![]() 20 NVA analysis is a common method for helping see beyond existing mental models and understand process inefficiencies, waste and workarounds. 16–19 Each process step was categorised by primary activity and identified as value add (achieving the process purpose itself, eg, actions that directly benefit the patient), value enabling (conducting work leading to a value-add step but not value add itself, eg, actions that indirectly support direct patient care) or non-value add (NVA) (unnecessary work, eg, actions that do not benefit patients and are not necessary to deliver care) as classically defined in the engineering and ‘lean’ literature. We therefore aimed to conduct an interdisciplinary formative study, combining SE and other approaches, of current diagnostic closed-loop processes in a small urban community-based health centre and a teaching practice within a large academic medical centre.įinal results were analysed from a systems perspective using a combination of process analysis frameworks based on the Toyota production system, Lean manufacturing, human factors and reliability science. While complementary to ‘quality improvement’, SE also is significantly different in terms of methods and ways of thinking about processes, performance and design. 9–13 Industries with more reliable processes that ensure completion make greater use of such methods to better understand process performance and design processes that perform with higher reliability. These general types of systems problems have been advocated to be studied and redesigned via systems engineering (SE) methods by the National Academy of Medicine, Agency for Healthcare Research and Quality, National Science Foundation and others. 7 Notably, greater use of electronic health records (EHRs) does not appear to have necessarily lightened workloads, due to their lack of interoperability and easy access to necessary information often necessitating additional effort and workarounds. Existing ‘open-loop’ processes are associated with (1) insufficient communication throughout the diagnostic processes, (2) delays in care, (3) diagnostic results not being tracked nor addressed, (4) specialist findings and notes not being received or reviewed, (5) patient charts not being updated, and (6) patients not being notified and engaged in their care. Information is not relayed back to the point of origin, limiting opportunities to address issues or errors and follow-up in timely manners. In contrast, current diagnostic specialty referral processes are largely open-loop ‘push’ systems, with few mechanisms to ensure completion, no automatic feedback and resilient adaptation systems. 4–6 Examples of closed feedback loops in other industries include inventory supply chains that track shipments and adapt delivery schedules to meet due dates, airlines that monitor flight delays and automatically reschedule travellers, and manufacturers that monitor equipment status and trigger maintenance when degradation occurs. ![]() 4 Closed loops, wherein an ordered test or referral is scheduled and completed, occur with alarmingly low reliability, with studies reporting 65%–73% not being completed. Diagnostic errors are a significant and costly patient safety issue, 1–3 including failures to complete diagnostic tests and referrals.
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