Digital Transformation in Healthcare: How IT is Changing the Polish Healthcare Sector

Monday, 7:30 AM, the emergency department of a Warsaw hospital. A patient with acute chest pain arrives at the ER. The attending physician opens the IT system to check the patient’s history — and encounters an empty field. Documentation from the previous hospital was never transmitted, test results from a private clinic are unavailable, and the patient himself cannot remember the names of his medications. The decision to administer blood thinners must be made within minutes, but without the complete clinical picture, every option carries risk.

This scenario repeats itself daily in Polish healthcare facilities. According to data from the Centre for e-Health (Centrum e-Zdrowia), although over 95% of hospitals in Poland have HIS-class information systems, only 67% fully utilize data exchange with the P1 platform — Poland’s central health data infrastructure. Interoperability between facilities remains a challenge — a patient moving between the public and private systems is often “invisible” to one side.

Digital transformation in healthcare is no longer an optional add-on to a facility’s strategy — it has become a fundamental requirement for delivering safe, effective, and patient-centered medical care. For CIOs and CTOs in the healthcare sector, 2026 brings both unprecedented opportunities (generative AI, advanced telemedicine, European Health Data Space) and growing regulatory pressure, along with expectations from patients accustomed to digital services in every other area of their lives.

This article is a comprehensive guide for technology leaders in the healthcare sector. We will analyze the key elements of digital transformation — from telemedicine through systems integration to data security — and present a practical implementation roadmap tailored to Polish regulatory and operational realities.

What is the current state of healthcare digitalization in Poland?

“70% of digital transformation initiatives fail, most often due to resistance from employees and lack of management support.”

— McKinsey & Company, Unlocking success in digital transformations | Source

Polish healthcare finds itself at a specific moment in its digital transformation journey. On one hand, we have an extensive central infrastructure — the P1 platform, e-prescriptions (e-recepta), e-referrals (e-skierowanie), and the Patient Internet Account (Internetowe Konto Pacjenta) — developed by the Centre for e-Health and forming the foundation for further digitalization. On the other hand — fragmentation at the facility level, varying degrees of digital maturity, and challenges integrating local systems with central resources.

Key achievements in recent years:

E-prescriptions became mandatory from 2020 — in 2025, over 500 million e-prescriptions were issued, representing virtually full adoption. The Patient Internet Account (IKP) now has over 15 million active users who can view their medical documentation, prescription history, and referrals. The P1 platform aggregates medical documentation from an increasing number of facilities, though the pace of incorporating additional data sources could be higher.

E-referrals, implemented gradually since 2021, now cover most specialist outpatient consultations and diagnostic tests. For patients, this means the end of paper referrals and the ability to use them at any facility contracted with the NFZ (National Health Fund — Poland’s public health insurance system). For facilities — the necessity of integrating systems with central infrastructure.

Another important step is Electronic Medical Documentation (EDM) — the obligation to maintain documentation in electronic form for all healthcare facilities. Implementation quality varies, but the legal and technical foundations are in place.

Systemic challenges:

Despite progress, many facilities struggle with outdated HIS systems designed in the pre-cloud and pre-mobile era. Monolithic, on-premise architectures that are difficult to integrate and expensive to maintain. Modernizing such a system is a multi-million investment and a multi-year project — which causes facilities to postpone the decision, deepening their technological debt.

Integration with the P1 platform is sometimes superficial — data is transmitted, but not in a way that enables full utilization. Example: a facility technically integrates with P1 but does not use the capability to retrieve documentation from other facilities because that would require changes to clinical processes and staff training.

Interoperability between facilities, especially public and private ones, remains limited. A patient having tests done at a private laboratory network while being treated at a public hospital often must physically bring printed results. The fragmentation of the HIS market — dozens of vendors, each with their own standards — deepens the problem.

According to a 2024 report from the Supreme Audit Office (NIK — Najwyzsza Izba Kontroli, Poland’s supreme audit institution), 34% of hospitals identified IT systems integration problems as the main barrier to digitalization. 28% identified insufficient funding, and 23% — the lack of qualified IT staff in the healthcare sector. This last factor is particularly painful — medical facilities compete for IT specialists with the commercial sector, often offering significantly lower salaries.

Why has telemedicine become a permanent element of healthcare?

The COVID-19 pandemic was the catalyst that within weeks forced telemedicine adoption on a scale unimaginable before 2020. But more importantly — telemedicine has remained a permanent element of the healthcare system even after the pandemic subsided. According to NFZ data, in 2025, teleconsultations accounted for 28% of all specialist outpatient consultations — compared to just 3% in 2019.

This change has profound implications for healthcare facility IT infrastructure. Systems must handle a growing volume of video sessions while maintaining clinical quality and data security. Integration with medical documentation must be seamless — a physician conducting a teleconsultation needs access to the patient’s history, test results, and previous recommendations.

Three levels of telemedicine maturity:

Basic level — video consultations. This is the simplest form of telemedicine, requiring only a video conferencing platform integrated with the medical documentation system. The patient connects with the physician via an app or browser, the physician conducts an interview, can view symptoms shown by the patient (e.g., a rash), and issues a prescription or referral electronically.

Key technical requirements: a stable and secure video platform (end-to-end encryption), integration with HIS for automatic documentation creation, patient identity verification mechanisms, availability across different devices (desktop, mobile).

Advanced level — Remote Patient Monitoring (RPM). Wearable devices and home sensors collect data on the patient’s vital parameters — blood pressure, heart rate, oxygen saturation, blood glucose, weight, physical activity. Data is transmitted to an analytics platform where algorithms identify deviations from normal and generate alerts for medical staff.

RPM is particularly applicable in chronic disease management — diabetes, heart failure, COPD, hypertension. It enables early intervention when health deteriorates, before the patient ends up in the ER. Clinical studies show a 20-40% reduction in hospitalizations in populations covered by RPM.

Technical requirements: integration of heterogeneous devices (each manufacturer uses their own protocols), IoT platform for data aggregation and processing, rules and alerts engine, dashboard for medical staff, integration with HIS and electronic documentation.

Strategic level — hybrid care model. The most advanced facilities design entire care pathways as a mix of in-person and remote visits, optimizing each element for clinical quality and efficiency.

Example for a cardiac patient: first visit in-person (physical examination, ECG, echocardiogram, calibration of RPM devices), subsequent follow-up visits — via telemedicine with analysis of data from home blood pressure monitor and smartwatch, return to in-person visit only when parameters worsen or after a specified time period.

Such a model requires not only technology but also redesigning clinical processes, staff training, and cultural change in the organization.

Telemedicine technology challenges:

• Network infrastructure — not every patient has access to stable internet, especially in rural areas. Video teleconsultations require a minimum of 2-3 Mbps upload.
• RPM device integration — the market is fragmented, there is no single communication standard, each manufacturer uses their own API.
• Scalability — a sudden surge in demand (like during COVID) can overwhelm systems unprepared for such scale.
• Cybersecurity — expanded attack surface to include patients’ home devices, often insufficiently secured.

How can you ensure medical data security in compliance with GDPR?

Medical data belongs to the category of sensitive data (special categories of personal data under Article 9 of GDPR — the EU’s General Data Protection Regulation), whose processing is subject to the strictest regulations. For CTOs in the healthcare sector, information security management is a fundamental responsibility — both ethical and legal.

A medical data breach has consequences far exceeding financial penalties. Disclosure of an oncology diagnosis, HIV status, or mental illness can destroy a patient’s life — professional relationships, family relationships, insurance relationships. Trust in the healthcare system depends on patients’ conviction that their most intimate information is safe.

Security architecture for healthcare IT systems:

Access control layer:

• Multi-factor authentication (MFA) as standard for all medical system users — especially for remote access
• Role and permission model aligned with the principle of least privilege — a doctor sees only patients from their ward, a nurse — only their patients
• Full traceability of patient data access (audit trail) — who, when, what data was viewed, for what reason
• “Break the glass” mechanism — access in emergency situations with justification requirement and notification

Data protection layer:

• Encryption of data at rest — full disk encryption, databases, backups
• Encryption of data in transit — TLS 1.3 for all connections, centrally managed certificates
• Pseudonymization and anonymization for analytical and research purposes — separation of identification data from clinical data
• Digital signatures for medical documentation (trusted profile, e-ID, qualified certificate)

Infrastructure security layer:

• Network segmentation — separation of clinical network (with medical devices), administrative, and public (Wi-Fi for patients)
• Intrusion detection and prevention systems (IDS/IPS)
• Vulnerability management — regular scanning, prioritization by risk, maintenance windows
• Backup and disaster recovery — regularly tested backup copies, RTO/RPO defined for different systems
• Endpoint protection — antivirus, EDR, mobile device management (MDM)

Special challenge — shadow IT:

Unauthorized use by medical staff of private devices and applications for processing patient data poses a serious risk. Messengers like WhatsApp used for consultations between doctors. Personal email accounts for sending test results. Unencrypted USB drives for transferring documentation. Photos of wounds taken with a personal phone and stored in the cloud.

These practices often result from frustration with impractical official systems — if the official internal messenger is slow and inconvenient, staff will reach for WhatsApp. The solution requires a combination of education, policy enforcement, and providing convenient, authorized tools.

Consequences of breaches:

GDPR provides for fines of up to 20 million euros or 4% of global turnover — for healthcare entities, the first option is usually applied. In Poland, several healthcare entities have already been penalized by UODO (the Polish Data Protection Authority) for insufficient security measures or data breaches.

Additionally, every breach requires notification to UODO within 72 hours, and in case of high risk to individuals — also notification to patients. The costs of incident handling (forensics, communication, potential legal proceedings) can far exceed the fine itself.

What are the challenges of IT systems integration in healthcare?

Systems integration is one of the most complex and underappreciated aspects of digital transformation in healthcare. A typical facility operates dozens of IT systems — from the main HIS, through laboratory systems (LIS), radiological systems (RIS/PACS), pharmacy systems, to specialized departmental systems (cardiology, oncology, dialysis) and medical devices with their own software.

Lack of integration means data silos — a physician must log into multiple systems to get a complete picture of the patient. Laboratory test results in one window, imaging in another, nursing documentation in a third. This is not just inconvenient — it is dangerous because it increases the risk of missing critical information.

Main integration challenges:

Technological heterogeneity. Systems from different vendors, based on different technologies (Java, .NET, Delphi, sometimes COBOL), implemented at different times (from the 1990s to today), with different architectural philosophies. Some have modern REST APIs, others communicate through CSV files exchanged via FTP.

Lack of true standardization. Despite the existence of healthcare communication standards (HL7, FHIR, DICOM), their implementation varies between vendors. Two systems “compliant with HL7 v2” may not be able to communicate without additional mapping because each interprets the standard differently.

Specificity of medical data. High semantic complexity — the same medical concept can be represented differently in different systems (different codes, different units, different granularity). “Blood pressure” in one system is a single value, in another — systolic and diastolic separately, in a third — a series of measurements with timestamps.

Communication standards in healthcare:

• HL7 v2.x — a standard developed in the 1980s, still widely used in legacy systems. Pipe-delimited format, difficult to parse and extend, but well-known and stable.
• HL7 CDA (Clinical Document Architecture) — structure for medical documents in XML. Used, among others, in Poland’s P1 platform for documentation exchange.
• HL7 FHIR (Fast Healthcare Interoperability Resources) — the newest standard, REST API + JSON/XML, modular, easy to implement. Dominant for new implementations, increasingly required by regulators (e.g., in the USA).
• DICOM (Digital Imaging and Communications in Medicine) — standard for medical imaging. Covers not only file format but the entire communication protocol.
• IHE (Integrating the Healthcare Enterprise) — integration profiles defining how to use the above standards in specific clinical scenarios.

Practical aspects of P1 platform integration:

The Centre for e-Health provides extensive technical documentation and a test environment for integration with the P1 platform. However, in practice, implementing integration takes significantly more time than the documentation alone suggests.

Typical problems:

• Inconsistencies between documentation and actual API behavior
• Changes introduced without adequate transition periods
• Performance limits with a large number of queries
• Complex certification and acceptance processes

Recommendation for CIOs: plan a 50-100% time buffer for unforeseen integration problems. Consider working with your HIS vendor or an integrator with documented experience in P1 integration.

What are the key criteria for selecting an HIS system?

The HIS (Hospital Information System) or its ambulatory equivalent is the central element of a medical facility’s IT infrastructure. The decision to select or replace an HIS affects the organization’s functioning for years — the average HIS lifecycle is 10-15 years. The wrong choice means years of struggling with an unsuitable tool or a costly premature replacement.

Functional criteria:

• Functional completeness — does the system handle all facility processes without “workarounds” and additional tools? NFZ settlements, medical documentation, orders, hospital pharmacy, operating block, emergency department, diagnostics.
• Ergonomics for clinical users — is the interface intuitive for physicians and nurses who have only seconds to interact between patients? The number of clicks to perform a typical operation matters.
• Support for terminological standards — ICD-10, ICD-11, SNOMED CT, LOINC — without these, data analysis and interoperability will be limited.
• Mobility — does the system work on tablets, or only on stationary computers? In many clinical scenarios, mobility is crucial.

Technical criteria:

• Architecture — monolithic vs. modular, on-premise vs. cloud, single-tenant vs. multi-tenant. Each approach has its trade-offs.
• Integration capabilities — API, supported standards (HL7, FHIR), documentation availability, sandbox for developers.
• Performance and scalability — will the system maintain responsiveness as the number of users and data grows?
• Security — certifications (ISO 27001), permissions model, encryption, audit trail.

Business criteria:

• Licensing model — one-time purchase vs. subscription, per-user vs. per-bed, what is included in the price and what is extra.
• Vendor’s financial condition — will the company exist in 10 years? Is it investing in product development?
• Partner ecosystem — are there independent integrators who can implement and develop the system?
• References — facilities with similar profiles that have successfully implemented the system.

Strategic criteria:

• Development roadmap — what are the vendor’s plans for the next 3-5 years? Is it investing in AI, telemedicine, interoperability?
• Alignment with regulatory direction — is the vendor keeping up with Centre for e-Health requirements, European regulations (EHDS)?
• Data migration capability — can you export data if you ever change systems, and how?

Selection process:

The recommended HIS selection process includes: (1) detailed requirements analysis with all user groups, (2) RFI to the broad market, (3) shortlist of 3-5 vendors, (4) demos on real clinical scenarios, (5) reference visits to working implementations, (6) negotiations and POC, (7) decision and contract.

The entire process takes 6-12 months. Attempts to shorten it usually result in a wrong decision or a contract unfavorable to the facility.

How do you build a digital transformation roadmap for a medical facility?

Digital transformation is a multi-year undertaking requiring strategic planning and phased implementation. Trying to do everything at once ends in chaos, budget overruns, and frustration. An effective roadmap divides the transformation into phases with clear deliverables and decision points.

Phase	Horizon	Main initiatives	Estimated budget (300-500 bed hospital)
Foundations	0-12 mo.	IT audit, cybersecurity baseline, full P1 integration, stabilization of current systems	EUR 0.5-1M
Consolidation	12-24 mo.	HIS modernization/replacement, departmental systems integration, basic analytics platform	EUR 2-3.5M
Optimization	24-36 mo.	Advanced telemedicine, process automation (RPA), staff mobility, patient portal	EUR 1-2M
Innovation	36-48 mo.	AI in diagnostic imaging, IoT and patient monitoring, advanced analytics, early EHDS	EUR 1.2-2.5M
Transformation	48+ mo.	Data-driven care model, treatment personalization, full interoperability, genomics	Ongoing investment

Foundations phase — cleaning house:

Before we start building new things, we need to organize what exists. An IT audit includes inventory of all systems, assessment of technical condition, identification of technological debt and risks. Result: system map, gap analysis report, prioritization.

Cybersecurity baseline is the minimum that must function before any expansion — because every new system is a new attack surface. MFA, working and tested backup, basic monitoring, security policies.

P1 integration in this phase means full, not symbolic integration — sending and receiving documentation, insurance verification, e-prescriptions, e-referrals.

Consolidation phase — building the foundation:

This is the most difficult and costly phase. If the existing HIS does not meet requirements, replacement is a 12-24 month project and EUR 2-3.5M for a medium-sized hospital. Alternatively — modernization (if the vendor offers an upgrade path) or integration wrapper (if the core HIS is stable but modules are missing).

Departmental systems integration means connecting all silos into a coherent whole. Goal: the physician sees everything in one place without switching between systems.

The analytics platform is a data warehouse collecting information from all sources, enabling operational and management reporting. Not fancy AI — just the ability to answer the question “how many ER admissions did we have last month.”

Optimization phase — building value:

With a solid foundation, we can build added value. Telemedicine beyond basic video — RPM, hybrid care pathways, integration with patient devices.

RPA (Robotic Process Automation) for administrative processes — automatic data entry from forms, report generation, handling repetitive workflows.

Staff mobility — physicians and nurses with tablets at the patient’s bedside, system access from anywhere.

Innovation and Transformation phases — building competitive advantage:

AI in diagnostic imaging as a “second observer” supporting radiologists. IoT and advanced patient monitoring. Predictive analytics — anticipating deterioration, optimizing patient flow.

These phases require maturity of earlier elements. Attempting to implement AI on uncleaned data will end in failure.

Key success factors:

• Strong sponsorship from management and the board — transformation requires decisions and resources at the strategic level
• Engaging clinicians as co-authors of changes — a system designed without physicians will be sabotaged by physicians
• Realistic planning with buffer for the unexpected — in healthcare, everything takes longer than anticipated
• Comprehensive change management — technology is 30% of success, the rest is people and processes
• Business continuity — transformation cannot disrupt patient care; a hospital cannot “shut down”

What role does artificial intelligence play in diagnostics and treatment?

Artificial intelligence is no longer a futuristic vision — it has become a tool with proven effectiveness in selected clinical applications. For CTOs in healthcare, the question is no longer “whether AI?” but “which AI applications make sense today, and which in 3-5 years?”

Diagnostic imaging — the most mature area:

Deep learning algorithms, particularly convolutional neural networks (CNNs), have achieved human expert-level accuracy in many tasks:

• Detection of pulmonary nodules on CT scans
• Identification of suspicious changes in mammography
• Diabetic retinopathy screening on fundus photographs
• Skin lesion analysis (dermoscopy)
• Classification of changes in histopathological specimens

The key to success is positioning AI as a “second observer,” not a radiologist replacement. Clinical studies show that the human-AI tandem achieves better results (higher sensitivity and specificity) than either alone. AI catches subtle changes that humans might miss with high volume; humans verify and correct AI errors, especially in atypical cases.

In Poland, several AI solutions for diagnostic imaging have already obtained certification as medical devices and are being used clinically — for example, systems supporting chest X-ray or mammography diagnostics.

Clinical Decision Support systems (CDS):

Modern CDS systems go beyond simple drug interaction alerts. They analyze all patient data and provide contextual recommendations:

• Differential diagnosis suggestions based on symptoms and test results
• Diagnostic test suggestions aligned with guidelines
• Dosing recommendations considering kidney function, age, interactions
• Reminders about screening tests and vaccinations
• Alerts about patients at risk of sepsis or deterioration

Predictive analytics:

Machine learning models trained on historical data identify patients at high risk of health deterioration, hospitalization, or readmission. This enables proactive intervention — for example, calling a chronically ill patient before expected decompensation.

Application examples: prediction of 30-day readmission, hospitalized patient fall risk, probability of pharmacotherapy non-compliance.

Requirements for successful AI implementation:

1. Clinical validation — an algorithm trained on American data may not work on the Polish population. Local validation is necessary.
2. Workflow integration — AI must be available in the clinician’s natural workflow without additional logging in and system switching.
3. Transparency — the physician must understand why AI is suggesting a given decision. A “black box” does not build trust.
4. Drift monitoring — AI models degrade over time as the population or clinical practice changes. Continuous performance monitoring is necessary.
5. Regulatory compliance — AI solutions for diagnostics are medical devices subject to MDR; AI supporting decisions may be subject to the AI Act.

How do you measure ROI on healthcare digitalization investments?

Business justification for digital transformation investments requires a rigorous approach to measuring return on investment. In the healthcare sector, this is particularly complex because many benefits are not directly financially measurable — better quality of care, patient safety, staff satisfaction.

Hard benefits (directly financially measurable):

• Reduction of operating costs — automation of administrative processes, paper elimination, patient flow optimization
• Revenue increase — higher throughput (more patients with the same resources), better coding and NFZ settlements (fewer underestimates), new services (telemedicine)
• Loss reduction — fewer medical errors (costly compensation), avoidance of regulatory penalties (GDPR, NFZ audits)

Soft benefits (measurable, but not directly financially):

• Care quality improvement — better treatment outcomes, shorter hospitalization time, fewer complications
• Staff efficiency — less time on documentation, more time with patients; higher job satisfaction, lower turnover
• Patient safety — fewer adverse events, faster detection of deterioration
• Patient satisfaction — shorter waiting times, better communication, access to own data

Example ROI calculation for modern HIS implementation in a 400-bed hospital:

Category	Value (5 years)
Costs
Licenses and implementation	EUR 2.8M
Infrastructure	EUR 1M
Maintenance (5 years)	EUR 1.4M
Total TCO	EUR 5.2M
Benefits
Administrative cost reduction (FTE, paper)	EUR 1.9M
Improved NFZ settlements (better coding)	EUR 2.8M
Reduction in medical errors and claims	EUR 1M
Resource optimization (beds, OR)	EUR 1M
Total benefits	EUR 6.7M
ROI	29%
Payback period	~3.5 years

Note: this is a simplified calculation for illustration. Actual analysis requires detailed facility data and context-specific assumptions.

Metrics to track:

• Documentation time per episode — how much time does a clinician spend on documentation
• Time from admission to first clinical decision
• Percentage of e-documentation vs. paper
• Data availability at admission (% of patients with accessible history)
• Number of IT-related adverse events (system unavailability, incorrect data)
• Patient and staff NPS

What are the most common mistakes in healthcare digital transformation projects?

Through years of working with medical facilities, we have identified recurring patterns of failure. Here are the most common mistakes and how to avoid them:

Mistake 1: Underestimating complexity.

A hospital’s digital transformation is not an IT project — it is a fundamental change in how the organization functions, affecting every process and every employee. Treating it as “installing new software” leads to failure.

How to avoid: Treat transformation as an organizational program with a sponsor at the director level. Dedicated PMO, involvement of all departments, comprehensive change management.

Mistake 2: Lack of clinician involvement from the start.

Systems designed by IT without end-user participation are unintuitive, do not address real clinical needs, and generate resistance. Medical staff will find ways around an inconvenient system — to the detriment of data quality and security.

How to avoid: The project team must include physicians and nurses. Prototypes and MVPs tested with users before full implementation. Continuous feedback collection and iteration.

Mistake 3: Inadequate data management.

Migrating “dirty” data to a new system transfers problems, it does not solve them. Patient duplicates, incorrect diagnosis codes, inconsistent formats — all of this migrates and worsens in the new environment.

How to avoid: Data governance as the foundation of transformation. Data cleaning and standardization BEFORE migration. Defined Master Data Management. Data quality responsibility in the organizational structure.

Mistake 4: Underestimating budget and timeline.

Planning optimism is a plague of IT projects, but in healthcare it is particularly dangerous — because systems must operate 24/7 and migration windows are limited.

How to avoid: Plan with a 25-40% buffer for budget and 40-60% for timeline. Assume integration problems, vendor delays, slower adoption than expected.

Mistake 5: Neglecting business continuity.

A hospital cannot “shut down” for a weekend for migration. Patients require 24/7 care, and every minute of system unavailability is a risk to health and life.

How to avoid: Detailed business continuity plan for each phase. Fallback procedures (how do we operate when the system is down?). Migration testing in a pre-production environment. Phased migration, not “big bang.”

Mistake 6: Lack of maintenance and development strategy.

System launch is the beginning, not the end. Without dedicated resources for maintenance, user support, and continuous development — the system degrades, users become frustrated, and technological debt grows.

How to avoid: Include maintenance costs in TCO from the start. Build or contract competencies for long-term support. Plan regular reviews and updates.

How do you prepare the organization and staff for digital change?

Technology is a necessary but insufficient condition for digital transformation. Research indicates that 70% of transformation projects do not achieve their goals — and in most cases, the reason is not technical issues but human and organizational ones.

Pillars of change management in healthcare:

Communication:

Staff must understand why change is necessary and what benefits it will bring — both for patients and for themselves. Key message: transformation is meant to help provide better care, not to replace staff with technology or add to their workload.

Communication must be continuous, multi-channel (meetings, intranet, posters, newsletters) and bidirectional — collecting questions and concerns, answering them transparently. Silence breeds rumors and resistance.

Training:

A training program tailored to different user groups and levels of digital competency. A physician with 30 years of experience needs a different approach than a young digital native resident.

Practical training on real clinical scenarios, not abstract “click here, then there.” Available in various formats (in-person, e-learning, video materials) for different learning styles and time availability.

Training is not a one-time event before implementation — it is an ongoing process with refreshers, training on new features, and support for new employees.

Super-users:

A network of “champions” or “super-users” in each department — people who have undergone in-depth training and can support colleagues on-site. The first line of help before the formal service desk.

Super-users are not just technical support — they are change ambassadors who can convince skeptical colleagues and gather feedback from the field.

User support:

Service desk prepared for a significant increase in tickets during implementation and the first months after. 24/7 availability (the hospital operates around the clock). Escalation for critical issues.

Clear SLAs — users must know what response and resolution times they can expect.

Engaging clinical leaders:

Department heads, clinic managers, head nurses are key influencers. Their support or resistance determines the attitude of entire teams.

Engage leaders as co-authors of changes, not just recipients. Give them the opportunity to influence the shape of the system and the implementation process. Their “ownership” will translate to their teams’ ownership.

Measuring and iterating:

Regular assessment of adoption, satisfaction, and problems. Using data for optimization — if a function is underused, maybe the problem is in UX or training, not in the users.

Celebrating successes — showing specific cases where the new system helped with patient care, saved time, or prevented an error.

What is the future of digital healthcare in Poland and Europe?

Healthcare digital transformation is a process that will continue for decades. Several trends define the direction of this evolution:

European Health Data Space (EHDS):

An EU regulatory proposal aiming to create a unified European health data space. Main elements:

• Patient’s right to access their own health data in electronic form, across the EU
• Right to transfer data between facilities and countries
• Framework for secondary use of health data for research, innovation, and health policy

For Polish facilities, this means the need for further investment in standardization (FHIR, SNOMED CT) and interoperability. Systems must be ready for data exchange with facilities in other EU countries.

AI expansion:

AI will permeate an increasing number of areas — not just diagnostic imaging, but also digital pathology, genomics, therapeutic decision support, documentation automation (speech-to-text, summarization), and patient-facing chatbots.

Key challenges: transparency and explainability of algorithms, legal responsibility for AI-assisted decisions, continuous validation and monitoring, compliance with the AI Act.

Personalized medicine and genomics:

Growing availability and declining costs of genome sequencing enable therapies tailored to the patient’s genetic profile — especially in oncology, but also in pharmacotherapy of other diseases.

IT systems must be ready for storing, processing, and integrating genomic data — this means gigabytes of data per patient, requiring specialized infrastructure and tools.

Internet of Medical Things (IoMT):

An explosion of network-connected medical devices — from implantable devices (pacemakers, insulin pumps) through wearables (smartwatches, fitness bands) to home sensors (scales, blood pressure monitors, glucometers).

Challenges: interoperability of hundreds of different devices, cybersecurity (implant as attack target), alert management (how to avoid “alarm fatigue”), integration with medical documentation.

Augmented and virtual reality:

AR/VR finds applications in medical staff training (procedure simulations), surgical planning (3D visualization of patient anatomy), rehabilitation (exercise gamification), and therapy for mental disorders (VR exposure therapy).

For IT, this means the need to support new types of devices and content, integration with clinical systems, and ensuring quality and security.

How does ARDURA Consulting support digital transformation in healthcare?

At ARDURA Consulting, we understand that digital transformation in healthcare requires combining deep technological knowledge with understanding of the healthcare sector’s specifics — regulations, clinical processes, and the organizational dynamics of medical facilities.

Our team includes specialists with experience both in delivering advanced IT projects and in working with medical facilities. This combination allows us to deliver solutions that not only work technically but are also accepted by clinical users and compliant with regulatory requirements.

Areas of our support:

• Digitalization strategy and roadmap — from auditing current state, through defining vision and goals, to a detailed implementation plan with budgets and timeline.
• System selection and implementation — support in the process of selecting HIS, LIS, RIS/PACS and other systems. Project management for implementation. Systems integration.
• P1 platform integration — experience in integrating with Poland’s central e-health infrastructure, overcoming typical technical and procedural problems.
• Telemedicine and RPM — designing and implementing telemedicine solutions, from basic video consultations to advanced patient monitoring.
• Cybersecurity — security audits, implementing controls compliant with GDPR and industry best practices.
• Staff Augmentation — supplementing healthcare facility IT teams with specialists experienced in healthcare: developers, architects, analysts, project managers.

Why ARDURA:

• Healthcare sector experience — we understand the specifics of medical facilities
• Access to a pool of IT talent with healthcare technology competencies (HL7, FHIR, DICOM)
• Flexible cooperation model — from strategic consulting through fixed-price projects to Staff Augmentation
• Long-term partnership — not a one-time project, but support throughout the transformation journey

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Digital transformation in healthcare is not a single project but a continuous journey. Facilities that treat it as a strategic priority will build lasting advantage — better patient care, higher efficiency, and readiness for future challenges.

If you face the challenge of digital transformation in your medical facility, we invite you to contact us. We will conduct a free consultation and propose optimal courses of action tailored to your context and capabilities.

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About the author: Lukasz Szymanski is COO of ARDURA Consulting with a versatile educational background combining IT with financial management. He specializes in IT process optimization, DevOps, and digital transformations for the enterprise sector. He has years of experience working with clients in the financial and healthcare sectors.