A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions

BACKGROUND

Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI).

OBJECTIVE

An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI.

METHODS

The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group.

RESULTS

Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI.

CONCLUSIONS

The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.

Covering digital health applications in the public insurance system: how to foster innovation in patient care while mitigating financial risks-evidence from Germany

Context

Digital health applications that support patients in managing their condition can have a positive impact on patients' health and improve the overall care process. In late 2019, as the first country worldwide, Germany included digital health applications in the benefit basket of the statutory health insurance (SHI) system to enable fast, broad-scale patient access and encourage innovation in the digital health industry. While the policy is widely recognized as a pioneering step toward improving patient care through digital technologies, there are concerns regarding the mechanics of the policy and the resulting financial risks for the SHI system.

Goals

The primary objective of this article is to provide a comprehensive and balanced overview of the German policy by evaluating its success in achieving its goals and by reviewing challenges that have emerged. The secondary objective is to delineate prospective policy options and areas warranting future research.

Approach

The article analyzes publicly available data of the Federal Institute for Drugs and Medical Devices collected between February 1st and July 17th, 2023, and complements it with empirical findings published by academic institutions and sickness funds. It discusses policy options and related areas of future research to overcome the identified challenges without jeopardizing the purpose of the legislation to encourage innovation in the digital health industry to improve patient care.

Conclusion

In line with the goals of the reimbursement policy, the inclusion of digital health applications in the SHI benefit basked has entailed new digital treatment options for patients across multiple disease areas. However, from a health policy perspective, the policy has several shortcomings, including low prescription rates, the temporary reimbursement of digital health applications that lack proven benefit, and a pricing framework that does not take into account the efficacy and efficiency of a treatment and may lead to a suboptimal allocation of public resources. Rather than the public system covering digital health applications without proven benefit, the authors suggest giving SHI organizations more budget authority to directly incentivize research and development activities and to introduce value-based pricing. More research is needed to determine the details of these mechanisms.

An Economic Impact Model for Estimating the Value to Health Systems of a Digital Intervention for Diabetes Primary Care: Development and Usefulness Study

Background

Diabetes is associated with significant long-term costs for both patients and health systems. Regular primary care visits aligned with American Diabetes Association guidelines could help mitigate those costs while generating near-term revenue for health systems. Digital interventions prompting primary care visits among unengaged patients could provide significant economic value back to the health system as well as individual patients, but only few economic models have been put forth to understand this value.

Objective

Our objective is to establish a data-based method to estimate the economic impact to a health system of interventions promoting primary care visits for people with diabetes who have been historically unengaged with their care. The model was built with a focus on a specific digital health intervention, Precision Nudging, but can be used to quantify the value of other interventions driving primary care usage among patients with diabetes.

Methods

We developed an economic model to estimate the financial value of a primary care visit of a patient with diabetes to the health system. This model requires segmenting patients with diabetes according to their level of blood sugar control as measured by their most recent hemoglobin A_1c value to understand how frequently they should be visiting a primary care provider. The model also accounts for the payer mix among the population with diabetes, documenting the percentage of insurance coverage through a commercial plan, Medicare, or Medicaid, as these influence the reimbursement rates for the services. Then, the model takes into consideration the population base rates of comorbid conditions for patients with diabetes and the associated current procedural terminology codes to understand what a provider can bill as well as the expected inpatient revenue from a subset of patients likely to require hospitalization based on the national hospitalization rates for people with diabetes. Physician reimbursement is subtracted from the total. Finally, the model also accounts for the level of patient engagement with the intervention to ensure a realistic estimate of the impact.

Results

We present a model to prospectively estimate the economic impact of a digital health intervention to encourage patients with documented diabetes diagnoses to attend primary care visits. The model leverages both publicly available and health system data to calculate the per appointment value (revenue) to the health system. The model offers a method to understand and test the financial impact of Precision Nudging or other primary care–focused diabetes interventions inclusive of costs driven by comorbid conditions.

Conclusions

The proposed economic model can help health systems understand and evaluate the estimated economic benefits of interventions focused on primary care and prevention for patients with diabetes as well as help intervention developers determine pricing for their product.

Moving to Personalized Medicine Requires Personalized Health Plans

When individuals, families, and employers select health plans in the United States, they are typically only shown the financial structure of the plans and their provider networks. This variation in financial structure can lead patients to have health plans aligned with their financial needs, but not with their underlying nonfinancial preferences. Compounding the challenge is the fact that managed care organizations have historically used a combination of population-level budget impact models, cost-effectiveness analyses, medical necessity criteria, and current medical consensus to make coverage decisions. This approach to creating and presenting health plan options does not consider heterogeneity in patient and family preferences and values, as it treats populations as uniform. Similarly, it does not consider that there are some situations in which patients are price-insensitive. We seek to highlight the challenges posed by presenting health plans to patients in strictly financial terms, and to call for more consideration of nonfinancial patient preferences in the health plan design and selection process.

Market access and value-based pricing of digital health applications in Germany

In December 2019, the Digital Health Care Act ("Digitale-Versorgung-Gesetz") introduced a general entitlement to the provision and reimbursement of digital health applications (DiGA) for insured persons in the German statutory health insurance. As establishing a new digital service area within the solidarity-based insurance system implies several administrative and regulatory challenges, this paper aims to describe the legal framework for DiGA market access and pricing as well as the status quo of the DiGA market. Furthermore, we provide a basic approach to deriving value-based DiGA prices.To become eligible for reimbursement, the Federal Institute for Drugs and Medical Devices evaluates the compliance of a DiGA with general requirements (e.g., safety and data protection) and its positive healthcare effects (i.e., medical benefit or improvements of care structure and processes) in a fast-track process. Manufacturers may provide evidence for the benefits of their DiGA either directly with the application for the fast-track process or generate it during a trial phase that includes temporary reimbursement. After one year of \]reimbursement, the freely-set manufacturer price is replaced by a price negotiated between the National Association of Statutory Health Insurance Funds and the manufacturer. By February 2022, 30 DiGA had successfully completed the fast-track process. 73% make use of the trial phase and have not yet proven their benefit. Given this dynamic growth of the DiGA market and the low minimum evidence standards, fair pricing remains the central point of contention. The regulatory framework makes the patient-relevant benefits of a DiGA a pricing criterion to be considered in particular. Yet, it does not indicate how the benefits of a DiGA should be translated into a reasonable price. Our evidence-based approach to value-based DiGA pricing approximates the SHI's willingness to pay by the average cost-effectiveness of one or more established therapy in a field of indication and furthermore considers the positive healthcare effects of a DiGA.The proposed approach can be fitted into DiGA pricing processes under the given regulatory framework and can provide objective guidance for price negotiations. However, it is only one piece of the pricing puzzle, and numerous methodological and procedural issues related to DiGA pricing are still open. Thus, it remains to be seen to what extent DiGA prices will follow the premise of value-based pricing.

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps, vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.