Economic Evaluation Associated With Clinical-Grade Mobile App-Based Digital Therapeutic Interventions: Systematic Review.

BACKGROUND: Digital therapeutics (DTx), a class of software-based clinical interventions, are promising new technologies that can potentially prevent, manage, or treat a spectrum of medical disorders and diseases as well as deliver unprecedented portability for patients and scalability for health care providers. Their adoption and implementation were accelerated by the need for remote care during the COVID-19 pandemic, and awareness about their utility has rapidly grown among providers, payers, and regulators. Despite this, relatively little is known about the capacity of DTx to provide economic value in care. OBJECTIVE: This study aimed to systematically review and summarize the published evidence regarding the cost-effectiveness of clinical-grade mobile app-based DTx and explore the factors affecting such evaluations. METHODS: A systematic review of economic evaluations of clinical-grade mobile app-based DTx was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Major electronic databases, including PubMed, Cochrane Library, and Web of Science, were searched for eligible studies published from inception to October 28, 2022. Two independent reviewers evaluated the eligibility of all the retrieved articles for inclusion in the review. Methodological quality and risk of bias were assessed for each included study. RESULTS: A total of 18 studies were included in this review. Of the 18 studies, 7 (39%) were nonrandomized study-based economic evaluations, 6 (33%) were model-based evaluations, and 5 (28%) were randomized clinical trial-based evaluations. The DTx intervention subject to assessment was found to be cost-effective in 12 (67%) studies, cost saving in 5 (28%) studies, and cost-effective in 1 (6%) study in only 1 of the 3 countries where it was being deployed in the final study. Qualitative deficiencies in methodology and substantial potential for bias, including risks of performance bias and selection bias in participant recruitment, were identified in several included studies. CONCLUSIONS: This systematic review supports the thesis that DTx interventions offer potential economic benefits. However, DTx economic analyses conducted to date exhibit important methodological shortcomings that must be addressed in future evaluations to reduce the uncertainty surrounding the widespread adoption of DTx interventions. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42022358616; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022358616.

Needed: system dynamics for the drug discovery process.

A system dynamics structure to characterize the link between technology and critical management decisions in the drug discovery process (DDP) is needed. The DDP includes many interacting feedback loops that impact decisions executed in early stages and that influence performance in later development stages. The complexity of the DDP and the absence of systemic perspective, contributes imprecision to decision-makers' own mental models, and limits their ability to generate decisions that provoke genuine process improvements. Hence, the emergence and use of new integrated technology- and knowledge-based approaches bridge to R&D concern at least early attrition rates, critical high-content chemical lead value identification and time delay reduction for the early phase DDP.

Bayesian versus Frequentist statistical modeling: a debate for hit selection from HTS campaigns.

The existing literature suggests the Bayesian-Frequentist debate could soon be involved in the prioritization of hits from HTS campaigns. The Bayesian-Frequentist debate reflects two archetypical attitudes regarding the process of conducting scientific and technological research. This review article covers recent advances in statistical analyses, currently in use, for hit selection in the drug discovery process. The impact of decisions (e.g. attrition) executed at early stages in the drug discovery process influences HTS performance in later development stages. It shows that, as the high content value of the information from HTS campaigns increases over time, the two statistical approaches aim to provide similar answers, but they might not succeed.

Costing bias in economic evaluations.

Determining the cost-effectiveness of healthcare interventions is key to the decision-making process in healthcare. Cost comparisons are used to demonstrate the economic value of treatment options, to evaluate the impact on the insurer budget, and are often used as a key criterion in treatment comparison and comparative effectiveness; however, little guidance is available to researchers for establishing the costing of clinical events and resource utilization. Different costing methods exist, and the choice of underlying assumptions appears to have a significant impact on the results of the costing analysis. This editorial describes the importance of the choice of the costing technique and it's potential impact on the relative cost of treatment options. This editorial also calls for a more efficient approach to healthcare intervention costing in order to ensure the use of consistent costing in the decision-making process.

Needed: models of biotechnology intellectual property.

Although never uncontroversial, intellectual property rights in biotechnological innovation are once more the focus of intense debate. The debate has yet to reach any result, largely because of several important errors in the way that various disciplines approach it. These errors include making assumptions without empirical basis and conflating various intellectual property regimes. What is needed is a transdisciplinary integrated method to correct these errors. Such a method can be implemented through the construction of alternative models of intellectual property protection designed to balance the various social, ethical and economic constraints that affect biotechnology.