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Systems engineering analysis of diagnostic referral closed-loop processes
BACKGROUND: Closing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%–73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods inc...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8634018/ https://www.ncbi.nlm.nih.gov/pubmed/34844935 http://dx.doi.org/10.1136/bmjoq-2021-001603 |
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author | Nehls, Nicole Yap, Tze Sheng Salant, Talya Aronson, Mark Schiff, Gordon Olbricht, Suzanne Reddy, Swapna Sternberg, Scot B Anderson, Timothy S Phillips, Russell S Benneyan, James C |
author_facet | Nehls, Nicole Yap, Tze Sheng Salant, Talya Aronson, Mark Schiff, Gordon Olbricht, Suzanne Reddy, Swapna Sternberg, Scot B Anderson, Timothy S Phillips, Russell S Benneyan, James C |
author_sort | Nehls, Nicole |
collection | PubMed |
description | BACKGROUND: Closing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%–73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods increasingly are advocated in healthcare for their value in studying and redesigning complex processes. OBJECTIVE: Conduct a formative SE analysis of process logic, variation, reliability and failures for completing diagnostic referrals originating in two primary care practices serving different demographics, using dermatology as an illustrating use case. METHODS: An interdisciplinary team of clinicians, systems engineers, quality improvement specialists, and patient representatives collaborated to understand processes of initiating and completing diagnostic referrals. Cross-functional process maps were developed through iterative group interviews with an urban community-based health centre and a teaching practice within a large academic medical centre. Results were used to conduct an engineering process analysis, assess variation within and between practices, and identify common failure modes and potential solutions. RESULTS: Processes to complete diagnostic referrals involve many sub-standard design constructs, with significant workflow variation between and within practices, statistical instability and special cause variation in completion rates and timeliness, and only 21% of all process activities estimated as value-add. Failure modes were similar between the two practices, with most process activities relying on low-reliability concepts (eg, reminders, workarounds, education and verification/inspection). Several opportunities were identified to incorporate higher reliability process constructs (eg, simplification, consolidation, standardisation, forcing functions, automation and opt-outs). CONCLUSION: From a systems science perspective, diagnostic referral processes perform poorly in part because their fundamental designs are fraught with low-reliability characteristics and mental models, including formalised workaround and rework activities, suggesting a need for different approaches versus incremental improvement of existing processes. SE perspectives and methods offer new ways of thinking about patient safety problems, failures and potential solutions. |
format | Online Article Text |
id | pubmed-8634018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-86340182021-12-10 Systems engineering analysis of diagnostic referral closed-loop processes Nehls, Nicole Yap, Tze Sheng Salant, Talya Aronson, Mark Schiff, Gordon Olbricht, Suzanne Reddy, Swapna Sternberg, Scot B Anderson, Timothy S Phillips, Russell S Benneyan, James C BMJ Open Qual Original Research BACKGROUND: Closing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%–73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods increasingly are advocated in healthcare for their value in studying and redesigning complex processes. OBJECTIVE: Conduct a formative SE analysis of process logic, variation, reliability and failures for completing diagnostic referrals originating in two primary care practices serving different demographics, using dermatology as an illustrating use case. METHODS: An interdisciplinary team of clinicians, systems engineers, quality improvement specialists, and patient representatives collaborated to understand processes of initiating and completing diagnostic referrals. Cross-functional process maps were developed through iterative group interviews with an urban community-based health centre and a teaching practice within a large academic medical centre. Results were used to conduct an engineering process analysis, assess variation within and between practices, and identify common failure modes and potential solutions. RESULTS: Processes to complete diagnostic referrals involve many sub-standard design constructs, with significant workflow variation between and within practices, statistical instability and special cause variation in completion rates and timeliness, and only 21% of all process activities estimated as value-add. Failure modes were similar between the two practices, with most process activities relying on low-reliability concepts (eg, reminders, workarounds, education and verification/inspection). Several opportunities were identified to incorporate higher reliability process constructs (eg, simplification, consolidation, standardisation, forcing functions, automation and opt-outs). CONCLUSION: From a systems science perspective, diagnostic referral processes perform poorly in part because their fundamental designs are fraught with low-reliability characteristics and mental models, including formalised workaround and rework activities, suggesting a need for different approaches versus incremental improvement of existing processes. SE perspectives and methods offer new ways of thinking about patient safety problems, failures and potential solutions. BMJ Publishing Group 2021-11-29 /pmc/articles/PMC8634018/ /pubmed/34844935 http://dx.doi.org/10.1136/bmjoq-2021-001603 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Original Research Nehls, Nicole Yap, Tze Sheng Salant, Talya Aronson, Mark Schiff, Gordon Olbricht, Suzanne Reddy, Swapna Sternberg, Scot B Anderson, Timothy S Phillips, Russell S Benneyan, James C Systems engineering analysis of diagnostic referral closed-loop processes |
title | Systems engineering analysis of diagnostic referral closed-loop processes |
title_full | Systems engineering analysis of diagnostic referral closed-loop processes |
title_fullStr | Systems engineering analysis of diagnostic referral closed-loop processes |
title_full_unstemmed | Systems engineering analysis of diagnostic referral closed-loop processes |
title_short | Systems engineering analysis of diagnostic referral closed-loop processes |
title_sort | systems engineering analysis of diagnostic referral closed-loop processes |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8634018/ https://www.ncbi.nlm.nih.gov/pubmed/34844935 http://dx.doi.org/10.1136/bmjoq-2021-001603 |
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