[MA 2024 02 ] Selection of data maturity model(s) for Amsterdam UMC and advice on implementation strategySelection of data maturity model(s) for Amsterdam UMC and advice on implementation strategy
Program Datadriven Healthcare, EvA Servicecentrum, AMC
Proposed by: Viola Brouwer, directeur EvA Servicecentrum (EPD voor Amsterdam UMC) en stuurgroep lid Datagedreven werken [v.brouwer@amsterdamumc.nl]
Introduction
The 3 main objectives of healthcare are accessibility, affordability, and quality. However, in the last decade the healthcare system has been under enormous pressure. The healthcare expenditures in the Netherlands have been rising and they are expected to increase until 2060 (1,2). To reform healthcare, the government, the healthcare sector and society have initiated a joint movement towards ‘passende zorg’ or appropriate care to safeguard the future quality of healthcare in the Netherlands (2,3,4). Digital excellence and digital maturity are seen as key for achieving the “quadriple aim” of health care (ie, reducing costs, improving patients experience, advancing population health, and improving the work life of healthcare providers) (5,6). To achieve digital excellence and digital maturity a lot of organizations use a maturity model. Maturity models (MM) are based on the premises that people, organizations, functional areas and processes evolve through a process of development or growth towards a more advanced maturity, going through a distinct number of levels (7).
Description of the SRP Project/Problem
Amsterdam UMC has written a position paper on data-driven healthcare. The goal is to realize demonstrable improvement of population health in the world (8). To achieve this goal, it states the ambition that in 2025, Amsterdam UMC will be a data-driven organization in which data value is used optimally and responsibly. Through the program Datadriven Healthcare Amsterdam UMC facilitates datadriven ways of working within the organization for continuous learning and making the right decisions at all levels in the organization for the best results. Within the program, the research for the determination of a suitable data maturity model incl. implementation, has been prioritized as a top 3 project for 2024. In line with the leading role as an academic hospital, it is to be expected that the outcomes of this (research) project will have an inspirational impact on other hospitals in the region and the Netherlands. Other hospitals have already shown interest in our approach on this topic.
As an organization we struggle to find the right model, matching our goals. The data maturity model field is highly diverse. To determine a fit for Amsterdam UMC, models must be examined in detail for their fit for the purpose provided above. There are at least 26 different maturity models that we know of that are used in healthcare, each model with their own primary goal (6,7). For example, a lot of models focus on the technical and/or organizational capability, only a few focus on improving the patient experience (6). These different goals can be contradictory to each other (5,6). Furthermore, few models address the important behavioural aspects of an organization’s data maturity, such as leadership and data driven culture. Another challenge is that most of the models are static. They focus on the current state of digital maturity and sometimes towards the desired digital maturity. But only a few maturity models or frameworks include continuous cycles of reassessment (5). Healthcare professionals and healthcare leaders need a maturity model or framework that sets clear targets and assesses progress for their setting, while also being agile to adjust to the rapid and continuous emergence of new technologies (5). So which models have the agility or the ability to adapt to the fast-changing environment and emerging technologies? Which models provide leadership and organizational support on different levels of the organization? In short: Which model is the right fit for Amsterdam UMC?
Research questions
Main question
How can an organisation such as Amsterdam UMC choose and implement a fitting data maturity model to achieve a data-driven organization that continuously learns and makes the right decisions at all levels of the organization?
Sub questions
• Which data maturity models exist at the moment?
• What are the requirements that a model for Amsterdam UMC should meet?
• Which existing data maturity model(s) would best fit and support the goals of Amsterdam UMC?
• What would Amsterdam UMC have to do to successfully use the chosen model in the organisation to achieve its goals?
Suggested methods
• To create an overview of the existing models, the student will have to study the literature, including the report of the steering committee Datadriven Healthcare.
• Additionally, the student will have to collect data on the requirements of Amsterdam UMC by performing qualitative methods such as interviews.
• Based on these results, and in close collaboration with the Program Datadriven Healthcare, the most fitting model will have to be decided upon, and an actionable implementation strategy will have to be devised and written.
Expected results
1. Advice on which data maturity model(s) are best suited to achieve a data-driven organization.
2. Advice on the implementation strategy for Amsterdam UMC.
Time period (usually 7 months)
Contact
Viola Brouwer, directeur EvA Servicecentrum (EPD voor Amsterdam UMC) en stuurgroep lid Datagedreven werken (v.brouwer@amsterdamumc.nl)
Marvin Stichling, Sr Adviseur Strategie & Innovatie en Programmamanager datagedreven werken. (m.p.stichling@amsterdamumc.nl)
References
1. RIVM-rapport 2020-0059. Vonk R.A.A et al. Toekomstverkenning zorguitgaven 2015-2060, kwantitatief vooronderzoek in opdracht van de Wetenschappelijke Raad voor het Regeringsbeleid. Deel 1: toekomstprojecties.
2. VWS-publicatie, Integraal Zorg Akkoord, Samenwerken aan Gezonde zorg, 2022, integraal-zorg-akkoord.pdf
3. VWS-publicatie, Nationale visie en strategie op het gezondheidsinformatiestelsel, 2023, https://open.overheid.nl/documenten/ronl-36667024db962a4962d0815e7cf2d3c9596d7255/pdf
4. ZonMW, appropriate care https://www.zonmw.nl/en/appropriate-care, geraadpleegd op 19-1-’24.
5. Kresswell K, Sheikh A, Krasuska M et al, Reconceptualising the digital maturity of health systems, 2019, The lancet-Digital Health, doi.org/10.1016/S2589-7500(19)30083-4
6. Duncan R, Eden R, Woods L, Wong I, Sullivan C, Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review, 2022, J Med Internet Research, doi.org/10.2196/32994
7. Gomez J, Romao M, Information System Maturity Models in Healthcare, 2018, Journal of Medical Systems, Springer, doi.org/10.1007/s10916-018-1097-0
8. Daemen, M, Schuchmann E, Brouwer, V et al, Postition Paper; Datagedreven werken in Amsterdam UMC. 2021, Amsterdam UMC