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      Access and reimbursement pathways for digital health solutions and in vitro diagnostic devices: Current scenario and challenges

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          Abstract

          Objectives

          Digital therapeutics (DTx) are innovative solutions that use meaningful data to provide evidence-based decisions for the prevention, treatment, and management of diseases. Particular attention is paid to software-based in vitro diagnostics (IVDs). With this point of view, a strong connection between DTx and IVDs is observed.

          Methods

          We investigated the current regulatory scenarios and reimbursement approaches adopted for DTx and IVDs. The initial assumption was that countries apply different regulations for the access to the market and adopt different reimbursement systems for both DTx and IVDs. The analysis was limited to the US, European countries (Germany, France, and UK), and Australia due to maturity in digital health product adoption and regulatory processes, and recent regulations related to IVDs. The final aim was to provide a general comparative overview and identify those aspects that should be better addressed to support the adoption and commercialization of DTx and IVDs.

          Results

          Many countries regulate DTx as medical devices or software integrated with a medical device, and some have a more specific pathway than others. Australia has more specific regulations classifying software used in IVD. Some EU countries are adopting similar processes to the Digital Health Applications (DiGA) under Germany's Digitale-Versorgung Gesetz (DVG) law, which deems DTx eligible for reimbursement during the fast access pathway. France is working on a fast-track system to make DTx available to patients and reimbursable by the public system. The US retains some coverage through private insurance, federal and state programs like Medicaid and Veterans Affairs, and out-of-pocket spending. The updated Medical Devices Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR) in the EU includes a classification system specifying how software integrated with medical devices, and IVDs specifically must be regulated.

          Conclusion

          The outlook for DTx and IVDs is changing as they are becoming more technologically advanced, and some countries are adapting their device classifications depending on specific features. Our analysis showed the complexity of the issue demonstrating how fragmented are regulatory systems for DTx and IVDs. Differences emerged in terms of definitions, terminology, requested evidence, payment approaches and the overall reimbursement landscape. The complexity is expected to have a direct impact on the commercialization of and access to DTx and IVDs. In this scenario, willingness to pay of different stakeholders is a key theme.

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          Beyond the Trial: Systematic Review of Real-World Uptake and Engagement With Digital Self-Help Interventions for Depression, Low Mood, or Anxiety

          Theresa Fleming, Lynda M. Bavin, Mathijs Lucassen … (2018)
          Background Digital self-help interventions (including online or computerized programs and apps) for common mental health issues have been shown to be appealing, engaging, and efficacious in randomized controlled trials. They show potential for improving access to therapy and improving population mental health. However, their use in the real world, ie, as implemented (disseminated) outside of research settings, may differ from that reported in trials, and implementation data are seldom reported. Objective This study aimed to review peer-reviewed articles reporting user uptake and/or ongoing use, retention, or completion data (hereafter usage data or, for brevity, engagement) from implemented pure self-help (unguided) digital interventions for depression, anxiety, or the enhancement of mood. Methods We conducted a systematic search of the Scopus, Embase, MEDLINE, and PsychINFO databases for studies reporting user uptake and/or usage data from implemented digital self-help interventions for the treatment or prevention of depression or anxiety, or the enhancement of mood, from 2002 to 2017. Additionally, we screened the reference lists of included articles, citations of these articles, and the titles of articles published in Internet Interventions, Journal of Medical Internet Research (JMIR), and JMIR Mental Health since their inception. We extracted data indicating the number of registrations or downloads and usage of interventions. Results After the removal of duplicates, 970 papers were identified, of which 10 met the inclusion criteria. Hand searching identified 1 additional article. The included articles reported on 7 publicly available interventions. There was little consistency in the measures reported. The number of registrants or downloads ranged widely, from 8 to over 40,000 per month. From 21% to 88% of users engaged in at least minimal use (eg, used the intervention at least once or completed one module or assessment), whereas 7-42% engaged in moderate use (completing between 40% and 60% of modular fixed-length programs or continuing to use apps after 4 weeks). Indications of completion or sustained use (completion of all modules or the last assessment or continuing to use apps after six weeks or more) varied from 0.5% to 28.6%. Conclusions Available data suggest that uptake and engagement vary widely among the handful of implemented digital self-help apps and programs that have reported this, and that usage may vary from that reported in trials. Implementation data should be routinely gathered and reported to facilitate improved uptake and engagement, arguably among the major challenges in digital health.
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            Digital health technology and mobile devices for the management of diabetes mellitus: state of the art

            Rongzi Shan, Sudipa Sarkar, Seth Martin (2019)
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              Artificial intelligence in clinical and genomic diagnostics

              Raquel Dias, Ali Torkamani (2019)
              Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.
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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Med Technol
                Journal ID (iso-abbrev): Front Med Technol
                Journal ID (publisher-id): Front. Med. Technol.
                Title: Frontiers in Medical Technology
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2673-3129
                Publication date (Electronic): 20 February 2023
                Publication date Collection: 2023
                Volume: 5
                Electronic Location Identifier: 1101476
                Affiliations
                [ 1 ]Alira Health , Milan, Italy
                [ 2 ]Alira Health , Basel, Switzerland
                [ 3 ] Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Graduate School of Health Economics and Management (ALTEMS), Rome, Italy
                Author notes

                Edited by: Janet Sultana, Mater Dei Hospital, Malta

                Reviewed by: Andrea Aiello, Intexo Società Benefit, Italy Patricia Vella Bonanno, University of Malta, Malta John Borg, Malta Medicines Authority, Malta

                [* ] Correspondence: Cassandra M. Nighswander cassandra.nighswander@ 123456alirahealth.com Claudia Leopaldi claudia.leopaldi@ 123456alirahealth.com
                [ † ]

                These authors have contributed equally to this work and share first authorship

                Specialty Section: This article was submitted to Regulatory Affairs, a section of the journal Frontiers in Medical Technology

                Abbreviations AI, artificial intelligence; ARTG, Australian register of therapeutic goods; BfArM, Bundesinstitut für Arzneimittel und Medizinprodukte; ASA, American society of anesthesiologists; CAGR, compound annual growth rate; CDx, companion diagnostics; CE, Conformitè Europëenne; CLFS, clinical laboratory fee schedule; CMS, centers for medicare and medicaid services; CNEDIMT, Commission nationale d’évaluation des dispositifs médicaux et des technologies de santé; CPT, current procedural terminology; CTTI, clinical trials transformation initiative; DHT, digital health technology; DiGA, digital health applications; DiGAV, Digitale-Gesundheitsanwendungen-Verordnung; DTAC, digital technology assessment criteria; DTC, direct-to-consumer; DTx, digital therapeutics; DVG, Digitale-Versorgung-Gesetz; EC, European commission; EDL, essential in vitro diagnostics; EFTA, European free trade association; EMA, European medicines agency; ESF, evidence standards framework; EU, European union; FDA, food and drug administration; FTC, federal trade commission; G-BA, Gemeinsame Bundesausschuss; GSP, genome sequencing procedure; HAS, Haute Autorité de Santé; HIPPA, health insurance portability and accountability act; HR-pQCT, high-resolution peripheral quantitative computed tomography; HTA, health technology assessment; ICER, incremental cost-effectiveness ratio; InEK, Institut für das Entgeltsystem im Krankenhaus; IT, information technology; IVD, in vitro diagnostics; IVDR, in vitro diagnostic regulation; MAC, medicare administrative contractor; MBS, medical benefits schedule; MDR, medical device regulation; MTEP, medical technologies evaluation program; MTG, medical technology guidance; NHS, national health service; NICE, national institute for health and care excellence; P&R, pricing and reimbursement; PAMA, protecting access to medicare act; PBAC, pharmaceutical benefits advisory committee; PLA, proprietary laboratory analyses; PMA, pre-market approval; SA, service attendu; SaMD, software as a medical device; SiMD, software in a medical device; SFS, structural fragility score; SHI, statutory health insurance; TGA, therapeutic goods administration; WP, work packages

                Article
                DOI: 10.3389/fmedt.2023.1101476
                PMC ID: 9986593
                PubMed ID: 36891483
                SO-VID: d3cb38cc-cc28-414e-aa11-fc174cd27964
                Copyright © © 2023 Mantovani, Leopaldi, Nighswander and Di Bidino.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 17 November 2022
                Date accepted : 01 February 2023
                Page count
                Figures: 0, Tables: 6, Equations: 0, References: 40, Pages: 0, Words: 0
                Categories
                Subject: Medical Technology
                Subject: Review

                Keywords: digital therapeutics,in vitro diagnostics,reimbursement,regulatory,access,software,device,digital health
                Data availability:
                Keywords: digital therapeutics, in vitro diagnostics, reimbursement, regulatory, access, software, device, digital health

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