本研究提出單一病症兩藥品項之採購決策模式稱購買決策模式(Purchasing Decision-PD Model)，本模式不僅考量該病症之年患者預估數目、評估藥品療效及療程、健保局對藥品的給付價格，並針對病患對醫療品質的知覺、數量折扣下的藥品價格改變、藥品的訂購與持有成本以及醫療無效率所造成之損失成本等因素，藉以決定每種藥品每年最適之需求量與每次訂購該藥品的經濟訂購量以求達成該病症兩藥品項採購決策時合理利潤最大化之目標。此外，本研究的逐步數學演算法乃以禁忌搜尋為主程式與全部單位型數量折扣經濟訂購量為副程式之框架下設計，並以Foxpro 套裝軟體發展一電腦化程式，期為此一複雜且難解的問題提供一快速的求解程序、方法與工具。 再者，本研究透過電腦模擬以實施當病患對醫療知覺的感受程度變動時對優選解與目標值的敏感性分析，且以高血脂症(Hyperlipidaemia)為例，並以實務上常用的兩種治療藥品 (Lipitor、Gemnpid)為本研究之單一病症多藥品項採購決策之範例以施行購買決策。接著將現行醫院之購買決策與本研究之優選決策進行利潤比較以作為本研究價值之強有力的佐證。綜言之，本研究可作為具有敏銳洞察力之醫院經營決策者在施行藥品購買決策時一模式化與數量化之參考依據。 This study submits a mathematical model to decide the purchase of two-itemized drugs for healing a given symptom called Purchasing Decision (PD) Model. This paper not only discusses the estimated number of patients having such a symptom per year, the evaluated efficiency and course of each drug for healing such a symptom, the paying price of each drug from Bureau of National Health Insurance (BNHI), but also concentrates on the perception of healing quality for patients, the purchasing price of each drug under quantity discounts, the ordering cost per order and the holding cost of each drug, and the losing cost of healing inefficiency to determine the optimal order quantity of each drug and its total demand per year for healing a given symptom to maximize profit. In addition, this study proposes a solving procedure to resolve such a combinatorial problem by using the Tabu Search technique as the main program and the Quantity Discounts for the Economic Order Quantity (EOQ) Model as the sub-program. Besides, a computerized program (written in Foxpro Program) is developed to provide a quick tool for resolving such a complicated problem. A computer simulation of changing the level of patient perception for healing quality is conducted to find out the sensitivity analysis of the optimal solution. Hyper-Cholesterolemia Symptom (Hyperlipidaemia) is regarded as a case example, two popular drugs, Lipitor and Gemnpid, to heal the Hyper-Cholesterolemia Symptom are considered in this case. Additionally, a comparison of present purchasing decision and proposed one of the case example in the hospital is conducted in this study. By a better profitability, the proposed purchasing decision can then be strongly recommended. In sum, this study indeed contributes a computerized tool to a decision maker with profound insight for purchasing decision of drugs.
