This use case was initiated in collaboration with the Data Sharing Coalition, one of the founding partners of the CoE-DSC.
An aging population is making the Dutch elderly care system less affordable and accessible. To improve the monitoring of health provisions and elderly care policies between the various stakeholders, data collaboration is required. One of the main challenges to realise data collaboration are ensuring privacy of patients’ data and reducing high costs for generating insights. Multi-Party Computation (MPC) has the potential to lower these barriers. In this use-case, our participant Linksight collaborates with DSW Health insurance and “Zorgkantoor”, the municipality of Delft and elderly care organisation Pieter van Foreest to monitor and improve care provisions for elderly citizens in the Delft, Schieland and Westland region, with a solution that can also be applied for national care monitoring. The Data Sharing Coalition supports Linksight by exploring the governance solutions to balance data control with pace of change when scaling up a data collaboration.
Use case ambition: ensuring affordable access to elderly care through large-scale data collaboration
The Dutch population is aging, which strains the elderly care system. Between 2015 and 2020, care costs for municipalities increased by 30 percent and waiting lists have surged, due to a lacking capacity of nursing homes and personnel. To ensure that elderly care remains affordable and accessible, regional collaboration is required to improve at a systems level and to continuously measure effectiveness of policies that impact elderly care (WLZ, ZVW, and WMO)*. Continuous monitoring allows care providers to benchmark against sector performance and enables data-driven decision making on designing new improved policies. However, a data collaboration between municipalities, health care offices (“zorgkantoren”), health insurers and care providers is required to enable such monitoring. *Notably, WLZ policies focus on life-long care by a care provider, and WMO policies focus on e.g. homecare provisions to support generic Wellbeing. Read more about the need for regional collaboration in care in the Dutch 2022 National Care Accord (IZA).
Three key challenges in setting up large-scale data collaboration for the Dutch elderly care sector
The Dutch elderly care sector currently faces three key challenges that hinder collaboration on data: privacy challenges, cost barriers and data fragmentation across the sector. Firstly, the privacy challenge occurs since the data collaboration deals with the patients’ health data which under the GDPR is considered a ‘special category of personal data’, that poses strong compliance requirements for data providers. Secondly, monitoring care performance is costly as it involves a trusted third party who aggregates and pseudonymises data from different sources, and external researchers who create insights. These types of analysis are usually done within a large interval (e.g. every 3 years) as the cost and setup time do not permit more frequent analysis, hence, limiting the possibility to gain continuous insights. Lastly, elderly care sector is very fragmented in the Netherlands, requiring an infrastructure that works for all participants involved. To put this fragmentation to numbers: 31 health offices (zorgkantoren) manage regional long-term care (WLZ), all working together with a subset of the 345 local municipalities that manage wellbeing (WMO), while regular healthcare (ZWV) is organised at the national level. Healthcare, elderly care and social care organisations often work in multiple regions. Such a plethora of parties requires data control mechanisms in the data collaboration.
Linksight, DSW, Delft municipality and Pieter van Foreest locally test value potential of MPC-based data collaboration to mitigate challenges
In this use case, our participant Linksight aims to prove the value of multi-party computation (MPC) in solving aforementioned data collaboration barriers in elderly care within the specific region in the Netherlands (Delft, Schieland, Westland). The ambition is to scale-up to a national coverage, after locally testing the value of MPC-based data collaboration. Here, MPC helps to lower the data collaboration barriers as follows. Firstly, GDPR compliance is easier met since the personal data is kept private, and only aggregated insights are shared. Secondly, automation of data collaboration results in cost reductions due to the replacement of the manual work mentioned before. The result of simplified compliance and lowered costs is that more frequent analysis for healthcare is possible, benefiting all involved: policy makers are better able to track policy changes (driving policy innovation), while healthcare providers can optimise their processes.
DSW Zorgkantoor explains the value of this data collaboration: “In order to organise care even better, to make it more compatible and more efficient, it is important that information from these domains is shared and combined. By combining and analysing the information from the various domains, care can be deployed more effectively, which ultimately improves the quality of care. MPC makes it possible to actually combine the information, which was a challenge in the past.”
Data Sharing Coalition supports Linksight with expertise to scale up MPC-based data collaboration, which can serve as inspiration for other sectors
In this use case, the Data Sharing Coalition is supporting Linksight with expertise to scale up the data collaboration by ensuring efficient data control for data providers while upkeeping the high pace of innovation and change in a future large-scale network. To tackle the scale up challenges the DSC organises a co-creation process with Linksight and provides knowledge from other use cases/domains on both technical and non-technical measures. The use case also strives to prove the added value of technology to a broad audience, hence, a privacy enhanced dashboard for monitoring care will be developed and tested for the region Delft, Schieland and Westland. The results from the use-case would provide an optimal preparation for the future scale-up of the data collaboration. Other data spaces can re-use the results on mechanisms for balancing data control and pace of change, as this is a generic challenge for all large-scale data collaborations.
Findings of this use case
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