Improving Wait Times and Patient Experience Through Implementation of a Provincial Expedited Diagnostic Pathway for BI-RADS 5 Breast Lesions

Laws, Alison; Crocker, Alysha; Dort, Joseph C.; Olson, David; Elwi, Adam; Anderes, Susan; Parker, Shannon; Estey, Angela; Keehn, Alysha; Quan, May Lynn

doi:10.1245/s10434-019-07558-7

Cited by 9 publications

(15 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients are eager to communicate, upload and share information but lack capability to filter or translate issues that are of utmost importance during an urgency [11,37,39]. Human intervention here, helped by a chatbot or automated filter tools (like lab values being in normal range) are potential intermediates and are well accepted by patients [22,23]. Adaption of automated tools by clinicians, depend on quality and pressure of volume/time which is highly variable and carries a changing landscape over time [39].…”

Section: Solvable Barriers In Symptom Monitoringmentioning

confidence: 99%

“…Thresholds to alter and double check need to be low for appropriate detection. Because IT tools never sleep, care teams need to organise correct 24 h shifts to respond to alerts, moreover they have to educate patients in what to do with early symptoms [22,23].…”

Section: Cautionsmentioning

confidence: 99%

“…Ideally, future tools in care coordination should take all the above factors into account, while allocating and guiding patient through their most suitable care path. Ideally, the model focusses on maximizing the combinatorial power of IT processing and the socio-psychological judgement of human director intervention steering coordination [ 19 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Human intervention in this process is essential, bringing both MDT and a nurse navigator to the forefront, becoming the mediator for each individual patient [ [21] , [22] , [23] , [24] ]. For both AI tools can help in estimating urgency and requirements of physical examinations and consultations to auto-plan care pathway and allocate.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits

Moser¹,

Narayan²

2020

The Breast

View full text Add to dashboard Cite

Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director's role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda's and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice and drive wellcoordinated, patient-centric cancer care in the complex web of modern healthcare today.

show abstract

Section: Solvable Barriers In Symptom Monitoringmentioning

confidence: 99%

Section: Cautionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits

Moser¹,

Narayan²

2020

The Breast

View full text Add to dashboard Cite

show abstract

“…7 The CSCN pathway, implemented in 2018, resulted in a more than 50% reduction in the median wait time to referral to a breast program (from 15 to 6 d) and high patient-reported wait-time satisfaction. 7,8 This pathway improved communication and notifications to primary care physicians and the breast programs, prompted an immediate referral to a surgeon and initiated early patient navigation during the diagnosis period. The CSCN also established relationships required to integrate data from non-AHS providers and develop a provincial measurement system in collaboration with the Alberta Society of Radiologists.…”

Section: Key Pointsmentioning

confidence: 99%