TY - JOUR

T1 - Modeling General Practitioners’ Total Drug Costs through GAMLSS and Collective Risk Models

AU - Clemente, Gian Paolo

AU - Savelli, Nino

AU - Spedicato, G. A.

AU - Zappa, Diego

PY - 2022

Y1 - 2022

N2 - Monitoring general practitioner prescribing costs is an important topic in order to efficiently allocate National Health Insurance resources. Using generalized additive models for location, scale, and shape with random effects, we investigate how second-order variables, related to patients, contribute to estimating the frequency, severity, and hence the total amount of costs. The total cost of prescriptions associated with a general practitioner is then derived following a collective risk theory approach by aggregating cumulants of patient cost distributions. By means of the fourth-order Cornish-Fisher expansion series of quantiles of the aggregate cost distribution of general practitioners, we construct a confidence interval for each doctor, which is used to select a subset of doctors that should be monitored to identify potential inefficiencies. A case study is developed by using structured data regarding the number and cost of prescriptions of about 900,000 patients linked to corresponding general practitioners. The prescription costs considered are only those paid fully by the national health coverage.

AB - Monitoring general practitioner prescribing costs is an important topic in order to efficiently allocate National Health Insurance resources. Using generalized additive models for location, scale, and shape with random effects, we investigate how second-order variables, related to patients, contribute to estimating the frequency, severity, and hence the total amount of costs. The total cost of prescriptions associated with a general practitioner is then derived following a collective risk theory approach by aggregating cumulants of patient cost distributions. By means of the fourth-order Cornish-Fisher expansion series of quantiles of the aggregate cost distribution of general practitioners, we construct a confidence interval for each doctor, which is used to select a subset of doctors that should be monitored to identify potential inefficiencies. A case study is developed by using structured data regarding the number and cost of prescriptions of about 900,000 patients linked to corresponding general practitioners. The prescription costs considered are only those paid fully by the national health coverage.

KW - Collective Risk Model

KW - GAMLSS

KW - Prescription Costs

KW - Collective Risk Model

KW - GAMLSS

KW - Prescription Costs

UR - http://hdl.handle.net/10807/196241

U2 - 10.1080/10920277.2022.2026229

DO - 10.1080/10920277.2022.2026229

M3 - Article

SN - 1092-0277

VL - 2022

SP - 610

EP - 625

JO - North American Actuarial Journal

JF - North American Actuarial Journal

ER -