Evaluation of a Multidisciplinary Team Approach for Generating Survivorship Care Plan Treatment Summaries in Patients With Breast Cancer

Abstract

INTRODUCTION: The optimal structure for survivorship care plan (SCP) programs and methodology for generating treatment summaries (TSs) has not yet been defined, but the Commission on Cancer and the National Accreditation Program for Breast Centers both mandate that participating oncology programs implement SCP-TS processes for patients that have completed treatment. METHODS: We used the Institute for Healthcare Improvement’s Plan-Do-Study-Act model for conducting a quality improvement project evaluating two different SCP-TS programs implemented at the Henry Ford Health System/Henry Ford Cancer Institute’s Breast Oncology Program in Detroit, Michigan. System I involved TSs drafted by nonspecialist breast clinic staff; System II involved TSs vetted through a multidisciplinary breast specialist conference approach. Accuracy of basic documentation entries related to dates and components of treatment were compared for the two approaches. RESULTS: Seventy-one System I and 93 System II documents were reviewed. Documentation was accurate in at least 90% of documents for both systems regarding delivery of chemotherapy and/or endocrine therapy and for documenting the identity of the various members of the cancer treatment team. Both systems had notable inaccuracies in documenting type of surgery performed, but System II had fewer inaccuracies than System I (33.78% v 51.67%, respectively; P = .05). System II, compared with System I, had fewer inaccuracies in documenting date of diagnosis (9.68% v 25.35%, respectively; P = .01) and had less missing information for dose of radiation delivered (9.33% v 33.9%, respectively; P < .01). CONCLUSION: A multidisciplinary team approach to drafting and reviewing SCP-TS documents improved content accuracy for our program, but ongoing education regarding documentation of various surgical procedures is warranted.

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

“…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].…”

“…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].…”

“…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.…”

“…Ideally when response evaluation and toxicities are reported back into the same system and all decision steps are made within the same MDT, the learning potential of AI prediction tools becomes stronger and locally representative [ 10 , 19 , [22] , [23] , [24] ].…”

“…AI can play an important role, being emotionless and able to compute urgency based on historic cohorts levelling indication, e.g. pain, risk, timing and cost, aiming at affordability [ [21] , [22] , [23] , [24] , [25] , [26] , [27] ] in data-collection.…”