Survey insights: 8 ways lab leaders can improve digital health solution adoption

• Stronger evidence builds trust that AI-enabled tools can truly aid clinicians 
• Digital workflows can boost efficiency without compromising quality
• Investing in continuous workforce development across key competencies is essential for enabling digital health adoption

In the rapidly evolving healthcare and diagnostics landscape, digital health adoption has become a strategic imperative for laboratories. Tools such as advanced analytics and artificial intelligence (AI)-enabled diagnostics accelerate turnaround times (TAT), improve accuracy, and support more personalized care, elevating both patient care and operational efficiency. Adoption isn’t merely a tech upgrade—it represents a shift in how labs create and deliver value. Leaders who embrace this transformation position their organizations at the forefront of quality, innovation, and competitive advantage. Realizing these benefits, however, requires overcoming key challenges and proceeding with a clear strategy for success.^1,2

Drawing on recent survey responses from laboratory and healthcare leaders, we map the real-world bottlenecks that stall digital health adoption—from interoperability hurdles and limited evidence of value to funding constraints and skills gaps.³

Here, we translate those findings into eight actionable strategies. Use this framework as a practical checklist to guide your lab’s priorities and accelerate digital health adoption in the next quarter.

How are lab leaders worldwide approaching digital adoption in healthcare? To answer this, a global survey was conducted in 2022, targeting professionals at the forefront of laboratory innovation. The survey gathered responses from 144 healthcare providers and executives across 52 countries, including lab directors, clinical managers, and C-suite leaders.³

Participants shared the main challenges they encounter when implementing digital health solutions and the types of evidence they rely on when evaluating new tools. This broad international perspective provided a comprehensive view of digital health adoption in labs around the world.

The diversity of job roles also offered a 360-degree perspective, from strategic decision-making at the executive level to operational insights on the lab floor. The survey results highlighted common hurdles while revealing a clear consensus on the information and support decision-makers need to confidently invest in digital solutions.

A global survey of 144 healthcare providers and executives conducted in 2022 uncovered several systemic challenges hindering digital adoption in healthcare laboratories. Interoperability tops the list: integrating new tools with existing laboratory information systems (LIS) and electronic health records (EHR) can be complex. Many labs are addressing this by prioritizing open standards and working with technology partners who design solutions with interoperability in mind.

Budget constraints follow closely, as funding digital initiatives is difficult amid competing priorities and tight healthcare budgets. Faced with this environment, successful labs are demonstrating return on investment early, by starting with pilot projects that show measurable efficiency gains.³

Leaders also cite data privacy and security risks when connecting devices and sharing data, a shortage of informatics talent with insufficient IT/data-science capacity to lead projects and the challenge of selecting solutions in a crowded vendor landscape.³

These barriers mirror broader healthcare trends. Large systems have faced similar struggles; for example, the National Health Service in the UK historically had a “poor track record” scaling digital technologies.^4,5

These insights show that while challenges exist, they are far from insurmountable. By taking a strategic and collaborative approach, lab leaders can turn these obstacles into building blocks for sustainable, scalable digital adoption that enhances both operations and care quality.

Successfully implementing a new digital tool requires more than budget approval or technical readiness; it depends on convincing evidence that the solution delivers measurable value. The 2022 survey explored the types of evidence lab leaders consider most compelling when evaluating digital health solutions and identified five categories, each addressing a critical dimension of a digital solution’s impact.³ Clinical value and patient safety emerged as the two highest-rated factors overall:³

• Clinical value: Real-world data or pilot studies showing meaningful benefits such as faster diagnosis or higher accuracy.
• Patient safety: Evidence (such as clinical trials or validation studies) that the tool performs at least as safely as current practices while minimizing unintended risks.
• User experience: Usability testing or adoption feedback confirming that both staff and patients can use the solution with minimal burden.
• Operational impact: Metrics showing workflow gains such as throughput, error reduction, shorter TAT, and time savings through system integration or automation.
• Financial return on investment (ROI): Health economic or business case analyses  demonstrating cost-effectiveness, downstream savings, and sustainable returns.

Overall, a digital solution must demonstrate it can improve care or maintain quality while boosting efficiency and safety. When supported by strong evidence, digital innovation earns both clinical and organizational trust.³

While many digital health tools have reached the market with limited validation, the landscape is rapidly improving. Historically, a lack of strong clinical trial data or regulatory documentation,  often referred to as low “clinical robustness”, made laboratory leaders cautious. After all, no organization wants to test unproven tech on patients, so adoption stalls.⁴

Encouragingly, the gap is narrowing. Developers are increasingly using clinical simulation, which employs realistic synthetic cases to generate performance data more quickly and cost-effectively. Regulators are refining approval pathways, and payers are introducing reimbursement models that reward proven digital therapeutics and diagnostics. Together, these advances define what “good evidence” looks like and incentivize solutions that show value.⁶

Stronger evidence builds trust that AI-enabled tools truly aid clinicians and that digital workflows boost efficiency without compromising quality. Global health organizations, including the World Health Organization (WHO), echo this, prioritizing the translation of research into action and improving data interoperability to support informed decision-making.   It is clear that closing the evidence gap is pivotal to accelerating confident, routine digital health adoption in everyday lab practice.^6,7

The survey distilled eight practical moves to turn evidence into impact—each targeting a different stage of digital health adoption. Below are the eight recommended actions and how you can implement them.

Choose tools with credible proof: clinical validation, peer-reviewed studies, pilot outcomes, and solid case studies. Require vendors to clearly show both clinical and economic value, such as measurable improvements in accuracy or TAT gains. Taking an evidence-first approach eases stakeholder buy-in and keeps adoption focused on measurable outcomes rather than marketing claims. This aligns with WHO’s recommendation to translate data and research into practical, real-world applications.⁷

Favor platforms that support modular upgrades, configurable workflows, and straightforward integration of new features. Interoperable, standards-based architectures prevent vendor lock-in and minimize the risk of future legacy constraints. In practice, prioritize LIS and data layers that plug easily into your EHR today while accommodating new instruments or data sources tomorrow, with minimal rework.^6,8

Start by inventorying your current tech and talent to identify hidden strengths. Activate underused analytics or integration functions already available in your LIS or middleware. Promote tech-savvy staff to “digital champions”, leading change from within. Extend existing systems with modular additions such as inventory management or workflow optimization tools, and use business intelligence to mine current device data for insights. These quick wins create momentum and help manage budgets while maintaining progress toward larger digital goals.^6,8

Implement recognized standards such as the Fast Healthcare Interoparability Resources (FHIR) developed by Health Level Seven International (HL7) for data exchange and Logical Observation Identifiers Names and Codes (LOINC) for laboratory terminology to ensure systems “speak” the same language. Standardization directly tackles interoperability, lowers integration cost, and future-proofs investments. Using common vocabularies also simplifies regulatory alignment and external data exchange (such as multi-center research or public-health reporting).^9,10,11

Foster secure, governed data sharing across departments and, where appropriate, with external partners. Integrated dashboards help clinicians access the right lab data at the right time. De-identified datasets can also be shared for research, AI model development, and performance benchmarking, advancing both science and service quality. With strong privacy and cybersecurity controls, the clinical, research, and operational benefits of responsible data sharing outweigh the risks.⁹

Ensure networks, computing power, storage, and security can carry digital workloads. Modernize endpoints where needed, and leverage cloud platforms for scalable storage, computing, and resilient backup or recovery. For low-latency instruments, consider edge computing to process information closer to the source. Equally important is cybersecurity: strengthen protections through encryption, firewalls, and intrusion detection systems (IDS). A sturdy backbone turns pilots into reliable operations.^10,12

Create a multidisciplinary digital steering group that includes representatives from the lab, IT, clinical operations, and privacy or safety teams. This group should set the overall digital strategy, prioritize projects, allocate resources, and monitor outcomes. Formalize evaluation and rollout criteria, such as pilot checklists and success metrics, to measure progress and maintain accountability. Align governance with institutional and national digital policies and data protection regulations to manage risk and sustain momentum.⁷

Invest in continuous workforce development across key competencies such as digital literacy, analytics, software proficiency, change management, and cybersecurity awareness. Establish super-user programs that empower experienced staff to mentor peers, and emphasize digital aptitude in recruitment. As healthcare grows increasingly data-driven, coordinated digital-skills development ensures technology is used to its fullest potential. Empowered teams turn “promising tech” into routine performance.^6,8

Digital health adoption can be challenging, but it’s achievable when approached strategically. Tackle the big blockers with a balanced focus on technology, evidence, and people. Use clinical evidence and safety data to build the case for change. Modern infrastructure and shared standards make adoption feasible, while skilled teams and firm governance make it last. This isn’t a tech swap; it’s an operational reset that delivers faster workflows, fewer errors, better care, and new capabilities. Done right, digital innovation makes health systems more efficient, connected, and equitable.^6,8

Use the survey’s insights as your strategic playbook. Prioritize interoperability, proven clinical value, and workforce readiness to break through early friction and secure real wins. The stakes are high: digital-ready labs will absorb rising demand, plug into personalized medicine, and power a data-driven healthcare future—future-proofing their institutions and accelerating global innovation.