By mapping patterns of prescription diversities from health administered standards, this study reveals prescription diversity among patients diagnosed with cancer in a rural area of Thailand. The purpose of this study is to provide the groundwork for future research on the interaction between activity-related coverage and economic models of social health benefits. In order to understand how prescription variations affect patients, this study maps prescribing trends from electronic medical records to individuals who have been diagnosed with cancer symptoms. This study demonstrated how cloud computing and big data technologies may be effectively used to assist the transfer of data from local hospital databases to cloud databases. In actual cloud service implementations, this approach focuses on optimizing corresponding software performance and finance. Despite the stated prescription standards, this study may indicate that judgments made by health services programs for the treatment of chronic illnesses are subject to influence. One factor contributing to system inefficiency has been identified as a lack of care coordination, particularly when tests and treatments are repeated because the results are not shared across providers. There are more than 1,000 preferred brand names and generic products for the treatment of chronic illnesses on the market for cancer therapies. This study utilizes multiple softwares to specify patterns of choices in order to prevent overprescribing and overdiagnosing because the list is triple-extended for non-preferred medications. The cost and profit associated with a drug's selection may be transformed into particular rules for programs managed by the health system. The results of this study suggested how recommendations specify medication orders for multiple coverage plans should be altered in order to uncover prescription trends in health administration programs, The association between benefit plan modification and reimbursement amount, as well as drug selection, dose, and manufacturer variability, were all quantified in the same illness using a regression model. The medical condition determines the amount per prescription, however the doctor may try to increase the dosage for the greatest possible benefit. The results are equivocal, but they also imply that alternative patient-type and brand selections result in better hospital benefit. The profitability of hospitals is significantly impacted by brand preference. The hospital's profit margins are larger when physicians are exposed to more brand alternatives, which may be the result of increasing brand rivalry. In the instance of hospitalization, the reimbursement index and the estimated dosage factor were both positive. According to this conclusion, more government reimbursements will result in a bigger profit margin for the hospital on each prescription. In order to comprehend the patterns of prescription for health services in Thailand, it may be useful to define the efficiency in healthcare procedures.