Systems approach to improving traumatic brain injury care in Myanmar: a mixed-methods study from lived experience to discrete event simulation

Abstract

Objectives: Traumatic brain injury (TBI) is a global health problem, whose management in low-resource settings is hampered by fragile health systems and lack of access to specialist services. Improvement is complex, given the interaction of multiple people, processes and institutions. We aimed to develop a mixed-method approach to understand the TBI pathway based on the lived experience of local people, supported by quantitative methodologies and to determine potential improvement targets.

Design: We describe a systems approach based on narrative exploration, participatory diagramming, data collection and discrete event simulation (DES), conducted by an international research collaborative.

Setting: The study is set in the tertiary neurotrauma centre in Yangon General Hospital, Myanmar, in 2019-2020 (prior to the SARS-CoV2 pandemic).

Participants: The qualitative work involved 40 workshop participants and 64 interviewees to explore the views of a wide range of stakeholders including staff, patients and relatives. The 1-month retrospective admission snapshot covered 85 surgical neurotrauma admissions.

Results: The TBI pathway was outlined, with system boundaries defined around the management of TBI once admitted to the neurosurgical unit. Retrospective data showed 18% mortality, 71% discharge to home and an 11% referral rate. DES was used to investigate the system, showing its vulnerability to small surges in patient numbers, with critical points being CT scanning and observation ward beds. This explorative model indicated that a modest expansion of observation ward beds to 30 would remove the flow-limitations and indicated possible consequences of changes.

Conclusions: A systems approach to improving TBI care in resource-poor settings may be supported by simulation and informed by qualitative work to ground it in the direct experience of those involved. Narrative interviews, participatory diagramming and DES represent one possible suite of methods deliverable within an international partnership. Findings can support targeted improvement investments despite coexisting resource limitations while indicating concomitant risks.

Keywords: health services administration & management; neurosurgery; organisation of health services; statistics & research methods; trauma management.

Figure 1

A systems approach to health and care improvement framed as a series of recursive questions reproduced with permission from Engineering Better Care, Royal Academy of engineering, 2017).

Figure 2

‘Rich pictures’ generated by workshop data reproduced from Bashford 2021 with permission of the author, with participant names redacted).

Figure 3

Des model structure showing the variables, patient flow and proportions the surgical patient pathway is denoted in red, the conservative/medical treatment pathway in black. Patients enter the des on the left at ‘arrivals’ and exit on the right into ‘home’, ‘referral’ or ‘death’. LoS, length of stay.

Figure 4

Effects of changing staff priorities on the patient load in different locations to improve CT flow in a high patient volume scenario (scenario 4). We adjusted the CT capacity (scenario 8) or the nursing staff task priorities (scenario 9a—priority to CT, scenario 9B—priority to CT and observation ward, scenario 9 c priority to CT, observation and neuroward). The figure shows the queues waiting for theatre, CT and the observation ward, the LoS for neuroward and to discharge with the values normalised to scenario 4. Additionally, we show per cent theatre utilisation. The locations are arranged in the order of patient flow. LoS, length of stay.

Figure 5

(A) Effect of a change in population by changing the percentage of patients classified as ‘surgical’ the increased length of stay on the observation ward is seen as an increase in delay for observation ward bed access. In yellow scenario 4 (50% surgical patients), in pink scenario 3 (20% surgical patients) and in purple is scenario 7 (80% surgical patients). (B). Effect of varying patient arrival rate with increased arrivals the waiting time for the observation ward bed increases. again, in yellow is the baseline scenario 4 (15 patients/day), in blue we show scenario 7 (13 patients/day).

Figure 6

Change in patient load on the observation ward when the capacity is increased in purple is the baseline scenario 0 (20 beds) and in yellow scenario 6 (30 beds). (A) shows the delay to an observation ward bed, (B) shows the observation ward occupancy through the simulation period. The moderate increase in bed capacity clearly reduces the pressure on observation ward beds.