Support Tool Research Articles

Developing an interprofessional decision support tool for diabetic foot ulcers management in primary care within the family medicine group model: a Delphi study in Canada

BackgroundPrimary care professionals encounter difficulties coordinating the continuum of care between primary care providers and second-line specialists and adhere to practice guidelines pertaining to diabetic foot ulcers management. Family medicine groups are providing primary care services aimed to improve access, interdisciplinary care, coordination and quality of health services, and reduce emergency department visits. Most professionals working in family medicine groups are primary care physicians and registered nurses. The aim of this study was to develop and validate an interprofessional decision support tool to guide the management of diabetic foot ulcers for primary care professionals working within the family medicine group model.MethodsA one-page decision tool developed by the research team was validated by an expert panel using a three-round Delphi protocol held between December 2019 and August 2021. The tool includes 43 individual actions and a care pathway from initial presentation to secondary prevention. Data collection was realized with both paper and electronic questionnaires, and answers were compiled in an electronic spreadsheet. Data was analyzed with use of descriptive statistics, and consensus for each item was defined as ≥ 80% agreement.ResultsExperts from 12 pre-identified professions of the diabetic foot ulcer interdisciplinary care team were included, 39 participants out of the 59 invited to first round (66.1%), 34 out of 39 for second (87.2%) and 22 out of 34 for third (64.7%) rounds. All items included in the final version of the decision support tool reached consensus and were deemed clear, relevant and feasible. One or more professionals were identified to be responsible for every action to be taken.ConclusionsThis study provided a comprehensive decision support tool to guide primary care professionals in the management of diabetic foot ulcers. Implementation and evaluation in the clinical setting will need to be undertaken in the future.

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department-Based Clinical Decision Support Tool to Prevent Future Falls.

Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation. This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians' use of the tool through an analysis of the resultant qualitative data. Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians' use of the CDS tool. The following categories of factors that impacted clinicians' use of the CDS were identified: (1) aspects of the CDS tool's design (2) clinicians' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool's implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians.

Eco-decisional well-being networks as a tool for community decision support

Eco-decisional well-being networks as a tool for community decision support

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application.

The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Construction and Application of a College English Blended Teaching System Based on Multi-Source Data Fusion

By using modern data analysis techniques, this study aims to construct an innovative university English teaching effectiveness evaluation model based on particle swarm algorithm and support vector machine. The model is designed to improve assessment accuracy and personalization. The research process includes the methodology of data collection, preprocessing, model construction and evaluation. The experimental results show that the model can more accurately assess students' English learning effectiveness and provide customized suggestions for personalized education. This research is important for improving the quality of university English education, promoting personalized learning, and providing support tools for educational decision makers.

A comparative study of fuzzy multi-objective investment project portfolio selection and optimization based on financial return and different risk measurements

Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples.

Decision Support Tool in the Selection of Powder for 3D Printing.

The work presents a tool enabling the selection of powder for 3D printing. The project focused on three types of powders, such as steel, nickel- and cobalt-based and aluminum-based. An important aspect during the research was the possibility of obtaining the mechanical parameters. During the work, the possibility of using the selected algorithm based on artificial intelligence like Random Forest, Decision Tree, K-Nearest Neighbors, Fuzzy K-Nearest Neighbors, Gradient Boosting, XGBoost, AdaBoost was also checked. During the work, tests were carried out to check which algorithm would be best for use in the decision support system being developed. Cross-validation was used, as well as hyperparameter tuning using different evaluation sets. In both cases, the best model turned out to be Random Forest, whose F1 metric score is 98.66% for cross-validation and 99.10% after tuning on the test set. This model can be considered the most promising in solving this problem. The first result is a more accurate estimate of how the model will behave for new data, while the second model talks about possible improvement after optimization or possible overtraining to the parameters.

Synchronous and Asynchronous Digital Tools for Real Life Support and Skills- Lessons Learnt from Covid 19

The current pandemic has surprised language educators. Globally, educators have been forced to implement online platforms and remote pedagogies. Online or virtual situations are challenging for students and teachers. Distance learning has become the norm. They must deal with a situation for which they never had any formal training or orientation. Although language learners have weathered this new crisis well, language instructors remain challenged with remote teaching's complexities. In this research proposal, the researcher examines how language educators have adapted to remote learning and discusses how they have implemented synchronous and asynchronous digital tools like OneNote in their language classrooms.

‘Too young to sit at home’: a qualitative study conducted among employees with young-onset dementia and their relatives

Objectives Young-onset dementia (YOD) symptoms often first present in the workplace, resulting in work performance challenges and eventually loss of employment. This study aims to investigate the experiences, work values, and support needs of employees with YOD and their relatives. Method Semi-structured interviews were conducted to explore the experiences, work values, and support needs of (former) employees with YOD and their relatives. Subsequently, separate focus group discussions were conducted for employees and relatives to review and prioritize interview findings. Inductive thematic analysis was applied to both datasets. Results A total of 15 interviews (six employees; nine relatives) and four focus group discussions (ten employees; six relatives) were conducted. Six themes emerged, with five revolving around the central theme: desire to work. The other themes represent essential contributing factors, including importance of receiving a YOD diagnosis; knowledge, awareness, and understanding regarding YOD; open communication, joint decision making, and collaboration; work adjustments, involvement, and support; phasing out work and future perspectives. Conclusion The findings highlight a strong desire to work post-YOD diagnosis, serving as a foundation for developing workplace support tools and guidance. This has the potential to help individuals with YOD preserve their self-confidence and identity while working within their capabilities.

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

De-imFAR phase II project: a study protocol for a cluster randomised implementation trial to evaluate the effectiveness of de-implementation strategies to reduce low-value statin prescribing in the primary prevention of cardiovascular disease

IntroductionThis study aims to reduce potentially inappropriate prescribing (PIP) of statins and foster healthy lifestyle promotion in cardiovascular disease (CVD) primary prevention in low-risk patients. To this end, we will...

Community Archetypes: An Empirical Framework for Guiding Research Methodologies to Reflect User Experiences of Sense of Virtual Community on Reddit

Humans need a sense of community (SOC), and social media platforms afford opportunities to address this need by providing users with a sense of virtual community (SOVC). This paper explores SOVC on Reddit and is motivated by two goals: (1) providing researchers with an excellent resource for methodological decisions in studies of Reddit communities; and (2) creating the foundation for a new class of research methods and community support tools that reflect users' experiences of SOVC. To ensure that methods are respectfully and ethically designed in service and accountability to impacted communities, our work takes a qualitative and community-centered approach by engaging with two key stakeholder groups. First, we interviewed 21 researchers to understand how they study community" on Reddit. Second, we surveyed 12 subreddits to gain insight into user experiences of SOVC. Results show that some research methods can broadly reflect user experiences of SOVC regardless of the topic or type of subreddit. However, user responses also evidenced the existence of five distinct Community Archetypes: Topical Q&A, Learning & Perspective Broadening, Social Support, Content Generation, and Affiliation with an Entity. We offer the Community Archetypes framework to support future work in designing methods that align more closely with user experiences of SOVC and to create community support tools that can meaningfully nourish the human need for SOC/SOVC in our modern world.

The Development of an Oral Health Nursing Tool for Patients with a Psychotic Disorder: A Human-Centred Design with a Feasibility Test.

Patients with psychotic disorders frequently report oral health problems, while mental health nurses (MHNs) seem not to be fully aware of these problems and the risk factors. Therefore, this study aimed to develop supportive tools for MHNs regarding oral health to increase sensitisation among MHNs and provide MHNs with the knowledge to recognise (potential) oral health problems in patients with a psychotic disorder. We used a human-centred design in which the user, MHNs and experts by experience were placed at the centre of the research process. Problems and needs in MHNs working with patients with a psychotic disorder were addressed. To identify key issues of problems as well as needs in terms of resources, we started with focus groups (n = 9). We analysed the data thematically based on the context of patients and MHNs in practice regarding oral health, preferred oral health tools focused on MHNs, and the intended outcomes of tools. A multi-criteria decision matrix was developed and analysed (n = 9) to identify the most optimal and viable solution based on established criteria and issues that are prevalent in the work of MHNs. The most promising result was the development of a brochure with an awareness screener. The brochure with the awareness screener was developed as a low-fidelity prototype for MHNs regarding oral health in patients with a psychotic disorder based on the latest scientific evidence. After testing it, the feasibility was tested through semi-structured interviews (n = 19). MHNs and experts by experience were satisfied with the tool and provided recommendations for adjustments to the content. Significant augmentations to the brochure included a clinical lesson and a toothbrush with toothpaste for patients. We can conclude that a brochure with an awareness screener is feasible for MHNs. Future steps aiming to further refine and optimise care processes for oral health tools in MHNs should take refining eligibility criteria for psychiatric populations and the language level of the target group of a tool into account.

DesignQuizzer: A Community-Powered Conversational Agent for Learning Visual Design

Online design communities, where members exchange free-form views on others' designs, offer a space for beginners to learn visual design. However, the content of these communities is often unorganized for learners, containing many redundancies and irrelevant comments. In this paper, we propose a computational approach for leveraging online design communities to run a conversational agent that assists informal learning of visual elements (e.g., color and space). Our method extracts critiques, suggestions, and rationales on visual elements from comments. We present DesignQuizzer, which asks questions about visual design in UI examples and provides structured comment summaries. Two user studies demonstrate the engagement and usefulness of DesignQuizzer compared with the baseline (reading reddit.com/r/UI_design). We also showcase how effectively novices can apply what they learn with DesignQuizzer in a design critique task and a visual design task. We discuss how to use our approach with other communities and offer design considerations for community-powered learning support tools.

Cadherin-17 as a target for the immunoPET of adenocarcinoma.

Cadherin-17 (CDH17) is a calcium-dependent cell adhesion protein that is overexpressed in several adenocarcinomas, including gastric, colorectal, and pancreatic adenocarcinoma. High levels of CDH17 have been linked to metastatic disease and poor prognoses in patients with these malignancies, fueling interest in the protein as a target for diagnostics and therapeutics. Herein, we report the synthesis, in vitro validation, and in vivo evaluation of a CDH17-targeted 89Zr-labeled immunoPET probe. The CDH17-targeting mAb D2101 was modified with an isothiocyanate-bearing derivative of desferrioxamine (DFO) to produce a chelator-bearing immunoconjugate - DFO-D2101 - and flow cytometry and surface plasmon resonance (SPR) were used to interrogate its antigen-binding properties. The immunoconjugate was then radiolabeled with zirconium-89 (t1/2 ~ 3.3 days), and the serum stability and immunoreactive fraction of [89Zr]Zr-DFO-D2101 were determined. Finally, [89Zr]Zr-DFO-D2101's performance was evaluated in a trio of murine models of pancreatic ductal adenocarcinoma (PDAC): subcutaneous, orthotopic, and patient-derived xenografts (PDX). PET images were acquired over the course of 5 days, and terminal biodistribution data were collected after the final imaging time point. DFO-D2101 was produced with a degree of labeling of ~ 1.1 DFO/mAb. Flow cytometry with CDH17-expressing AsPC-1 cells demonstrated that the immunoconjugate binds to its target in a manner similar to its parent mAb, while SPR with recombinant CDH17 revealed that D2101 and DFO-D2101 exhibit nearly identical KD values: 8.2 × 10-9 and 6.7 × 10-9 M, respectively. [89Zr]Zr-DFO-D2101 was produced with a specific activity of 185 MBq/mg (5.0 mCi/mg), remained >80% stable in human serum over the course of 5 days, and boasted an immunoreactive fraction of >0.85. In all three murine models of PDAC, the radioimmunoconjugate yielded high contrast images, with high activity concentrations in tumor tissue and low uptake in non-target organs. Tumoral activity concentrations reached as high as >60 %ID/g in two of the cohorts bearing PDXs. Taken together, these data underscore that [89Zr]Zr-DFO-D2101 is a highly promising probe for the non-invasive visualization of CDH17 expression in PDAC. We contend that this radioimmunoconjugate could have a significant impact on the clinical management of patients with both PDAC and gastrointestinal adenocarcinoma, most likely as a theranostic imaging tool in support of CDH17-targeted therapies.

Implications of Switching from Conventional to Electronic Cigarettes on Quality of Life and Smoking Behaviour: Results from the EQualLife Trial

Introduction: Electronic cigarettes and “vaping” have become popular since their appearance in Europe and the USA in approximately 2006. They are often perceived as having fewer health risks than conventional cigarettes, which makes them of interest as a support tool in smoking cessation. However, its efficacy regarding cessation or reduction of smoking under real-life conditions remains controversial. Our objective was to clarify this question in an observational study of smoking habits after initiating vaping without targeted intervention, as compared to a validated cessation programme. Methods: From October 2015 to April 2018, 80 subjects (60 in the e-cigarette group and 20 in the supervised smoking cessation group) were included in two trial visits, one at the start of the trial and the second after 3 months, plus 4 questionnaire surveys: at the start of the trial and after a 1, 2, and 3 month period. The questionnaire included a nicotine use inventory, a modified Fagerström test for nicotine dependence, and the WHO-QOL-BREF survey. Results: E-cigarettes were effective, leading to a significant (p p p p = 0.011 after 12 weeks) and lower (p Discussion/Conclusions: The use of electronic cigarettes reduced nicotine dependence and tobacco consumption, but a supervised smoking cessation programme was superior in terms of achieved cessation in both regards. Electronic cigarettes did not improve the quality of life. Since e-cigarettes could be associated with long-term health risks, their usefulness in smoking cessation remains questionable, and a professionally guided and validated smoking cessation programme still appears to be superior and preferable, in terms of achieved cessation. Although this trial is limited regarding the number of participants and follow-up time, it highlights the need for additional, large clinical trials evaluating the efficacy of e-cigarettes for smoking cessation in comparison to a professionally guided smoking cessation programme.

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach.

Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface. We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool. Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity. The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.

Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.

Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.

Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.

Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed. Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives. Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.

A single-arm pilot of MyInspiration: a novel digital resource to support spiritual needs of patients undergoing cancer-directed surgery

PurposeThis study aimed to assess the feasibility, acceptability, and satisfaction associated with the MyInspiration intervention, a digital spiritual support tool for patients undergoing cancer surgery. Additionally, we evaluated changes in spiritual well-being and the ability to find meaning in their experience with cancer before and after the intervention.MethodsThis was a prospective, single-arm pilot study. Feasibility and acceptability were assessed by ratio of participants who completed all assessments among individuals who had signed consent forms. Satisfaction was assessed with 5 Likert-style questions around user experience. Patient spiritual well-being and finding meaning in their experience with cancer were measured at baseline and post-intervention.ResultsForty patients were enrolled, the majority of whom were female (80.0%) and diagnosed with breast cancer (52.5%), with an average age of 54.4 years (SD = 13.7, range 29.0–82.0). Regarding feasibility and acceptability, 76.9% of patients who consented to participate completed the full study protocol. In assessing satisfaction, 59% of patients were satisfied with the overall experience of MyInspiration. There was no difference in spiritual well-being pre-/post-intervention. There was a difference in pre (M = 1.95, SD = .95) and post (M = 2.23, SD = .86) scores relative to “finding meaning in the cancer experience” with a mean difference of 0.28 (p = 0.008).ConclusionMyInspiration was feasible and acceptable to patients, and the majority were satisfied with the tool. The intervention was associated with changes in patients’ ability to find meaning within their cancer experience. A randomized control trial is needed to evaluate the efficacy of the tool in a broader population of patients with cancer.