TY - JOUR
T1 - FRAM and LEAN as tools for describing and improving the referral process between outpatient clinics in a Danish Hospital
T2 - Complementary or conflicting?
AU - Safi, Mariam
AU - Clay-Williams, Robyn
AU - Ursin Grau, Tine
AU - Brandt, Frans
AU - Ravnborg Thude, Bettina
N1 - Funding Information:
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The work was supported by the University Hospital of Southern Denmark as part of a Ph.D. project.
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/10
Y1 - 2023/10
N2 - Although LEAN and Resilience Engineering (RE) are distinct process improvement approaches, recent research evidence suggests that these two approaches have some similarities and can be used jointly for process optimisation. This study aimed to understand and improve the referral process between internal medicine outpatient clinics at the University Hospital of Southern Denmark, Hospital Sønderjylland, using RE and LEAN principles. The referral process was mapped from the RE perspective using Functional Resonance Analysis Method (FRAM) and from the LEAN perspective using Value Stream Mapping (VSM). The FRAM and VSM outputs were also compared to identify learning opportunities and to understand how these two methods complement each other in practice. The study drew on sources of data from interviews, observations, a VSM workshop and registry data on referrals from the hospital's electronic patient journal system. The FRAM analysis showed that organisational structure, time constraints, interruptions and lack of a standard for referral may explain the inefficient referral process. The VSM showed that long processing times may explain bottlenecks in the referral flow: while the processing time of a referral took between 6 and 17 min in the outpatient clinics, the actual time was longer, taking between 22 h 42 min and 5 days 14 h 25 min. By using FRAM and VSM methodologies synergistically, we identified potential quality improvement initiatives to improve the referral process. Our findings may guide healthcare or other industries in how to use FRAM and VSM in practice for process optimisation.
AB - Although LEAN and Resilience Engineering (RE) are distinct process improvement approaches, recent research evidence suggests that these two approaches have some similarities and can be used jointly for process optimisation. This study aimed to understand and improve the referral process between internal medicine outpatient clinics at the University Hospital of Southern Denmark, Hospital Sønderjylland, using RE and LEAN principles. The referral process was mapped from the RE perspective using Functional Resonance Analysis Method (FRAM) and from the LEAN perspective using Value Stream Mapping (VSM). The FRAM and VSM outputs were also compared to identify learning opportunities and to understand how these two methods complement each other in practice. The study drew on sources of data from interviews, observations, a VSM workshop and registry data on referrals from the hospital's electronic patient journal system. The FRAM analysis showed that organisational structure, time constraints, interruptions and lack of a standard for referral may explain the inefficient referral process. The VSM showed that long processing times may explain bottlenecks in the referral flow: while the processing time of a referral took between 6 and 17 min in the outpatient clinics, the actual time was longer, taking between 22 h 42 min and 5 days 14 h 25 min. By using FRAM and VSM methodologies synergistically, we identified potential quality improvement initiatives to improve the referral process. Our findings may guide healthcare or other industries in how to use FRAM and VSM in practice for process optimisation.
KW - FRAM
KW - Healthcare
KW - LEAN
KW - Referral process
KW - Resilience engineering
KW - VSM
U2 - 10.1016/j.ssci.2023.106230
DO - 10.1016/j.ssci.2023.106230
M3 - Journal article
AN - SCOPUS:85162157584
SN - 0925-7535
VL - 166
JO - Safety Science
JF - Safety Science
M1 - 106230
ER -