The net effect of the coronavirus disease 2019 (COVID-19) pandemic and the response to it on the emergence of antimicrobial resistance is yet unknown. Positive impacts on the spread of multiresistant pathogens and infections in general may be observed with the implementation of general preventative measures for the spread of infectious disease such as social distancing, reduced travel and increased personal hygiene. This pandemic has accelerated the development of novel technologies, such as mRNA vaccines, that may be used to fight other diseases. These should be capitalized upon to manage the ongoing antimicrobial resistance pandemic in the background. However, it is likely that the COVID-19 pandemic is fueling the emergence of antimicrobial resistance due to high rates of inappropriate antimicrobial prescribing, the high use of biocides and the interruption of treatment for other conditions. Clinical uncertainty driven by the lack of effective diagnostics and practice of telemedicine may have driven the inappropriate use of antimicrobials. As pathogens know no borders, increased focus is needed for infectious diseases still threatening low- and middle-income countries such as tuberculosis. Stewardship measures for future outbreaks should stress the importance of social distancing and hand washing but discourage the overuse of disinfectants and antimicrobials that are not proven effective.

Aims: The aim of this study was to evaluate pharmacists' and physicians' attitudes and knowledge about antimicrobial treatment and resistance and based on the results to identify possible gaps and suggest opportunities for collaboration between the two professions. Methods: The physician's questionnaire consisted of 47 items and the pharmacists' questionnaire consisted of 50 items. Participants were asked to identify situations in which they were more likely to prescribe/dispense an antimicrobial without a firm indication, to identify the greatest contributors to antimicrobial resistance (AMR) among health care workers, to rate statements related to AMR, how frequently they used different sources of information and to rate the possible causes of AMR. Furthermore, preparedness for specific actions in practice was self-rated and knowledge was evaluated. The questionnaires were anonymous and participation in the study was voluntary. Results: This research included 180 community pharmacists and 181 physicians. As many as 76 (42.0%) physicians stated they would prescribe an antimicrobial when unsure whether the infection was of viral or bacterial etiology. More than half of the participants considered family medicine doctors the greatest contributors to AMR ( N = 216, 59.8%), followed by patients ( N = 175, 48.5%). In questions focusing on knowledge, physicians scored significantly higher compared with pharmacists (4.06 ± 1.01 vs. 3.71 ± 1.08, p = 0.001). Conclusion: This research revealed practices among pharmacists and physicians that should be improved and it highlighted gaps in knowledge by both professions. More attention should be given to patient consultation when dispensing and prescribing an antimicrobial as this may reduce their contribution to AMR.

Community pharmacists recognize the need to implement antimicrobial stewardship activities in community pharmacies. They are in a unique position to provide triage for common primary care indications and to lower the burden of patients at general practitioners’ offices. However, research shows that, in some areas, dispensing of antimicrobials without valid prescription is still highly prevalent. Regardless of training, every community pharmacist can give his contribution to antimicrobial stewardship. One of the basic elements should be antimicrobial dispensing according to regulations, either prescription only, or according to guidelines where pharmacists have prescribing authority. Patient consultation supported with educational materials, such as leaflets, may reduce patients’ expectations to receive antibiotics for self-limiting infections and reduce pressure on general practitioners to prescribe antibiotics on patients’ demand. Treatment optimization may be achieved in collaboration with the prescribing general practitioners or by providing feedback. At last, pharmacists provided with additional training may be encouraged to provide consultation services to long-term care facilities, to introduce point-of-care testing for infectious diseases in their pharmacies or prescribe antimicrobials for uncomplicated infections. These services are welcomed by patients and communities. Expanding pharmacy services and pharmacists’ prescribing autonomy have shown a positive impact by reducing antibiotics consumption, thus ensuring better compliance with treatment guidelines.

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.