2020 № 2 Multimodal data analysis, “Human” and “Machine” approaches difference, social problematics of biomedical data collection and turnover
Artificial intelligence technologies based physicians decision support systems is an important step of healthcare digital transformation. Despite of neuronet algorithms implementation into analytical systems benefits there are questions that have to be solved for digital healthcare successful launch. In addition to knowledge expert level for systems development and privacy warranties work with professional medical society and general public is essential for psychological and social barriers overcoming during transmission to digital economics.
2020 № 2 Informatization in public health: from standards to expert systems
According to the updated requirements of the Ministry of Health of Russia, the main document regulating the treatment and diagnostic process are clinical recommendations. The guidelines for clinical recommendations, based on the results of randomized trials, determine the doctor’s actions when making a diagnosis, and choosing a rational treatment. However, the rudimentary format for the placement of information on paper significantly limits their operation. The capabilities of IT technologies allow integrating clinical recommendations into the structure of expert systems. On the example of the expert system “Treatment of chronic heart failure” the possibilities and prospects of informatization of the diagnostic process are presented.
2020 № 2 Personnel education for digital healthcare and professional standards analysis
Most important task of healthcare is medical service continuous improving through implementation of new technologies. Modern technologies development is largely based on big biological data analysis. Ability to fully utilize modern technologies requires as new level of training for medical personnel, so reconsidering of professional standards with paying more attention to scientific and technical development.
Materials and methods: Physicians going through professional skills improvement process were questioned, professional standards, legislation for medical services and education were investigated.
Results: conclusions made on physicians readiness for work at digital healthcare system and on concordance of existing pro
fessional standards to healthcare development trends.
Conclus ion: scientific and technical aspects of physicians education have to be enforced, education time for physicians working at high-tech healthcare segment should be increased, research activity should be included into professional standards for some specialties.
Modern healthcare develops towards neuronet based big data analysis digital technologies implementation. Healthcare becomes more technologized and scientifically capacious. Information volume increase requires more teaching of skills for obtained within personalized digital medicine development data mining and interpreting; professional standards widening for personnel providing medical service at this field.
2020 № 2 Evaluation of innovations for healthcare
Actuality : evaluation system for healthsaving technologies innovation level development is defined by need of innovative healthcare development strategy implementation and insufficient definition of innovative healthcare technologies criteria. Implementation of new healthcare technologies into systemic healthsaving requires managerial decision making support for innovations integration.
Goal: healthsaving technologies innovation level multicriteria evaluation scientific approval and development.
Materials and methods: program includes systemic and content analysis, expert evaluation, analytical and statistical methods. Working with Public Health experts focus group three steps algorithm was used. Excel based data processing was performed with indicative indicators and indicative space calculation.
Results: healthsaving technologies innovative level multicriteria analysis method was designed. Method novelty consists of first time introduced for healthcare innovative level criteria, expert card and evaluation algorithm. Healthsaving technologies final evaluation indicative indicators and space were calculated as a base for managerial decision choice support for their integration.
Conclusion presented evaluation scale allows medical technologies ranging according to needs of solutions for certain tasks.