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Start quick check
You answer 15 questions in 5 dimensions. Evaluate each statement from the perspective of your entire organization, not individual departments.
There are no right or wrong answers. Answer as honestly as possible. The results are not stored and not forwarded.
Dimension 1: Leadership & Governance
Basis: Duncan et al. (2022) [2]: “Governance and Management”; AIR-5D [4]: “Risk, Privacy & Governance”
F1.1 The digital strategy of our organization is documented in writing and approved by management.
[2,6]
Duncan et al. 2022: “Strategy” as one of the 7 core dimensions of digital maturity. DigitalRadar DE: “Structures & Systems” with a written digital strategy as a basic indicator.
Why relevant: A missing or informal digital strategy can lead to wrong decisions and parallel structures in digitalization projects.
Does not apply at allFully applies
F1.2 There is a clearly identified responsibility (role/person) for digitalization decisions at management level.
[2,4]
Duncan et al. 2022: “Leadership and Management” as a core indicator in the governance dimension. AIR-5D: Governance structures as a prerequisite for AI readiness.
Why relevant: Unclear decision-making responsibilities delay projects and lead to parallel structures.
Does not apply at allFully applies
F1.3 Regulatory requirements (GDPR, MDR, EU AI Act) are systematically assessed when making digital decisions.
[4,5]
AIR-5D: “Risk, Privacy & Governance” (weight 0.101). HAIRA: Compliance as a core element of 7 AI governance domains. The EU AI Act (2024) obliges healthcare actors to carry out risk assessments before AI use.
Why relevant: The EU AI Act (2024) classifies many healthcare AI applications as high-risk systems with specific compliance requirements.
Does not apply at allFully applies
Dimension 2: Process stability & IT capacity
Basis: Duncan et al. (2022) [2]: “IT Capability” + “Interoperability”; HIMSS EMRAM Stages 1-4 [1]
F2.1 Our core processes are standardized, documented and regularly reviewed.
[2,6]
Duncan et al. 2022: Process stability as a prerequisite for meaningful digitalization. DigitalRadar DE: “Clinical processes” as one of the weak dimensions of German hospitals.
Why relevant: AI optimizes existing processes - unstable or undocumented processes are not improved by AI, but scaled as errors.
Does not apply at allFully applies
F2.2 Our IT systems are reliable, up-to-date and systematically maintained.
[1,2]
HIMSS EMRAM Stage 1: Complete integration of laboratory, imaging and pharmacy systems as a basic requirement. Duncan et al. 2022: “IT Capability” as a fundamental dimension.
Why relevant: Outdated or unstable IT infrastructure is a central technical barrier to AI implementation.
Does not apply at allFully applies
F2.3 Our digital systems can exchange data with each other in a structured manner (interoperability).
[1,2]
Duncan et al. 2022: “Interoperability” as an independent maturity dimension. HIMSS EMRAM Stage 6–7 requires that data can flow between systems without media disruption.
Why relevant: Lack of interoperability prevents data access that is essential for AI training and inference processes.
Does not apply at allFully applies
Dimension 3: Data quality & analytics
Basis: Duncan et al. (2022) [2]: “Data Analytics”; AIR-5D [4]: “Data Management” (weight 0.22)
F3.1 Our data is systematically recorded, stored in a structured manner and checked for quality.
[4,10]
AIR-5D: Data Management as the second most important AI readiness dimension (weight 0.22). Wasylewicz & Scheepers-Hoeks (2019): Data quality as a critical success factor for clinical AI applications.
Why relevant: “Garbage in, garbage out” – AI models are only as good as the quality of the data on which they are trained and applied.
Does not apply at allFully applies
F3.2 We use data actively and regularly for decisions (e.g. dashboards, reporting, analyses).
[2,3]
Duncan et al. 2022: Data-driven decision-making culture as an indicator of “analytics” maturity level. Adler-Milstein et al. 2024: Higher digital maturity significantly correlates with better quality and patient safety.
Why relevant: Organizations that do not have a data-based decision-making culture can neither evaluate nor meaningfully use AI outputs.
Does not apply at allFully applies
F3.3 There are clear responsibilities for data quality, data governance and data access.
[4,5]
AIR-5D: Data management explicitly includes governance structures for data access. HAIRA: “Algorithm Development” and “Data Governance” as independent AI governance domains.
Why relevant: Unclear data ownership puts AI projects at significant risk. Data quality, access and accountability are central prerequisites for trustworthy AI use.
Does not apply at allFully applies
Dimension 4: Willingness to change & culture
Basis: Duncan et al. (2022) [2]: “People, Skills and Behavior”; PMC 2021 [7]: Change management in European hospitals
F4.1 Employees and clinical specialists are actively involved in digital transformation processes at an early stage.
[7]
Borghouts et al. 2021 (PMC8625074, five European countries): Change management tools and structured stakeholder involvement are relevant for digital innovation in hospitals. Clinical specialists are important enablers of change.
Why relevant: Lack of involvement of clinical specialists is a central non-technical risk factor for digitalization projects in healthcare.
Does not apply at allFully applies
F4.2 Our leadership culture supports experiments, allows learning from mistakes and accepts uncertainty in digital change.
[7,2]
Duncan et al. 2022: “Cultural Values” as a core indicator of the governance dimension. PMC 2021: Fault tolerance and psychological safety as significant predictors of successful digital innovation in European hospitals.
Why relevant: Risk-averse leadership cultures without tolerance for errors systematically block innovation and AI exploration - regardless of technical infrastructure.
Does not apply at allFully applies
F4.3 There are targeted measures to develop digital skills for all relevant employee groups.
[2,11]
Duncan et al. 2022: “People, Skills and Behavior” as one of seven core dimensions, including skills development and digital fluency. WHO Europe (2025): workforce capacity building is relevant to AI readiness in health systems.
Why relevant: Technology without competent use does not create added value. Digital competence gaps are an important implementation barrier in healthcare.
Does not apply at allFully applies
Dimension 5: Focus & AI-Readiness
Basis: AIR-5D [4]: “Opportunity Discovery” (highest priority, weight 0.44); HAIRA [5]: “Problem Formulation”
F5.1 We have identified and evaluated specific, prioritized use cases for digitalization and/or AI.
[4,5]
AIR-5D: “Opportunity Discovery” is a central AI readiness dimension. HAIRA: “Problem Formulation” is an early and critical step in AI governance. Lack of use case identification increases the risk of AI projects starting without a clear goal.
Why relevant: “Opportunity Discovery” is by far the most important dimension in the AIR-5D framework (weight 0.44 out of 1.0) - without a clear benefit case, AI adoption has no strategic foundation.
Does not apply at allFully applies
F5.2 We make a clear distinction between digitalization (process automation) and AI (learning systems) and prioritize accordingly.
[4,5]
AIR-5D: Clear understanding of AI categories as a prerequisite for strategic prioritization. HAIRA: “Problem Formulation” explicitly includes the distinction between AI and non-AI solutions. Confounding both concepts leads to systematic misallocation of resources.
Why relevant: Many organizations refer to simple automation as “AI” – or, conversely, use AI where conventional digitalization would be sufficient and more cost-effective.
Does not apply at allFully applies
F5.3 Decisions for the use of AI are made based on clear criteria (e.g. proof of benefit, risk class, governance requirements).
[5,4]
HAIRA: “External Product Evaluation” and “Deployment Integration” as independent AI governance domains. AIR-5D: Systematic decision criteria as a core requirement for AI readiness at the optimization level. EU AI Act (2024): Risk classification as a legal obligation for high-risk AI in healthcare.
Why relevant: The EU AI Act (2024) requires healthcare organizations to carry out risk assessments before using AI. Missing decision criteria increase liability risks and endanger patient safety.
Does not apply at allFully applies
Scientific basis & methodology
This Digital Quick Check synthesizes three established frameworks from international healthcare digitalization research into a practical self-assessment tool for leaders.
Additional implementation and governance aspects were checked against current WHO Europe and DiMe recommendations.[11][12]
The five Quick Check dimensions were derived from a systematic review of maturity models in healthcare [2] and from the AIR-5D framework for AI readiness in healthcare organizations [4]. Findings from the German DigitalRadar program [6] and the HIMSS EMRAM model [1] were also included.
| # | Dimension | Scientific basis | Indicators (selection) |
|---|---|---|---|
| D1 | Leadership & Governance | Duncan et al. 2022 [2]: “Governance and Management”; AIR-5D [4]: “Risk, Privacy & Governance” | Leadership, change management, compliance, risk management, data governance |
| D2 | Process stability & IT capacity | Duncan et al. 2022 [2]: “IT Capability” + “Interoperability”; HIMSS EMRAM Stages 1-4 [1] | Process standardization, IT reliability, system integration, interoperability |
| D3 | Data quality & analytics | Duncan et al. 2022 [2]: “Data Analytics”; AIR-5D [4]: “Data Management” (weight: 0.22) | Data collection, quality assurance, use for decisions, data governance |
| D4 | Willingness to change & culture | Duncan et al. 2022 [2]: “People, Skills and Behavior”; Kotter (2012) & ADKAR; Borghouts et al. 2021 [7] | Stakeholder integration, error culture, competence development, leadership culture |
| D5 | Focus & AI readiness | AIR-5D [4]: “Opportunity Discovery” (weight: 0.44 – highest priority); HAIRA [5] | Use case identification, AI vs. digitalization distinction, decision criteria |
The instrument uses a 5-point Likert scale (1 = does not apply at all to 5 = completely applies) – oriented toward the instrument by Duncan et al. for general practices (JMIR 2025) [8] as well as best practices from scaling research [9]. Three questions per dimension (min. 3, max. 15 points) result in a total score of 15-75 points.
The thresholds for the traffic-light assessment are heuristic orienting thresholds based on three sources:
- Lower threshold (45% = 34/75): DigitalRadar reporting shows that many hospitals still have structural development needs in digital maturity [6]. The lower threshold marks a conservative orientation value below which the digital basis for AI use cases should first be stabilized.
- Upper threshold (73% = 55/75): AIR-5D emphasizes structured readiness across data, governance, opportunity discovery and implementation capability [4]. The upper threshold therefore marks an orienting level at which targeted AI exploration can be considered under clear governance.
- Plausibility check: Digital maturity is associated with quality and safety outcomes in hospitals [3]. A green assessment should therefore remain ambitious and should not be interpreted as formal certification.
- Tolerance band around the thresholds: The 45 % and 73 % thresholds are heuristic orienting lines, not empirically validated cut-offs. Results within ±5 percentage points of a threshold should be read as a transition zone – not as a categorical statement.
⚠ What this instrument does NOT provide
- It is not a psychometrically validated assessment – no Cronbach's α reliability, no factor analysis, no external calibration against a reference population.
- It reflects a single perspective. Organisational maturity should be assessed with at least 3 people from different functions and hierarchical levels.
- The thresholds are heuristically plausibilised, not empirically validated. They serve as orientation, not as scientific benchmarking.
For a robust organisational analysis we recommend triangulation with additional data sources (multi-respondent survey, document analysis, external perspective).
Rating scale (benchmark)
🔴 Foundations missing
15-34 points (<45%)
Digital basis is not sufficient for AI. Prioritise foundational investments.
🟡 Development potential
35-54 points (46-72%)
Partial readiness. Focus and prioritisation are needed. AI may be possible selectively.
🟢 Good digital basis
55-75 points (≥73%)
Digital prerequisites for targeted AI exploration are met.
Heuristic benchmark orientation: DigitalRadar DE 2024 [6] • AI-readiness maturity logic [4] • digital maturity and quality/safety outcomes in hospitals [3]
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out of 75 possible points
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ℹ Note on robustness:
This result reflects your individual perspective. For a robust organisational view we recommend running this instrument with 3–5 people from different functions and hierarchical levels and comparing the results.
Diverging assessments are themselves a valuable indicator of organisational dynamics – and often the most rewarding starting point for a structured advisory conversation.
Scientific classification of your results
Recommendations for action
Data protection & notice: Your answers are processed only locally in your browser – no transmission, no storage. Parts of the content were created with AI support (EU AI Act Art. 50).
Methodological note: This instrument is a structured screening tool, not a validated clinical or scientific assessment procedure in the strict sense. Self-assessment instruments can be influenced by social desirability and organizational perspective bias [9]. The results provide orientation and strategic decision support and do not replace professional advice. For a reliable organizational analysis, we recommend a supplementary external perspective (e.g. as part of a structured consulting process).
References
[1]
HIMSS Analytics. Electronic Medical Record Adoption Model (EMRAM): Overview and Stage Descriptions. Healthcare Information and Management Systems Society; 2024.
Available at: https://www.himss.org/what-we-do-solutions/maturity-models-emram
[2]
Duncan R, Eden R, Woods L, Wong I, Sullivan C. Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review.
J Med Internet Res. 2022;24(3):e32994. DOI: 10.2196/32994.
PMID: 35353050. PMC: PMC9008527
[3]
Adler-Milstein J, Holmgren AJ, Kralovec P, Worzala C, Searcy T, Patel V. Digital Maturity as a Predictor of Quality and Safety Outcomes in US Hospitals: Cross-Sectional Observational Study.
J Med Internet Res. 2024;26:e56316. DOI: 10.2196/56316.
PMC: PMC11336495
[4]
Velez CM et al. Five Dimensions of AI Readiness (AIR-5D) Framework – A Preparedness Assessment Tool for Healthcare Organizations.
Hospital Topics. 2024. DOI: 10.1080/00185868.2024.2427641.
PMID: 39543793
[5]
Advancing Healthcare AI Governance through a Comprehensive Maturity Model (HAIRA): Based on Systematic Review of 35 Frameworks.
npj Digital Medicine. 2026. DOI: 10.1038/s41746-026-02418-7
[6]
DigitalRadar Consortium (HIMSS, HIMSS Analytics, hih). Results of the national digitalization measurement of German hospitals – DigitalRadar 2021 & 2024.
Federal Ministry of Health; 2024.
Available at: HIMSS Digital Radar 2024
[7]
Borghouts J et al. Change Management and Digital Innovations in Hospitals of Five European Countries.
Healthcare (Basel). 2021;9(11):1508. PMC: PMC8625074
[8]
Development and Validation of a Questionnaire to Measure Digital Maturity of General Practitioner Practices: Web-Based Cross-Sectional Survey Study.
J Med Internet Res. 2025;27:e81416. DOI: 10.2196/81416
[9]
Taherdoost H. A Review of Key Likert Scale Development Advances: 1995–2019.
Int J Res Methodol Soc Sci. 2019.
[10]
Wasylewicz ATM, Scheepers-Hoeks AMJW. Clinical Decision Support Systems. In: Kubben P, Dumontier M, Dekker A (eds).
Fundamentals of Clinical Data Science. Cham: Springer; 2019. DOI: 10.1007/978-3-319-99713-1_11
[11]
World Health Organization Regional Office for Europe. Artificial Intelligence is Reshaping Health Systems: State of Readiness across the WHO European Region. WHO Europe; 2025.
Available at: WHO Europe 2025
[12]
Digital Medicine Society (DiMe). The Playbook: Implementing AI in Healthcare. Digital Medicine Society; 2025.
Available at: dimesociety.org
— Global best practice reference. Primarily US market (FDA, HIPAA); Governance, equity and algorithmic vigilance principles applicable internationally.
Application in the European legal framework: EU AI Act (Regulation (EU) 2024/1689), GDPR, MDR 2017/745, EHDS (Regulation (EU) 2025/327). US-specific regulations (FDA, HIPAA) are not transferable.