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Background <p>The global healthcare landscape is currently facing significant challenges driven by rising pharmaceutical expenditures, an aging population, and the increasing prevalence of chronic diseases. The widespread practice of polypharmacy, particularly among the elderly, has led to higher risks of drug-drug interactions, adverse events, and escalating costs. Traditional clinical guidelines often fail to address the complexities of multimorbidity. The integration of Artificial Intelligence (AI) and Digital Therapeutics (DTx) presents a strategic opportunity to mitigate these issues by optimizing medication management and adherence.</p> Objective <p>Rather than providing an exhaustive overview, this paper aims to illustrate the clinical and economic impact of Digital Therapeutics (DTx) and AI-driven decision support systems through targeted examples in specific therapeutic areas. Specifically, it evaluates the efficacy of these technologies in reducing polypharmacy, minimizing inappropriate prescriptions, and lowering overall healthcare costs while maintaining or improving clinical outcomes, focusing on representative models of care for patients with chronic and metabolic-cardiovascular conditions.</p> Methods <p>A narrative review of recent clinical evidence, including Randomized Controlled Trials (RCTs) and economic models, was conducted by searching major scientific databases (e.g., PubMed, Scopus). The analysis focuses on studies involving elderly populations with multimorbidity (e.g., Rieckert et al., Brünn et al.) and metabolic-cardiovascular conditions (e.g., Nordyke et al., Davison et al.). The review further categorizes various DTx platforms across therapeutic areas such as diabetes, cardiovascular rehabilitation, respiratory diseases, and chronic pain.</p> Results <p>The application of electronic Clinical Decision Support Systems (CDSS) in geriatric care has been associated with a significant reduction in medication burden (Mean Difference − 0.45 drugs) without increasing hospitalization or mortality rates. In the metabolic-cardiovascular domain, DTx interventions for Type 2 Diabetes indicated potential cost-effectiveness, with pharmaceutical cost reductions ranging from 22% to 29% and estimated savings of $97–145 per patient per month. Clinical benefits suggested a trend toward reduced HbA1c levels (-0.5% to -0.7%) and projected decreases in long-term microvascular complications due to improved behavioral adherence and deprescribing of unnecessary medications.</p> Conclusion <p>Digital Therapeutics have evolved from experimental interventions to viable, potentially cost-effective structural components of chronic disease management. Evidence supports their promising role in enhancing clinical outcomes and optimizing resource allocation. However, widespread adoption requires the establishment of clear regulatory frameworks, value-based reimbursement models, and full integration into longitudinal care pathways (e.g., local frameworks such as the Italian PDTA or global Chronic Care Models). DTx are positioned to become a fundamental pillar of future hybrid healthcare models, with the potential to support sustainability and personalized patient care.</p>

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The Use of Digital Therapeutics in the Management of Chronic Diseases: State of the Art, Evidence and Prospects

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Francesco Ferrara,
  • Flavia De Berardinis

摘要

Background

The global healthcare landscape is currently facing significant challenges driven by rising pharmaceutical expenditures, an aging population, and the increasing prevalence of chronic diseases. The widespread practice of polypharmacy, particularly among the elderly, has led to higher risks of drug-drug interactions, adverse events, and escalating costs. Traditional clinical guidelines often fail to address the complexities of multimorbidity. The integration of Artificial Intelligence (AI) and Digital Therapeutics (DTx) presents a strategic opportunity to mitigate these issues by optimizing medication management and adherence.

Objective

Rather than providing an exhaustive overview, this paper aims to illustrate the clinical and economic impact of Digital Therapeutics (DTx) and AI-driven decision support systems through targeted examples in specific therapeutic areas. Specifically, it evaluates the efficacy of these technologies in reducing polypharmacy, minimizing inappropriate prescriptions, and lowering overall healthcare costs while maintaining or improving clinical outcomes, focusing on representative models of care for patients with chronic and metabolic-cardiovascular conditions.

Methods

A narrative review of recent clinical evidence, including Randomized Controlled Trials (RCTs) and economic models, was conducted by searching major scientific databases (e.g., PubMed, Scopus). The analysis focuses on studies involving elderly populations with multimorbidity (e.g., Rieckert et al., Brünn et al.) and metabolic-cardiovascular conditions (e.g., Nordyke et al., Davison et al.). The review further categorizes various DTx platforms across therapeutic areas such as diabetes, cardiovascular rehabilitation, respiratory diseases, and chronic pain.

Results

The application of electronic Clinical Decision Support Systems (CDSS) in geriatric care has been associated with a significant reduction in medication burden (Mean Difference − 0.45 drugs) without increasing hospitalization or mortality rates. In the metabolic-cardiovascular domain, DTx interventions for Type 2 Diabetes indicated potential cost-effectiveness, with pharmaceutical cost reductions ranging from 22% to 29% and estimated savings of $97–145 per patient per month. Clinical benefits suggested a trend toward reduced HbA1c levels (-0.5% to -0.7%) and projected decreases in long-term microvascular complications due to improved behavioral adherence and deprescribing of unnecessary medications.

Conclusion

Digital Therapeutics have evolved from experimental interventions to viable, potentially cost-effective structural components of chronic disease management. Evidence supports their promising role in enhancing clinical outcomes and optimizing resource allocation. However, widespread adoption requires the establishment of clear regulatory frameworks, value-based reimbursement models, and full integration into longitudinal care pathways (e.g., local frameworks such as the Italian PDTA or global Chronic Care Models). DTx are positioned to become a fundamental pillar of future hybrid healthcare models, with the potential to support sustainability and personalized patient care.