Time saved is time earned: Implementation of an agile workflow system in a high-volume radiation oncology centre Workflow optimization in radiation oncology

Time saved is time earned: Implementation of an agile workflow system in a high-volume radiation oncology centre Workflow optimization in radiation oncology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Time saved is time earned: Implementation of an agile workflow system in a high-volume radiation oncology centre Workflow optimization in radiation oncology Kundan Singh Chufal, Irfan Ahmad, Alexis Andrew Miller, Preetha Umesh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4015333/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Aims and Objectives: To evaluate operational efficiency gains when utilizing an agile digital workflow system (DWS; OncFlow®) in the Radiation Oncology clinic over standard workflow (SW). Materials and Methods Two Radiation Oncology teams in the same institution, one using DWS and the other SW, were prospectively assessed to compare the following operational parameters: consultation waiting time, communication errors, and data retrieval. We employed non-parametric tests and an unpaired t-test for statistical analysis. Results Digital workflow patients experienced a median consultation waiting time of 5.5 minutes (95% CI: 4.7–6.3) compared to 17.9 minutes (95% CI: 14.2–21.6) in the standard workflow, with the difference being significant (p < 0.0001). Communication-related incidents each month were significantly lower in the DWS group, with a median of 1 incident (range: 0–3) compared to 4 incidents (range: 1–5) in the SW (p < 0.001). Planned data retrieval was also considerably faster with DWS. Conclusion Digital workflow systems significantly reduce consultation waiting times and communication errors, enhancing efficiency in the Radiation Oncology clinic. Faster data retrieval also reduced research turnaround time. Broader application in more diverse working environments is warranted. Digital Workflow Efficiency Radiation Oncology Patient Wait Times Communication in Healthcare Data Management Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Digitization impacts modern healthcare delivery, especially oncology, specifically with the emergence of Routine Clinical Data as a viable alternative to conducting randomized clinical trials.[1,2] However, software like Electronic Health Records (EHR) implemented in large medical environments are designed to capture information from all departments (including allied specialties and ancillary services), assist in transparent billing for services provided, avoid prescription-related errors and potentially improve care.[3–5] Real world EHR data might address a specific context if appropriate and retrievable, but the onus of data extraction falls to clinicians, dedicated research staff, and/or data miners.[6,7] For data scientists, which includes clinicians, the EHR falls in the category of an impenetrable data repository with an onerous process of analyzable data extraction.[8,9] Despite intentionally designing compliant data security, digital theft and/or hijacking by malicious actors still occurs.[10–12] For patients and treating doctors, EHRs impede the development of a strong doctor-patient relationship.[13,14] Consultations in clinics are dictated by time, which is dominated by doctors filling EHRs, either while engaging with patients or prior to a patient encounter.[15,16] The former is often perceived as disinterest by patients, and the latter increases waiting time, both leading to doctor dissatisfaction. Increasing dissatisfaction, burnout rates, and reduced time devoted to direct patient care are associated with the EHR’s clerical work, and concerns of consequent lower-quality patient care are emerging.[17] Finally, most EHR vendors currently do not have a workable solution for flagging incorrect or missing information, or preventing duplication using copy/paste text operations, all of which ultimately require future rectification and validation.[18] Radiation oncologists uniquely curate an idiosyncratic, separate patient information system, the treatment planning system (TPS) with patients' CT scans, radiotherapy volumes, treatment plans, treatment and quality assurance data.[19] The TPS does not integrate with the EHR and attempts to integrate have been costly and disruptive.[20,21] Vendors who supply TPS and Oncology Information Systems (OIS) have been more successful with oncology-specific data.[22] While the data is better structured, extracting TPS/OIS data for analysis is difficult. Most end-users lack the technical expertise to extract this data from either, and any add-on modules for this purpose require additional cost. While working with the TPS is the sine qua non for the practice of radiation oncology, the additional effort expended on also working with OIS/EHRs could be optimized. Designing an agile Digital Workflow System (DWS) with the core principles of translating radiation oncology knowledge and workflow structures translation into a relational database should:(a) minimize time spent on repetitive data entry; (b) achieve analyzable data export; (c) reduce errors specific to the radiation oncology workflow; (d) reduce error-perpetuating, bloating copy/paste of clinical notes [23], and; (e) allow a local Large Language Model (pre-trained on the structured database) to generate concise summaries. Simplification of the individual patient view can provide all relevant treatment-related information in a single page with collapsible sections, minimizing repeated referral to multiple data sources (radiology, pathology, lab results).[24] Finally, the system could assess data integrity/completeness. Appropriate software development processes for medical systems are rarely discussed. These project management processes are described as sequential ('waterfall') or agile workflows.[25] The sequential methodology is a linear, phase-by-phase approach with clear, structured stages and set goals ( a la Gantt chart), but is rigid against changes and typically starts with a specification and no further interaction with users until completion.[26] Agile methodology is flexible and iterative, focusing on customer involvement, continuous feedback, and team collaboration, with high adaptability, which is well-suited for projects with expert or evolving needs and uncertainties.[27] Most EHR/OIS/TPSs are developed using a sequential methodology, whereas the DWS was and is developed using an agile methodology to adapt to the needs of the oncologist end-user.[18] The diverse literature on EHR adoption comes from developed nations with mandated and funded EHRs.[28] Research from lower-middle-income countries (LMICs) on the general topic of EHR adoption is sparse.[29,30] Research in LMICs is challenging, however a DWS system focused on ease of use and data quality with assist research output. The collection of data at source and in appropriate categories, democratizes and globalizes the process of gathering research quality data, while enhancing operational efficiency. Analysis of treatment efficacy will be supported. The study’s primary objectives were to compare the DWS with a traditional physical file-based system (SW) by analyzing: (a) waiting time in the outpatient clinic; (b) errors related to miscommunication; (c) time taken to retrieve analyzable data on radiation dermatitis. We also analyzed the time taken to fill data in the DWS modules. MATERIALS & METHODS Our department delivers ~ 3000 courses of radiotherapy (RT) annually on 5 linear accelerators (TrueBeam, CyberKnife, RadiXact, Clinac, Synergy) and a multi-channel brachytherapy machine (MicroSelectron HDR) using CT-based treatment planning (SOMATOM go. Sim) to deliver external beam radiotherapy, stereotactic radiosurgery and radiotherapy, and interstitial and intracavitary brachytherapy. The usual time from CT simulation to RT start is 1–2 working days, and the departmental staffing (Radiation Oncologists, Registrars, Fellows, Residents, Medical Physicists, and Therapists) of ~ 105 individuals are distributed in four separate work areas (Outpatient Clinics, treatment areas, simulation area, and planning area). In September 2022, the Radiation Oncology department implemented a Digital Workflow System (DWS)(OncFlow®, Dashamlav AI Labs, Pune, India). This DWS is an integrated suite of modules designed to digitally capture the entire patient journey with its generated data. Unlike an EHR, DWS data is entered through a friendly oncologist-designed web page, which is the front-end to a knowledge-structured, relational mySQL database. The patient’s unique ID serves as a foreign key linking all subsequent patient-related records entered in categorical formats, so that structured data retrieval enables import into standard statistical software. The DWS was designed for prospective, contemporaneous, categorical data entry to reflect the patient’s cancer journey as well as the oncologist’s knowledge structure and workflow, from diagnosis through follow-up to survivorship and death. The DWS data is entered by radiation oncologists or trained data entry operators (verified by radiation oncologists) of the DWS oncology team. This structured data is then used to generate dynamic patient summaries/notes based on the available information and displayed for each patient. Typical EHR attempt to capture ‘all’ patient information, but the DWS was designed to capture oncologically relevant patient information. To determine the efficiency gained by switching to the DWS system, we designed a prospective study to compare with the existing physical file-based workflow, focusing on the outpatient clinics, and simulation/planning workflow. In the former setting, we measured waiting time and, in the latter, communication-related errors. Additionally, we compared the effort (time taken) to extract analyzable data on radiation dermatitis. These were defined as follows: 1. Waiting time Outpatients can attend the clinic with or without a prior appointment. Unlike High-Income Countries (HIC), insurance or government policies do not dictate or influence the patient’s choice to seek treatment; therefore, the HIC waiting time literature does not apply. We defined waiting time as the time difference between reporting to the reception area of the outpatient clinic and being seen by the radiation oncologist. This measure is a validated assessment tool developed by the Institute for Healthcare Improvement (IHI) for evaluating healthcare efficiency.[31] In the physical records-based workflow (SW), each patient has a unique ID used to accumulate the paper reports of all investigations, interventions, and consultations. This record is managed and stored in a medical records department (MRD), which coordinates the task of providing the record to outpatient clinics, in-patient wards, and day-care facilities. A physical file documenting radiation therapy details, plan parameters and delivery data is held in the Radiation Oncology department duplicating the TPS repository. All charts for scheduled patients are retrieved before outpatient visits, and all unscheduled outpatient visits result in ad hoc chart retrievals. The subsequent workflow in the outpatient clinic differed between the two oncology teams. a. Standard Workflow Irrespective of the arrival time at the outpatient desk, the Standard Workflow oncology team was only notified once the chart was physically present. A time period was required by the oncologist to become familiar with the chart before calling the patient into the room. b. Digital Workflow System (OncFlow®) Irrespective of the arrival time at the outpatient desk, the DWS oncology team is notified immediately after the patient arrives. The oncologist then became familiarized with the case details by reviewing the entries in the DWS before calling the patient into the room. When the physical chart arrived in the Outpatient department, it was delivered to the room where the patient was already being seen. 2. Communication-related incident All radiation oncology department have a communication chain from the treating radiation oncologist, registrars, fellows, residents, and medical physicists to the therapists in order to translate the radiation oncologist’s intent of treatment through the hierarchy of processes and reviews to ensure the delivered treatment is what was intended. We defined a communication-related incident as an RT plan not being delivered on the scheduled date due to: (a) inadequate target/organ-at-risk delineation, or; (b) unacceptable plan parameters on evaluation by the treating radiation oncologist. Machine breakdown/maintenance or non-deliverable plan due to failed pre-treatment quality assurance were excluded from the definition. 3. Analyzable data We defined analyzable data as extracted data presented in a specific, interchangeable format, electronic file (CSV, XLS, or XLSX) with individual patient data in a single row that includes demographics, diagnosis details, RT details, and radiation dermatitis data (assessed during and up to 1-month post-completion of RT) in predefined numerical and categorical formats. On the day of assessment, the date, radiation dermatitis grade (Common Terminology Criteria for Adverse Events version 5), fractions delivered or days after RT completion, was recorded (on paper by the SW team; electronically by the DWS team). Missing data was handled as missing-at-random. The DWS data entry time was provided by three independent data entry operators (beginner-, intermediate- and expert-level) and a radiation oncologist. Data was entered during the out-patient clinic consultation. The DWS metadata comprising timestamps and user IDs was querying in the server logs to assign times to each clinical process step. Users were unaware of the data extraction to minimize the Hawthorne effect.[32] Study Design The Chief of Radiation Oncology enrolled two independent radiation oncology teams (one using DWS, the other using SW) to assess their relative performance in three key departmental quality variables – outpatient waiting times, radiotherapy communication-related errors over one year, and finally, efficiency of radiation skin toxicity data extraction and analysis. 1. Waiting Time The receptionists reported data on waiting time during the specified period directly to the Chief of Radiation Oncology. 2. Communication-related error Data on monthly communication-related errors was collected from an in-house departmental analytics dashboard. 3. Analyzable data Without warning and independently to either team, the Chief of Radiation Oncology requested the collation of analyzable data into a spreadsheet describing five sub-tasks: (a) Identify patients treated in the preceding three months; (b) identify the radiation dermatitis toxicity grade for each patient; (c) present data in machine-readable format; (d) pre-processing the data into numerical categories; (e) demonstrate the validity of the data. Progress on each sub-task was monitored by TeamGantt (Maryland, USA). The two teams were blinded to the other team's activities to minimize the Hawthorne effect.[32] Statistical Analysis The measured time delay was analyzed using non-parametric tests (Mann-Whitney test & Kruskal-Wallis) using Prism v10 (Dotmatics, USA) due to its non-normal distribution. An unpaired t-test assessed the cumulative difference in communication-related incidents. No formal statistical analysis was used to compare the time taken to extract research data. Since the data on time taken to enter information was for different patients, it was analyzed as an aggregate rather than stratified by the level of expertise. The two-tailed significance was set at less than 0.05 RESULTS OPD waiting times One hundred seventeen patients were enrolled in this study (59 patients in SW and 58 in DWS). Overall, the median waiting time for patients in the DWS workflow was significantly less compared to the standard workflow [mean, 95% CI: 5.5 min (4.7–6.3) vs 17.9 min (14.2–21.6), p < 0.0001] (Fig. 1 ). The time taken for the physical file retrieval was not significantly different [mean, 95% CI: 16.6 min (9.4–23.8) vs 13.3 min (9.4–17.1), p = 0.646] (Fig. 2 ). For scheduled outpatient visits where the chart was already retrieved, the DWS workflow was faster as the patient data familiarization time was reduced [mean, 95% CI: 5 min (4–5.9) vs 13 min (9.6–16.5), p < 0.0001] (Fig. 3 ). For unscheduled outpatient visits, the DWS workflow was only slightly slower but not significantly [mean, 95% CI: 6.2 min (4.8–7.6) vs 5 min (4–5.9), p = 0.7487] (Fig. 2 ) and much less than the average time needed to retrieve the paper chart [mean, 95%CI: 28.6 min (13.5–43.6)] (Supplementary Fig. 1). The Standard Workflow was significantly longer [mean, 95% CI: 29 min (21.6–36.3) vs 13 min (9.6–16.5), p < 0.0001] (Fig. 3 ) due to the additional time for paper chart retrieval before familiarization could begin. Communication-related incidents Figure 4 shows the overall communication-related incidents over 12 months. Overall, the cumulative incident rate over 12 months was significantly different [DWS vs SW, median (incidents/month), range: 1 (0–3) vs 4 (1–5), p < 0.001]. Time taken to extract analyzable data The task was divided into five sub-tasks, and Fig. 5 shows the time taken by both systems. Overall, the DWS system took substantially less time [< 1 month vs 4 months]. The SW team spent more time finding data and extraction from paper records. In contrast, only pre-processing the data extracted from the DWS database took significant time. Time taken to enter data into DWS 114 unique patient entries were randomly extracted from the server logs for analysis. Of 12 total modules in the DWS, 9 took less than two minutes each to complete across all users and 7 took less than one minute (Fig. 6 ). It is worth noting that data entry outside of the Radiation Oncology process [e.g., entering Dose-Volume Histogram (DVH) and chemotherapy drug data] is more onerous. The average time taken for completing each patient’s chart [median, IQR: 212 sec (54–427)] is less than the addition of each module individually, because data re-use during the lifetime of the patient’s care journey, and repetitive entries like on-treatment clinical assessments and follow-up assessments are brief. DISCUSSION This study clearly demonstrates the inefficiencies inherent in conventional workflows with slower data retrieval, longer waits, and less effective patient processing mechanisms, and supports the efficacy of an agile digital workflow system in enhancing the patient care process within Radiation Oncology departments. The benefits of design according to oncology knowledge structure and workflow use are multiple and obvious.[33] A significant reduction in overall waiting times was delivered, which should be reflected in patient satisfaction.[34] Despite similar physical chart retrieval times for both workflows, the DWS team could complete their familiarization and start their consultation before the physical chart was available for unbooked outpatients. This speed results from the small, incremental and additive amounts of time taken to expand the structured OncFlow® record contemporaneously with data discovery. The benefit comes from being digital rather than electronic. Since the diagnosis is evident in the diagnosis field, histopathology reports do not need to be found and read again; the value was entered at first consultation. This digital function permits the software to succinctly summarize the patient's clinical circumstance within the construct of oncological knowledge structure to yield benefits not available in the large EHR, which does not cater to any particular branch of medical knowledge but instead acts as a repository for electronic paper.[5,12] The underlying design of the relational database incorporating oncology knowledge and workflow structure permits the customization of information presented to the individual oncologist. This permits the display of oncological summaries that are rapidly interpretable, more so than wading through free-text medical documentation.[24] Reducing waiting times by increasing efficiency addresses patient dissatisfaction and discomfort, and reduces oncologists stress over scheduling pressures.[35] In particular, unscheduled outpatient visits is more likely to be symptomatic from recurrence, side effects, and/or disease, yet in the standard workflow wait 29 minutes before an oncological assessment even starts. Similar research on waiting time optimization in radiation oncology clinics using patient flow analysis also provides improvement with reduced waiting times (51.2 min to 27.1 min, Johns Hopkins group; 45 min to 16 min, MD Anderson Group).[36,37] From the oncologist’s perspective, if one were to assume an oncologist-patient face-to-face time of 15 minutes, the time in clinic for an oncologist to see ten scheduled and two unscheduled patients will be 247 min using the DWS [(15x10 + 5.5x10) + (15x2 + 6x2)], and 368 min using SW [(15x10 + 13x10) + (15x2 + 29x2)]. This workload reduction should ameliorate fatigue, burnout and reduce medical error.[17] From the hospital manager’s perspective, waiting rooms become less crowded, parking demands and overtime charges arising from clinics running late will be reduced.[38] The research and reporting benefits are similarly enormous. The collation of acute skin toxicity data over 4 months using paper charts is sadly the norm for most clinicians' experience of retrospective reviews. The DWS team, however, demonstrated the new paradigm of Routine Clinical Data where contemporaneously entered data is into a digital system according to established skin toxicity classifications, and is therefore immediately available for real-time data mining, display, and research. Whether RT imaging, contours and RT plan data can be stored in the DWS requires deliberate investigation, but when implemented, the time taken to import approved DVH data in the DWS will be significantly reduced.[39] The DWS also mitigated communication-related incidents, a research area which has received relatively less attention in radiation oncology.[40] Previous research on the topic found that written communication errors, mainly involving the use of the Electronic Medical Record (EMR) accounted for 62% of communication events and the majority occurred during patient assessment or treatment planning. Disconcertingly, 20% of these were potentially severe events. The medical community wants generalizable research, and while this study shows a path to digital transformation, even this institution shows two workflows. As with all electronic systems, the software deficits are usually minor compared to the implementation deficits.[22] Maximizing the DWS benefit will require changes to established workflow patterns.[33] Since this evaluation has occurred in a single large center, we encourage future studies to include multiple institutions and more diverse clinical environments, therefore encompassing a broader demographic. Additionally, a cost-benefit analysis would be instrumental in quantifying the economic impact of DWS implementation, providing a more comprehensive understanding of its value proposition within healthcare operations. Finally, a patient and oncologist satisfaction survey comparing DWS with physical file/EHR-based workflows would help identify potential avenues for improvement. CONCLUSION In conclusion, transitioning from traditional patient management workflows to digital workflow systems enhances the efficiency of healthcare services by reducing waiting times and communication-related incidents. Investing time in entering data into the DWS could accelerate research focused on real-world outcomes in oncology, especially from lower-middle-income countries. As the global healthcare landscape progresses towards digitization, our findings highlight the potential of workflow management systems to serve as catalysts for positive change, conferring significant benefits to all stakeholders. DECLARATIONS Ethics approval: Not Applicable Consent for Publication: Not Applicable Data Availability Statement: This study was performed at Rajiv Gandhi Cancer Institute & Research Centre and is stored in the institutions data repository. The authors do not own these data and hence are not permitted to share them in the original form (only in aggregate form). Reasonable requests for access to data will be considered on an individual basis, by contacting the corresponding author. Competing interests: None Funding Statement: None Author Contributions: All authors contributed equally in the design, conception and writing of this manuscript. Acknowledgements: We are grateful to Mr Sameer Koul, Mr Nakul Goswami & Mr Nitesh Waghmare. REFERENCES Saesen R, Van Hemelrijck M, Bogaerts J, et al. Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer. Eur J Cancer . 2023;186:52–61. Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol . 2019;16:312–25. Crossing the Quality Chasm: A New Health System for the 21st Century . Washington, D.C.: National Academies Press 2001. https://doi.org/10.17226/10027 Romano MJ, Stafford RS. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Aug, 2024 Reviews received at journal 03 Jul, 2024 Reviewers agreed at journal 25 Jun, 2024 Reviews received at journal 24 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 15 Jun, 2024 Reviewers invited by journal 29 May, 2024 Editor invited by journal 20 Mar, 2024 Editor assigned by journal 20 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 05 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4015333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276552579,"identity":"fdc8a9ed-c3f8-429a-9156-aadb74690f0a","order_by":0,"name":"Kundan Singh Chufal","email":"","orcid":"","institution":"Rajiv Gandhi Cancer Institute \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Kundan","middleName":"Singh","lastName":"Chufal","suffix":""},{"id":276552580,"identity":"19611f16-ea88-4ba6-83ad-77eeb0f97b4a","order_by":1,"name":"Irfan Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACNgbGByBajo2BuQHEJUYLswGINgbqJVILA1RLYgPRWvikDzN+LmCwS+9jP9j4mKeMQZ5f7AABh/ElM0vPYEjObeNJbDbmOcdgOHN2AgEtPPwHpHkYmHPbJBjbpHnbGBIMbhPUwsz8m4ehPp2NFC1sQFsOJ5CmxZrH4LghyC+Gc85JEPaLfA8z822eimp5+fbDBx+8KbOR55cmoAUCDOAsCWKUj4JRMApGwSggBAADfy8aT3p0CgAAAABJRU5ErkJggg==","orcid":"","institution":"Rajiv Gandhi Cancer Institute \u0026 Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Irfan","middleName":"","lastName":"Ahmad","suffix":""},{"id":276552581,"identity":"c7fa34bf-b281-49d4-b497-26eec9c3545c","order_by":2,"name":"Alexis Andrew Miller","email":"","orcid":"","institution":"Illawarra Cancer Care Centre","correspondingAuthor":false,"prefix":"","firstName":"Alexis","middleName":"Andrew","lastName":"Miller","suffix":""},{"id":276552582,"identity":"80507e3e-fce4-4d1b-8648-a97a4c9b1ef1","order_by":3,"name":"Preetha Umesh","email":"","orcid":"","institution":"Rajiv Gandhi Cancer Institute \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Preetha","middleName":"","lastName":"Umesh","suffix":""},{"id":276552583,"identity":"8fb59131-3a3c-41f8-897c-1006c9239561","order_by":4,"name":"Alok Dwivedi","email":"","orcid":"","institution":"Discover Financial Services","correspondingAuthor":false,"prefix":"","firstName":"Alok","middleName":"","lastName":"Dwivedi","suffix":""},{"id":276552584,"identity":"bc78c9e5-41ba-4396-bc04-4b4c7dbb9f0a","order_by":5,"name":"Kratika Bhatia","email":"","orcid":"","institution":"Rajiv Gandhi Cancer Institute \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Kratika","middleName":"","lastName":"Bhatia","suffix":""},{"id":276552585,"identity":"95a27a88-d2db-44af-a39f-21ed5bb1f515","order_by":6,"name":"Munish Gairola","email":"","orcid":"","institution":"Rajiv Gandhi Cancer Institute \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Munish","middleName":"","lastName":"Gairola","suffix":""}],"badges":[],"createdAt":"2024-03-05 05:34:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4015333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4015333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52105618,"identity":"22ce0519-9549-4432-ad9f-5e6418fd3070","added_by":"auto","created_at":"2024-03-06 19:29:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236165,"visible":true,"origin":"","legend":"\u003cp\u003eMean (with 95% CI) outpatient clinic waiting times for the DWS team and Standard Workflow team.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015333/v1/dc3bbf98c7e7d65961d2d6ad.jpg"},{"id":52105617,"identity":"f7f24580-7049-4241-9f87-607a6fad5953","added_by":"auto","created_at":"2024-03-06 19:29:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250800,"visible":true,"origin":"","legend":"\u003cp\u003eMean (with 95% CI) time required to retrieve the physical records for patients managed by DWS team or the Standard Workflow team.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015333/v1/797039caaa42685084ba5ae0.jpg"},{"id":52105616,"identity":"e2dc662e-5a5d-4d4e-aa4b-33f0f1409193","added_by":"auto","created_at":"2024-03-06 19:29:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":507033,"visible":true,"origin":"","legend":"\u003cp\u003eMean (with 95% CI) outpatient clinic waiting times for patients stratified by scheduled and unscheduled appointments under DWS team and Standard Workflow team.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015333/v1/47e5c004925244661347158d.jpg"},{"id":52105615,"identity":"36f316a7-e6e7-41ad-86c3-181c099b2bc6","added_by":"auto","created_at":"2024-03-06 19:29:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":488314,"visible":true,"origin":"","legend":"\u003cp\u003eCommunication-related incidents per month between the DWS team and Standard Workflow team over one year.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015333/v1/7bdc75c0f811298265ce37b0.jpg"},{"id":52105619,"identity":"64851e8b-0aff-4b20-81dd-c15b4609bc40","added_by":"auto","created_at":"2024-03-06 19:29:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3956223,"visible":true,"origin":"","legend":"\u003cp\u003eGantt Chart displaying the progress and timelines for tasks completed by the DWS Team and Standard Workflow Team from September to December 2023 (Prepared using TeamGantt).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015333/v1/b08c20d995025ab7c11f979f.jpg"},{"id":52105621,"identity":"90e2f774-7630-4833-af0f-bec808dba719","added_by":"auto","created_at":"2024-03-06 19:29:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14137203,"visible":true,"origin":"","legend":"\u003cp\u003eBox and Whiskers plot (median with 10\u003csup\u003eth\u003c/sup\u003e-90\u003csup\u003eth\u003c/sup\u003e Percentile) of aggregate time four operators took to enter data into each module of the DWS. Exact values are shown in the text inset.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015333/v1/1f21ddc3318afb995f1af257.jpg"},{"id":52106242,"identity":"2f499743-3d60-46af-bd1d-97e981efe9fe","added_by":"auto","created_at":"2024-03-06 19:37:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":665227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4015333/v1/511b6dc4-aef7-47bf-ac36-cc5409d18774.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time saved is time earned: Implementation of an agile workflow system in a high-volume radiation oncology centre Workflow optimization in radiation oncology","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDigitization impacts modern healthcare delivery, especially oncology, specifically with the emergence of Routine Clinical Data as a viable alternative to conducting randomized clinical trials.[1,2] However, software like Electronic Health Records (EHR) implemented in large medical environments are designed to capture information from all departments (including allied specialties and ancillary services), assist in transparent billing for services provided, avoid prescription-related errors and potentially improve care.[3\u0026ndash;5] Real world EHR data might address a specific context if appropriate and retrievable, but the onus of data extraction falls to clinicians, dedicated research staff, and/or data miners.[6,7]\u003c/p\u003e \u003cp\u003eFor data scientists, which includes clinicians, the EHR falls in the category of an impenetrable data repository with an onerous process of analyzable data extraction.[8,9] Despite intentionally designing compliant data security, digital theft and/or hijacking by malicious actors still occurs.[10\u0026ndash;12] For patients and treating doctors, EHRs impede the development of a strong doctor-patient relationship.[13,14] Consultations in clinics are dictated by time, which is dominated by doctors filling EHRs, either while engaging with patients or prior to a patient encounter.[15,16] The former is often perceived as disinterest by patients, and the latter increases waiting time, both leading to doctor dissatisfaction. Increasing dissatisfaction, burnout rates, and reduced time devoted to direct patient care are associated with the EHR\u0026rsquo;s clerical work, and concerns of consequent lower-quality patient care are emerging.[17] Finally, most EHR vendors currently do not have a workable solution for flagging incorrect or missing information, or preventing duplication using copy/paste text operations, all of which ultimately require future rectification and validation.[18]\u003c/p\u003e \u003cp\u003eRadiation oncologists uniquely curate an idiosyncratic, separate patient information system, the treatment planning system (TPS) with patients' CT scans, radiotherapy volumes, treatment plans, treatment and quality assurance data.[19] The TPS does not integrate with the EHR and attempts to integrate have been costly and disruptive.[20,21] Vendors who supply TPS and Oncology Information Systems (OIS) have been more successful with oncology-specific data.[22] While the data is better structured, extracting TPS/OIS data for analysis is difficult. Most end-users lack the technical expertise to extract this data from either, and any add-on modules for this purpose require additional cost. While working with the TPS is the \u003cem\u003esine qua non\u003c/em\u003e for the practice of radiation oncology, the additional effort expended on also working with OIS/EHRs could be optimized.\u003c/p\u003e \u003cp\u003eDesigning an agile Digital Workflow System (DWS) with the core principles of translating radiation oncology knowledge and workflow structures translation into a relational database should:(a) minimize time spent on repetitive data entry; (b) achieve analyzable data export; (c) reduce errors specific to the radiation oncology workflow; (d) reduce error-perpetuating, bloating copy/paste of clinical notes [23], and; (e) allow a local Large Language Model (pre-trained on the structured database) to generate concise summaries. Simplification of the individual patient view can provide all relevant treatment-related information in a single page with collapsible sections, minimizing repeated referral to multiple data sources (radiology, pathology, lab results).[24] Finally, the system could assess data integrity/completeness.\u003c/p\u003e \u003cp\u003eAppropriate software development processes for medical systems are rarely discussed. These project management processes are described as sequential ('waterfall') or agile workflows.[25] The sequential methodology is a linear, phase-by-phase approach with clear, structured stages and set goals (\u003cem\u003ea la\u003c/em\u003e Gantt chart), but is rigid against changes and typically starts with a specification and no further interaction with users until completion.[26] Agile methodology is flexible and iterative, focusing on customer involvement, continuous feedback, and team collaboration, with high adaptability, which is well-suited for projects with expert or evolving needs and uncertainties.[27] Most EHR/OIS/TPSs are developed using a sequential methodology, whereas the DWS was and is developed using an agile methodology to adapt to the needs of the oncologist end-user.[18]\u003c/p\u003e \u003cp\u003eThe diverse literature on EHR adoption comes from developed nations with mandated and funded EHRs.[28] Research from lower-middle-income countries (LMICs) on the general topic of EHR adoption is sparse.[29,30] Research in LMICs is challenging, however a DWS system focused on ease of use and data quality with assist research output. The collection of data at source and in appropriate categories, democratizes and globalizes the process of gathering research quality data, while enhancing operational efficiency. Analysis of treatment efficacy will be supported.\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s primary objectives were to compare the DWS with a traditional physical file-based system (SW) by analyzing: (a) waiting time in the outpatient clinic; (b) errors related to miscommunication; (c) time taken to retrieve analyzable data on radiation dermatitis. We also analyzed the time taken to fill data in the DWS modules.\u003c/p\u003e"},{"header":"MATERIALS \u0026 METHODS","content":"\u003cp\u003eOur department delivers\u0026thinsp;~\u0026thinsp;3000 courses of radiotherapy (RT) annually on 5 linear accelerators (TrueBeam, CyberKnife, RadiXact, Clinac, Synergy) and a multi-channel brachytherapy machine (MicroSelectron HDR) using CT-based treatment planning (SOMATOM \u003cem\u003ego.\u003c/em\u003eSim) to deliver external beam radiotherapy, stereotactic radiosurgery and radiotherapy, and interstitial and intracavitary brachytherapy. The usual time from CT simulation to RT start is 1\u0026ndash;2 working days, and the departmental staffing (Radiation Oncologists, Registrars, Fellows, Residents, Medical Physicists, and Therapists) of ~\u0026thinsp;105 individuals are distributed in four separate work areas (Outpatient Clinics, treatment areas, simulation area, and planning area).\u003c/p\u003e\n\u003cp\u003eIn September 2022, the Radiation Oncology department implemented a Digital Workflow System (DWS)(OncFlow\u0026reg;, Dashamlav AI Labs, Pune, India). This DWS is an integrated suite of modules designed to digitally capture the entire patient journey with its generated data. Unlike an EHR, DWS data is entered through a friendly oncologist-designed web page, which is the front-end to a knowledge-structured, relational mySQL database. The patient\u0026rsquo;s unique ID serves as a foreign key linking all subsequent patient-related records entered in categorical formats, so that structured data retrieval enables import into standard statistical software.\u003c/p\u003e\n\u003cp\u003eThe DWS was designed for prospective, contemporaneous, categorical data entry to reflect the patient\u0026rsquo;s cancer journey as well as the oncologist\u0026rsquo;s knowledge structure and workflow, from diagnosis through follow-up to survivorship and death. The DWS data is entered by radiation oncologists or trained data entry operators (verified by radiation oncologists) of the DWS oncology team. This structured data is then used to generate dynamic patient summaries/notes based on the available information and displayed for each patient. Typical EHR attempt to capture \u0026lsquo;all\u0026rsquo; patient information, but the DWS was designed to capture oncologically relevant patient information.\u003c/p\u003e\n\u003cp\u003eTo determine the efficiency gained by switching to the DWS system, we designed a prospective study to compare with the existing physical file-based workflow, focusing on the outpatient clinics, and simulation/planning workflow. In the former setting, we measured waiting time and, in the latter, communication-related errors. Additionally, we compared the effort (time taken) to extract analyzable data on radiation dermatitis. These were defined as follows:\u003c/p\u003e\n\u003cp\u003e1. Waiting time\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eOutpatients can attend the clinic with or without a prior appointment. Unlike High-Income Countries (HIC), insurance or government policies do not dictate or influence the patient\u0026rsquo;s choice to seek treatment; therefore, the HIC waiting time literature does not apply. We defined waiting time as the time difference between reporting to the reception area of the outpatient clinic and being seen by the radiation oncologist. This measure is a validated assessment tool developed by the Institute for Healthcare Improvement (IHI) for evaluating healthcare efficiency.[31]\u003c/p\u003e\n\u003cp\u003eIn the physical records-based workflow (SW), each patient has a unique ID used to accumulate the paper reports of all investigations, interventions, and consultations. This record is managed and stored in a medical records department (MRD), which coordinates the task of providing the record to outpatient clinics, in-patient wards, and day-care facilities. A physical file documenting radiation therapy details, plan parameters and delivery data is held in the Radiation Oncology department duplicating the TPS repository.\u003c/p\u003e\n\u003cp\u003eAll charts for scheduled patients are retrieved before outpatient visits, and all unscheduled outpatient visits result in \u003cem\u003ead hoc\u003c/em\u003e chart retrievals. The subsequent workflow in the outpatient clinic differed between the two oncology teams.\u003c/p\u003e\n\u003cp\u003ea. Standard Workflow\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eIrrespective of the arrival time at the outpatient desk, the Standard Workflow oncology team was only notified once the chart was physically present. A time period was required by the oncologist to become familiar with the chart before calling the patient into the room.\u003c/p\u003e\n\u003cp\u003eb. Digital Workflow System (OncFlow\u0026reg;)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eIrrespective of the arrival time at the outpatient desk, the DWS oncology team is notified immediately after the patient arrives. The oncologist then became familiarized with the case details by reviewing the entries in the DWS before calling the patient into the room. When the physical chart arrived in the Outpatient department, it was delivered to the room where the patient was already being seen.\u003c/p\u003e\n\u003cp\u003e2. Communication-related incident\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eAll radiation oncology department have a communication chain from the treating radiation oncologist, registrars, fellows, residents, and medical physicists to the therapists in order to translate the radiation oncologist\u0026rsquo;s intent of treatment through the hierarchy of processes and reviews to ensure the delivered treatment is what was intended. We defined a communication-related incident as an RT plan not being delivered on the scheduled date due to: (a) inadequate target/organ-at-risk delineation, or; (b) unacceptable plan parameters on evaluation by the treating radiation oncologist. Machine breakdown/maintenance or non-deliverable plan due to failed pre-treatment quality assurance were excluded from the definition.\u003c/p\u003e\n\u003cp\u003e3. Analyzable data\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eWe defined analyzable data as extracted data presented in a specific, interchangeable format, electronic file (CSV, XLS, or XLSX) with individual patient data in a single row that includes demographics, diagnosis details, RT details, and radiation dermatitis data (assessed during and up to 1-month post-completion of RT) in predefined numerical and categorical formats.\u003c/p\u003e\n\u003cp\u003eOn the day of assessment, the date, radiation dermatitis grade (Common Terminology Criteria for Adverse Events version 5), fractions delivered or days after RT completion, was recorded (on paper by the SW team; electronically by the DWS team). Missing data was handled as missing-at-random.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe DWS data entry time was provided by three independent data entry operators (beginner-, intermediate- and expert-level) and a radiation oncologist. Data was entered during the out-patient clinic consultation. The DWS metadata comprising timestamps and user IDs was querying in the server logs to assign times to each clinical process step. Users were unaware of the data extraction to minimize the Hawthorne effect.[32]\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eStudy Design\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe Chief of Radiation Oncology enrolled two independent radiation oncology teams (one using DWS, the other using SW) to assess their relative performance in three key departmental quality variables \u0026ndash; outpatient waiting times, radiotherapy communication-related errors over one year, and finally, efficiency of radiation skin toxicity data extraction and analysis.\u003c/p\u003e\n\u003cp\u003e1. Waiting Time\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe receptionists reported data on waiting time during the specified period directly to the Chief of Radiation Oncology.\u003c/p\u003e\n\u003cp\u003e2. Communication-related error\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eData on monthly communication-related errors was collected from an in-house departmental analytics dashboard.\u003c/p\u003e\n\u003cp\u003e3. Analyzable data\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eWithout warning and independently to either team, the Chief of Radiation Oncology requested the collation of analyzable data into a spreadsheet describing five sub-tasks:\u003c/p\u003e\n\u003cp\u003e(a) Identify patients treated in the preceding three months;\u003c/p\u003e\n\u003cp\u003e(b) identify the radiation dermatitis toxicity grade for each patient;\u003c/p\u003e\n\u003cp\u003e(c) present data in machine-readable format;\u003c/p\u003e\n\u003cp\u003e(d) pre-processing the data into numerical categories;\u003c/p\u003e\n\u003cp\u003e(e) demonstrate the validity of the data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eProgress on each sub-task was monitored by TeamGantt (Maryland, USA). The two teams were blinded to the other team's activities to minimize the Hawthorne effect.[32]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eThe measured time delay was analyzed using non-parametric tests (Mann-Whitney test \u0026amp; Kruskal-Wallis) using Prism v10 (Dotmatics, USA) due to its non-normal distribution. An unpaired t-test assessed the cumulative difference in communication-related incidents. No formal statistical analysis was used to compare the time taken to extract research data. Since the data on time taken to enter information was for different patients, it was analyzed as an aggregate rather than stratified by the level of expertise. The two-tailed significance was set at less than 0.05\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cspan class=\"Underline\"\u003eOPD waiting times\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOne hundred seventeen patients were enrolled in this study (59 patients in SW and 58 in DWS).\u003c/p\u003e\n\u003cp\u003eOverall, the median waiting time for patients in the DWS workflow was significantly less compared to the standard workflow [mean, 95% CI: 5.5 min (4.7\u0026ndash;6.3) vs 17.9 min (14.2\u0026ndash;21.6), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe time taken for the physical file retrieval was not significantly different [mean, 95% CI: 16.6 min (9.4\u0026ndash;23.8) vs 13.3 min (9.4\u0026ndash;17.1), p\u0026thinsp;=\u0026thinsp;0.646] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor scheduled outpatient visits where the chart was already retrieved, the DWS workflow was faster as the patient data familiarization time was reduced [mean, 95% CI: 5 min (4\u0026ndash;5.9) vs 13 min (9.6\u0026ndash;16.5), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor unscheduled outpatient visits, the DWS workflow was only slightly slower but not significantly [mean, 95% CI: 6.2 min (4.8\u0026ndash;7.6) vs 5 min (4\u0026ndash;5.9), p\u0026thinsp;=\u0026thinsp;0.7487] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and much less than the average time needed to retrieve the paper chart [mean, 95%CI: 28.6 min (13.5\u0026ndash;43.6)] (Supplementary Fig.\u0026nbsp;1). The Standard Workflow was significantly longer [mean, 95% CI: 29 min (21.6\u0026ndash;36.3) vs 13 min (9.6\u0026ndash;16.5), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) due to the additional time for paper chart retrieval before familiarization could begin.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eCommunication-related incidents\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the overall communication-related incidents over 12 months. Overall, the cumulative incident rate over 12 months was significantly different [DWS vs SW, median (incidents/month), range: 1 (0\u0026ndash;3) vs 4 (1\u0026ndash;5), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eTime taken to extract analyzable data\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe task was divided into five sub-tasks, and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the time taken by both systems. Overall, the DWS system took substantially less time [\u0026lt;\u0026thinsp;1 month vs 4 months]. The SW team spent more time finding data and extraction from paper records. In contrast, only pre-processing the data extracted from the DWS database took significant time.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eTime taken to enter data into DWS\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e114 unique patient entries were randomly extracted from the server logs for analysis. Of 12 total modules in the DWS, 9 took less than two minutes each to complete across all users and 7 took less than one minute (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). It is worth noting that data entry outside of the Radiation Oncology process [e.g., entering Dose-Volume Histogram (DVH) and chemotherapy drug data] is more onerous. The average time taken for completing each patient\u0026rsquo;s chart [median, IQR: 212 sec (54\u0026ndash;427)] is less than the addition of each module individually, because data re-use during the lifetime of the patient\u0026rsquo;s care journey, and repetitive entries like on-treatment clinical assessments and follow-up assessments are brief.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study clearly demonstrates the inefficiencies inherent in conventional workflows with slower data retrieval, longer waits, and less effective patient processing mechanisms, and supports the efficacy of an agile digital workflow system in enhancing the patient care process within Radiation Oncology departments. The benefits of design according to oncology knowledge structure and workflow use are multiple and obvious.[33]\u003c/p\u003e \u003cp\u003eA significant reduction in overall waiting times was delivered, which should be reflected in patient satisfaction.[34] Despite similar physical chart retrieval times for both workflows, the DWS team could complete their familiarization \u003cem\u003eand\u003c/em\u003e start their consultation before the physical chart was available for unbooked outpatients. This speed results from the small, incremental and additive amounts of time taken to expand the structured OncFlow\u0026reg; record contemporaneously with data discovery. The benefit comes from being digital rather than electronic. Since the diagnosis is evident in the diagnosis field, histopathology reports do not need to be found and read again; the value was entered at first consultation. This digital function permits the software to succinctly summarize the patient's clinical circumstance within the construct of oncological knowledge structure to yield benefits not available in the large EHR, which does not cater to any particular branch of medical knowledge but instead acts as a repository for electronic paper.[5,12]\u003c/p\u003e \u003cp\u003eThe underlying design of the relational database incorporating oncology knowledge and workflow structure permits the customization of information presented to the individual oncologist. This permits the display of oncological summaries that are rapidly interpretable, more so than wading through free-text medical documentation.[24] Reducing waiting times by increasing efficiency addresses patient dissatisfaction and discomfort, and reduces oncologists stress over scheduling pressures.[35] In particular, unscheduled outpatient visits is more likely to be symptomatic from recurrence, side effects, and/or disease, yet in the standard workflow wait 29 minutes before an oncological assessment even starts. Similar research on waiting time optimization in radiation oncology clinics using patient flow analysis also provides improvement with reduced waiting times (51.2 min to 27.1 min, Johns Hopkins group; 45 min to 16 min, MD Anderson Group).[36,37]\u003c/p\u003e \u003cp\u003eFrom the oncologist\u0026rsquo;s perspective, if one were to assume an oncologist-patient face-to-face time of 15 minutes, the time in clinic for an oncologist to see ten scheduled and two unscheduled patients will be 247 min using the DWS [(15x10\u0026thinsp;+\u0026thinsp;5.5x10) + (15x2\u0026thinsp;+\u0026thinsp;6x2)], and 368 min using SW [(15x10\u0026thinsp;+\u0026thinsp;13x10) + (15x2\u0026thinsp;+\u0026thinsp;29x2)]. This workload reduction should ameliorate fatigue, burnout and reduce medical error.[17] From the hospital manager\u0026rsquo;s perspective, waiting rooms become less crowded, parking demands and overtime charges arising from clinics running late will be reduced.[38]\u003c/p\u003e \u003cp\u003eThe research and reporting benefits are similarly enormous. The collation of acute skin toxicity data over 4 months using paper charts is sadly the norm for most clinicians' experience of retrospective reviews. The DWS team, however, demonstrated the new paradigm of Routine Clinical Data where contemporaneously entered data is into a digital system according to established skin toxicity classifications, and is therefore immediately available for real-time data mining, display, and research. Whether RT imaging, contours and RT plan data can be stored in the DWS requires deliberate investigation, but when implemented, the time taken to import approved DVH data in the DWS will be significantly reduced.[39]\u003c/p\u003e \u003cp\u003eThe DWS also mitigated communication-related incidents, a research area which has received relatively less attention in radiation oncology.[40] Previous research on the topic found that written communication errors, mainly involving the use of the Electronic Medical Record (EMR) accounted for 62% of communication events and the majority occurred during patient assessment or treatment planning. Disconcertingly, 20% of these were potentially severe events.\u003c/p\u003e \u003cp\u003eThe medical community wants generalizable research, and while this study shows a path to digital transformation, even this institution shows two workflows. As with all electronic systems, the software deficits are usually minor compared to the implementation deficits.[22] Maximizing the DWS benefit will require changes to established workflow patterns.[33]\u003c/p\u003e \u003cp\u003eSince this evaluation has occurred in a single large center, we encourage future studies to include multiple institutions and more diverse clinical environments, therefore encompassing a broader demographic. Additionally, a cost-benefit analysis would be instrumental in quantifying the economic impact of DWS implementation, providing a more comprehensive understanding of its value proposition within healthcare operations. Finally, a patient and oncologist satisfaction survey comparing DWS with physical file/EHR-based workflows would help identify potential avenues for improvement.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, transitioning from traditional patient management workflows to digital workflow systems enhances the efficiency of healthcare services by reducing waiting times and communication-related incidents. Investing time in entering data into the DWS could accelerate research focused on real-world outcomes in oncology, especially from lower-middle-income countries. As the global healthcare landscape progresses towards digitization, our findings highlight the potential of workflow management systems to serve as catalysts for positive change, conferring significant benefits to all stakeholders.\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThis study was performed at Rajiv Gandhi Cancer Institute \u0026amp; Research Centre and is stored in the institutions data repository. The authors do not own these data and hence are not permitted to share them in the original form (only in aggregate form). Reasonable requests for access to data will be considered on an individual basis, by contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed equally in the design, conception and writing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe are grateful to Mr Sameer Koul, Mr Nakul Goswami \u0026amp; Mr Nitesh Waghmare.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eSaesen R, Van Hemelrijck M, Bogaerts J, \u003cem\u003eet al.\u003c/em\u003e Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer. \u003cem\u003eEur J Cancer\u003c/em\u003e. 2023;186:52\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eBooth CM, Karim S, Mackillop WJ. 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Philadelphia: Wolters Kluwer 2018.\u003c/li\u003e\n\u003cli\u003eKirkpatrick JP, Light KL, Walker RM, \u003cem\u003eet al.\u003c/em\u003e Implementing and Integrating a Clinically Driven Electronic Medical Record for Radiation Oncology in a Large Medical Enterprise. \u003cem\u003eFront Oncol\u003c/em\u003e. 2013;3. doi: 10.3389/fonc.2013.00069\u003c/li\u003e\n\u003cli\u003eSolanki AA, Surucu M, Bajaj A, \u003cem\u003eet al.\u003c/em\u003e Improving the Accessibility of Patient Care Through Integration of the Hospital and Radiation Oncology Electronic Health Records. \u003cem\u003eJCO Clin Cancer Inform\u003c/em\u003e. 2017;1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eYu P, Gandhidasan S, Miller AA. Different usage of the same oncology information system in two hospitals in Sydney\u0026mdash;Lessons go beyond the initial introduction. \u003cem\u003eInt J Med Inf\u003c/em\u003e. 2010;79:422\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eAl Bahrani B, Medhi I. 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Managing the development of large software systems: concepts and techniques. \u003cem\u003eProceedings of the 9th International Conference on Software Engineering\u003c/em\u003e. Washington, DC, USA: IEEE Computer Society Press 1987:328\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eLarman C, Basili VR. Iterative and incremental developments. a brief history. \u003cem\u003eComputer\u003c/em\u003e. 2003;36:47\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eTHE HITECH ACT\u0026mdash;An Overview. \u003cem\u003eAMA J Ethics\u003c/em\u003e. 2011;13:172\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eTiangco B, Daguit SEJ, Astrologo NC, \u003cem\u003eet al.\u003c/em\u003e Challenges in the maintenance of an open hospital-based cancer registry system in a low-to-middle-income country (LMIC): 2017\u0026ndash;2022 experience. \u003cem\u003ePLOS Digit Health\u003c/em\u003e. 2024;3:e0000328.\u003c/li\u003e\n\u003cli\u003eSrivastava SK. 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Satisfactions, dissatisfactions, and causes of stress in medical practice. \u003cem\u003eJAMA\u003c/em\u003e. 1979;241:1483\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eConley K, Chambers C, Elnahal S, \u003cem\u003eet al.\u003c/em\u003e Using a real-time location system to measure patient flow in a radiation oncology outpatient clinic. \u003cem\u003ePract Radiat Oncol\u003c/em\u003e. 2018;8:317\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eMesko S, Weng J, Das P, \u003cem\u003eet al.\u003c/em\u003e Using patient flow analysis with real-time patient tracking to optimize radiation oncology consultation visits. \u003cem\u003eBMC Health Serv Res\u003c/em\u003e. 2022;22:1517.\u003c/li\u003e\n\u003cli\u003eKesteloot K, Lievens Y, Van Der Schueren E. Improved management of radiotherapy departments through accurate cost data. \u003cem\u003eRadiother Oncol\u003c/em\u003e. 2000;55:251\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eMiller A. Mandatory Datasets - Storing the ICHOM Lung Cancer Data Collection in an OIS. \u003cem\u003e2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)\u003c/em\u003e. Karlstad: IEEE 2018:436\u0026ndash;7. https://doi.org/10.1109/CBMS.2018.00083\u003c/li\u003e\n\u003cli\u003eBlakaj A, Wootton L, Zeng J, \u003cem\u003eet al.\u003c/em\u003e Let\u0026rsquo;s Talk: Communication Errors in Radiation Oncology. \u003cem\u003eInt J Radiat Oncol\u003c/em\u003e. 2017;99:E547.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Figure","content":"\u003cp\u003eSupplementary Figure 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital Workflow Efficiency, Radiation Oncology, Patient Wait Times, Communication in Healthcare, Data Management Systems","lastPublishedDoi":"10.21203/rs.3.rs-4015333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4015333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims and Objectives:\u003c/h2\u003e \u003cp\u003eTo evaluate operational efficiency gains when utilizing an agile digital workflow system (DWS; OncFlow\u0026reg;) in the Radiation Oncology clinic over standard workflow (SW).\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eTwo Radiation Oncology teams in the same institution, one using DWS and the other SW, were prospectively assessed to compare the following operational parameters: consultation waiting time, communication errors, and data retrieval. We employed non-parametric tests and an unpaired t-test for statistical analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDigital workflow patients experienced a median consultation waiting time of 5.5 minutes (95% CI: 4.7\u0026ndash;6.3) compared to 17.9 minutes (95% CI: 14.2\u0026ndash;21.6) in the standard workflow, with the difference being significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Communication-related incidents each month were significantly lower in the DWS group, with a median of 1 incident (range: 0\u0026ndash;3) compared to 4 incidents (range: 1\u0026ndash;5) in the SW (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Planned data retrieval was also considerably faster with DWS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDigital workflow systems significantly reduce consultation waiting times and communication errors, enhancing efficiency in the Radiation Oncology clinic. Faster data retrieval also reduced research turnaround time. Broader application in more diverse working environments is warranted.\u003c/p\u003e","manuscriptTitle":"Time saved is time earned: Implementation of an agile workflow system in a high-volume radiation oncology centre Workflow optimization in radiation oncology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 19:29:00","doi":"10.21203/rs.3.rs-4015333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-21T15:20:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-03T18:03:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276851964972439959580111334394623766706","date":"2024-06-25T11:18:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-24T10:32:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32396364696320454521495065689175703974","date":"2024-06-20T12:33:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300131346979193235354843472747299623259","date":"2024-06-15T15:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-29T06:53:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-20T05:05:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-20T05:03:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-20T05:03:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2024-03-05T05:25:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"77d0135b-d4e2-41f6-8edb-4f0f9e209400","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-05T17:45:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 19:29:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4015333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4015333","identity":"rs-4015333","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}