Top HealthTech Software Development Trends, Challenges & Best Practices in 2025

The intersection of technology and healthcare, aka HealthTech, has come a long way in the past couple of decades.

Last year, more than 1.3 billion individuals engaged with digital health technologies. These include fitness trackers and smartwatches, virtual doctor consultations, and many other forms of digital treatment and care. The market value of digital health reached approximately $172 billion in 2024, and there is an expectation of continued expansion.

And now, in 2025, more tech innovations, such as artificial intelligence (AI), big data, and blockchain, are transforming the delivery and management of healthcare services.

In this guide, we'll comprehensively explore the state of HealthTech in 2025. We'll dive into the major trends shaping HealthTech software development, explore some challenges, and outline some best practices so you can set superior standards for success.

Major Trends in HealthTech Software Development

Let’s start by looking into some key trends driving HealthTech software development.

AI and Machine Learning

Just over a couple of years ago, when ChatGPT entered the scene, AI became more than a buzzword—from a thing of the future into the here and now.

Since then, tools like ChatGPT have become mainstream. And new AI tools aimed at optimizing efficiency across industries and organizational departments are popping up every day.

Healthcare has been no exception.

In a nutshell, AI is now a pivotal piece of tech in:

• Making healthcare more efficient by automating tasks.
• Analyzing huge amounts of patient and business data for insights.
• Supporting decision-making processes that lead to improvements in patient care.

AI systems can provide surgeons with real-time data to predict patient risks, personalize treatment plans, and even assist in complex surgeries.

AI algorithms are getting really good at spotting diseases from X-rays and MRIs faster and more accurately than ever. For instance, the Mayo Clinic uses AI to scan images, and it's making a massive difference in spotting health issues quickly and correctly.

In terms of patient management, AI tools help track patient progress, manage chronic conditions, and optimize emergency treatments based on real-time data.

AI is helping cut down healthcare costs by optimizing resource allocation, reducing wasteful expenses, and enhancing patient scheduling.

Telemedicine and Remote Monitoring

The demand for digital health services, especially telemedicine, surged notably during the COVID-19 pandemic when traditional in-person consultations were less accessible.

The pandemic was the catalyst for the expansion of telemedicine.

As health systems were overwhelmed and non-emergency visits became a risk, healthcare providers rapidly adopted virtual care models to continue treating patients. This shift wasn't just a temporary adjustment but a total transformation that permanently altered healthcare delivery.

Telemedicine remains a preferred option due to its convenience, accessibility, and ability to eliminate travel.

Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) is an interconnected ecosystem of medical devices like wearables, mobile apps, and health systems and services. These devices collect, analyze, and transmit health data, offering incredible levels of real-time patient care.

IoMT also greatly enhances diagnostic precision and aids in tailoring treatment strategies. By consistently collecting data from various sources like wearable heart monitors and glucose sensors, these systems can detect irregularities that might be missed during infrequent hospital visits.

Wearables like fitness bands and glucose monitors send important health data straight to patient records. This helps doctors diagnose more accurately, create personalized treatment plans, and manage health proactively.

For example, if a wearable notices something off with your vitals, it can quickly alert both you and your doctor, allowing for fast action.

Blockchain for Health Data Security

Like AI and IoMT, blockchain isn’t a new kid on the block, either.

By design, blockchain creates decentralized and immutable records of all transactions. So, once patient data is recorded, it cannot be altered or deleted.

In other words, it creates secure, unchangeable records that no one can mess with. This helps prevent unauthorized access or data tampering.

For healthcare providers, this enables enhanced tracking of data integrity across different stages of patient care, from diagnosis to treatment and follow-up. It eliminates the fear of data being altered maliciously.

This boosts patient trust, too. They gain more control over their data. And they can access their health records securely and provide permissions for specific data use.

Big Data and Analytics

Healthcare data is vast. This means it is prime for applying analytics to dig up patterns, predict trends, and make systemic improvements.

Modern healthcare professionals use big data to personalize treatments and improve efficiency.

Predictive models in healthcare can spot patients who might face chronic diseases or need hospital readmission. This early insight lets doctors step in sooner with preventive care or tailored treatments.

Hospitals also use data to streamline their use of resources, such as reducing wait times and better scheduling patients. This makes healthcare more efficient and satisfying for patients.

Augmented Reality and Virtual Reality

AR and VR aren’t just for fun and games.

AR gives surgeons real-time, 3D visual reconstructions during operations. It enhances precision by showing parts of the anatomy usually hidden from the naked eye.

Virtual Reality (VR) is proving to be a powerful tool in pain management and the treatment of mental health conditions like Post-Traumatic Stress Disorder (PTSD).

It helps distract patients from pain during treatments. It also helps create a safe space for exposure therapy, allowing them to confront and manage their fears more effectively.

In mental health, VR facilitates exposure therapy in a controlled setting, allowing patients to face and work through their fears in a manageable way.

AR and VR also enable better medical education by providing students and professionals with immersive training experiences. Creating realistic simulations of medical scenarios allows learners to practice their skills without risks to real patients.

For example, AR can overlay virtual conditions onto mannequins, providing students with interactive and dynamic learning experiences.

Then, there’s the potential of AR in patient engagement and rehabilitation. AR can motivate patients during physical therapy by incorporating gamified elements into rehabilitation exercises. It can provide real-time feedback on their performance and progress.

Key Challenges in HealthTech Software Development

With great amounts of data and stringency come great challenges in HealthTech software development.

Regulatory Compliance & Data Privacy

Staying compliant with health laws like HIPAA in the U.S. and GDPR in Europe is no small feat. These regulations demand strict data privacy to protect patient information.

For instance, HIPAA sets standards for protecting sensitive patient information. It requires healthcare providers and their business associates to implement physical, network, and process security measures.

Similarly, GDPR imposes strict rules on data processing and grants patients significant control over their personal data. It emphasizes the need for consent and transparency in handling health information.

Cybersecurity & Data Protection Risks

There’s sensitive information, there’s highly sensitive information, and then there’s healthcare data.

Safeguarding it, particularly patient records, is absolutely critical for healthcare organizations.

The healthcare sector is a prime target for cyberattacks.

In fact, a staggering 92% of healthcare organizations reported experiencing a cyberattack in 2024, with the average cost of the most damaging attacks reaching as high as $4.7 million.

With patient records fetching high prices on the black market, fortifying defenses against ransomware, phishing, and other cyber threats is crucial.

To defend against these dark arts, healthcare institutions must implement strong encryption methods to safeguard data at rest and in transit.

Authentication measures are equally important.

Multi-factor authentication (MFA) provides an additional layer of security by requiring multiple forms of verification before granting user access. This could include something the user knows (a password), something the user has (a security token), and something the user is (biometric verification).

Additionally, real-time monitoring of network activities can help catch and counteract threats before they cause harm.

Rigorous access controls and audit trails can help prevent unauthorized access and tampering. Comprehensive security policies that define who can access what data and under what circumstances can also be invaluable.

Finally, regular security training for all healthcare employees helps minimize risks. Well-informed staff are less likely to fall prey to phishing attacks or other common cyber threats.

Integration & Interoperability Challenges

Many hospitals and clinics operate on legacy systems that utilize proprietary Electronic Health Records (EHR) formats, such as those provided by Epic, Cerner, and other vendors.

Old, clunky systems can make data sharing between healthcare providers a headache. This restricts the ability to provide coordinated care.

Moving to more modern solutions that support standards like FHIR or HL7, which help different systems work together, is essential but complex.

FHIR provides a framework for exchanging, integrating, sharing, and retrieving electronic health information. It is designed to make healthcare information available, discoverable, and understandable globally.

Similarly, HL7 facilitates the exchange of information between medical applications.

However, achieving seamless interoperability remains a challenge.

While FHIR and HL7 aim to standardize data formats and simplify data exchange, the widespread adoption necessary to make a substantial impact is still ongoing.

Balancing Functionality with Usability

HealthTech software must be powerful enough to handle complex medical data and simple enough for everyone to use.

Striking this balance is key.

This balance is crucial because software that is too complicated can deter use. Whereas overly simplified software may not meet all clinical and compliance requirements.

So, the challenge lies in designing systems that provide all the necessary functionalities without overwhelming the users.

On top of that, poor UX in healthcare software can have dire consequences, including medical errors, healthcare delivery inefficiencies, and a general user reluctance to adopt new tech.

For example, if an EHR system is not user-friendly, it might lead to incorrect data entry or inadequate access to vital patient information. This could compromise patient care.

When software is user-friendly, it encourages its adoption by everyone, from senior doctors to administrative staff. This means that the technology's benefits are fully realized.

So, in HealthTech software development, engaging with end-users early in the software development life cycle is essential to ensure the final product meets clinical needs.

Scalability & Performance Issues

Healthcare applications deal with large volumes of real-time patient data, medical images, and records. This demand puts a premium on the scalability of healthcare IT systems.

Scalability ensures that as the volume of data grows, the system can expand without losing performance.

Cloud computing offers scalable resources and infrastructure that can adjust dynamically to the needs of healthcare providers.

Reliability is another critical aspect of healthcare IT systems, especially for telemedicine, remote monitoring, and emergency response applications. These systems must operate smoothly under all conditions since they are often used in critical care situations.

For instance, remote monitoring systems for patients with chronic conditions must function reliably to continuously track vital signs like heart rate and blood glucose levels.

Evolving Technologies & Industry Standards

AI, IoMT, blockchain, cloud computing—all those buzzwords we discussed earlier are transforming healthcare delivery. They’re making it more efficient, secure, and patient-centric.

However, they present a challenge for developers: future-proofing their software.

Future-proofing involves designing software systems that are flexible enough to integrate new technologies without the need for complete overhauls.

For instance, AI and machine learning algorithms are continuously improving. This requires building healthcare systems that can accommodate newer, more advanced models.

Likewise, IoMT devices are proliferating, which means systems must be capable of handling an increasingly complex network of connected devices.

Not just tech—industry standards are constantly changing, too.

Standards such as FHIR and HL7 are essential for ensuring that systems can communicate with one another effectively. As these standards evolve, the entire suite of interconnected healthcare software systems must be updated to remain compliant and secure.

High Development & Maintenance Costs

Compliance, security, and reliability are all critical elements of HealthTech software development. And they come at a cost.

Regulations like HIPAA, GDPR, and others require ongoing attention and investment to meet legal standards and protect patient data effectively.

Security measures, especially in a targeted sector like healthcare, involve sophisticated solutions to protect data from breaches. These solutions are expensive to implement.

As such, HealthTech systems require continuous updates to address emerging security vulnerabilities and adapt to regulatory changes. Each update can entail a good chunk of change associated with the development, testing, and deployment phases.

The cycle of continuous improvement and adherence to high compliance and security standards is a heavy financial burden on healthcare organizations.

Yet, these investments are almost a necessary evil for delivering safe, reliable, and effective healthcare services.

Resistance to Digital Transformation

People typically don’t like change. Healthcare professionals accustomed to traditional workflows and practices often resist adopting new HealthTech solutions.

This resistance often stems from comfort with established routines and skepticism about the reliability or effectiveness of digital technologies.

For example, old practitioners with decades of experience may hesitate to count on digital systems. They may fear that the systems will fail or complicate their daily tasks rather than simplify them.

Introducing AI tools and digital systems requires thorough training to ensure everyone is comfortable using them. This training can take a lot of time and resources and is often not a top priority.

Still, healthcare organizations must invest in effective training programs. They should create easy-to-understand modules, offer continuous support, and clearly show how these new tools benefit users.

Best Practices for HealthTech Software Development

Clearly, HealthTech software development is riddled with challenges.

If you work in this space, the following best practices can help you navigate these intricacies more easily.

Emphasizing Ethical AI

Ethics in AI means creating systems that comply with legal standards and operate within moral boundaries.

Particularly in healthcare, AI can inadvertently perpetuate or even exacerbate existing biases if not carefully managed.

For instance, if an AI system is trained on data that lacks diversity, its outputs may be less accurate for underrepresented groups. This can lead to disparities in the quality of care delivered to different populations.

To combat this, developers must ensure that the datasets they use for training AI are diverse and representative of the entire population they serve.

HealthTech AI applications should ideally also be able to explain their decision-making processes. This transparency builds trust with healthcare providers and patients. It ensures they are aware of how and why specific recommendations are made.

Here are some best practices for implementing ethical AI in HealthTech:

• Diverse Data Sets: Use diverse and comprehensive data sets to train AI and ensure its effectiveness across different demographics.
• Bias Testing: Regularly test AI systems for biases and take corrective measures if any are found.
• Transparency: Develop transparent AI systems about their functionality and decision-making processes. This could involve using techniques from explainable AI (XAI) that provide insights into the AI’s operations and decisions.
• Diverse Stakeholder Engagement: Involve many stakeholders, including ethicists, community representatives, and potential patients, in the development process.
• Continuous Monitoring and Evaluation: Set up mechanisms for ongoing monitoring and evaluation of AI systems to ensure they operate ethically over time.

Prioritizing Regulatory Compliance & Security

A huge priority for healthcare companies is abiding by rules and regulations such as HIPAA, HITRUST, etc.

Encryption, authentication, security policies, and real-time network monitoring are all essential cybersecurity elements that we discussed. Beyond those, deploying firewalls, intrusion detection systems, and anti-malware software to protect against external attacks is also necessary.

Mandatory regular security audits are essential for ongoing compliance. These reviews all aspects of a Health Tech system, from the physical security of servers to the effectiveness of cybersecurity protocols.

Staying compliant protects patients. It safeguards healthcare providers from legal and financial penalties for data breaches and non-compliance.

Seamless Interoperability & Integration

Any healthcare software cannot work in a silo. It needs to bring in data from various sources, such as EHRs, IoMT devices, and third-party health systems.

This means HealthTech software developers must keep integration and interoperability top-of-mind when beginning a project. It cannot be an afterthought.

For this, as we touched upon earlier, using industry standards such as Fast Healthcare Interoperability Resources (FHIR) and Health Level Seven International (HL7) is imperative.

Developers can ensure that different systems can interoperate effectively by building software that adheres to these standards.

For example, when EHRs and IoMT devices use the same communication standards, data from IoT devices can be directly fed into EHR systems without requiring manual entry. This reduces errors and saves time.

User-Centric Design & Accessibility

Legacy healthcare software is usually ugly and unintuitive.

Sure, they may do the job just fine. But the lousy interface, which takes a while to understand, hurts efficiency and can even impact patient care.

So, the user interface (UI) and user experience (UX) of any modern HealthTech software matter big time.

The clarity of information can directly improve patient outcomes.

The goal of user-centric design in healthcare technology is to make software tools accessible and straightforward. Any tool must minimize the learning curve and reduce the potential for errors.

For healthcare providers, this means designing interfaces that fit seamlessly into their workflow. Interfaces that allow them to access patient data, input information, and utilize decision-support tools quickly and effectively.

For patients, it involves creating applications that are easy to navigate and understand. Apps that enable them to check their health data, manage appointments, and communicate with their healthcare providers without technical snags.

In addition to focusing on intuitive design, HealthTech software must comply with the Web Content Accessibility Guidelines (WCAG).

Compliance with these guidelines ensures the software is accessible to all users, including those with disabilities. This requires incorporating features such as screen reader compatibility, keyboard navigation, and text-to-speech functionality.

Cloud-Based Healthcare Solutions & Scalability

Cloud computing isn’t novel, but it has a lot going for it. Cloud-based healthcare solutions offer excellent scalability and remote access without compromising security.

They facilitate remote monitoring and telemedicine. Cloud accessibility improves the continuity of care by allowing doctors to make informed decisions, even when not physically present at a healthcare facility.

Cloud-based solutions efficiently accommodate varying data loads and patient interactions. They can adapt quickly to changes, such as sudden increases in telehealth visits. Healthcare organizations can easily scale their IT resources up or down as needed.

Furthermore, cloud computing's cost efficiency comes from its subscription-based, pay-as-you-go pricing models. These allow healthcare organizations to pay only for the resources they use.

AI-Powered Predictive Analytics & Big Data Utilization

By far the biggest trend (that we opened with) in software development and tech in general, AI is enabling superior healthcare software capabilities in various ways.

First, AI can analyze large volumes of data to identify patterns that human analysts might overlook.

Predictive analytics in healthcare leverage this capability to forecast disease progression, identify at-risk patients, and anticipate healthcare outcomes.

By processing and analyzing medical images, patient records, and genetic information, AI can detect signs of diseases such as cancer, cardiovascular diseases, and neurological disorders much earlier than traditional methods.

Early detection can immensely increase the success rate of treatments and reduce the cost of care over time.

Then, AI also drives operational efficiency in healthcare settings.

For example, AI systems can analyze patterns in appointment bookings to optimize schedules and reduce no-shows. They can predict peak times for patient admissions to manage staffing needs better.

Additionally, AI-driven tools can automate repetitive tasks such as data entry, allowing medical staff to focus more on patient care rather than administrative duties.

Here are a few best practices for implementing AI-powered predictive analytics and big data in HealthTech development:

• Develop Predictive Models: Incorporate machine learning algorithms to develop predictive models that can analyze historical data and predict future outcomes. Focus on identifying at-risk patients and forecasting disease progression to allow for early interventions.
• Validate and Refine Models: Regularly validate the accuracy of your predictive models against new data and refine them as needed to improve their predictive power. Ensure your models are trained on diverse and representative datasets to minimize bias.
• Enhance Diagnostic Tools: Implement machine learning algorithms that can analyze medical images, genetic information, and patient histories to detect early signs of diseases.
• Continuous Learning and Updating: Set up systems for continuous learning where your AI tools regularly update their diagnostic algorithms based on the latest medical research and clinical data.

And, of course, ensure any AI software development or integration project is free from biases (that is, ethical, as talked about in the first best practice).

What is the Future of AI in Healthcare?

Let’s quickly recap what the future holds in the landscape of HealthTech:

• AI-Powered Personalized Healthcare: AI-driven diagnostics and precision medicine are becoming more prevalent, allowing for more tailored treatment plans based on an individual’s genetic makeup, lifestyle, and past health records.
• Smart Hospitals & Automation: IoT-enabled smart hospitals represent the next step in healthcare evolution, where everything from patient monitoring to asset management is interconnected.
• Blockchain for Secure Health Records: By creating decentralized and tamper-proof records, blockchain enables the secure sharing of patient data. Smart contracts could automate insurance claims, reducing processing times and preventing fraudulent claims.

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• AR & VR in Healthcare: AR-assisted surgeries will provide surgeons with real-time data and imaging, enhancing precision during operations. VR is set to expand its role in mental health therapy and in training medical professionals, offering realistic simulations and therapeutic environments that improve training and treatment outcomes.
• Digital Therapeutics (DTx) & AI in Drug Discovery: AI accelerates drug development by identifying potential drug candidates more quickly and accurately. Digital therapeutics, often integrated with wearable technologies, support the continuous care and management of chronic conditions outside traditional healthcare settings.
• Global Interoperability Standards: The adoption of FHIR and HL7 is crucial for the seamless exchange of health data across borders. These standards will facilitate better data integration and sharing between different healthcare systems worldwide, enhancing care for patients moving across geographical areas.

Overall, the integration of AI and other technologies into HealthTech is shaping a future where healthcare is more efficient, personalized, and secure.

Looking to stay ahead of the curve in HealthTech 2025? Check out 10 virtual healthcare conferences worth attending to hear from industry leaders

How SoluteLabs Can Help You With Healthcare Software Development?

At SoluteLabs, we specialize in crafting secure, efficient, and innovative healthcare software solutions tailored to healthcare providers, administrators, and startups' unique needs.

With deep expertise in HealthTech, we help organizations navigate the industry's complexities, ensuring compliance, scalability, and seamless user experiences.

Why Choose SoluteLabs for Healthcare Software Development?

Here are three key reasons why we are at the forefront of HealthTech software development:

• Compliance & Security First: We place a high priority on compliance and security. We understand the importance of adhering to HIPAA, GDPR, and other regulatory standards. Our commitment ensures that your software meets all industry requirements for data protection and privacy.
• Interoperability & Integration Expertise: Our solutions integrate seamlessly with existing EHRs, IoMT devices, and third-party health systems. This expertise enhances operational efficiency and facilitates smooth data exchange, making your healthcare services more coordinated and efficient.
• User-Centric Design: We believe in the power of building intuitive and engaging healthcare applications to enhance the experiences of both patients and providers. Our user-centric design approach focuses on creating software that is not only functional but also easy to use. This means lower learning curves, better adoption rates, and, ultimately, higher overall quality of care provided.

By partnering with SoluteLabs, you gain an experienced team adept at navigating the technical and regulatory challenges of the healthcare industry.

Our Healthcare Software Development Services

We offer a comprehensive range of HealthTech software development services to meet the specific needs of the healthcare industry:

• Custom Healthcare Software Development: We specialize in creating bespoke healthcare software solutions tailored to each client's unique requirements. Whether you need a system for patient management, data analysis, or any other specific function, our team has the expertise to develop software that meets your precise needs.
• Legacy System Modernization: Many healthcare providers operate on outdated systems that hinder their efficiency and compliance. At SoluteLabs, we offer legacy system modernization services to help healthcare organizations upgrade their old systems to modern platforms with enhanced capabilities.
• Quality Assurance & Compliance Testing: Ensuring your software is reliable and compliant with industry regulations is paramount. We provide comprehensive quality assurance and compliance testing services so your healthcare software meets all regulatory requirements and operates without glitches.
• Maintenance & Ongoing Support: To ensure your healthcare software continues to perform optimally over time, ongoing maintenance and support are crucial. SoluteLabs offers continuous maintenance and support services to handle any issues post-deployment. This includes regular updates, bug fixes, and system enhancements.
• Advanced Technology Integration: Integrating the latest advancements into your existing systems can greatly enhance your capabilities. We help integrate advanced technologies such as AI, blockchain, and IoMT into your healthcare systems, improving service delivery and operational efficiency.

By choosing SoluteLabs for your healthcare software development needs, you partner with a team of experts committed to delivering high-quality, compliant, and cutting-edge solutions that make your healthcare organization future-ready.

Redefining Healthcare Through Innovation

AI, IoMT, AR, VR—these acronyms fundamentally reshape how healthcare services will be delivered, experienced, and managed.

Integrating these innovative technologies will enhance healthcare operations' efficiency, improve diagnostics and treatment accuracy, and personalize patient care to a whole new level.

SoluteLabs is spearheading this transformative journey for various healthcare organizations. With our deep expertise in developing secure, compliant, and user-centric software solutions, we are well-equipped to help healthcare companies and providers navigate the complexities of modern HealthTech.

Learn more about how we can help you with HealthTech product discovery, UX Strategy, design, engineering, integration, modernization, maintenance, and more. Schedule a free, no-obligation meeting with our team today!