Table 3. Overview of themes, axial codes, and stakeholders’ notes.

Themes and axial codes	Stakeholders’ notes (direct quotations)
Despite uncertainty, the benefits of using data outweigh the associated risks
Knowledge about data	Data is just a word–data is not used (yet)–there is a lot of data in LTC.
Negative feelings	Data collection should not become the aim–fear of the unknown–risk that data steers a professional too much.
Current applications	AI in practice–eHealth (data that is not necessarily added or used by humans)–going from retrospective to real-time insights, to prospective improvements.
Ethical dilemmas	Uncertainty about reliability of data-Ethical dilemmas about data usage.
Data’s potential for care	Data usage of quality of care–data supports tailored care to the client–identifying group selections.
The lack of accessibility and uniformity hinders integrating data-informed care
Indicating variety of data	Demographical information – Clinical Measurements – kinds of data, including audio, video, qualitative and quantitative.
Uniformity in systems	Standardizing definitions in the systems—System often requires jargon, should switch to layman’s’ language—database structure is crucial for enhancing uniformity.
Simplification	Translating data to present understandable information for clients, informal caregivers and public–professionals will be supported if data becomes more comprehensive and easy to use–simplifying data improves multidisciplinary learning and improving.
Interoperability	Sharing data and associated solutions to increase data usage–LTC can continuously improve by connecting data between systems–Link external sources and strive for open data.
Electronic health record	Double checks in EHR–Data could support medication registration–Compliance cannot be tracked with data yet.
Information gain	Systems should be unambiguously and easy to use–Making data both findable and usable–With LTC, agreements are needed for accessibility and governance of data.
Accessibility	Creating overview of aging population (in region)–Matching eHealth and specific client(characteristics)–Insight into data of future clients.
Centralizing storage	Working towards one platform and format for different disciplines-Centralizing information–Reducing workload or pressure as data entry and retrieval are in one place.
HR and finance departments pioneer data usage, yet the potential lies in clinical decision-making
Organizational opportunities	Determination of long-term vision, supported by financial numbers–Organize care differently (more care with less resources–Data can be used positively or negatively to finance care.
Data collection and analysis techniques	Collecting information on different levels, to gain insights what is important and for who–classifying systematic problem analysis–Identifying the different instruments for data collection will result in less administration costs.
Predictions	Data can support in findings patterns and trends–Estimating lifestyle and preferences of clients–Predicting care intensity.
Data-informed care demands individual, collective, and organizational prerequisites
Education, training, and support	Education to understand the goal of data and information–Support needed for individuals with less digital literacy–Education or train employees systematically.
Teamwork and collective skills	Communicational skills within a team–Dare to innovate-mentality needed in a team–Every team should include an attention raiser or data specialist.
Individual contribution toward data-informed working	Feeling the urge to do own contribution independently or together with informal caregiver)–Taking control and responsibility–Understanding that data should support and not influence.
Requirements for learning and improving	Multidisciplinary collaboration with different layers (care, cure, management, IT)–Organizational or team objective should be formulated and communicated more clearly with everyone–Define responsibilities and roles.
Multidisciplinary collaboration enriches collective knowledge regarding data
Central role for data	Data supports individuals in multidisciplinary approaches with shared and offering new ideas–Adhering to Evidence Based Practice working principles–Enhance dialogue and understanding between different professions.
Collaborative and shared decision-making	Integrating multiple sources and professions allows for better consideration and substantiation–Appropriately implementing SDM result in more, better dialogue with clients–making decisions as a team.
Striving for overarching goals	Teams should mitigate working with limited perspectives from other professions–by working multidisciplinary, the shared objective should be accepted by all–Crucial advantage is “multi-perspectivity” on the one subject.
Collective knowledge	Leaning-by-doing enhances individual digital literacy levels–Gaining knowledge about digital systems, finding information and using that information – Collective understanding acquired by individual contributions.