S16 / Healthcare delivery
Planning for concentrated healthcare expenditure
A healthcare planning team needs to anticipate next-year utilization and spending concentration. Determine whether baseline utilization plus access indicators predicts future expenditure better than demographics alone, without treating high spending as synonymous with clinical need.
Planned topic
Proposed design & source
Two-part models; complex survey inference; longitudinal prediction
Use eligible two-year MEPS panel records and the matching longitudinal weights. Predict second-year total expenditure and utilization from first-year information. Compare a two-part expenditure model with a flexible challenger; preserve survey strata and clusters in inference and analyze loss to follow-up.
Evaluation: Hold out later panels where comparable definitions exist. Compare weighted mean, demographic and prior-spending baselines. Report weighted error, top-risk-group calibration, expenditure concentration captured and subgroup uncertainty; bootstrap or linearize according to the survey design.
Boundary: These are population planning estimates, not individual care recommendations. MEPS is not hospital operational telemetry; predicted cost must not be presented as a measure of treatment value or unmet clinical need.
Medical Expenditure Panel Survey (MEPS) ↗
S17 / Healthcare delivery
Emergency-department waiting-time inequality and uncertainty
A healthcare operations leader wants to know which visit groups experience the longest waits and how stable those patterns are. Estimate adjusted waiting-time distributions, not just a systemwide average.
Planned topic
Proposed design & source
Survey-weighted quantiles; standardization; tail-risk analysis
Select NHAMCS emergency-department years with comparable waiting-time, arrival and triage fields after codebook review. Use survey-weighted distributional or quantile models and a long-wait indicator model. Audit missing, capped and special-code values and distinguish visits that were never seen where identifiable.
Evaluation: Compare weighted empirical quantiles with adjusted estimates and leave-year-out predictions. Report uncertainty, effective sample sizes, weighted calibration and sensitivity to missing waits. Suppress unreliable fine-grained cells instead of producing precise-looking rankings.
Boundary: NHAMCS is a sampled visit survey ending in 2022, not a complete hospital arrival/service log. It cannot directly calibrate a particular hospital's queue or prove staffing changes reduce waits.
NHAMCS emergency department public-use files ↗
S18 / Healthcare delivery
Medicare service concentration and regional market coverage
A healthcare strategy team needs to understand service mix and dependence on a small number of providers. Identify regional concentrations and changing specialty/service patterns that merit a closer market assessment.
Planned topic
Proposed design & source
Concentration analysis; matrix factorization; multilevel trends
Construct provider-service-year panels from the public CMS aggregates. Harmonize procedure codes and geographic definitions; distinguish provider location from beneficiary residence. Use service-mix factorization or clustering plus multilevel trend models. Add compatible Medicare enrollment denominators only as an explicitly sourced extension.
Evaluation: Test cluster stability across years and alternative volume definitions. Compare trend forecasts against last-year values, using later years as holdouts. Report suppression-related missingness and sensitivity to geographic boundaries and provider identifiers.
Boundary: Original Medicare Part B is only part of the market. Provider location does not prove patient access, service volume is not clinical quality, and concentration alone does not establish market power.
Medicare Physician & Other Practitioners ↗
