cost-savings calculations. Take these caveats to heart — and enjoy exploring the numbers.

Your population

Patients enrolled in swallowing therapy, not total admissions
100
Age ≥70 and HFRS ≥5
50%
2.0
δFOIS 2 ≈ e.g. tube-feeding → pureed diet, or pureed → regular texture. Achievable with targeted therapy (FEES-guided dietary adaptation, EMST, PES).

Therapy costs

e.g. device purchase, staff training, programme implementation
e.g. consumables, therapist time, session fees
DRG revenue + Pflegeerlös per day. Default: BWR-weighted average across MDC 01 neurology DRGs (BBFW 2026 €4,571, PEW €250). Stroke unit ≈€1,000, standard neurology ward ≈€800.

Per-patient savings estimate

LOS reduction × cost per bed-day. Intuitive and transparent, but assumes linear cost-per-day.
Cohort Mean savings / patient 95% CrI
Non-geriatric – –
Geriatric – –
Weighted average – –
P(savings > 0)
–
P(savings > therapy cost)
–

Annual programme economics

Net annual savings
–
after setup + therapy costs
Break-even patients
–
to recover setup cost
P(positive ROI at N)
–
at chosen patient volume
Approach Weighted mean / patient 95% CrI P(savings > 0)
LOS-derived – – –
Direct cost model – – –

These are two independent Bayesian estimates of the same effect. The LOS-derived approach multiplies posterior LOS reduction by a bed-day rate. The direct cost model estimates cost differences from a separate Gamma-log regression. Do not add them together.

Method: Savings are posterior predictive estimates from Bayesian Gamma-log regression models (brms/Stan) fitted to N = 10,375 neurological inpatients (University Hospital RWTH Aachen, 2021–2024). Models adjust for age, sex, stroke, emergency admission, Hospital Frailty Risk Score, and Selbstpflegeindex. Geriatric cohort: age ≥ 70 and HFRS ≥ 5. δFOIS represents a therapy-agnostic improvement on the Functional Oral Intake Scale (1–7). Interpolation is piecewise-linear between posterior summaries at δ = 0, 1, 2, 3. Probabilities use a normal approximation to the posterior.

Reference: Werner CJ. Independent economic impact of neurogenic dysphagia on hospital length of stay and costs: a Bayesian analysis of 10,375 neurological inpatients. medRxiv 2026. doi:10.1101/2026.04.08.26350417
Research use only. This calculator is provided for health-economic orientation and educational purposes. It is not a certified planning tool and must not be the sole basis for budget decisions. Estimates are derived from a single-centre retrospective cohort and may not generalise to other settings. All computation is client-side — no patient data is transmitted.