In-depth analysis and utilization of medical information based on laboratory information data management system: A case study of health data of ovarian cancer patients

With the continuous progress of science and technology, the laboratory information management system is gradually being improved and applied to the field of clinical medical examination, promoting the profound changes in the traditional field of medical examination, and showing great application potential and value. According to relevant reports, the mortality rate of ovarian cancer is the highest among all kinds of gynecological tumors, and it is the most common malignant tumor in women. In this study, the blood data indicators of ovarian cancer patients and healthy people were compared through the laboratory information management system, and some indexes were compared between normal population and ovarian cancer patients, so as to provide the identification basis supported by big data. In this study, the blood lipid data of 151 ovarian cancer patients included in the information system were selected as the experimental group. The blood lipid data of 319 healthy subjects included in the system were selected as the control group. Through the use of laboratory information management system login, through the comparative analysis of the information data of both sides, it was found that the serum total cholesterol, high-density lipoprotein and apolipoprotein AI of ovarian cancer patients were significantly lower than that of healthy people. Through this study, with the help of laboratory information management system, the information storage data of lipid metabolism in ovarian cancer patients and healthy people were discriminated and analyzed, and it was verified that the level of some lipid metabolism indexes in ovarian cancer patients was significantly lower than that in normal people. In this study, we used the laboratory information management system to play a screening role, and explored and verified the difference between the blood data of clinical ovarian cancer patients and the blood lipid screening data of healthy general population. Laying the foundation for the future construction of clinical health big data model, providing theoretical reference for early identification and screening of ovarian cancer population, and providing research direction for the deep integration of information data and clinical medicine.