M Fung-Kee-Fung1,2, D E Maziak1,3, J R Pantarotto1,4, J Smylie1, L Taylor1, T Timlin1, T Cacciotti1, P J Villeneuve1,3, C Dennie1,5, C Bornais1, S Madore1, J Aquino1,5, P Wheatley-Price1,6, R S Ozer1, D J Stewart1,6. 1. The Ottawa Hospital, ON. 2. Division of Gynecologic Oncology, University of Ottawa, ON; and. 3. Department of Surgery, Division of Thoracic Surgery, University of Ottawa, Ottawa Hospital Research Institute, ON. 4. Division of Radiation Oncology, University of Ottawa, ON. 5. Department of Medical Imaging, The Ottawa Hospital, Ottawa Hospital Research Institute, University of Ottawa, ON. 6. Department of Medicine, University of Ottawa, ON.
Abstract
BACKGROUND: The Ottawa Hospital (toh) defined delay to timely lung cancer care as a system design problem. Recognizing the patient need for an integrated journey and the need for dynamic alignment of providers, toh used a learning health system (lhs) vision to redesign regional diagnostic processes. A lhs is driven by feedback utilizing operational and clinical information to drive system optimization and innovation. An essential component of a lhs is a collaborative platform that provides connectivity across silos, organizations, and professions. METHODS: To operationalize a lhs, we developed the Ottawa Health Transformation Model (ohtm) as a consensus approach that addresses process barriers, resistance to change, and conflicting priorities. A regional Community of Practice (cop) was established to engage stakeholders, and a dedicated transformation team supported process improvements and implementation. RESULTS: The project operationalized the lung cancer diagnostic pathway and optimized patient flow from referral to initiation of treatment. Twelve major processes in referral, review, diagnostics, assessment, triage, and consult were redesigned. The Ottawa Hospital now provides a diagnosis to 80% of referrals within the provincial target of 28 days. The median patient journey from referral to initial treatment decreased by 48% from 92 to 47 days. CONCLUSIONS: The initiative optimized regional integration from referral to initial treatment. Use of a lhs lens enabled the creation of a system that is standardized to best practice and open to ongoing innovation. Continued transformation initiatives across the continuum of care are needed to incorporate best practice and optimize delivery systems for regional populations.
BACKGROUND: The Ottawa Hospital (toh) defined delay to timely lung cancer care as a system design problem. Recognizing the patient need for an integrated journey and the need for dynamic alignment of providers, toh used a learning health system (lhs) vision to redesign regional diagnostic processes. A lhs is driven by feedback utilizing operational and clinical information to drive system optimization and innovation. An essential component of a lhs is a collaborative platform that provides connectivity across silos, organizations, and professions. METHODS: To operationalize a lhs, we developed the Ottawa Health Transformation Model (ohtm) as a consensus approach that addresses process barriers, resistance to change, and conflicting priorities. A regional Community of Practice (cop) was established to engage stakeholders, and a dedicated transformation team supported process improvements and implementation. RESULTS: The project operationalized the lung cancer diagnostic pathway and optimized patient flow from referral to initiation of treatment. Twelve major processes in referral, review, diagnostics, assessment, triage, and consult were redesigned. The Ottawa Hospital now provides a diagnosis to 80% of referrals within the provincial target of 28 days. The median patient journey from referral to initial treatment decreased by 48% from 92 to 47 days. CONCLUSIONS: The initiative optimized regional integration from referral to initial treatment. Use of a lhs lens enabled the creation of a system that is standardized to best practice and open to ongoing innovation. Continued transformation initiatives across the continuum of care are needed to incorporate best practice and optimize delivery systems for regional populations.
Entities:
Keywords:
Learning health system; community of practice; health information; lean improvement; lung cancer; regional; theory of constraints
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