ACADSTAFF UGM
| Title | : | Critical Illness Insurance Model for Breast Cancer Patients Based on Chemotherapy Responses |
| Author | : |
M IVAN ARIFUL F (1) Dr. Drs. Gunardi, M.Si. (2) Dr. Fajar Adi Kusumo, S.Si., M.Si. (3) dr. Susanna Hilda Hutajulu, Ph.D. Sp.PD-KHOM (4) Dr. dr. Ibnu Purwanto, Sp.PD., K.HOM. (5) |
| Date | : | 11 2022 |
| Keyword | : | Critical Illness Insurance,Multi State Model,Breast Cancer,Pure Premium Rate,Chemotherapy Critical Illness Insurance,Multi State Model,Breast Cancer,Pure Premium Rate,Chemotherapy |
| Abstract | : | The insurance model in the form of Critical Illness (CI) is generally structured by a multi-state model that allows us to describe changes in insurance policies based on status changes experienced. The model in this study discusses the Markov process, which describes the critical illness insurance policy in each state for a continuous-time. Critical illness of breast cancer is modeled by several states consisting of A is healthy or disease-free, B is early cancer, C is cancer increase after chemo, and Y is dead from cancer. This condition is based on the response to treatment after chemotherapy. The first steps in this study are to assign a function to the transition intensity from state to state and the transition probability. The transition probability of the multi-state model is the solution of the Kolmogorov forward differential equation. The following discussion is to create a formula for calculating the pure premium rate based on age intervals. A case study based on medical record data at dr.Sardjito Hospital is applied to calculate insurance premiums based on policies and age groups. A case study based on med- ical record data at dr.Sardjito Hospital is applied to calculate insurance premiums based on policies and age groups. The premium generated in this study is assumed to only depend on the number and time of state transfers. This insurance model can be an alternative to a more accurate insurance calculation based on the incidence of displacement of critically ill patients, especially breast cancer patients. |
| Group of Knowledge | : | Statistik |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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