Digitalisation in the Health Care Sector – A Long Way to Go
OpinionOpinion Piece by Achim Wambach and Simon Reif
The digital transformation in the health care sector, with the resulting availability of comprehensive data, enables the provision of personalised therapies and performance-based payments. If the digital health sector is to be regulated effectively, the threat of data-based monopolies due to proprietary data access must be counteracted. The Digital Markets Act (DMA) addresses this threat by requiring platform companies with market dominance to share data. The planned European Health Data Space (EHDS) is intended to create economically usable data access through standardised specifications for data preparation. In Germany, short-term payment incentives, individual solutions (such as selective contracts) and lacking infrastructure too often turn into obstacles to digital innovation. From an economic perspective, what is needed are long-term incentive systems and a more competitive setting, emphasise ZEW President Achim Wambach and ZEW Research Group Head Simon Reif in their article for the magazine Wirtschaftsdienst.
As data become increasingly available, the health care sector is undergoing a fundamental digital transformation. This affects both the types of treatment and the associated organisational structures. With comprehensive data on individual predispositions and (pre-existing) conditions, it is possible to shift more to personalised medicine: for example, providing gene medicines that are personalised for individual patients. Because more data are available, this personalised medicine also enables more targeted prevention, depending on factors such as genetic predispositions and individual health behaviour. The changes in the types of treatment also lead to data-driven changes in organisational structures, for example in the payment or reimbursement mechanisms. These are predominantly based on services rendered. With new data, performance-based payment formats also become possible. However, this development towards data-based care also harbours the risk that proprietary data access could lead to data-based monopolies. Such market power has been observed in the past with purely digital business models. In view of these developments, the question arises as to what is the best possible way to regulate data collection and use in the health care sector.
The specifics of digital business models
The ever-increasing availability of data and the broader application possibilities of machine learning processes and generative artificial intelligence mean that the regulation of service provision and care planning needs to be modernised. In order to unlock the potential of digitalisation for improving health care quality and achieving cost savings through efficiency gains, the regulations designed need to take into account the peculiar characteristics of digital business models. These characteristics can be defined in terms of speed and market power.
Digital products have a much shorter time to market and faster innovation cycles than analogue offerings. In the health care sector, this can be seen in the rapidly growing market for digital therapeutics or in the frequent addition of digital components to medical devices. However, many stages in the regulatory processes are not designed for this high pace. The development of a new drug takes more than ten years on average (Schuhmacher et al., 2016) and even under the accelerated Breakthrough Therapy procedure of the U.S. Food and Drug Administration (FDA) the approval process still takes an average of 32 months after completion of the decisive Phase 3 studies (Chandra et al., 2024). While attempts are now being made at the regulatory level to at least adapt the approval processes to the new digital health care innovations, the payment mechanisms applicable to digital technologies in the health care sector continue to follow the rigid mechanism of negotiating prices based on the benefits determined in clinical studies after approval. Regulators need to create a framework that allows the dynamic adjustment of prices to new developments in order to incentivise the further development of health care solutions that are already on the market (Brönneke et al., 2023).
An increasingly data-based health care system is – like other digital business models – exposed to the risk of welfare-reducing market power. Examples could be platforms for making appointments in medical practices or algorithms for diagnostics. Market power often arises either through proprietary access to data with corresponding economies of scale, or through the (indirect) network effects on platforms: both of these factors make it difficult for potential newcomers to establish themselves in the market. It is as yet not clear whether the competition will lead to the emergence of sufficient alternative health care platforms. With regard to algorithms there is also both a risk and an opportunity: On the one hand, providers with high market penetration and, consequently, ever-larger proprietary data sources could build up a welfare-reducing market power; on the other hand, innovations based on data-efficient algorithms may be launched, as is the case with the current providers of generative AI models. In the health care sector, the costs of market power in the form of patent-protected monopolies for innovative drugs are well accepted. The resulting financial incentive is intended to promote research and development activities by pharmaceutical companies. Unlike patent-protected monopolies, however, data-based monopolies have no expiry date. Digital offers must therefore be monitored carefully from a competition perspective.
Regulation of digital business models
European regulation of digital business models is primarily a response to the potential risks posed by these innovations. Initially, the focus was on sanctioning abusive behaviour by digital giants such as Alphabet or Meta. However, this approach proved to be little effective, as the market power of the dominant companies was more robust than expected. The Digital Markets Act (DMA) in 2023 therefore marked a change in strategy: Large digital platforms must now meet certain ex-ante requirements, such as the ban on self-preferencing or, under certain circumstances, the data-sharing obligation. It remains to be seen to what extent this change in strategy can help strengthen the European digital economy, for example by giving European companies more access to data. This development should also be taken into account in the health care sector: Merely hoping for competition and abuse control is not enough. Access to data is essential, and where the market power of companies becomes dominant, a level playing field for competitors must be ensured.
While the DMA is intended to strengthen market forces, the EU has simultaneously restricted companies' room for manoeuvre through a large number of regulations. The General Data Protection Regulation (GDPR), praised as harmonisation within Europe and as a contribution to universal data protection, has at the same time significantly restricted the innovation activities of companies and start-ups. There are also fears that the 2024 AI Act will represent a market barrier for small companies in particular, rather than provide new freedoms as intended (Janeba, 2024).
There is another angle that concerns the regulation of health data. The planned European Health Data Space (EHDS) is to lay the foundation for business models based on health data. The large online platforms only generated data because of their new business models. In contrast, there is a large number of data sources in the health care sector, some spanning decades. In addition to the data collected in clinical registers or clinical studies, hospital information systems have been available since the 1980s, the data from which provide insights into treatment pathways (Prokosch, 2001), and billing data, which can be used to trace medical histories at an individual level. In many cases, these health data are stored in a decentralised manner and in most cases they cannot be easily linked. The legal and economic foundations for developing business models with these data are lacking. This is why the EU is now developing a regulatory framework which is to enable research and industry to access these data sources. This includes requirements for data preparation and data provision that must be implemented by all EU countries.
The German health care system
The example of the EHDS requirements for health data in the individual European countries shows that very good health data infrastructures already exist in many places. However, Germany lags far behind its European counterparts in this respect. (Kessissoglou et al., 2024). This means that German’s backlog in health care data infrastructure in particular, but also in the digitalisation of the health care system in general (Thiel et al., 2018), cannot be attributed to Europe-wide data protection requirements such as the GDPR. There are many causes, but the significant factor is the short-term perspective adopted by the majority of decision-makers in the German health care system (Reif et al., 2024).
The lack of focus on long-term investments, e.g. digitalisation and prevention, is partly due to the fact that at least since 1994, when the scheme for risk adjustment (‘Risikostrukturausgleich’, RSA) between the health insurance providers was introduced, long-term cost increases have been passed on to the overall system due to the absence of innovation. As a result, there is no incentive on the part of the health insurance providers to cut costs. Patient-centred digital health applications are one example of such lost innovations. For 25 years, individual health insurers have been able to conclude so-called “selective contracts” with service providers. Although some of these contracts were used to make digital health solutions available to policyholders, the range of offerings was limited. The health insurance companies obviously saw no added value in offering more of these services. The legislator responded by introducing the scheme for digital health applications (‘Digitale Gesundheitsanwendungen’, DiGAs) in 2019 and thus requiring all health insurers to reimburse the providers of these apps according to a structured procedure. This procedure was unique worldwide (Gerke et al., 2020) and has since become a model for similar reimbursement channels in France and Belgium, for example.
However, such a new reimbursement mechanism would not have been necessary in a functioning competition between health insurance providers. Provided with the right economic incentives, health insurance funds would compete in looking for the best investments in the health care system. For such competition-related incentives to be effective, however, the allocation mechanism in the risk adjustment between health insurance companies must be designed for the long term, as we explain in detail from an economic perspective in Reif et al. (2025).
If the self-administration in the health care system cannot provide sufficient impetus for innovation, regulatory measures such as the introduction of the DiGA reimbursement mechanism or the requirement for data preparation and data provisioning as part of the EHDS act as important drivers. Nevertheless, to ensure faster digital transformation in health care, economic incentives need to ensure that long-term investments in the health care system are worthwhile.
