Analysis and evaluation of healthcare delivery to patients with coronary artery disease using PC-based medical data - A. M. Nazarov; S. N. Tolpygina; I. P. Bolodurina (Кардиоваскулярная терапия и профилактика)
Aim. Using PC-based medical information technologies, analyze and evaluate inpatient and outpatient care for patients with coronary artery disease (CAD) with the development and testing of machine learning algorithms.Material and methods. In 2017, 586 patients with myocardial infarction (MI) under the age of 70 years were identified according to the electronic database of the Uniform State Health Information System. Of these, 349 patients received reperfusion therapy, and 237 did not. In all 586 patients, lethal outcomes, hospital and ICU length of stay were determined. In 342 out of 586 patients after MI, using PC-based medical data, number of completed outpatient treatment cases, ambulance calls, and emergency hospitalizations for CAD were identified. According to the Uniform State Health Information System, the dynamics of heart failure (HF) and angina class was determined in patients at the end of the year.Results. In patients with reperfusion therapy, in comparison with MI patients without it, significantly shorter hospital (13,17±0,17 vs 15,35±0,46 days) and ICU (1,73±0,06 vs 2,56±0,29 days) length of stay, as well as significantly lower (2,9 times) mortality. In the post-infarction period, there were no differences between these groups in the number of completed cases of outpatient CAD treatment and ambulance calls due to CAD. The number of emergency hospitalizations was significantly (1,4 times) more in the group of patients who did not receive reperfusion therapy. In both groups of patients, there was a positive dynamic in angina class and HF stage.Conclusion. PC-based personalized medical services are informative material for assessing the results of inpatient and outpatient care for patients with CAD. It is rational to consider assessing deaths, hospital and ICU length of stay, ambulance calls, emergency hospitalizations, angina class and HF stage changes after MI as an algorithm for machine learning and computer analysis of the treatment results in patients with CAD.