A Data-Driven Analytical Framework To Learn And Improve Clinical Workflow In Radiation Oncology

Munbodh, Reshma; Leonard, Kara L.; Schwer, Michelle L.; Brindle, Jim; Klein, Eric E.

doi:10.1016/j.ijrobp.2020.07.223

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such a solution would allow us to verify the correctness of the constraint specifications in addition to solving for inconsistencies and understanding the impact of additional checks introduced in the future 16,26 . The methods proposed in this study are synergistic with ongoing efforts to improve clinical practice through modeling and automation 27,28 …”

Section: Discussionmentioning

confidence: 99%

Graph‐based risk assessment and error detection in radiation therapy

2021

View full text Add to dashboard Cite

Purpose The objective of this study was to formalize and automate quality assurance (QA) in radiation oncology. Quality assurance in radiation oncology entails a multistep verification of complex, personalized radiation plans to treat cancer involving an interdisciplinary team and high technology, multivendor software and hardware systems. We addressed the pretreatment physics chart review (TPCR) using methods from graph theory and constraint programming to study the effect of dependencies between variables and automatically identify logical inconsistencies and how they propagate. Materials and methods We used a modular approach to decompose the TPCR process into tractable units comprising subprocesses, modules and variables. Modules represented the main software entities comprised in the radiation treatment planning workflow and subprocesses grouped the checks to be performed by functionality. Module‐associated variables served as inputs to the subprocesses. Relationships between variables were modeled by means of a directed graph. The detection of errors, in the form of inconsistencies, was formalized as a constraint satisfaction problem whereby checks were encoded as logical formulae. The sequence in which subprocesses were visited was described in an activity diagram. Results The comprehensive model for the TPCR process comprised 5 modules, 19 subprocesses and 346 variables, 225 of which were distinct. Modules included “Treatment Planning System” and “Record and Verify System.” Subprocesses included “Dose Prescription,” “Documents,” “CT Integrity,” “Anatomical Contours,” “Beam Configuration,” “Dose Calculation,” “3D Dose Distribution Quality,” and “Treatment Approval.” Variable inconsistencies, and their source and propagation were determined by checking for constraint violation and through graph traversal. Impact scores, obtained through graph traversal, combined with severity scores associated with an inconsistency, allowed risk assessment. Conclusions Directed graphs combined with constraint programming hold promise for formalizing complex QA processes in radiation oncology, performing risk assessment and automating the TPCR process. Though complex, the process is tractable.

show abstract

Section: Discussionmentioning

confidence: 99%

Graph‐based risk assessment and error detection in radiation therapy

2021

View full text Add to dashboard Cite

show abstract

“…The dashboard was designed to prospectively gather data that provides insight into how care path activities unfold over time, where delays are introduced, and the effect of introducing new care path activities on clinical workflow. 13 In this study, we also present preliminary quantitative measures that characterize the treatment planning process including on-time performance, staff compliance in using the standardized workflow, and how various treatment planning activities unfold over time. In addition, we present preliminary results which quantify the longitudinal effect of COVID-19 on patient volumes using data obtained from the dashboard.…”

Section: Introductionmentioning

confidence: 99%

Real‐time analysis and display of quantitative measures to track and improve clinical workflow

Munbodh

Roth

Leonard

et al. 2022

J Applied Clin Med Phys

View full text Add to dashboard Cite

Purpose: Radiotherapy treatment planning is a complex process with multiple, dependent steps involving an interdisciplinary patient care team. Effective communication and real-time tracking of resources and care path activities are key for clinical efficiency and patient safety. Materials and Methods:We designed and implemented a secure, interactive web-based dashboard for patient care path, clinical workflow, and resource utilization management. The dashboard enables visualization of resource utilization and tracks progress in a patient's care path from the time of acquisition of the planning CT to the time of treatment in real-time. It integrates with the departmental electronic medical records (EMR) system without the creation and maintenance of a separate database or duplication of work by clinical staff. Performance measures of workflow were calculated. Results:The dashboard implements a standardized clinical workflow and dynamically consolidates real-time information queried from multiple tables in the EMR database over the following views: (1) CT Sims summarizes patient appointment information on the CT simulator and patient load; (2) Linac Sims summarizes patient appointment times, setup history, and notes, and patient load; (3) Task Status lists the clinical tasks associated with a treatment plan, their due date, status and ownership, and patient appointment details; (4) Documents provides the status of all documents in the patients' charts; and (5) Diagnoses and Interventions summarizes prescription information, imaging instructions and whether the plan was approved for treatment. Real-time assessment and quantification of progress and delays in a patient's treatment start were achieved. Conclusions: This study indicates it is feasible to develop and implement a dashboard, tailored to the needs of an interdisciplinary team, which derives and integrates information from the EMR database for real-time analysis and display of resource utilization and clinical workflow in radiation oncology.The framework developed facilitates informed, data-driven decisions on clinical workflow management as we seek to optimize clinical efficiency and patient safety.

show abstract

A Data-Driven Analytical Framework To Learn And Improve Clinical Workflow In Radiation Oncology

Cited by 2 publications

References 0 publications

Graph‐based risk assessment and error detection in radiation therapy

Graph‐based risk assessment and error detection in radiation therapy

Real‐time analysis and display of quantitative measures to track and improve clinical workflow

Contact Info

Product

Resources

About