Oncology Information System Research Articles

A system for equitable workload distribution in clinical medical physics.

BackgroundClinical medical physics duties include routine tasks, special procedures, and development projects. It can be challenging to distribute the effort equitably across all team members, especially in large clinics or systems where physicists cover multiple sites. The purpose of this work is to study an equitable workload distribution system in radiotherapy physics that addresses the complex and dynamic nature of effort assignment.MethodsWe formed a working group that defined all relevant clinical tasks and estimated the total time spent per task. Estimates used data from the oncology information system, a survey of physicists, and group consensus. We introduced a quantitative workload unit, “equivalent workday” (eWD), as a common unit for effort. The sum of all eWD values adjusted for each physicist's clinical full‐time equivalent yields a “normalized total effort” (nTE) metric for each physicist, that is, the fraction of the total effort assigned to that physicist. We implemented this system in clinical operation. During a trial period of 9 months, we made adjustments to include tasks previously unaccounted for and refined the system. The workload distribution of eight physicists over 12 months was compared before and after implementation of the nTE system.ResultsPrior to implementation, differences in workload of up to 50% existed between individual physicists (nTE range of 10.0%–15.0%). During the trial period, additional categories were added to account for leave and clinical projects that had previously been assigned informally. In the 1‐year period after implementation, the individual workload differences were within 5% (nTE range of 12.3%–12.8%).ConclusionWe developed a system to equitably distribute workload and demonstrated improvements in the equity of workload. A quantitative approach to workload distribution improves both transparency and accountability. While the system was motivated by the complexities within an academic medical center, it may be generally applicable for other clinics.

Linear accelerator utilization: Concept and tool to aid the scheduling of patients for radiotherapy.

Resources in radiotherapy (RT) need to be used effectively to meet the current clinical demand. The aim of this data-driven study is to identify temporal trends in the scheduling of patients for RT and to develop a tool for a visual overview of future scheduling levels. Scheduling data at an eight-linac modern RT department in Sweden were collected twice daily for planned and observed linac use in 2018-2020. Information was retrieved each day for the present (Day 0) and the forthcoming 100 weekdays with total linac utilization rates (LURs) calculated for two activity categories: treatment and non-treatment. An in-house tool based on the LUR concept, database queries from the oncology information system (OIS)/automatic calculations was developed and evaluated by RT managers and scheduling staff (n=10). Overall median LURs were 87%/89% (planned/observed; p<0.01) with more frequent and larger daily increase for non-treatment activities compared with treatment activities. LUR increased with shorter planning horizons and reached 100% for fully-operating linacs ≈3 weeks before Day 0. The tool was reported by 88% to ease the work and to contribute towards an even scheduling of patients (responses: 8/10). Alterations from a planned RT schedule occurs frequently. Having a tool that helps to reduce the abundance of booking information into clinically relevant overviews promise to increase the understanding of present and future scheduling levels. Our proposed concept and tool suggest that this is a feasible approach to schedule patients for RT more evenly.

Decision support system for the proton therapy implementation

The authors propose the method that is intended for automating patient treatment technological process in a proton therapy center and based on a decision support system. The developed information system provides the implementation of the proton therapy protocols. The technological process of the proton therapy is strictly regulated. It includes not only the process of therapy itself (irradiation of the patient), but also the preliminary stages of treatment, as well as the stages following the irradiation process. Proton therapy centers use information systems, such as radiological and hospital information systems (oncology information systems), that provide information support for separate stages. At the moment, there is no system that controls the entire technological process in a proton therapy center. The proposed system ensures that the operator complies with the protocol of the treatment’s stage and supports the entire technological process of the proton therapy. The system sets the schedule of stages, order and possibility of their parallel implementation, determines the conditions for allowing the start of the stage, provides the subsystems with the protocol of the stage and necessary data of protocol to manage the stage by the operator, and also records the results of the completed stage. The system is built on the “client-server” technology. The server has a software part (that implements the concept the server) and a database. For each operator workstation that manages the stage, the client software of the system is additionally installed. The paper describes an approach to creating a decision support system for the personnel of a proton therapy center. Such a system provides the correct implementation and sequence of the proton therapy stages. The system has been included in the project of the Research Proton Therapy Center for Ophthalmic Oncology at Saint Petersburg Nuclear Physics Institute of National Research Center “Kurchatov Institute”. The necessary information is transmitted from the system to the proton therapy center equipment via radiological and hospital information systems using standard protocols. It allows the solution to serve as a part of any automated control system of a radiation therapy center (not only for proton therapy).

Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology dataextraction, reporting and distributed learning.

There is significant potential to analyse and model routinely collected data for radiotherapy patients to provide evidence to support clinical decisions, particularly where clinical trials evidence is limited or non-existent. However, in practice there are administrative, ethical, technical, logistical and legislative barriers to having coordinated data analysis platforms across radiation oncology centres. A distributed learning network of computer systems is presented, with software tools to extract and report on oncology data and to enable statistical model development. A distributed or federated learning approach keeps data in the local centre, but models are developed from the entire cohort. The feasibility of this approach is demonstrated across six Australian oncology centres, using routinely collected lung cancer data from oncology information systems. The infrastructure was used to validate and develop machine learning for model-based clinical decision support and for one centre to assess patient eligibility criteria for two major lung cancer radiotherapy clinical trials (RTOG-9410, RTOG-0617). External validation of a 2-year overall survival model for non-small cell lung cancer (NSCLC) gave an AUC of 0.65 and C-index of 0.62 across the network. For one centre, 65% of Stage III NSCLC patients did not meet eligibility criteria for either of the two practice-changing clinical trials, and these patients had poorer survival than eligible patients (10.6m vs. 15.8m, P=0.024). Population-based studies on routine data are possible using a distributed learning approach. This has the potential for decision support models for patients for whom supporting clinical trial evidence is not applicable.

P-23 The utility of the MOSAIQ oncology information system in head and neck cancer research

P-23 The utility of the MOSAIQ oncology information system in head and neck cancer research

Application programming interface guided QA plan generation and analysis automation.

PurposeLinear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process.MethodsAutomating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light‐radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6‐month period.ResultsThe AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field‐matched junction and ball‐bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%.ConclusionThe automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.

A methodology for the extraction and analysis of real-world radiographic and electronic medical record data to reconstruct treatment arms of historical prostate cancer clinical trials.

5033 Background: Real-world evidence (RWE), including synthetic comparator arms created from historical real-world data (RWD), has the potential to support the safety and efficacy evaluation of new medical products. However, many available RWD sources lack the details necessary to reliably identify patients comparable to clinical trial cohorts or to assess essential oncologic efficacy endpoints. This project demonstrates the ability to extract and analyze RWD to identify patients matching eligibility criteria to four historical clinical trials in metastatic castration-resistant prostate cancer (mCRPC), and calculate outcome measures. Methods: A total of 5,741 patients treated for prostate cancer at multiple institutions (2010-2020) were analyzed in two cohorts using data extracted from the EMR, Tumor Registry, Oncology Information System, and Picture Archiving and Communication System. Of 3,486 patients with prostate cancer in Cohort 1, 422 mCRPC patients were identified: those treated with ADT who achieved castration-level testosterone ( &lt; 50 ng/dL), had evidence of metastatic disease, and exhibited rising PSA (PCWG2). These patients were further matched to four historical clinical trial treatment arms (COU-AA-301: 49, COU-AA-302: 143, AFFIRM: 30, PREVAIL: 79), based on prior chemotherapy and receipt of Abiraterone or Enzalutamide. Overall survival (OS) and time to skeletal related events (SRE) (pathological fracture, spinal compression, surgery to bone, and radiotherapy to bone) were calculated based on diagnosis and procedure codes using the Kaplan-Meier (KM) Estimator. Of 2,255 patients with prostate cancer in Cohort 2, 101 patients received Abiraterone or Enzalutamide and 59 patients had sufficient baseline and follow-up imaging to be scored. Radiographic progression-free survival (rPFS) was calculated from the start of treatment to the time of progression (RECIST 1.1) or loss to follow-up using the KM estimator. Results: In Cohort 1, median OS was 37.7 months (95% CI: 31.5-NR), and median time to SRE was 17.9 months (13.5-22.6). Median OS per patient cohort matched to historical trial treatment arm was COU-AA-301: 23.7 months (10.7-NR), COU-AA-302: 45.9 months (34.9-NR), AFFIRM: 35.3 months (6.34-NR), PREVAIL: 41.5 months (21.9-NR). In Cohort 2, median rPFS was 37.2 months (13.3-NR). Conclusions: The methodology employed in this analysis not only successfully identified a cohort of RWD patients similar to clinical trial-defined patients, but also curated sufficiently reliable data to calculate essential endpoints (e.g., rPFS). At scale, this methodology can be used to generate RWE, including synthetic comparator arms to support clinical trials with radiographic endpoints.

Anal squamous cell carcinoma: a retrospective case series.

Anal cancer is a relatively rare cancer with 660 cases diagnosed in 2000-2015 in Ireland (1). The current standard treatment is radical chemoradiotherapy (CRT). The aim of our study was to review the treatment and outcomes of patients with localised anal squamous cell carcinoma (SCC), who received radical treatment in our radiation oncology network between 2008 and 2014 inclusive. Data were collected retrospectively from ARIA® oncology information system and patient charts. Statistical analyses were performed using IBM® SPSS® statistical software version 25.0. Seventy-nine cases of anal SCC were identified. Mean age of patients at commencement of radiotherapy (RT) was 60.2years (standard deviation: 13.1years). The most common total RT dose was 50.4Gy in 28 fractions (N=58; 73.4%). Median follow-up was 5.6years. Two (2.6%) patients had persistent disease, seventeen (21.8%) patients developed loco-regional recurrence and nine (11.5%) patients developed solid organ metastases, four of whom had complete treatment response at the primary site. Eight patients underwent salvage anal surgery following completion of RT. Median overall survival was 10.5years (95% confidence interval (CI) 5.1-15.8years), median loco-regional relapse-free survival was 10.4years (95% CI 4.4-16.3years) and median disease-free survival was 9.3years (95% CI 6.3-12.2years). Our study demonstrates that treatment for anal SCC and outcomes following definitive CRT in Ireland during the study period were comparable to international standards.

Pattern of care in radiotherapy at a University Hospital in Spain: the RENORT project.

RENORT is a novel data mining application developed to extract relevant clinical data from oncology information systems (OIS; ARIA and Mosaiq) used in radiation oncology (RO). We used RENORT to extract demographic and clinical data from the OIS of all patients treated at the RO Department at the General Hospital of Valencia during the year 2019. A total of 1158 treatments were performed. The female/male ratio was 39.3%/60.7%, with a mean age of 66years. The mean waiting time between the treatment decision/proposal to the first visit was 10.1days. Mean duration of the treatment preparation process was 21days. Most patients (90.4%) completed treatment within the prescribed time ± 7days. The most common sites/treatment types were: metastatic/palliative treatments (n = 300; 25.9%), breast (209; 18.0%), genitourinary (195; 16.8%), digestive (116; 10.0%), thoracic (104; 9.0%), head and neck (62; 5.4%), and skin cancer (51; 4.4%). The distribution according to treatment intent was as follows: palliative (n = 266; 23.0%), adjuvant curative (335; 28.9%), radical without adjuvant treatment (229; 19.8%), radical with concomitant treatment (188; 16.2%), curative neoadjuvant (70; 6.0%), salvage radiotherapy (61; 5.3%); and reirradiation (9; 0.8%). The most common treatment techniques were IMRT/VMAT with IGRT (n = 468; 40.4%), 3D-CRT with IGRT (421; 36.4%), SBRT (127; 11.0%), 2DRT (57; 4.9%), and SFRT (56; 4.8%). A mean of 15.9 fractions were administered per treatment. Hypofractionated schemes were used in 100% of radical intent breast and prostate cancer treatments. The RENORT application facilitates data retrieval from oncology information systems to allow for a comprehensive determination of the real role of radiotherapy in the treatment of cancer patients. This application is valuable to identify patterns of care and to assess treatment efficacy.

Structure of an Oncology Information System Based on a Cost-Effective Relational Database for Small Departments of Radiation Oncology

Structure of an Oncology Information System Based on a Cost-Effective Relational Database for Small Departments of Radiation Oncology

Validation and practical implementation of seated position radiotherapy in a commercial TPS for proton therapy.

This work aims to validate new 6D couch features and their implementation for seated radiotherapy in RayStation (RS) treatment planning system (TPS). In RS TPS, new 6D couch features are (i) chair support device, (ii) patient treatment option of "Sitting: face towards the front of the chair", and (iii) patient support pitch and roll capabilities. The validation of pitch and roll was performed by comparing TPS generated DRRs with planar x-rays. Dosimetric tests through measurement by 2D ion chamber array were performed for beams created with varied scanning and treatment orientation and 6D couch rotations. For the implementation of 6D couch features for treatments in a seated position, the TPS and oncology information system (Mosaiq) settings are described for a commercial chair. An end-to-end test using an anthropomorphic phantom was performed to test the complete workflow from simulation to treatment delivery. The 6D couch features were found to have a consistent implementation that met IEC 61712 standard. The DRRs were found to have an acceptable agreement with planar x-rays based on visual inspection. For dose map comparison between measured and calculated, the gamma index analysis for all the beams was >95% at a 3% dose-difference and 3mm distance-to-agreement tolerances. For an end-to end-testing, the phantom was successfully set up at isocenter in the seated position and treatment was delivered. Chair-based treatments in a seated position can be implemented in RayStation through the use of newly released 6D couch features.

Challenges Of The Clinical Machine Learning Implementation For The Prospective System For High Intensity Evaluation During Radiation Therapy (SHIELD-RT) Study

Patients undergoing outpatient cancer radiation therapy (RT) or chemoradiation (CRT) may require evaluation in the emergency department (ED) or hospitalization. We previously developed a machine learning (ML) algorithm utilizing electronic health records (EHR) to predict risk of such events, and this algorithm was used to stratify high risk patients for randomization as part of the recently completed prospective System for High Intensity Evaluation During Radiation Therapy (SHIELD-RT) study. This study demonstrated that ML-directed mandatory supplemental evaluations reduced the rate of acute care visits during RT and CRT. Here we describe the workflow report on the operational challenges encountered while using the oncology information system (OIS) ARIA (Varian Medical Systems, Palo Alto) for clinical implementation of the ML algorithm. In this single institution randomized QI study (NCT04277650), all outpatient adult courses of RT and CRT started from January 7, 2019 to June 30, 2019 were evaluated during their first week of treatment by the ML algorithm. New courses of treatment in the prior week were identified via the OIS, using a preexisting OIS designation of patient appointments as “new start” (new patient beginning new course), “old start” (previously treated patient starting new course) or “final treatment” (either final fraction of multi-fraction treatment or single fraction treatment). To ensure that the ML algorithm was run on newly started courses, “old start” and “final treatment” appointments were checked to verify the appointment corresponded to new treatment courses or to new single fraction treatment courses, respectively. The final patient list was used to query pre-treatment EHR histories and treatment prescription information. Weekly time requirements by the physics staff were documented over the course of the study. Manual review of patient and treatment prescription data from the OIS required on average 4.9 hours per week. Patient appointments labeled “old start” sometimes corresponded to the start of a new treatment plan (i.e. boost plans) during a treatment course that was already underway and previously evaluated for the study. At the time of the treatment start, some sequential boost prescriptions were entered as drafts that required systematic confirmation. Ideally, extraction of patient and radiation therapy treatment plan data would happen on a rolling basis as patients started treatment, but the need for review of the data limited data extraction to once weekly. The workflow for implementation will be presented at the conference. Our study demonstrated a successful clinical implementation of an ML-based algorithm, but manual review of treatment course identification for algorithm assessment was required. Full operationalization beyond the SHIELD-RT study would necessitate changes to data management practices in the OIS centering on identifying new starts.

Semantically Interoperable Oncology Information System - Oncology 4.0

Realizing the possibilities of eHealth will depend on the capacity to share information across time and space. Multiple actors, systems, machines, and organizations must seamlessly and unambiguously communicate. Therefore, we created a semantically interoperable oncology information system (Oncology 4.0). The objective of the Oncology 4.0 project was to validate the technology framework for oncology information management. We developed a system around four loosely coupled modules: shared meta-thesaurus, dynamic form creator, Fast Healthcare Interoperability Resources (FHIR) RestFull Application Programming Interface, and semantic engine. To validate the technology, we have created a cohort of synthetic head and neck cancer patients. We have described a single patient with 1432 data points, which represents clinical, pathological, genomic, treatment, and workflow data. The data collection instruments, forms, documents, and interfaces were created while respecting the FHIR framework. We have assigned random values to each variable while respecting data from the literature (median and standard deviation for a continuous variable, frequencies of occurrence for categorical variables). To test the real-world scenario, we changed the document structure in 10% of the patient, we have exchange synthetic patient data with the radio-oncology treatment planning system (ROTPS), and we have exchange data with a pathology information system (PIS). We have successfully created 10.000 in-silico head and neck cancer patients. Every data point was accessible through the FHIR interface and defined within shared thesaurus entry. We have achieved bi-directional intra-institutional data exchange with ROTPS and inter-institutional data exchange with PIS. We were capable to dynamically change the content of the clinical documentation without endangering the underlying database model. We have overcome significant technical barriers and demonstrated semantical interoperability between disparate healthcare systems. Further steps will include solving problems on the political and functional interoperability level.

Organic generation of real-world real-time data for clinical evidence in radiation oncology

PurposeWe introduce a system devoted to automatically produce structured data in radiotherapy to: (i) relate clinical outcomes with any variable; and (ii) optimize resources and procedures. Methods and materialWe have designed a detailed workflow for a patient to follow during radiotherapy treatments. Four elements of Oncology Information Systems (OISs) can be mainly interrelated in our system: (a) task lists to be accomplished by the staff; (b) forms to fill in at each step of the workflow; (c) generation of reports; and (d) a system to trigger new tasks, forms or reports when an needed, either automatically or manually. We handle the data dumped into reports with Visual Basic for Word code to store structured data for patients in electronic medical records (EMRs). These EMRs can be further analyzed, generating clinical real-world data in real time, i.e., at any step of the process. ResultsOur system was implemented about the beginning of 2019, producing a database filled with a pool of 1,184 patients in a year. Although one year is not long enough to produce statistically clinical outcomes, we show our results for cancer by anatomical location so far to meet the first goal stated above. With respect to the second goal, we here (1) show the distribution of times taken for the whole radiotherapy process divided by anatomical locations for, (2) study the fractionations schemes used throughout 2019, and (3) evaluate the number of missed sessions of treatment in our institution. Our system also leads to better communication among staff members, dramatically reducing misunderstandings because of the centralization of the information. ConclusionsWe present an integrated customization of an OIS, yet adaptable to others, that makes possible an optimized performance of the department by driving an automatized paperless workflow; and allows for an automatized and effortless collection of structured data throughout the radiotherapy process.

The implementation of MOSAIQ-based image-guided radiation therapy image matching within radiation therapy education.

Image‐guided radiation therapy (IGRT) technologies are routinely used by radiation therapists (RTs) in clinical departments. However, there is limited literature on the acquisition and assessment of IGRT image‐matching competencies in undergraduate educational environments. This commentary paper aims to share the authors’ experiences in the development of teaching IGRT and image‐matching concepts in an undergraduate radiation therapy programme. It outlines how MOSAIQ oncology information systems (OIS) have enabled the university to embed hands‐on IGRT image matching on a range of clinical cases. The hands‐on exposure to case‐based planar and volumetric kilovoltage (kV) image matching has resulted in improved teaching and better preparation of students for clinical IGRT encounters. Students are likely to benefit from critical image assessment and decision‐making as well as the improved engagement in teaching and learning.

Bortezomib use and outcomes for the treatment of multiple myeloma.

The public subsidy in Australia of bortezomib (Velcade) for untreated non-transplant multiple myeloma patients was based on the VISTA trial. To ascertain the health outcomes of bortezomib in 'real world' transplant-ineligible elderly patients, compared to trial data. Patient and treatment data were extracted from an oncology information system, laboratory information system and medical chart audits for three Queensland public hospitals. We identified 74 patients; the median age was 75 years. Our cohort comprised 47% patients who were International Staging System stage III, 45% at stage II and 8% at stage I. Patients who had comorbidities, such as cardiac disease (41%), pulmonary disease (14%), diabetes (22%), peripheral neuropathy (14%) and other comorbidities (41%) at baseline were included. The common regimens prescribed were VMP, CVD and VD, and most patients (n = 73) received bortezomib on a once-weekly or twice-a-week basis. The overall response rate was 81%. Half (53%) of the patients did not complete their planned therapy due to toxicity (30%), suboptimal response or disease progression (15%), or death on treatment (8%). Overall survival was 40.7 months and progression free survival was 17.7 months. Our patients were older, had worse disease characteristics and more comorbidities than patients in the VISTA trial. While response rates were similar, survival outcomes appeared worse. Bortezomib-based treatment in the real world setting still carries a high risk of toxicity in the elderly population.

A Conceptual Model of an Oncology Information System.

IntroductionOncologists are usually faced with a huge amount of diagnostic and therapeutic data in the process of cancer care. However, they do not have access to the integrated data. This research aimed to present a conceptual model of an oncology information system based on the users’ requirements.MethodsThis study was conducted in 2019 and composed of two phases. Initially, a questionnaire was designed, and clinical experts (n=34) were asked to identify the most important data elements and functional requirements in an oncology information system. In the second phase, conceptual, structural and behavioral diagrams of the system were drawn based on the results of the first phase. These diagrams were also reviewed and validated by five experts.ResultsMost of the data elements and all functional requirements were found important by the experts. The data elements were related to different phases of cancer care including screening, prevention, diagnosis, treatment, mental care and pain relief, and end-of-life care. Then, conceptual, structural and behavioral diagrams of the system were designed and approved by the experts or revised based on their comments.ConclusionThe conceptual model and the diagrams presented in the current study can be used for developing an oncology information system. This system will be able to manage patients’ cancer data from screening to the end-of-life care. However, the system needs to be designed and implemented in a real healthcare setting to see how it can meet users’ requirements.

A practical cyberattack contingency plan for radiation oncology.

PurposeThis article presents a solution for continuing radiation therapy without interruption in the event of a cyberattack to the radiation oncology information systems (ROIS). This process could be easily deployed to any radiation oncology practice, with little clinical overhead or burden.Methods and MaterialsThe solution automatically retrieves all essential information from the clinical ROIS for each under‐treatment patient and periodically (e.g., daily) saves these data to a dedicated secure server for recovery. In the event that the clinical ROIS is not functioning as a result of a cyberattack, this essential information is used to build a new secondary ROIS server to continue radiotherapy treatments until the main ROIS is recovered. Once the cyberattack threat is cleared, the clinical ROIS server is rebuilt from the institution’s enterprise backup. The newly accumulated treatment information for each patient is then exported from the secondary ROIS to bring the clinical ROIS up to date.ResultsThe Department of Radiation Oncology at the University of Maryland Medical System implemented this solution for clinical use with the Varian ARIA ROIS in the management of ~250 daily radiotherapy treatments, inclusive of a proton center. This solution was determined to be a feasible and affordable business continuity plan for the radiation oncology practice by minimizing radiation treatment downtime to a couple of hours in a simulated cyberattack drill.ConclusionsThe proposed solution can achieve continuation of radiation therapy treatment without treatment breaks in the event of a cyberattack. It also provides cushion time for radiation oncology departments to rebuild their clinical ROIS systems from the enterprise data backup.

Analysis of Patient-Physician Concordance in the Understanding of Chemotherapy Treatment Plans Among Patients With Cancer

The treatment plans of patients with cancer involve multiple modalities that oncologists need to explain to patients. One such modality is chemotherapy, in which information about the goals, duration, and expected complications of therapy are considered fundamental to achieving optimal patient understanding. Therefore, effective communication between patients and their treating physicians is important to ensure patient adherence to treatment and achieve better outcomes. To investigate the concordance in the understanding of chemotherapy treatment plans between patients and their treating oncologists and to identify the potential factors associated with concordance. A cross-sectional study was conducted among adult patients (aged >18 years) with cancer who consented to receive chemotherapy between October 4, 2017, and November 8, 2018. The study also included the treating oncologists of patients receiving chemotherapy. An interview-based structured questionnaire was administered in both inpatient and outpatient settings at the Princess Noorah Oncology Center in Jeddah, Saudi Arabia. The demographic data of patients and physicians were obtained from the ARIA Oncology Information System, a chemotherapy-prescribing software database used at the center. Patients who had a personal history of cancer or were unwilling to be involved in the decision-making process were excluded. Data were analyzed from November 15 to December 20, 2018. Patients' comprehension and concordance with their treating physicians regarding the aspects of the intended treatment plan. The main outcomes measured were the patient-physician concordance level in the understanding of treatment plans and the identification of patient-related and physician-related factors associated with the level of concordance. A total of 151 adult patients (77 men [51.0%] and 74 women [49.0%]) were interviewed. Of those, 144 patients (75.5%) were younger than 60 years, and 52 patients (34.4%) had a college or advanced degree. A total of 20 treating oncologists were interviewed, of whom 14 (70.0%) were men and 6 (30.0%) were women. Arabic was the primary language of 19 oncologists (95.0%), and 19 oncologists (95.0%) had medical practice experience outside of Saudi Arabia. Only 20 patients (13.7%) had full concordance with their physicians regarding the aspects of their treatment plans. The remaining 131 patients (86.2%) had discordance with 1 or more aspect of their treatment plans. The most common aspect of discordance was the planned duration of the chemotherapy regimen, with 104 patients (68.4%) having full discordance. Full patient-physician concordance was more likely among patients with college or advanced degrees (χ21 = 17.73; P < .001) and patients with a family history of cancer (χ21 = 15.88; P < .001). In addition, older physicians (>40 years) compared with younger physicians (aged 30-40 years) were more likely to achieve higher rates of full patient-physician concordance, whereas older patients (>60 years) were associated with only partial concordance compared with patients younger than 60 years (χ21 = 5.84; P = .008), with an AR of 2.7. Most patients showed suboptimal understanding of aspects of their chemotherapy treatment plans. More effort and time should be dedicated to enhancing the understanding of chemotherapy plans among patients with lower educational levels. In addition, self-report surveys that evaluate patients' understanding of their chemotherapy treatment plans should be developed and added to the informed consent process to objectively assess how much a patient understands and to develop a stepwise patient education program that targets patients with the lowest levels of understanding.

Oncology Information System: A Qualitative Study to Identify Cancer Patient Care Workflows

Oncology information systems provide solutions for managing the information of cancer patients and enable monitoring of different aspects of cancer patient care. Since the use of oncology information systems enhances the quality of care, improves documentation, optimizes resource allocation, and increases the cost-effectiveness of care services, attention to these systems’ performance and their adaptation to workflows seems necessary. The purpose of this study was to identify cancer patient care workflows to design an oncology information system for Iran.This study employed a qualitative design and was conducted in 2019. Semi-structured interviews were conducted with 25 experts to determine their views on identifying workflows for cancer patients’ care. The participants were clinical and non-clinical staff at six university hospitals equipped with oncology wards. The method of data analysis was framework analysis.The cancer patient care workflows consisted of two categories, including cancer diagnosis workflows and cancer treatment workflows. Cancer diagnosis workflows fall into three subcategories, i.e., the patient’s referral to the clinic, an examination of the patient’s condition, and pathology workflows. On the other hand, cancer treatment workflows are divided into various treatments offered to cancer patients and workflows in the chemotherapy and radiotherapy wards.Given the variety of services and the complexity of caring for cancer patients as well as the involvement of various specialists in the process of care, identifying and optimizing workflows in the oncology information system reduces errors, enhances data accuracy, eliminates unnecessary steps, and ultimately improve the service delivery to cancer patients.