System dynamics modelling to formulate policy interventions to optimise antibiotic prescribing in hospitals

Figures & data

Table 1. Influence factors affecting prescribing behaviour categorised using a hierarchical framework.

	Individual level	Organisational level	Health system level
Facilitators	Adopting education programmes and obtaining prescribers’ feedback for future intervention design (Ashiru-Oredope et al., 2012; Chambers et al., 2019; Cooke et al., 2010; Davey et al., 2013; McNulty et al., 2012; Thakkar et al., 2011)	Introducing new technology for testing or changes to laboratory turnaround time by substantively changing work patterns (Davey et al., 2013)	Sharing antibiotic prescribing and patient outcome data nationally (Wickens et al., 2013) Reviewing and updating research and collaboration plans to set priorities in antimicrobial stewardship research (Rzewuska et al., 2019)
Barriers		Low guideline accessibility; low hospital budget for AMS implementation; unregulated de- escalation practices (Ashiru-Oredope et al., 2013; Bal & Gould, 2011)	Low prescribers’ participation in guideline making; national level guideline not suitable for local institutes (Charani et al., 2011, 2019)
	Delayed delivery of pathogen information due to prolonged laboratory turnaround time; senior prescribers’ instruction overriding guidelines (Charani et al., 2011, 2013; Hulscher et al., 2010; Skodvin et al., 2015)

Figure 1. Flow chart of antibiotic prescribing decision-making in hospitals in the National Health System in England.

Figure 2. Causal loop diagram of doctors’ decision-making processes.

Figure 3. Stock and flow diagram of doctors’ decision-making processes.

Table 2. Assumptions made to simplify the system behaviour.

Assumptions in system structure and procedures	The model focusses on a system with closed boundaries within which prescribers’ decision-making processes takes place. It simplifies the real-life practice in medical wards and microbiology laboratories in English hospitals Each patient has at most one blood sample taken for microbiology laboratory testing. A proportion of patients will not receive microbiology laboratory test results as either were not taken, or blood samples went missing, or became contaminated. All stock variables in the model are assumed to have a constant rate of change, meaning patients progress within the model at a constant speed.
Assumptions in doctors	The total number of doctors within the model is constant. One doctor makes only one prescribing decision at the empiric stage. Doctors’ behaviour is predictable and does not change randomly. Doctors’ seniority can be used to predict their behaviour. Senior doctors (specialist registrars and consultants), junior doctors (core trainees, Foundation year 1 and 2 doctors, nurses and pharmacists), and microbiologists are the three groups of health professionals with distinctive behaviour included in the model. Doctors are motivated to prescribe optimally; however, they are not perfectly rational because of the limitations in their cognitive capability (‘bounded rationality’) when other influences are present.
Assumptions in patients	Patients passively accept treatment and are not involved in the decision-making processes.

Table 3. ‘What if’ scenarios simulated using the SD model.

	Senior staff		Junior staff	Microbiology			Simulation results
	Complying consultants (%)	Complying specialist registrars (%)	Complying junior staff (%)	Turnaround time	Blood samples taken (%)	Blood samples processed (%)	Compliant decisions (%)
Baseline	72.2	88.4	99.8	1.22 days	89.8	93.4	92.9
Empiric stage
1	–	–	–	<3 days	–	–	92.9
2	–	–	–	<2 days	–	–	92.9
3	–	–	–	<1 day	–	–	93.3
4	100	–	–	–	–	–	95.3
5	–	100	–	–	–	–	96.8
6	100	100	–	–	–	–	99.0
7	100	Influenced	Influenced	–	–	–	95.2
8	–	100	Influenced	–	–	–	96.9
9	100	100	Influenced	–	–	–	99.3
10	100	100	Influenced	<1 day	–	–	99.4
Review stage
11	–	–	–	–	100	–	88.3
12	–	–	–	–	–	100	78.7

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