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HR leaders and corporate benefits managers must balance organizational costs with decisions about which new tools and treatments will be covered by their employee health insurance plans. Getting it right can mean the difference between life and death for cancer patients. Most HR leaders, however, are not experts in cancer treatment and do not know how to make sure their plan benefits do not create roadblocks to treatment.

A total of 295 people who were diagnosed with cancer from 2019 to 2022 participated in the 2023 CancerCare Biomarker Survey, which was conducted in January 2023.

CancerCare’s 2023 survey of cancer patients found that biomarker testing helped doctors tailor therapy for nearly all the patients (93%) whose cancers were tested over the past three years. Two in 10 cancer patients (20%) avoided unnecessary chemotherapy and/or radiation and one in 10 (10%) became eligible for a clinical trial because of biomarker testing.

Biomarker testing is a necessary tool in the advancing world of precision cancer treatment. Despite the significant and demonstrable benefits to surveyed patients, three out of 10 respondents (29%) who received biomarker testing did not have the test covered by their insurance. Some survey respondents reported that biomarker test coverage was originally denied and they had to fight to get it covered. Others had to find ways to pay out-of-pocket or seek financial assistance to cover the cost of the testing.

Unfortunately, health insurance plans often limit cancer patients’ access to recommended biomarker testing, impose burdensome prior authorization (PA) protocols or require unaffordable cost-sharing, which can prevent or delay cancer patients’ access to optimal treatments. PA, a significant source of roadblocks to timely testing and treatment, was required by a quarter (25%) of the cancer patients surveyed.

Biomarker testing is increasingly a health care equity issue and there are significant gaps in the rate of biomarker testing between black and white lung and colorectal cancer patients, which can lead to disparities in clinical trial participation and hinder access to the most effective treatments. A key way to address these barriers is to broaden insurance coverage of biomarker testing, as recommended by medical experts.

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

Background: Quality of life is a core concern for cancer patients, which can be negatively affected by illness-related death anxiety; yet understanding of how to appropriately target psycho-oncological interventions remains lacking. We aimed to explore experiential acceptance in cancer patients, and whether acceptance – as an alternative to avoidant coping – was related to and predictive of better quality of life and death anxiety outcomes.

Methods: We used a longitudinal, quantitative design with a follow-up after three months. Seventy-two participants completed a questionnaire-battery measuring illness appraisals, acceptance and non-acceptance coping-styles, quality of life, and death anxiety; 31 participants repeated the battery after three months.

Results: Acceptance was an independent explanatory and predictive variable for quality of life and death anxiety, in the direction of psychological health. Acceptance had greater explanatory power for outcomes than either cancer appraisals or avoidant response styles. Avoidant response styles were associated with greater death anxiety and poorer quality of life.

Conclusions: The findings support the role of an accepting response-style in favourable psychological outcomes, identifying a possible target for future psychological intervention. Response styles that might be encouraged in other therapies, such as active coping, planning, and positive reframing, were not associated with beneficial outcomes.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

Cancer has become a priority among today’s health problems. Therefore, providing facilities that ensure high-quality cancer treatment has become an essential design problem. Additionally, a considerable number of studies have introduced the ‘healing environment concept’ as a substantial input for healthcare buildings. The purpose of this paper is to present a design guide for cancer treatment services that is compatible with the healing environment concept.

In this context, studies on the healing environment have been analyzed, and the legislation of some selected countries has been assessed. Then, all the filtered data are used to form the design guideline for chemotherapy department, radiation oncology department and inpatient care services under a new series of analysis criteria.

The resulting principles are revealed according to the criteria of general settlement principles, internal function relations, medical necessities, user experience, interior design, social interaction/privacy, safety, landscape design and outdoor relations by the help of proposed plans, diagrams and schematic drawings.

This research constitutes the first and yet only study in its field that aims to increase efficiency and user satisfaction and provide better patient-centered care while providing a design guide on health-care architecture.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

High levels of depression and anxiety in the family caregivers of a patient with cancer affect their quality of life. The purpose of this study was to investigate the effects of a mindful self-compassion (MSC) training program on self-compassion in the family caregivers of patients with cancer.

In 2020, this quasi-experimental study used convenience sampling to select 92 family caregivers of patients with cancer presenting to the oncology ward of Seyed-Al-Shohada Hospital, Isfahan, Iran. The subjects randomly assigned to two groups participated in an online MSC program for 1.5 months. All the participants completed a self-compassion scale (Neff) before, immediately after and one month after the intervention. The data were analyzed using the Mann–Whitney U test, the Chi-squared test, the LSD test, the t-test and repeated measures ANOVA.

The total mean score of self-compassion, respectively, obtained as 64.64 ± 8.23 and 64.44 ± 4.94 in the experimental and control groups before the intervention significantly increased to 81.15 ± 7.94 and 64.06 ± 5.22 immediately after and 78.94 ± 8.22 and 64.22 ± 4.85 one month after the intervention (P < 0.001).

Given the potential for negative psychological impacts for patients, caregivers and clinicians in cancer care, the online MSC program can be recommended to support and reduce psychological distress in them.

This paper examined the effect of the online MSC program on self-compassion in the family caregivers of patients with cancer and can contribute to our understanding of the value of integrating mental health of caregivers and care of patients with cancer.

The mortality rate due to skin cancers has been increasing over the past decades. Early detection and treatment of skin cancers can save lives. However, due to visual resemblance of normal skin and lesion and blurred lesion borders, skin cancer diagnosis has become a challenging task even for skilled dermatologists. Hence, the purpose of this study is to present an image-based automatic approach for multiclass skin lesion classification and compare the performance of various models.

In this paper, the authors have presented a multiclass skin lesion classification approach based on transfer learning of deep convolutional neural network. The following pre-trained models have been used: VGG16, VGG19, ResNet50, ResNet101, ResNet152, Xception, MobileNet and compared their performances on skin cancer classification.

The experiments have been performed on HAM10000 dataset, which contains 10,015 dermoscopic images of seven skin lesion classes. The categorical accuracy of 83.69%, Top2 accuracy of 91.48% and Top3 accuracy of 96.19% has been obtained.

Early detection and treatment of skin cancer can save millions of lives. This work demonstrates that the transfer learning can be an effective way to classify skin cancer images, providing adequate performance with less computational complexity.

Value-based healthcare suggested using patient-reported information to complement the information available in the medical records and administrative healthcare data to provide insights into patients' perceptions of satisfaction, experience and self-reported outcomes. However, little attention has been devoted to questions about factors fostering the use of patient-reported information to create value at the system level.

Action research design is carried out to elicit possible triggers using the case of patient-reported experience and outcome data for breast cancer women along their clinical pathway in the clinical breast network of Tuscany (Italy).

The case shows that communication and engagement of multi-stakeholder representation are needed for making information actionable in a multi-level, multispecialty care pathway organized in a clinical network; moreover, political and managerial support from higher level governance is a stimulus for legitimizing the use for quality improvement. At the organizational level, an external facilitator disclosing and discussing real-world uses of collected data is a trigger to link measures to action. Also, clinical champion(s) and clear goals are key success factors. Nonetheless, resource munificent and dedicated information support tools together with education and learning routines are enabling factors.

Current literature focuses on key factors that impact performance information use often considering unidimensional performance and internal sources of information. The use of patient/user-reported information is not yet well-studied especially in supporting quality improvement in multi-stakeholder governance. The work appears relevant for the implications it carries, especially for policymakers and public sector managers when confronting the gap in patient-reported measures for quality improvement.

This article aims to examine how users' involvement in value co-creation influences the development and orchestration of well-being ecosystems to help tackle complex societal challenges. This research contributes to the public management literature and answers recent calls to investigate novel public service governances by discussing users' involvement and value co-creation for novel well-being solutions.

The authors empirically explore this phenomenon through a case study of a complex ecosystem addressing increased well-being, focussing on the formative evaluation stage of a longitudinal evaluation of Sweden's first support centre for people affected by cancer. Following an abductive reasoning and action research approach, the authors critically discuss the potential of user involvement for the development of well-being ecosystems and outline preconditions for the success of such approaches.

The empirical results indicate that resource reconfiguration of multi-actor collaborations provides a platform for value co-creation, innovative health services and availability of resources. Common themes include the need for multi-actor collaborations to reconfigure heterogeneous resources; actors' adaptive change capabilities; the role of governance mechanisms to align the diverse well-being ecosystem components, and the engagement of essential actors.

Although using a longitudinal case study approach has revealed stimulating insights, additional data collection, multiple cases and quantitative studies are prompted. Also, the authors focus on one country but the characteristics of users' involvement for value co-creation in innovative well-being ecosystems might vary between countries.

The findings of this study demonstrate the value of cancer-affected individuals, with “lived experiences”, acting as sources for social innovation, and drivers of well-being ecosystem development. The findings also suggest that participating actors in the ecosystem should utilise wider knowledge and experience to tackle complex societal challenges associated with well-being.

Policymakers should encourage the formation of well-being ecosystems with diverse actors and resources that can help patients navigate health challenges. The findings especially show the potential of starting from the user's needs and life situation when the ambition is to integrate and innovate in fragmented systems.

The proposed model proposes that having a user-led focus on innovating new solutions can play an important role in the development of well-being ecosystems.