bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-user physician perspectives about an oncology clinical decision-support system: A qualitative study.
Authors: Rocha Hermano Alexandre Lima , Emani Srinivas , Arruda Carlos Andre Moura , Rizvi Rubina , Garabedian Pamela , Machado de Aquino Camila , Felix Winnie , Hekmat Rezzan , Sands-Lincoln Megan , Meneleu Pedro , Jackson Gretchen Purcell , Bates David Westfall , Juaçaba Sergio Ferreira 
Published: May 2020

e14061 Background: Advances in artificial intelligence (AI) continue to expand capabilities within the healthcare domain, particularly in the discipline of oncology. Watson For Oncology (WfO) is an AI-enabled clinical decision support system that presents potential therapeutic options for cancer-treating physicians. The objectives of this study were to identify non-user physicians’ expectations, perceived challenges and benefits of WfO use in Brazil. Methods: The study took place at Instituto do Câncer do Ceará (ICC), a Brazilian oncology hospital that implemented WfO in December 2017, but not all physicians adopted the tool. Physicians who had not used WfO (n = 5) were recruited through purposive sampling identified with the assistance of local research personnel. Semi-structured interviews were conducted in Portuguese and later de-identified and transcribed into English. A thematic analysis of interview data based on grounded theory by two members of the research team with extensive experience in qualitative data analysis was conducted. Results: Non-user physicians had positive perceptions about WfO, along with several concerns and uncertainties. They expected that WfO would be easy to learn, useful, and helpful. Physicians perceived that WfO would provide a more standardized approach to treatment than care without it. They also believed that WfO would play a supportive and not a substitute role in care especially for complex cases in which the physicians had more in-depth knowledge of a patient and already had an established patient-provider relationship. Physicians did expect WfO use to negatively impact productivity, specifically through longer office times per patient because of the need to enter data and review recommendations. Physicians questioned whether the use of WfO would negatively impact their autonomy and role in providing care. Finally, physicians also questioned whether the treatment suggested by WfO would fit the social context of a low-middle income country such as Brazil with limited technological and economic resources. Conclusions: The implementation of US-developed AI technologies, such as WfO, should be further explored in different social and economic contexts. Physician concerns about productivity and autonomy need to be assessed and addressed in AI implementation; one strategy is to leverage previous lessons learned from electronic health record (EHR) implementations. This study is a critical step in understanding potential user perspectives in adopting a new AI tool in different social contexts.
Using oral morphine-equivalent values to track the analgesic effect of treatments in clinical trials.
Authors: Raymond Stephen A , Hutchin John F , Degboe Arnold , Faulkner Kenneth G 
Published: May 2020

e14075 Background: Patient reported pain is subjective, requiring individuals to grade the feeling of pain as “high” or “low”, typically using a numeric scale. As a supplement to self-reported pain, monitoring the dosage and type of treatment needed to keep pain at a tolerable or comfortable level has been suggested. However, differences in treatments can complicate the ability to compare analgesic effects. One approach to this problem uses a 30 mg oral dose of morphine as a standard to quantify the relative analgesic strength of different pain medications. Methods: For use in clinical research, we developed an application on a handheld device for logging opioid consumption and supporting automated determination of oral morphine equivalents (OME) for commonly prescribed analgesics. We reviewed the literature to prepare a comprehensive database of OMEs for more than 450 commonly prescribed pain medications. From treatment logs (time, dose, and route of administration), the application determines a daily “OME Score” in standardized units of mg of oral morphine. Results: As prescriptions change, the daily OME Score gives a comparable measure to track the utilization of diverse analgesic medications. OME Scores can be categorized using the Analgesic Quantification Algorithm (AQA scale) developed by Chung (1). The AQA scale has eight categories and provides 5 gradations for patients in advanced pain who receive strong opioids, each gradation reflecting adoubling in the daily OME Score. The impact of a therapy on pain may be considered clinically significant depending on the changes in self-reported pain and AQA scores related to the treatment. Conclusions: We have developed a method and associated application for easily logging the time, dose, and route of administration of analgesic treatments. This information can be used to compute the daily OME Score for a wide variety of analgesics. The daily OME score can then be tracked and used to report the AQA score alongside self-reported pain for evaluating pain relief in clinical studies. Reference: 1) Chung KC, Barlev A, Braun AH, Qian Y, Zagari M. Pain Medicine 2014; 15:225-232.
Informed consent in phase I clinical trials: Implications for trends in design.
Authors: Frankel Paul Henry , Groshen Susan G. 
Published: May 2020

e14077 Background: Informed Consent (IC) is a critical aspect of human subjects protection. Institutional Review Boards are tasked with insuring proper IC as one aspect of protecting participants in clinical trials. Phase I trials in oncology present special issues with IC, as often neither the risks nor the benefits are well-known. This has resulted in carefully worded IC templates for Phase I studies based on the traditional use of dose-finding designs that are geared towards finding the “Maximum Tolerated Dose (MTD)”. As the definition of this term varies by study, the implication for patient risk and informed consent are rarely discussed. Methods: We reviewed Phase I designs to present options for improving the informed consent process for Phase I oncology trials. Results: Phase I studies have seen an increase in designs based on work from the early 1990s seeking a dose that results in a targeted percent of patients experiencing a “Dose Limiting Toxicity (DLT)” to define the MTD. The most common definition of a DLT is a treatment-related toxicity that results in a particularly concerning severe toxicity (grade 3 or higher) in the first cycle of therapy and the most common rate targeted (in designs that define toxicity as a goal) is 25%. In that setting, while lower doses may have a lower likelihood of DLT, higher doses or the expansion cohort are likely to have a 25% chance of DLT if the target is pursued. This information is rarely quantitatively communicated in the informed consent. Conclusions: IRBs and investigators should consider communicating through informed consent the quantitative summary of goals of the study and related risk. For example, transparency suggests conveying when the goal (target) of the study is to find the dose where there is a one in four chance of experiencing a severe adverse event in the first cycle.
Recruiting patients with advanced cancer to participate in a non-opioid intervention for chronic pain management.
Authors: Selvan Preethi , Myers-Coffman Katherine , Bryl Karolina , Lama Jasmine Tenpa , Sutton Brigette , Biondo Jacelyn , Cottone Carrie , Lacson Clarissa , Millstein Allison , Banks Joshua , Worster Brooke , Leader Amy , Bradt Joke 
Published: May 2020

e14085 Background: Chronic pain related to advanced cancer is difficult to treat. In addition to traditional analgesics, non-pharmacological interventions, such as music therapy, may help alleviate pain in this population. Research studies to test the efficacy of these non-pharmacological interventions are necessary and important. However, recruiting patients with advanced cancer to participate in such interventions can be challenging. Identifying effective sources of referrals and barriers to participation may help increase recruitment rates in the future. Methods: We recruited patients with advanced cancer (stage III or IV) with chronic pain from two major hospital systems to participate in a pain management interventional study. The experimental group received individualized music therapy, while the control group received talk therapy. Participants attended six weekly sessions at the hospital and were compensated for time and travel. Recruitment methods included referrals from care teams, advertisement through flyers and posters, EMR chart reviews, and in-person recruitment at multiple infusion centers. Missing data was excluded from analyses. Chi-square tests assessed significant differences between groups. Results: Of 594 patients that were referred to the study, 7% enrolled (n = 40), 35% declined (n = 208), and 58% were ineligible (n = 346). Forty-seven percent (n = 19) of enrolled participants were female; race/ethnic distributions were the following: African American/Black (55%; n = 22), Caucasian/White (28%; n = 11), or other (17%; n = 7). The most effective recruitment method was through self-referral; of the 40 patients enrolled in the study, 9 (23%) were enrolled through this method. There were no statistically significant differences between referral source and enrollment. The top three reasons patients declined to participate included lack of interest (32%; n = 67), lack of time and/or energy (28%; n = 59), and lack of transportation (16%, n = 34). Conclusions: Although there were no differences in referral sources, more patients were enrolled through self-referral or in-person recruitment, showing the power of personal motivation and personal touch. While lack of participation due to interest, time, or energy are hard to overcome, future research should consider campaigns to reach patients who are motivated to participate. In addition to referrals, resources for in-person recruitment and transportation to help alleviate barriers to research participation should be considered.
Use of Twitter among cancer clinicians and researchers and the associations with scientific impact of its users and followers.
Authors: Cheung Winson Y. , Rigo Rodrigo , Batra Atul 
Published: May 2020

e14101 Background: Social media channels, such as Twitter, represent relatively new technology platforms for scientific users to disseminate research findings and communicate their views and interpretations to colleagues and followers. To date, the associations between the use of Twitter and the scientific impact of its users and followers are unclear. Methods: All Canadian oncologists who are full members of the American Society of Clinical Oncology were identified from the online membership directory. Users of Twitter were defined as those with an active Twitter account, as of June 2019, and posted at least one tweet within the past 12 months. Data regarding the number of tweets, likes, and followers were collected by an online search of Twitter. Scientific impact of each individual was assessed based on a user’s h-index and number of citations from Google Scholar as well as score from Research Gate. Associations were examined with summary statistics and correlation coefficients. Results: We identified 676 eligible oncologists of whom 80 (12%) and 596 (88%) currently use and do not use Twitter. Among the users, the median number (IQR) of tweets, likes, and followers were 196 (45-865), 325 (86-1,246), and 198 (89-449), respectively. The scientific impact of Twitter users versus non-users was statistically similar (see Table). Likewise, within the group of users, there were no correlations between the number of tweets, likes, and followers and the scientific impact of individuals (correlation coefficients 0.38, 0.34, and 0.41, respectively, all p > 0.05). Conclusions: Only 1 in 10 oncologists use Twitter, but those who use Twitter leveraged this technology platform frequently. There were no associations between the use of Twitter and the scientific impact of its users. Views from a minority of oncologists are represented on Twitter. Such potential bias may underscore the potential need to exercise caution when using social media for scientific knowledge exchange. Regular evaluations of new technologies are warranted to ensure the quality and rigor of their scientific content in order to reduce misrepresentation. [Table: see text]
Initial Experience with Cancer Guidelines Navigator, a tool to standardize and improve the quality of cancer care in Sub-Saharan Africa, at Ocean Road Cancer Institute in Tanzania.
Authors: Suarez Saiz Fernando Jose , Yuravlivker Leemor , Ndumbalo Jerry , Mwaiselage Julius , Siu Sadiq Maalim , Koh Wui-Jin , Dawe Natalie , Strauss Daniel 
Published: May 2020

e14106 Background: The IBM Cancer Guidelines Navigator (CGN) is a digital reference system to support treatment planning that allows clinicians to enter a cancer patient’s clinical characteristics and presents the corresponding treatment options in the NCCN Harmonized Guidelines (TM) for Sub-Saharan Africa. In October 2019, Ocean Road Cancer Institute (ORCI) in Tanzania became the first site in Africa to initiate a hospital-wide implementation of the tool to help clinicians reduce cancer treatment variability by increasing adherence to standard evidence-based care. We describe training and lessons learned from system introduction. Methods: Training for clinical staff at ORCI occurred over one week and included daily one-hour lectures, followed by personalized hands-on training. A survey was administered to assess usability and use cases of the tool. Results: Thirty-one ORCI clinical and IT staff members participated in training, and 12 completed the survey. Responses indicated that the most beneficial uses for CGN were at point of care and for self-learning. Participants indicated that the top benefits of the tool were quick access to guidelines and evidence (75%) and ease of use (58%). Expanding cancer coverage (42%), offline access and better integration into the workflow (25%) were identified as areas for improvement. Post-training, ORCI implemented easier access to CGN on each computer and tablet used for consultation and care management. Conclusions: CGN is a digital reference system that is designed to support easy and efficient access to regionalized cancer-treatment guidelines for point-of-care treatment planning and education. Expansion of this program has been planned for other hospitals in Tanzania. Future studies will examine whether CGN usage affects guideline adherence.
Evaluation of cancer patients comfort with and interest in interacting with various social robot configurations in a clinical environment.
Authors: Hajdenberg Julio , Dvorak Tomas , Robinson Diane , Spencer Christopher 
Published: May 2020

e14112 Background: Our group is interested in designing and programming social robots that augment our staff capabilities in performing different cognitive tasks in the clinic. This study examined patients' interest and comfort with various robot form factors, when screened for distress as part of a routine patient evaluation. Methods: 199 Patients (N) (Power > .80) were presented 7 commercially available robot forms and rated their comfort and interest in interacting with each one on a 5-point Likert scale (1 = Strongly Agree, 3 = Neutral, 5 = Strongly disagree).One sample t-tests compared the difference between the overall mean ratings and neutral point for comfort and interest. Independent samples t-test examined sex differences in overall ratings. Robust ANOVAs and post-hoc tests compared ratings across forms. Results: Results indicated significant interest and comfort in interacting with any robot tested, with men more interested and comfortable (see Table). Robust ANOVA indicated significant main effects for robot type ( Qcomfort = 31 .12; Qinteraction = 26 .52 , ps < .005) and sex ( Qcomfort = 21 .74; Qinteraction = 19 .35 , ps < .005) on comfort and interest. Post-hoc tests indicated that taller and narrower robots were least favored; however, there was no clear preferred form. Conclusions: Our findings suggest that patients are comfortable and interested in interacting with a robot, with men more comfortable and interested. Across robot forms, the taller and narrower forms were less favorable to patients. Future implementations of robotic interfaces may wish to avoid taller and narrower form factors. These data have informed the current model being tested in our clinics, which will be available for display. [Table: see text]
Mobile health applications in oncology.
Authors: Upadhyay Vivek , Landman Adam , Hassett Michael J. 
Published: May 2020

e14115 Background: Over 325,000 mobile health (mhealth) applications (apps) have been developed. There has been a substantial increase in mhealth investment, with over $8.1 billion invested in digital health startups in 2018. While apps have been studied within clinical oncology, we are aware of no comprehensive evaluation of the commercial footprint of oncology-specific apps. We sought to describe the state of oncology-specific apps and highlight notable areas of development. Methods: We conducted a systemic search for oncology apps in the Apple iOS and Google Play app stores in January 2020. Search terms included “cancer,” “oncology,” “radiotherapy,” and “chemotherapy.” All apps were manually reviewed and classified by English language support, date of last update, downloads, intended audience, intended purpose, and developer type. We also compared commercially available apps with those described in a recently conducted meta-analysis of oncology-app studies. We performed descriptive statistics using RStudio V1.2.335. Results: We identified 794 oncology-specific, English-language applications, but only 257 (32%) met basic quality standards and were considered evaluable. The primary reason for exclusion was lack of a recent update. Of included apps, almost half (47%) were found in the “Medical” Store Category and the majority were free (88%). The most common intended audience was healthcare professionals (45%), with 28% being geared towards the general public and 27% being intended for patients. The intended function was education for 37%, clinical decision support (CDS) for 19%, and patient support for 18%. Only 22% of education apps and 40% of CDS apps reported any formal app content review process. Web developers created 61% of apps, scientific societies created 10%, and hospitals/healthcare organizations created just 6% (Table). The most frequently downloaded apps tended to be geared toward educating/supporting the public. Of 54 studies that utilized mobile apps in oncology identified by a recent meta-analysis, only 2 could be matched to commercially available apps from our study, suggesting a substantial divide between investigation and product dissemination. Conclusions: Our analysis of oncology-related apps in the commercial marketplace found few high-quality, up-to-date apps, and a notable absence of key oncology stakeholders in app development. Future studies should explore barriers to developing and disseminating apps designed to advance oncology care delivery. [Table: see text]
Oncology early development technology to facilitate early analysis of safety and efficacy data.
Authors: Vaca Mireya , Naumovski Louie , Soh Chang-Heok , Leal Jailene 
Published: May 2020

e14116 Background: The lack of a standard data review platform for the surveillance of early oncology trials led to the formation of a cross-functional initiative with the objective of creating a data surveillance framework that would provide clinical study teams with access to near real-time data in order to be able to generate fast insights into the safety and efficacy findings of oncology early development trials so that early signal detection or clinical proof-of-concept could take place. Methods: To determine the requirements that would drive the framework, the cross-functional team first conducted user interviews with study physicians to gather information on the types of tables, graphs, and visual outputs that would be most useful to the clinical study team, as well as most useful to re-create as a standard across the early oncology portfolio, focusing at this stage, on solid tumor trials using RECIST 1.1 for the efficacy output. Once these requirements were determined, specification details, including mock-ups of the visual output and guidelines to follow, were created. Programming and development of the pilot dashboard then began using R and Spotfire as the tools of choice. Results: The resulting dashboard branded “OED Fast Insights” included a standard set of pages composed of dynamic tables and graphs organized around 3 components: study disposition, disease response, and treatment-emergent adverse events. The output closely resembled tables and graphs the clinical study team were familiar with through statistical programming output using SAS. However, leveraging the interactive functionality of Spotfire allowed the team to produce dynamic tables and graphs that could be readily filtered by different pre-specified criteria (e.g. cohort, dose level, gender, lines of therapy, best overall response, type of cancer, biomarker status, etc.) or by subject or aggregate-level views, changed by different color indication, or adjusted in other ways by the user. The dynamic aspect of spider plots, waterfall plots, and swimmer lanes were of special interest in identifying early signal detection. Conclusions: The cross-functional initiative began in 2018 and by the end of 2019 the data surveillance framework was successfully scaled and rolled-out to the entire early oncology portfolio of existing trials utilizing RECIST 1.1 solid tumor criteria. The next phase of the initiative will be to extend the framework beyond RECIST 1.1 efficacy surveillance to include hematologic cancers.