## 4 Discussion  
In this study, specific recommendations for customized servers and DB designs for medical institutions were proposed, considering the computing resource status of medical institutions using OHDSI OMOP CDM, and validated using actual examples of the proposed recommendation. This study proposed two types of server designs (combined/separated) and three types of DB designs (DA/OLTP/DW). Each design was applied to OMOP CDM systems, which were employed in one tertiary hospital and two secondary hospitals in Korea. The recommendation presented here is not based on the hospital itself but on the hospital's HW resources. Therefore, this recommendation can be applied to hospitals in other countries because it is independent of the hospital's locations. We demonstrated that the configuration tuning of the computing resource needs to be customized according to the size of the data and the CDM service plan according to the medical institution. The obtained tuning results are presented in Table 3. To observe the effect of tuning itself, we only changed the configuration tuning according to the purpose, without altering the server specification, data amount, and service amount. The performance varied, depending on the tuning. TPS refers to the number of unit tasks processed per second, and latency is the time it takes for the server to receive a request from a client and send a response. The larger the scale, the smaller the TPS, and the larger the latency (Table 4). At the same scale, OLTP exhibited the highest TPS because the setting at OLTP had the largest number of max_connections. Latency was the highest in OLTP because the higher the throughput, the slower the response time (Table 4). If the number of clients is increased within the max_connection, the TPS increases because the number of unit tasks processed per second increases. Increasing the number of clients increased the throughput and thus increased the latency (Table 5). From the results, when HW resources are the same, the TPS and latency performance varies according to the hospital's requirements (DB scale, the number of clients). To maintain the maximum performance on the same resource, it is important to properly tune according to the recommendations presented in this study. For example, in the case of a primary hospital, since the DB scale and number of clients are smaller than those of secondary and tertiary hospital, TPS and latency values can be reduced even with the same resource if the DA of Table 3 is used as the base when tuning. The results of OLTP and DW were similar using the benchmark tool. In this study, we adopted the same system and measured the parameters by altering the DB configurations. OLTP and DW shared similar configurations in the shared_buffers, effective_cache_size, and maintenance_work_mem (max = 2 GB) fields. This explains why they exhibited similar results in the virtual memory status (Table 6). Disk performance exhibited similar results in OLTP and DW, which originated from the same disk speed and configuration (Table 7). From the results, we confirm that the resource usage rate varies according to configuration tuning even in the same resource. Therefore, this study proved that the configuration tuning should be different according to the type of hospital based on the recommendation. The limitation of this study is that the number of max_connections is small. This is because it is designed assuming an environment where only the minimum applications provided by OHDSI are installed, and the number of researchers simultaneously accessing it is small. Unlike this assumption, as in the IBM cloud, there are cases where super-users reserve 15 connections by default to maintain DB status and integrity [39]. Max_connection must be increased according to the medical institution's research needs. However, because too much max_connection reduces the working_mem that can be used per connection, it is not recommended to increase it unconditionally.