This paper aims to describe the clinical and regulatory aspects of new drugs and indications that were approved for lung, breast, prostate, and colorectal cancer, from 2016 to 2018, in order to provide health technology assessment trends in oncology.

Data were collected from the US Food and Drug Administration (FDA) online database for new medications and indications approved for the above-mentioned types of cancer. Data regarding clinical study characteristics and regulatory information were collected.

From 2016 to 2018, 53 percent of the FDA approvals of new drugs and indications for the most incident cancers were for oral protein kinase inhibitor monotherapy for advanced lung cancer. Since 2018, four drugs were approved as tumor-agnostic therapies. A biomarker was included in 72 percent of indications, and 58 percent of approvals were for targeted therapies, potentially heralding an end to research into conventional cytotoxic agents. A special designation for faster approval was granted in 78 percent of new approvals. The majority of the studies were open label randomized controlled trials (RCTs) (44 percent), followed by blind RCTs, single-arm clinical trials, and cohort studies. Only 14 percent of studies used overall survival as the primary end point; the vast majority used surrogate end points, and did not use patient-important outcomes. Three biosimilars were approved in the period.

Advanced lung cancer therapy, mainly targeted drugs, accounted for 53 percent of approvals. Special designations for faster approval were used in 78 percent of FDA approvals, and four drugs were approved for tumor-agnostic treatment—a new form of approval.

Consideration of ethical, legal, and social issues plus patient values (ELSI+) in health technology assessment (HTA) is challenging because of a lack of conceptual clarity and the multi-disciplinary nature of ELSI+. We used concept mapping to identify key concepts and inter-relationships in the ELSI+ domain and provide a conceptual framework for consideration of ELSI+ in HTA.

We conducted a scoping review (Medline and EMBASE, 2000–2016) to identify ELSI+ issues in the HTA literature. Items from the scoping review and an expert brainstorming session were consolidated into eighty ELSI+-related statements, which were entered into Concept Systems® Global MAX™ software. Participants (N = 38; 36 percent worked as researchers, 21 percent as academics; 42 percent self-identified as HTA experts) sorted the statements into thematic groups, and rated them on importance in making decisions about adopting technologies in Canada, from 1 (not at all important) to 5 (extremely important). We used Concept Systems® Global MAX™ software to create and analyze concept maps with four to sixteen clusters.

Our final ELSI+ map consisted of five clusters, with each cluster representing a different concept and the statements within each cluster representing the same concept. Based on the concepts, we named these clusters: patient preferences/experiences, patient quality of life/function, patient burden/harm, fairness, and organizational. The highest mean importance ratings were for the statements in the patient burden/harm (3.82) and organizational (3.92) clusters.

This study suggests an alternative approach to ELSI+, based on conceptual coherence rather than academic disciplines. This will provide a foundation for incorporating ELSI+ into HTA.

To synthetize the state of the art of methods for identifying candidate technologies for disinvestment and propose an evidence-based framework for executing this task.

An interpretative review was conducted. A systematic literature search was performed to identify secondary or tertiary research related to disinvestment initiatives and/or any type of research that specifically described one or more methods for identifying potential candidates technologies, services, or practices for disinvestment. An iterative and critical analysis of the methods described alongside the disinvestment initiatives was performed.

Seventeen systematic reviews on disinvestment or related terms (health technology reassessment or medical reversal) were retrieved and methods of 45 disinvestment initiatives were compared. On the basis of this evidence, we proposed a new framework for identifying these technologies based on the wide definition of evidence provided by Lomas et al. The framework comprises seven basic approaches, eleven triggers and thirteen methods for applying these triggers, which were grouped in embedded and ad hoc methods.

Although identification methods have been described in the literature and tested in different contexts, the proliferation of terms and concepts used to describe this process creates considerable confusion. The proposed framework is a rigorous and flexible tool that could guide the implementation of strategies for identifying potential candidates for disinvestment.

We designed, developed, and implemented a new hospital-based health technology assessment (HB-HTA) program called Smart Innovation. Smart Innovation is a decision framework that reviews and makes technology adoption decisions. Smart Innovation was meant to replace the fragmented and complex process of procurement and adoption decisions at our institution. Because use of new medical technologies accounts for approximately 50 percent of the growth in healthcare spending, hospitals and integrated delivery systems are working to develop better processes and methods to sharpen their approach to adoption and management of high cost medical innovations.

The program has streamlined the decision-making process and added a robust evidence review for new medical technologies, aiming to balance efficiency with rigorous evidence standards. To promote system-wide adoption, the program engaged a broad representation of leaders, physicians, and administrators to gain support.

To date, Smart Innovation has conducted eleven HB-HTAs and made clinician-led adoption decisions that have resulted in over $5 million dollars in cost avoidance. These are comprised of five laboratory tests, three software-assisted systems, two surgical devices, and one capital purchase.

Smart Innovation has achieved cost savings, avoided uncertain or low-value technologies, and assisted in the implementation of new technologies that have strong evidence. The keys to its success have been the program's collaborative and efficient decision-making systems, partnerships with clinicians, executive support, and proactive role with vendors.

User experience (UX) plays a key role in uptake and usage of mental health smartphone interventions, yet remains underinvestigated. This review aimed to characterize and compare UX evaluation approaches that have been applied in this specific context, and to identify implications for research and practice.

A narrative review was conducted of UX-themed studies published in PubMed, PsycINFO, and Scopus up to February 2019. Eligible studies reported on data reflecting users' interactions with a smartphone intervention for any mental health condition. Studies were categorized into “situated” versus “construct-based” methods according to whether or not an established UX construct was used to acquire and analyze data.

Situated approaches used bespoke UX metrics, including quantitative measures of usage and performance, as well as grounded interview data. Construct-based approaches such as assessments of usability and acceptability were based on conceptual frameworks, with methodologically stronger versions featuring construct definitions, validated measurement tools, and an ability to compare data across studies. Constructs and measures were sometimes combined to form bespoke construct-based approaches.

Both situated and construct-based UX data may provide benefits during design and implementation of a mental health smartphone intervention by helping to clarify the needs of users and the impact of new features. Notable however was the omission of UX methods, such as split testing. Future research should consider these unaddressed methods, aim to improve the rigor of UX assessment, integrate their use alongside clinical outcomes, and explore UX assessment of more complex, adaptive interventions.