Database Modernization Services

What is HIPAA-compliant Database?  A database is an organized collection of structured information controlled by a database management system. To be HIPAA-compliant, the database must follow administrative, physical, and technical safeguards of the HIPAA Security Rule. Often it means limiting access to PHI, as well as safely processing, transmitting, receiving, and encrypting data, plus having a proactively breach mitigation strategy. Administrative, physical, and technical safeguards of the HIPAA Security Rule HIPAA Rules for Database Security If your database contains even a part of PHI, it is covered by the HIPAA Act of 1996 and can attract the attention of auditors. PHI is the information containing any identifiers that link an individual to their health status, the healthcare services they have received, or their payment for healthcare services. The HIPAA Security Rule (the part of HIPAA Act) specifically focuses on protecting electronic PHI. Technical safeguards (the part of HIPAA Security Rule) contain requirements for creating a HIPAA-compliant database. Centers for Medicare & Medicaid Services (CMS) covers HIPAA Technical Safeguards for database security in their guidance. The first question that can arise is whether you should use any specific database management system to address the requirements? The answer is absolutely no. The Security Rule is based on the concept of technology neutrality. Therefore, no specific requirements for types of technology are identified. Businesses can determine themselves which technologies are reasonable and appropriate to use. There are many technical security tools, products, and solutions that a company may select. However, the guidance warns that despite the fact that some solutions may be costly, it can’t be the cause of not implementing security measures. "Required" (R) specifications are mandatory measures. "Addressable" (A) specifications may not be implemented if neither the standard measure nor any reasonable alternatives are deemed appropriate (this decision must be well-documented and justified based on the risk assessment). Here are the mandatory and addressable requirements for a HIPAA-compliant database. Mandatory HIPAA Database Security Requirements HIPAA Compliant Database Access Control Database authentication. Verify that a person looking for access to ePHI is the one claimed. Database authorization. Restrict access to PHI according to different roles ensuring that no data or information is made available or disclosed to unauthorized persons. Encrypted PHI PHI must be encrypted both when it is being stored and during transit to ensure that a malicious party cannot access information directly. Unique User IDs You need to distinguish one individual user from another followed by the ability to trace activities performed by each individual within the ePHI database.  Database security logging and monitoring All usage queries and access to PHI must be logged and saved in a separate infrastructure to archive for at least six years.  Database backups Must be created, tested, and securely stored in a separate infrastructure, as well as properly encrypted.  Patching and updating database management software Regular software upgrades, as soon as they are available, to ensure that it’s running the latest tech. ePHI disposal capability Methods of deleting ePHI by trained specialists without the ability to recover it should be implemented. By following the above requirements you create a HIPAA-compliant database. However, it’s not enough. All HIPAA-compliant databases must be settled in a high-security infrastructure (for example, cloud hosting) that itself should be fully HIPAA-compliant. HIPAA-Compliant Database Hosting You need HIPAA-compliant hosting if you want either to store ePHI databases using services of hosting providers, or/and to provide access to such databases from the outside of your organization. Organizations can use cloud services to store or process ePHI, according to U.S. Department of Health & Human Services. HIPAA compliant or HIPAA compliance supported? Most of the time, cloud hosting providers are not HIPAA compliant by default but support HIPAA compliance, which means incorporating all the necessary safeguards to ensure HIPAA requirements can be satisfied. If healthcare business wants to start collaborating with a cloud hosting provider, they have to enter into a contract called a Business Associate Agreement (BAA) to enable a shared security responsibility model, which means that the hosting provider takes some HIPAA responsibility, but not all.  deloitte.com/content/dam/Deloitte/us/Documents/risk/us-hipaa-compliance-in-the-aws-cloud.pdf In other words, it is possible to utilize HIPAA compliance supported services and not be HIPAA compliant. Vendors provide tools to implement HIPAA requirements, but organizations must ensure that they have properly set up technical controls - it's their responsibility only. Cloud misconfigurations can cause an organization to be non-compliant with HIPAA. So, healthcare organizations must: be ensured that the ePHI is encrypted during transit, in use, and at rest; enable data backup and disaster recovery plan to create and maintain retrievable exact copies of ePHI, including secure authorization and authentication  even during times where emergency access to ePHI is needed; implement authentication and authorization mechanisms to protect ePHI from being altered or destroyed in an unauthorized manner as well as include procedures for creating, changing, and safeguarding passwords; implement procedures to monitor log-in attempts and report discrepancies; conduct assessments of potential risks and vulnerabilities to the confidentiality, integrity, and availability of ePHI; include auditing capabilities for their database applications so that security specialists can analyze activity logs to discover what data was accessed, who had access, from what IP address, etc. In other words, one needs to track, log, and store data in special locations for extended periods of time. PaaS/DBaaS vs IaaS Database Hosting Solutions Healthcare organizations may use their own on-premise HIPAA-compliant database management solutions or utilize cloud hosting services (sometimes with managed database services) offered by external hosting providers.  Selecting between different hosting options is often selecting between PaaS/DBaaS and IaaS.  For example, Amazon Web Services (AWS) provides Amazon Relational Database Services (Amazon RDS) that not only gives you access to already cloud-deployed MySQL, MariaDB, PostgreSQL, Oracle, Microsoft SQL Server or Amazon Aurora relational database management software, but also removes almost all administration tasks (so-called PaaS/DBaaS solution). In turn, Amazon's Elastic Compute Cloud (Amazon EC2) services are for those who want to control as much as possible with their database management in the cloud (so-called IaaS solution).  on-Premise vs PaaS/DBaaS vs IaaS Database Hosting Solution PaaS/DBaaS vs IaaS Database Hosting Solution Azure also provides relational database services that are the equivalent of Amazon RDS: Azure SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL, and Azure Database for MariaDB. Other database engines such as SQL Server, Oracle, and MySQL can be deployed using Azure VM Instances (Amazon EC2 equivalent in Azure). Our company is specializing in database development and creates databases for large and smaller amounts of data storage. Belitsoft’s experts will help you prepare a high-level cloud development and cloud migration plan and then perform smooth and professional migration of legacy infrastructure to Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. We also employ experts in delivering easy to manage HIPAA-compliant solutions and technology services for medical businesses of all sizes. Contact us if you would like to get a HIPAA risk assessment and analysis.

Why Financial Institutions Migrate Data Legacy systems are dragging them down Most migrations start because something old is now a blocker. Aging infrastructure no one wants to maintain, systems only one person understands (who just resigned), workarounds piled on top of workarounds. Eventually, the cost of not migrating becomes high. Compliance doesn’t wait New regulations show up, and old systems cannot cope. GDPR, SOX, PCI, local data residency rules. New audit requirements needing better lineage, access logs, encryption. If your platform cannot prove control, migration becomes the only way to stay in business. M&A forces the issue When banks merge or acquire, they inherit conflicting data structures, duplicate records, fragmented customer views. The only path forward is consolidation. You cannot serve a unified business on mismatched backends. Customer expectations got ahead of tech Customers want mobile-first services, real-time transactions and personalized insights. Legacy systems can’t provide that. They weren’t designed to talk to mobile apps, stream real-time data, or support ML-powered anything.  Analytics and AI hit a wall You can’t do real analytics if your data is trapped in ten different systems, full of gaps and duplicates, updated nightly via broken ETL jobs. Modern data platforms solve this. Migrations aim to centralize, clean, and connect data. Cost pressure from the board Everyone says “cloud saves money.” That’s only half true. If you’re running old on-premises systems with physical data centers, licenses, no elasticity or automation …then yes, the CFO sees migration as a way to cut spending. However, smart teams don’t migrate for savings alone. They migrate to stop paying for dysfunction. Business wants agility. IT can’t deliver When the business says “launch a new product next quarter,” and IT says “that will take 8 months because of system X,” migration becomes a strategy conversation. Cloud-native platforms, modern APIs, and scalable infrastructure are enablers. But you can’t bolt them onto a fossil. Core system upgrades that can’t wait anymore This is the “we’ve waited long enough” scenario. A core banking system that can’t scale. A data warehouse from 2007. A finance platform with no support. It’s not a transformation project. It’s triage. You migrate because staying put means stagnation, or worse, failure, during a critical event. We combine automated tools and manual checks to find hidden risks early before they become problems through a discovery process, whether you’re consolidating systems or moving to the cloud. Database Migration Strategy Start by figuring out what you really have Inventory is what prevents a disaster later. Every system, every scheduled job, every API hook: it all needs to be accounted for. Yes, tools like Alation, Collibra, and Apache Atlas can speed it up, but they only show what is visible. The real blockers are always the things nobody flagged: Excel files with live connections, undocumented views, or internal tools with hard-coded credentials. Discovery is slow, but skipping it just means fixing production issues after cutover. Clean the data before you move it Bad data will survive the migration if you let it. Deduplication, classification, and data profiling must be done before the first trial run. Use whatever makes sense: Data Ladder, Spirion, Varonis. The tooling is not the hard part. The problem is always legacy data that does not fit the new model. Data that was fine when written is now inconsistent, partial, or unstructured. You cannot automate around that. You clean it, or you carry it forward. Make a real call on the strategy — not just the label Do not pick a migration method because a vendor recommends it. Big Bang works, but only if rollback is clean and the system is small enough that a short outage is acceptable. It fails hard if surprises show up mid-cutover. Phased is safer in complex environments where dependencies are well-mapped and rollout can be controlled. It adds overhead, but gives room to validate after each stage. Parallel (or pilot) makes sense when confidence is low and validation is a high-priority. You run both systems in sync and check results before switching over. It is resource-heavy, you are doubling effort temporarily, but it removes guesswork. Hybrid is a middle ground. Not always a cop-out, it can be deliberate, like migrating reference data first, then transactions. But it requires real planning, not just optimism. Incremental (trickle) migration is useful when zero downtime is required. You move data continuously in small pieces, with live sync. This works, but adds complexity around consistency, cutover logic, and dual writes. It only makes sense if the timeline is long. Strategy should reflect risk, not ambition. Moving a data warehouse is not the same as migrating a trading system. Choose based on what happens when something fails. Pilot migrations only matter if they are uncomfortable Run a subset through the full stack. Use masked data if needed, but match production volume. Break the process early. Most failures do not come from the bulk load. They come from data mismatches, dropped fields, schema conflicts, or edge cases the dev team did not flag. Pilot migrations are there to surface those, not to "prove readiness." The runbook is a plan, not a document If people are confused during execution, the runbook fails. It should say who does what, when, and what happens if it fails. All experts emphasize execution structure: defined rollback triggers, reconciliation scripts, hour-by-hour steps with timing buffers, a plan B that someone has actually tested. Do not rely on project managers to fill in gaps mid-flight. That is how migrations end up in the postmortem deck. Validation is part of the job, not the cleanup If you are validating data after the system goes live, you are already late. The validation logic must be scripted, repeatable, and integrated, not just “spot checked” by QA. This includes row counts, hashing, field-by-field matching, downstream application testing, and business-side confirmation that outputs are still trusted. Regression testing is the only way to tell if you broke something. Tools are fine, but they are not a strategy Yes, use DMS, Azure Data Factory, Informatica, Google DMS, SchemaSpy, etc. Just do not mistake that for planning. All of these tools fail quietly when misconfigured. They help only if the underlying migration plan is already clear, especially around transformation rules, sequence logic, and rollback strategy. The more you automate, the more you need to trust that your input logic is correct. Keep security and governance running in parallel Security is not post-migration cleanup. It is active throughout. Access must be scoped to migration-only roles PII must be masked in all non-prod runs Logging must be persistent and immutable Compliance checkpoints must be scheduled, not reactive Data lineage must be maintained, especially during partial cutovers This is not a regulatory overhead. These controls prevent downstream chaos when audit, finance, or support teams find data inconsistencies. Post-cutover is when you find what you missed No matter how well you planned, something will break under load: indexes will need tuning, latency will spike, some data will have landed wrong, even with validation in place, reconciliations will fail in edge cases and users will see mismatches between systems. You need active monitoring and fast intervention windows. That includes support coverage, open escalation channels, and pre-approved rollback windows for post-live fixes. Compliance, Risk, and Security During Migration Data migrations in finance are high-risk by default. Regulations do not pause during system changes. If a dataset is mishandled, access is left open, records go missing, the legal and financial exposure is immediate. Morgan Stanley was fined after failing to wipe disks post-migration. TSB’s failed core migration led to outages, regulatory fines, and a permanent hit to customer trust. Security and compliance are not post-migration concerns. They must be integrated from the first planning session. Regulatory pressure is increasing The EU’s DORA regulation, SEC cyber disclosure rules, and ongoing updates to GDPR, SOX, and PCI DSS raise the bar for how data is secured and governed.  Financial institutions are expected to show not just intent, but proof: encryption in transit and at rest, access logs, audit trails, and evidence that sensitive data was never exposed, even in testing. Tools like Data Ladder, Spirion, and Varonis track PII, verify addresses, and ensure that only necessary data is moved. Dynamic masking is expected when production data is copied into lower environments. Logging must be immutable. Governance must be embedded. Strategy choice directly affects your exposure The reason phased, parallel, or incremental migrations are used in finance has nothing to do with personal preference — it is about control. These strategies buy you space to validate, recover, and prove compliance while the system is still under supervision. Parallel systems let you check both outputs in real time. You see immediately if transactional records or balances do not match, and you have time to fix it before going live. Incremental migrations, with near-real-time sync, give you the option to monitor how well data moves, how consistently it lands, and how safely it can be cut over — without needing full downtime or heavy rollback. The point is not convenience. It is audit coverage. It is SLA protection. It is a legal defense. How you migrate determines how exposed you are to regulators, to customers, and to your own legal team when something goes wrong, and the logs get pulled. Security applies before, during, and after the move Data is not less sensitive just because it is moving. Testing environments are not immune to audit. Encryption is not optional — and access controls do not get a break. This means: Everything in transit is encrypted (TLS minimum) Storage must use strong encryption (AES-256 or equivalent) Access must be restricted by role, time-limited, logged, and reviewed Temporary credentials are created for migration phases only Any non-production environment gets masked data, not copies Belitsoft builds these controls into the migration path from the beginning — not as hardening after the fact. Access is scoped. Data is verified. Transfers are validated using hashes. There is no blind copy-and-paste between systems. Every step is logged and reversible. The principle is simple: do not treat migration data any differently than production data. It will not matter to regulators that it was “temporary” if it was also exposed. Rely on Belitsoft’s database migration engineers and data governance specialists to embed security, compliance, and auditability into every phase of your migration. We ensure your data remains protected, your operations stay uninterrupted, and your migration meets the highest regulatory standards. Reconciliation is the compliance checkpoint Regulators do not care that the migration was technically successful. They care whether the balances match, the records are complete, and nothing was lost or altered without explanation. Multiple sources emphasize the importance of field-level reconciliation, automated validation scripts, and audit-ready reports. During a multi-billion-record migration, your system should generate hundreds of real-time reconciliation reports. The mismatch rate should be in the double digits, not thousands, to prove that validation is baked into the process. Downtime and fallback are also compliance concerns Compliance includes operational continuity. If the system goes down during migration, customer access, trading, or payment flows can be interrupted. That triggers not just customer complaints, but SLA penalties, reputational risk, and regulator involvement. Several strategies are used to mitigate this: Maintaining parallel systems as fallback Scheduling cutovers during off-hours with tested recovery plans Keeping old systems in read-only mode post-cutover Practicing rollback in staging Governance must be present, not implied Regulators expect to see governance in action, not in policy, but in tooling and workflow: Data lineage tracking Governance workflows for approvals and overrides Real-time alerting for access anomalies Escalation paths for risk events Governance is not a separate track, it is built into the migration execution. Data migration teams do this as standard. Internal teams must match that discipline if they want to avoid regulatory scrutiny. No margin for “close enough” In financial migrations, there is no tolerance for partial compliance. You either maintained data integrity, access control, and legal retention, or you failed. Many case studies highlight the same elements: Drill for failure before go-live Reconcile at every step, not just at the end Encrypt everything, including backups and intermediate outputs Mask what you copy Log everything, then check the logs Anything less than that leaves a gap that regulators, or customers, will eventually notice. Database Migration Tools There is no single toolset for financial data migration. The stack shifts based on the systems involved, the state of the data, and how well the organization understands its own environment. Everyone wants a "platform" — what you get is a mix of open-source utilities, cloud-native services, vendor add-ons, and custom scripts taped together by the people who have to make it work. Discovery starts with catalogs” Cataloging platforms like Alation, Collibra, and Apache Atlas help at the front. They give you visibility into data lineage, orphaned flows, and systems nobody thought were still running. But they’re only as good as what is registered. In every real migration, someone finds an undocumented Excel macro feeding critical reports. The tools help, but discovery still requires manual effort, especially when legacy platforms are undocumented. API surfaces get mapped separately. Teams usually rely on Postman or internal tools to enumerate endpoints, check integrations, and verify that contract mismatches won’t blow up downstream. If APIs are involved in the migration path, especially during partial cutovers or phased releases, this mapping happens early and gets reviewed constantly. Cleansing and preparation are where tools start to diverge” You do not run a full migration without profiling. Tools like Data Ladder, Spirion, and Varonis get used to identify PII, address inconsistencies, run deduplication, and flag records that need review. These aren’t perfect: large datasets often require custom scripts or sampling to avoid performance issues. But the tooling gives structure to the cleansing phase, especially in regulated environments. If address verification or compliance flags are required, vendors like Data Ladder plug in early, especially in client record migrations where retention rules, formatting, or legal territories come into play. Most of the transformation logic ends up in NiFi, scripts, or something internal For format conversion and flow orchestration, Apache NiFi shows up often. It is used to move data across formats, route loads, and transform intermediate values. It is flexible enough to support hybrid environments, and visible enough to track where jobs break. SchemaSpy is commonly used during analysis because most legacy databases do not have clean schema documentation. You need visibility into field names, relationships, and data types before you can map anything. SchemaSpy gives you just enough to start tracing, but most of the logic still comes from someone familiar with the actual application. ETL tools show up once the mapping is complete. At this point, the tools depend on environment: AWS DMS, Google Cloud DMS, and Azure Data Factory get used in cloud-first migrations.AWS Schema Conversion Tool (SCT) helps when moving from Oracle or SQL Server to something modern and open. On-prem, SSIS still hangs around, especially when the dev team is already invested in it. In custom environments, SQL scripts do most of the heavy lifting — especially for field-level reconciliation and row-by-row validation. The tooling is functional, but it’s always tuned by hand. Governance tooling Platforms like Atlan promote unified control planes: metadata, access control, policy enforcement, all in one place. In theory, they give you a single view of governance. In practice, most companies have to bolt it on during migration, not before. That’s where the idea of a metadata lake house shows up: a consolidated view of lineage, transformations, and access rules. It is useful, especially in complex environments, but only works if maintained. Gartner’s guidance around embedded automation (for tagging, quality rules, and access controls) shows up in some projects, but not most. You can automate governance, but someone still has to define what that means. Migration engines Migration engines control ETL flows, validate datasets, and give a dashboard view for real-time status and reconciliation. That kind of tooling matters when you are moving billions of rows under audit conditions. AWS DMS and SCT show up more frequently in vendor-neutral projects, not because they are better, but because they support continuous replication, schema conversion, and zero-downtime scenarios. Google Cloud DMS and Azure Data Factory offer the same thing, just tied to their respective platforms. If real-time sync is required, in trickle or parallel strategies, then Change Data Capture tooling is added. Some use database-native CDC. Others build their own with Kafka, Debezium, or internal pipelines. Most validation is scripted. Most reconciliation is manual Even in well-funded migrations, reconciliation rarely comes from off-the-shelf tools. Companies use hash checks, row counts, and custom SQL joins to verify that data landed correctly. In some cases, database migration companies build hundreds of reconciliation reports to validate a billion-record migration. No generic tool gives you that level of coverage out of the box. Database migration vendors use internal frameworks. Their platforms support full validation and reconciliation tracking and their case studies cite reduced manual effort. Their approach is clearly script-heavy, format-flexible (CSV, XML, direct DB), and aimed at minimizing downtime.  The rest of the stack is coordination, not execution. During cutover, you are using Teams, Slack, Jira, Google Docs, and RAID logs in a shared folder. The runbook sits in Confluence or SharePoint. Monitoring dashboards are built on Prometheus, Datadog, or whatever the organization already uses.  What a Serious Database Migration Vendor Brings (If They’re Worth Paying) They ask the ugly questions upfront Before anyone moves a byte, they ask, What breaks if this fails? Who owns the schema? Which downstream systems are undocumented? Do you actually know where all your PII is? A real vendor runs a substance check first. If someone starts the engagement with “don’t worry, we’ve done this before,” you’re already in danger. They design the process around risk, not speed You’re not migrating a blog. You’re moving financial records, customer identities, and possibly compliance exposure. A real firm will: Propose phased migration options, not a heroic “big bang” timeline Recommend dual-run validation where it matters Build rollback plans that actually work Push for pre-migration rehearsal, not just “test in staging and pray” They don’t promise zero downtime. They promise known risks with planned controls. They own the ETL, schema mapping, and data validation logic Real migration firms write: Custom ETL scripts for edge cases (because tools alone never cover 100%) Schema adapters when the target system doesn’t match the source Data validation logic — checksums, record counts, field-level audits They will not assume your data is clean. They will find and tell you when it’s not — and they’ll tell you what that means downstream. They build the runbooks, playbooks, and sanity checks This includes: What to do if latency spikes mid-transfer What to monitor during cutover How to trace a single transaction if someone can’t find it post-migration A go/no-go checklist the night before switch The good ones build a real migration ops guide, not a pretty deck with arrows and logos, but a document people use at 2AM. They deal with vendors, tools, and infrastructure, so you don’t have to They don’t just say “we’ll use AWS DMS.” They provision it, configure it, test it, monitor it, and throw it away clean. If your organization is multi-cloud or has compliance constraints (data residency, encryption keys, etc.), they don’t guess; they pull the policies and build around them. They talk to your compliance team like adults Real vendors know: What GDPR, SOX, PCI actually require How to write access logs that hold up in an audit How to handle staging data without breaking laws How to prepare regulator notification packets if needed They bring technical project managers who can speak of “risk”, not just “schema.” So, What You’re Really Hiring You’re not hiring engineers to move data. You’re hiring process maturity, disaster recovery modeling, DevOps with guardrails and legal fluency. With 20+ years of database development and modernization expertise, Belitsoft owns the full technical execution of your migration—from building custom ETL pipelines to validating every transformation across formats and platforms. Contact our experts to get a secure transition, uninterrupted operations, and a future-proof data foundation aligned with the highest regulatory standards.

Sectors Driving Modernization in 2025 Healthcare Providers (Hospitals & Health Systems) Modernization backlog in the U.S. hospitals has been growing for more than a decade under the weight of legacy EHRs, disconnected workflows, and documentation systems that force clinicians to copy-paste. Most hospitals replace core infrastructure before building anything new. That means EHR migrations, ERP consolidations, and cloud-hosted backend upgrades to scale across facilities. The Veterans Health Administration is the most public example - now deploying Oracle Health across 13 new sites with the goal of creating a unified record that spans different departments. Similar moves play out quietly inside regional systems that have been running unsupported software since the Obama era. Clinician-facing modernization, however, is where momentum is most welcome. At Ohio State’s Wexner Medical Center, 100 physicians piloted Microsoft’s DAX Copilot and gained back 64 hours from documentation duties. That’s literal time restored to patient care, without hiring anyone new. And it’s exactly the kind of small-scope, high-impact win that other systems are now copying. Children’s National Hospital is going broader, experimenting with generative AI to reshape how providers interact with clinical data by reducing search. Modernization used to mean cost. Now it means capacity. Digital tools are being deployed where FTEs are short, where burnout spike, and where attrition has already created blind spots in workflows. And that’s why boards are green lighting infrastructure projects that would have been stuck in committee five years ago.  The barrier, in most cases, is coherence. Hospitals know they need to modernize, but don’t always know where to start or how to sequence. Teams want automation, but they’re still duct-taping reports together from five systems that don’t talk. That’s where most providers are stuck in 2025: trapped between urgency and fragmentation. The systems that are breaking through are mapping out modernization in terms of what actually improves the patient and staff experience: real-time BI dashboards instead of retrospective reports, mobile-first scheduling tools that sync with HR systems, ambient listening that captures the record without forcing clinicians to become transcriptionists. Belitsoft’s healthcare software experts modernize legacy systems, simplify processes, and implement clinician-facing tools that reduce friction in care delivery. We help providers align modernization with clinical priorities, supporting everything from building custom EHR systems to healthcare BI and ambient documentation. Health Insurance Payers (Health Plans) In 2025, health plans replace brittle adjudication systems with cloud-native core platforms built around modular, API-first design.  They pursue more narrow networks, value-based care contracts, and hybrid offerings like telehealth-plus-pharmacy bundles. Legacy systems were never designed to track those parameters, let alone price them dynamically or support real-time provider feedback loops. That’s why firms like HealthEdge and their integration partners are getting traction — for enabling automation, and for embedding claims payment integrity and fraud detection directly into the workflow. In 2025, that’s the move: shift from audit-and-chase to real-time correction. Not post-event fraud analytics - preemptive denial logic, powered by AI. Member experience modernization is the other front. Health plans can’t afford to lose members over clunky app experiences, slow pre-auth workflows, or incomplete provider directories.  Payers are investing in: API-integrated portals that allow self-service claims and virtual ID cards Telehealth services, especially for behavioral health, built into benefit design Real-time benefits lookups, connected directly to provider systems Omnichannel engagement platforms that consolidate outreach, alerts, and support They’re expectations. And insurers that delay will watch their NPS scores erode — along with their employer group contracts. Regulatory pressure is also reshaping the agenda. Payer executives now list security and compliance as top risks in any tech upgrade. Only a third of them feel confident they’re ready for incoming regulatory changes That means modernization isn’t just a technology lift. New systems are being evaluated based on: Audit-readiness Data governance visibility API traceability Identity and access control fidelity Integration with CMS-mandated interoperability endpoints Pharmaceutical & Life Sciences Companies In 2025 most large life sciences companies have finally accepted what startups realized years ago: you can’t do AI-powered anything on top of fragmented clinical systems. Top-20 pharma companies are actively overhauling their clinical development infrastructure - migrating off the siloed, custom-coded platforms that once made sense in a regional, paper-heavy world, but now slow everything from trial design to regulatory submissions. According to McKinsey, nearly half of big pharma has invested heavily in modernizing their clinical development stack. That number is still growing. The pain points driving this shift are familiar: trial startup timelines that drag on for quarters, data systems that can’t integrate real-world evidence, and analytics teams forced to export CSVs just to compare outcomes across geographies. That’s a strategic bottleneck. Modernized platforms are solving it. Companies that have replaced legacy CTMS and EDC tools with integrated cloud systems are reporting 15–20% faster site selection and up to 30% shorter trial durations - just from clean workflow automation and real-time visibility across sites.  Modernizing clinical trial systems opens the door to better ways of running studies. Adjusting them as they go, letting people join from anywhere, predicting how trials will play out, or using AI to design the trial plan. All of that sounds like the future, but none of it works on legacy platforms. The AI can’t model if your data is spread across four systems, six countries, and seventeen formats.   That’s why companies like Novartis, Pfizer, and AstraZeneca are rebuilding their infrastructure to make that possible. Faster trials mean faster approvals. Faster approvals mean more exclusive runway. Every month saved can mean tens of millions in added revenue.  McKinsey notes that 30% of top pharma players have only modernized one or two applications - usually as isolated pilots. These companies are discovering that point solutions don’t scale unless the underlying platform does. It’s not enough to deploy an AI model or launch a digital trial portal. Without a harmonized application layer beneath it, the benefits stall. You can automate one process, but you can’t orchestrate the whole trial. Outside of R&D, the same dynamic is playing out in manufacturing and commercial. Under the Pharma 4.0 banner, companies are digitizing batch execution, tracking cold-chain logistics in real time, and using analytics to reduce waste - not just to report it. On the commercial side, modern CRMs help sales teams target the right providers with better segmentation, and integrated data platforms are feeding real-time feedback loops into brand teams. But again, none of that matters if the underlying systems can’t talk to each other. Health Tech Companies and Vendors The biggest EHR vendors are no longer just selling systems of record. They’re rebuilding themselves as data platforms with embedded intelligence. Oracle Health (formerly Cerner) is shipping a cloud-native EHR built on its own OCI platform, with analytics and AI tools hardwired into the experience. This is a complete rethinking of how health data flows across settings - including clinical, claims, SDoH, and pharmacy - and how clinicians interact with it. Oracle’s voice-enabled assistant is the new UI. Epic is taking a similar turn. By early 2025, its GPT-powered message drafting tool was already generating over 1 million drafts per month for more than 150 health systems. Two-thirds of its customers have used at least one generative feature. They’re high-volume use cases that clinicians now expect in their daily workflows. What used to be “will this work?” is now “why doesn’t our system do that?” Vendor modernization is now directly reshaping clinician behavior, admin efficiency, and patient experience - whether you’re ready or not. On the startup side, digital health funding has rebounded - with $3B raised in Q1 2025 alone. Startups are leapfrogging legacy tools with focused apps: Virtual mental health that delivers within hours Remote monitoring platforms that plug directly into EHRs AI tools that triage diagnostic images before radiologists ever see them Key Technologies and Approaches in 2025 Modernization Cloud Migration On-premises infrastructure can’t keep up with the bandwidth, compute, or integration demands of modern healthcare. Providers are now asking “how many of our systems can we afford not to migrate?” Cloud lets healthcare organizations unify siloed data - clinical, claims, imaging, wearables - into a single stack. It enables shared analytics. It allows for disaster recovery, real-time scaling, and AI deployment. It’s also the only path forward for regulatory agility. As interoperability rules change, cloud platforms can update fast.  Microservices and Containerization Legacy platforms are so big that if one module needs a patch, the whole stack often has to be touched. Nobody can afford this in 2025 - especially when the systems are built around scheduling, billing, or inpatient documentation. That's why organizations break apart monoliths. Microservices and containers (via Docker, Kubernetes, and similar platforms) let IT teams refactor old systems one piece at a time - or build new services without waiting for an enterprise release cycle. It’s how CHG Healthcare built a platform to deploy dozens of internal apps in containers - standardizing workflows and cutting deployment times dramatically. It’s how hospitals are now plugging in standalone scheduling tools or analytics layers next to their EHR. EHR Modernization EHRs are still the spine of provider operations. For a decade, usability and interoperability were the two top complaints from clinicians and CIOs alike. In 2025, EHR vendors deliver fixes. Epic now supports conversational interfaces, automated charting, and GPT-powered patient message replies. Oracle’s cloud EHR is designed with built-in AI assistants and analytics  from the start. Meditech’s Expanse is delivering mobile-native UX and modern cloud hosting. These are new baselines. And they’re being adopted because: Clinicians need workflows that reduce clicks Health systems need interoperability without middleware hacks Regulators are demanding FHIR APIs and real-time data sharing When the VA replaces VistA with Oracle across its entire footprint, it’s a national signal: modern EHRs are not just record systems now. Low-Code The staffing shortage in healthcare tech is real. And waiting months for a development team to deliver a small app is no longer acceptable. That’s why low-code platforms (Salesforce, PowerApps, ServiceNow) are gaining ground in hospital IT. Low-code enables clinical and operational teams to launch small, high-impact tools on their own. Examples in the field: A bedside tablet app that pulls data via FHIR API, built in weeks - not quarters Custom staff scheduling flows tied to the HR system, updated on the fly Patient outreach tools that route data back into the CRM without custom middleware Artificial Intelligence and Machine Learning Integration From clinical documentation to insurance claims to pharmaceutical R&D, AI has moved from pilot status to production use - and it’s quietly reshaping cost structures and workflows. Clinical AI The most visible adoption is inside hospitals and physician groups, where AI-powered scribes now operate as real-time note-takers. These ambient tools transcribe conversations and structure them into the clinical record as a usable encounter note. Early deployments are showing tangible gains: fewer hours spent documenting, faster throughput, and  happier physicians. Patient-facing apps now routinely include AI chatbots for triage, appointment scheduling, and FAQ handling, offloading low-complexity interactions that would otherwise clog up call centers or front desks.   Operational AI: Driving Down Admin Overhead in Payers and Providers Insurers have leaned hard into AI for process-heavy work: claims adjudication, fraud detection, and summarization of policies and clinical guidelines. Automating portions of the revenue cycle has reduced manual review, improved coding accuracy, and accelerated payment timelines. Deloitte’s 2025 survey confirms that AI is now a strategic priority for over half of payer executives, and not just for cost reduction. Underwriting, prior authentication decisioning, and customer service bots are now all AI-enabled domains -  because manual handling simply doesn’t scale. Provider systems are adopting similar logic. AI-driven tools now assist billing teams with denial management and code validation - helping recover missed revenue and reduce rejected claims, often without increasing staffing.  Pharma AI In pharma, algorithms screen compounds, predict trial success based on patient stratification, and optimize site selection based on population health patterns. One major biopharma firm uses machine learning to model which trial protocols are most likely to succeed - and which recruitment strategies have the highest yield. McKinsey estimates $50 billion in annual value is on the table if AI is fully leveraged across R&D. And the only thing blocking that is the systems. That’s why the smartest companies are modernizing trial management platforms, integrating real-world data, and building AI into their analytics infrastructure. Governance Is Now Mandatory Because AI is Embedded Once AI starts generating visit summaries, triaging patients, or flagging claims for denial - the risk of error becomes systemic. Most provider organizations deploying clinical AI tools now have AI governance committees reviewing: Model accuracy and performance Bias and equity auditsRegulatory alignment with FDA’s evolving AI guidance Interoperability Interoperability is the hidden engine powering everything that matters in healthcare modernization. If your systems can’t share data through APIs,  then every other investment you make will eventually stall. AI, analytics, virtual care, population health management -none of it works without integration.   The 21st Century Cures Act mandated that EHRs expose patient data through standardized FHIR APIs as a legal requirement. That mandate hit everyone who integrates with patient data: providers, payers, labs, and app developers. Cloud integration platforms, HL7/FHIR toolkits, and master patient indexes are now readily available and built-in to most modern systems.  Modern EHRs are now deployed with real APIs. Health plans open claims data to other payers. Patients expect apps to access their records with one click. And regulators expect interoperability to be a default. Modern health apps - whether built in-house or purchased - are expected to offer FHIR APIs, user-level OAuth security, and plug-and-play integration with at least half a dozen external systems. If they can’t? They’re not even considered in procurement. Challenges and Barriers to Modernization in 2025 Cybersecurity 2023 and 2024 were record-setting years for healthcare data breaches, and ransomware is still a daily risk. The challenge is modernizing with zero-trust architectures, embedded encryption, real-time monitoring. Security-first modernization is slower.  Legacy Systems  Modernizing one system often means breaking five others. So teams modernize in slices. They update scheduling without touching the billing core. They roll out new patient apps while the back-end is still on-prem. And that piecemeal approach - while pragmatic - creates technical debt. The challenge is the dependencies. It’s the billing logic no one can rewrite. The custom reporting your compliance team depends on. The integrations held together with scripts from 2011. In 2025, the health systems making real progress are doing three things differently: Mapping dependencies before they pull the cord Using modular wrappers and APIs to isolate change Sequencing modernization around business impact - not tech idealism Regulatory Requirements Every platform you touch has to stay compliant with HIPAA, ONC, CMS, and increasingly, FDA guidance - especially if you’re embedding AI. Replace your EHR? Make sure it’s still ONC-certified. Launch a new patient engagement app? Don’t forget consent management and audit trail requirements. Build a clinical decision tool with GPT? You may be walking into a software-as-a-medical-device (SaMD) zone. Many payers are holding off on major IT overhauls. The risk of investing in the wrong architecture - or too early - is real. But waiting also costs. The CEOs who are moving forward are doing so by baking compliance into the project timeline. They involve legal and clinical governance from day one. And they’re designing for flexibility  because the policy won’t stop shifting. And above all: they’re resisting the urge to rip and replace without a migration path that keeps operations intact. Cultural Resistance You can buy platforms but not adoption. Every new system - no matter how well designed - shows up as another thing to learn. Innovation fatigue goes away when teams believe the new tools actually give them time back, reduce clicks, and make their lives easier. In 2025, the organizations breaking through cultural resistance are doing two things well: Involving clinicians early - in co-design Delivering early wins - like AI scribes that give doctors back 15 minutes per visit, not promises of better care someday They also hire tech-savvy “physician champions,” embed superusers in departments, and give staff the support and agency to adopt at their pace. Because if modernization is delivered as a top-down mandate? It will stall. No matter how good the system is. Interoperability and Data Silos: Progress with Pain Ironically, modernization projects often make interoperability harder before they make it better. That’s because new systems speak modern languages — but your data is still in the old ones. Migrating patient records. Reconciling code sets. Building crosswalks between legacy EHRs and new cloud platforms. It all takes time. Even when the target system is FHIR-native, the data coming in isn’t. And until all entities in your network modernize in sync, you’re living in a hybrid world - with clinical, claims, and patient-generated data split across modern APIs and legacy exports. This isn’t a short-term challenge. It’s the operating condition of modernization in 2025.  The solution is to design for coexistence. Build middleware. Accept data friction. And keep moving. ROI Pressure Modernization costs money. Licenses, subscriptions, cloud costs, consultants — the sticker price is high. And even if you believe in the strategy, your CFO wants proof. That’s why the smartest CEOs are phasing modernization into value-based tranches: Replace the billing system after the front-end is streamlined Layer AI into existing documentation tools before replacing the EHR Roll out low-code apps to hit immediate ops gaps while core platforms evolve And they’re tying every dollar to metrics that matter: reduced call center volumes, faster claim approvals, shortened length of stay. Because in 2025, you need to modernize the things that move the business. How Belitsoft Can Help Belitsoft helps healthcare organizations modernize legacy systems with modular upgrades, smart integrations, and cloud-native tools that match the pace of clinical and business needs. Whether it’s rebuilding trial platforms, fixing disconnected EHRs, or making patient apps usable again, Belitsoft turns modernization from a bottleneck into a competitive advantage. For Providers (Hospitals & Health Systems) Belitsoft can support modernization efforts through: Custom EHR migration support: migrating from legacy systems or outdated on-premises EHRs to modern, cloud-native platforms. Frontend modernization: building mobile-native apps, ambient voice tools, or clinician-facing interfaces that reduce clicks and documentation overload. Integration layers: connecting fragmented billing, lab, and scheduling systems via FHIR APIs and custom middleware. Low-code tools: creating lightweight apps for patient check-in, nurse scheduling, or discharge planning without waiting for full-stack releases. Microservices architecture: decoupling legacy hospital software to enable modular upgrades - scheduling, reporting, documentation, etc. Belitsoft can act as both a modernization contractor and strategic tech partner for health systems stuck between urgency and fragmentation. For Health Plans (Payers) Belitsoft can deliver: Custom modernization of adjudication and payment systems, designed with modular APIs and cloud-native infrastructure. Member experience modernization: building digital self-service portals, real-time benefits lookup, and omnichannel messaging tools. Interoperability solutions: developing APIs for CMS mandates, FHIR integration, identity management, and secure audit-ready logs. AI-powered automation: embedding fraud detection, denial logic, or claim prioritization into claims processing. Compliance-focused upgrades: modern systems built for traceability, audit-readiness, and evolving ONC/CMS requirements. Belitsoft’s strength lies in building solutions that integrate legacy claims engines with new digital layers - enabling real-time interaction, transparency, and regulatory resilience. For Pharma and Life Sciences Belitsoft can offer: CTMS and EDC modernization: replacing siloed legacy systems with cloud-native platforms for trial design, patient recruitment, and data capture.Analytics and BI dashboards: real-time visibility into site performance, recruitment status, and trial outcomes. Integration of real-world evidence (RWE) into trial and commercial data pipelines. Manufacturing and supply chain visibility tools: real-time batch tracking, cold-chain monitoring, yield optimization. CRM modernization for sales teams: segmentation, real-time performance tracking, and better targeting tools. Belitsoft can serve as a modernization partner for pharma companies looking to move beyond pilots and point solutions toward scalable digital infrastructure. For HealthTech Vendors & Startups Belitsoft can support healthtech vendors with: Cloud-native platform development: building core SaaS tools for remote monitoring, virtual care, and diagnostics. Modern EHR integrations: FHIR API development, SDoH data handling, and embedded analytics. Product-grade AI/ML integration: powering triage tools, image screening, or care recommendations with custom models and audit-ready pipelines. Governance tooling: dashboards for model performance, bias monitoring, and regulatory alignment. Interoperability-first design: plug-and-play modules that are procurement-ready (FHIR, OAuth2, audit logs). Belitsoft can function as a full-cycle tech partner for healthtech companies - from prototype to compliance-ready production systems.