What findings do we have from the literature to assess the benefits of digital health solutions?
Knowledge database Organisation Strategy & control D.4: Method kit for quantifying the process potential of digital health toolsThe increasing life expectancy of the population poses numerous challenges for our healthcare system. The European Commission assumes that the increased introduction of digital health solutions will play a decisive role in solving these problems (European Commission, n.d.). But how can these benefits be quantified? This article summarises results from the literature.
Problem description, research question and relevance
Digital health solutions could represent a turning point in dealing with the challenges in the healthcare system. However, one obstacle to their introduction is the lack of concrete ways of quantifying the benefits of digital health solutions.
This raises the following questions: How can we compare the value that these solutions create for the healthcare system and make evidence-based decisions when selecting the most beneficial solutions? We need efficient and effective methods to determine the value. For organisations competing in the digital health market, measuring and proving the value of their digital solutions will be a key competitive advantage. These companies need to find ways to clearly measure and communicate the potential value they can create. However, this is not easy.
When we talk about value in healthcare, we have to consider many different stakeholders. One of the most important stakeholders is the patient. Companies should be clear about the value they create for patients. However, they must also demonstrate that they bring short and long-term benefits to the healthcare institutions they sell to. For example, when selling their solutions to hospitals, they must not only demonstrate the financial benefits, but also consider how they can improve the work of the various stakeholders in a hospital. The challenge is that not all stakeholders are aware of the potential benefits and they may have very different ideas about how these technologies can help them do their jobs better or more cost-effectively. Therefore, organisations should take a clear and tailored approach to explaining the benefits of their technologies to different stakeholders.
Methods and procedures in the project
In the SHIFT-D.4 sub-project, we are working with heyPatient as a practice partner to develop a methodological toolkit to quantify the impact of digital health interventions. The use of the method kit should enable the targeted use of limited resources for digital transformation and thus also serve to streamline and shorten decision-making processes.
Digital health applications facilitate the tracking of health-related behaviour, the monitoring of potential health risks as well as communication and interaction between patients and doctors. In Switzerland alone, an average of up to 70 per cent of online data traffic in the healthcare sector takes place on mobile devices(statista, 2019). However, the categorisation of quantification methods is both complex and wide-ranging, making it one of the key challenges. Based on a literature review, a categorisation of possible evaluation tools was carried out on the topic of quantification methods for the benefits of digital health interventions.
The literature research was carried out as part of a term paper in the Intergrated Projects module by students of the ZHAW Masters in Health Economics and Healthcare Management. We would like to take this opportunity to thank Sandra Bruno de Schumann, Laura Kunz and Lukas Kurpat for their contribution to the project!
Results and findings
The most important results of the literature research are summarised in the following table, which is an excerpt and does not claim to be exhaustive of all possible assessment instruments.
Benefit category | Criterion | Measurement methods (examples) |
Comprehensive economic evaluations | Cost-effectiveness analysis | Costs: measured in monetary units Benefits: measured in years of life gained or cases of illness identified |
Cost-utility analysis | Costs: monetary units Benefits: QUALYs, DALYs | |
Cost-benefit analysis | Costs: monetary units Benefits: Monetary units | |
Cost-consequence analysis | Costs: monetary units Benefits: medical or natural units | |
Cost-minimisation analysis | Costs: Monetary units Benefit: The same value is assumed as for the costs | |
Organisational benefit (influences the economic benefit) | Reduction in the time spent by doctors | TARMED tax point values, less utilisation |
Reduction in admin. time expenditure | Number of customer phone calls for appointment booking | |
Increase in health literacy | Patient survey | |
Clinical benefit | Quality of life | QALYs and DALYs |
Effect on certain vital signs | Networked measuring devices for home use | |
Shorter recovery time | Duration from onset of symptoms to freedom from symptoms | |
Prolongation of survival | Outcome measurements for certain diagnoses | |
Pain | Pain scale, questionnaires | |
Improvement in function | Standardised assessments, e.g. to record independence | |
Data protection and data security | Lawfulness of processing | |
Purpose limitation, data minimisation and storage limitation | ||
Rights of data subjects | ||
Information security and service management | ||
Prevention of data leakage | ||
Authentication | ||
Integration of data and functions | ||
Technical benefits | Ease of use | |
User friendliness | ||
Robustness against operating errors and malfunctions or external events | ||
Functionality | ||
Recommendations for practice
- Categorising the digital health intervention: The world of digital health interventions is diverse. Accordingly, before developing a set of methods to quantify the benefits of a corresponding digital health intervention, the most important target variables associated with it should be defined in advance, as these largely determine which methods are most suitable for the evaluation (LeFevre et al., 2017).
- Create prototypes and test them: These require regular repetition of the evaluation process and appropriate adjustments (Jochimsen, 2021).
- Do not only look at the cost dimension in the benefit evaluation: measurement parameters such as the reduction in treatment duration, patient adherence or the completeness of patient data could serve as potential alternatives (Bambigura et al., 2021).
The knowledge contribution aims to create an understanding of the complexity of benefit quantification, while at the same time highlighting the need and necessity for the development of a methodological toolbox to address this challenge.
Literature and other sources
Babigumira, J., Dolan, S., Shade, S., Puttkammer, N., Bale, J., Tolentino, H., & M. Santas, X. (2021). Applied Economic Evaluation of Digital Health Interventions. US Center for Disease Control and Prevention. 74.
DiGA - Digital health applications and apps on prescription. (2021, November 30). vfa.de. https://www.vfa.de/de/wirtschaft-politik/abcgesundheitspolitik/diga-schnell-er-klaert.html
Digital health applications regulation. (2022, May 19). https://www.gesetze-im-in-ternet.de/digav/anlage_1.html
European Commission. (n.d.). Electronic health services (eHealth): Digital health and care. Overview. Retrieved from https://health.ec.europa.eu/ehealth-digital-health-and-care/overview_de
Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), e20. https://doi.org/10.2196/jmir.3.2.e20
Gehring, H., Pramann, O., Imhoff, M., & Albrecht, U.-V. (2014). The future trend of "medical apps": From the app store directly into the medical application? Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 57(12), 1402-1410. https://doi.org/10.1007/s00103-014-2061-x
Ghadiri, A., Ternès, A., & Peters, T. (Eds.). (2016). Trends in occupational health management. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-07978-9
Jochimsen, B. (2021). Digitalisation for Health - Economic Aspects of the SVR Health Report. Wirtschaftsdienst, 101(5), 376-380. https://doi.org/10.1007/s10273-021-2916-3
LeFevre, A. E., Shillcutt, S. D., Broomhead, S., Labrique, A. B., & Jones, T. (2017). Defining a staged-based process for economic and financial evaluations of mHealth programmes. Cost Effectiveness and Resource Allocation, 15(1), 5. https://doi.org/10.1186/s12962-017-0067-6
Statista. (2019, December). statista. https://de.statista.com/statistik/daten/studie/369792/um-frage/anteil-des-mobilen-online-traffics-in-der-schweiz-in-einzelnen-branchen/
Thranberend, T., & Bittner, J. (2020). AppQ 1.1 quality criteria core set for more quality transparency in digital health applications. Bertelsmann Stiftung. https://docplayer.org/193668308-Appq-1-1-guetekriterien-kernset-fuer-mehr-qualitaet-stransparenz-bei-digitalen-gesundheitsanwendungen.htm