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Bild eines medizinischen Fachpersonals bei der Analyse klinischer Daten am Laptop, überlagert mit Grafiken und Zahlen – symbolisiert moderne, validierte Analyseumgebungen in den Life Sciences.
Tailored to your needs.

Clinical Data Management and Analysis

Use your preferred tools, interfaces and infrastructure—HMS designs analytics environments that accelerate delivery and meet regulatory requirements.

When analytics environments reach their limits

Legacy systems, manual validation steps, or isolated toolchains can slow down clinical data workflows. Modern technologies promise increasing computational power to gain efficiency.
Typical challenges include:
Open-source tools need to be used in a compliant environment.
Existing systems no longer support scale, automation, or collaboration.
Automate previous manual and time-consuming validation steps.
Analytics workflows are fragmented or difficult to audit.
Customize the user interface to better fit user needs.
To address these needs, HMS provides customized solutions that strengthen critical components without requiring a complete platform rebuild.

From Package to Platform—What HMS Delivers

HMS supports life science organizations in building and operating modular Statistical Computing Environments—from individual components to complete validated platforms. Our services are flexibly bookable and cover all critical areas across data, processes, and technology.

Support for Analysts and Data Scientists

We provide development services and reproducible workflows in compliant analytics:
  • R programming and R package development and validation (CSV-compliant)

  • SAS programming, macro development, and migration to R or Python

  • Python programming support on request

Engineering and Integration of SCE Platforms

We design, implement, and validate tailored computing environments:
  • Platform engineering: on-premise, cloud, or hybrid

  • Integration with R (e.g. via container platforms), SAS or Python tools

  • Modular setup: from assessment to architecture, deployment, validation, and knowledge transfer

  • Manage data assets 

Package Development and Validation

We help teams manage and maintain validated R packages or develop custom functionality:
  • Development and validation according to CSV or GxP requirements

  • Integration into SCE platforms and version control 

Change Management and Migration Support

We support teams in modernizing their analytics infrastructure:
  • System migration from legacy environments (e.g. SAS9)

  • Modernization of processes and toolchains within regulatory frameworks

How we can support your workflow
HMS services are aligned with the operational flow of modern analytics environments. Whether your team needs support in developing code, setting up a secure and scalable platform, validating critical components, or managing change—each service area connects directly to one or more of the six core SCE functions. Our modular offering ensures that you can engage HMS exactly where value and urgency align.
Let’s talk about your environment.
In a 30-minute discovery call, we clarify where your analytics workflows stand today and where support from HMS could bring immediate value.
Book a free consultation
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Please note that we require at least one week’s notice for scheduling appointments to avoid overlaps. We will confirm your requested appointment within 24 hours. Unfortunately, we are unable to accommodate next-day appointments.
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The services provided by HMS align with the core capabilities of a modern Statistical Computing Environment (SCE). Even if you're not building a complete platform today, we ensure that your components are scalable, traceable, and compliant by design.

Why a Modern SCE Matters

Fragmented systems and outdated tools slow down clinical trial data management and analysis, ultimately delaying report submissions to regulatory authorities. A modern SCE enables scalable, reproducible analytics that meet compliance standards and accelerate market access.
A modern SCE is essential to
  • Automate deployment and testing processes, aiming for agile development and short release cycles (continuous validation)

  • Support exploratory and submission workflows

  • Follow IT strategy requiring platform modernization

What defines a modern SCE?
A capable SCE supports both compliance and innovation. It finds the agreement between reproducibility, flexibility, and functionality you need. 
Core functions a modern SCE should support include:
Build Analyses & Models
Analysts explore data flexibly and prepare submission-ready analyses in
validated environments using tools they embrace.
Communicate
Analysis outputs are shared reproducibly.
Consume & Decide
Reports are accessible through secure, role-specific interfaces.
Trace & Document
Compliance is ensured from input to output as needed.
Manage Technical Assets
Individual scripts, shared, validated code (SAS macros, R/Python packages), etc.
Manage Data Assets
Data sources and transformation ensuring GxP-compliant user access management.
These core functions form the operational backbone of modern SCEs—whether built as standalone solutions or embedded within broader analytics ecosystems.
The diagram below illustrates how a modern SCE orchestrates the key functional building blocks across teams, tools, and assets. Each element contributes to a validated, efficient analytics workflow:
Visual representation of a validated Statistical Computing Environment (SCE), illustrating the flow from analysis and modeling to communication and decision-making, supported by managed data, technical assets, and traceable documentation.

Turning SCE Principles into Real-World Solutions

Each of the core functions of a modern Statistical Computing Environment can be strengthened individually—or addressed step by step. HMS offers modular services that align with these principles and deliver tangible value across clinical analytics workflows.
Whether you are starting with a single validated R package or planning a full platform  transformation, HMS is your partner for scalable, compliant, and efficient analytics environments. Our cross-functional team combines deep engineering know-how with life science domain expertise to support you wherever you are in your journey.
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How We Work With You

HMS combines engineering expertise with regulatory awareness to deliver solutions that fit your environment and pace. Our projects are modular and collaborative.
End-to-End Support​
We support all aspects of data analysis and compliance across regulated and non-regulated environments.​
Step-by-step or all-in—you choose
Whether you’re validating a single package, designing a full platform or modernizing legacy workflows, we tailor our engagement to your scope and timeline.
Collaborative from day one
We work closely with internal stakeholders, from IT and QA to biostatistics and regulatory teams, to ensure solutions are practical, accepted, and sustainable.

Our Typical Project Flow

We structure our projects in transparent, modular phases, each aligned with your internal processes and regulatory needs.
Step 1
Assessment & Planning
Understand existing systems, define goals, and clarify validation requirements.
Step 2
Architecture Design
Develop technical and procedural concepts, toolchains, infrastructure, and governance.
Step 3
Implementation & Deployment
Configure environments, integrate tools, and prepare compliant workflows.
Step 4
Validation & Documentation
Apply GAMP principles and CSV standards to test, document, and qualify the solution.
Step 5
Knowledge Transfer & Support
Enable your teams through training, documentation, and long-term support options.
Our typical workflow starts with a structured assessment—and so can our conversation.
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Benefit from Our Experience

Why HMS Is the Right Partner for Validated Clinical Analytics 

With HMS, you choose a partner who combines analytics expertise with technological depth and a strong understanding of regulated environments. Our services are modular and designed to fit into your existing infrastructure.
Proven in regulated environments
HMS combines deep technological know-how with an understanding of what it takes to deliver in regulated contexts.
Engineering mindset meets analytics
We apply professional software engineering principles to clinical analytics workflows— ensuring reproducibility, validation, and maintainability at scale.
Technology-neutral, domain-aware
Whether you prefer R, SAS, or Python as a statistical programmer and whether your IT architects favor on-premise solutions or cloud services —we design solutions that integrate into your existing architecture and support your domain-specific needs.
Trusted partner in life sciences
Our clients include global diagnostics and pharma companies. We are familiar with the requirements, constraints, and expectations in clinical data analysis.
Trust That Counts – Confirmed by BARC
Our clients value our work – as shown by Europe's largest independent user survey in Data & Analytics (BARC, 2025):
100%
Recommendation Rate
100%
Project Goal Achievement
96%
Value Our Tech Expertise
... to the HMS study results
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Benefit from Our Experience

Our Success Stories

These selected use cases show how HMS helps life science teams improve validation, accelerate delivery, and meet regulatory expectations.

Validated R environment for analysis of clinical studies

Life Science, Healthcare

Pharmaceutical companies increasingly use the R programming language for planning and analysing clinical studies. Our validated R environment enables the use of modern statistical methods while meeting the high regulatory requirements in a GxP setting.

Benefits:

  • Reduced licensing costs by replacing standard software
  • Use of modern statistical methods
  • Exploratory work in compliance with regulatory requirements
Case Study anschauen

GenAI-Supported Transformation: Migration from SAS to AWS

Finance

Our solution contributed to the modernization of core SAS BI landscapes by successfully facilitating the transition to the cloud. We effectively used GenAI for automated code migration to Python.

Benefits:

  • Long-term cost-effectiveness and efficiency of BI applications
  • Architecture meets BaFin requirements
  • Process optimization through consolidation and professionalisation of processes
Case Study anschauen

LLM-Based Natural Language SQL Queries

Healthcare / Life Science

Our solution enables natural language SQL queries through a chatbot interface, allowing users without technical expertise to access corporate data.

Benefits:

  • Democratised data access
  • Increased efficiency
  • Informed decision-making
Case Study anschauen

Analysis of Free Text Data Through Automated Content Tagging

Healthcare / Pharma

Our solution for tagging and structuring content transforms previously untapped free text data into valuable, analysable information, thereby enhancing the data foundation within companies.

Benefits:

  • Faster classification, evaluation, and structuring of data
  • Expansion of the data foundation
  • Informed analysis and decision-making
Case Study anschauen

Innovating Clinical Research: AI for Secondary Clinical Data Analysis

Healthcare / Life Science

We supported Novartis in exploring the potential of machine learning in clinical research. By analysing study data with machine learning and explainable AI, we identified unknown correlations and improved the predictive power for treatment outcomes and safety profiles.

Benefits:

  • Discovery of unknown correlations
  • Improved prediction of treatment outcomes
  • Optimised identification of critical features through explainable AI
Case Study anschauen

GenAI-Supported Transformation: Migration from SAS to AWS

Finance

Our solution contributed to the modernization of core SAS BI landscapes by successfully facilitating the transition to the cloud. We effectively used GenAI for automated code migration to Python.

Benefits:

  • Long-term cost-effectiveness and efficiency of BI applications
  • Architecture meets BaFin requirements
  • Process optimization through consolidation and professionalisation of processes
Case Study anschauen

LLM-Based Natural Language SQL Queries

Healthcare / Life Science

Our solution enables natural language SQL queries through a chatbot interface, allowing users without technical expertise to access corporate data.

Benefits:

  • Democratised data access
  • Increased efficiency
  • Informed decision-making
Case Study anschauen

Analysis of Free Text Data Through Automated Content Tagging

Healthcare / Pharma

Our solution for tagging and structuring content transforms previously untapped free text data into valuable, analysable information, thereby enhancing the data foundation within companies.

Benefits:

  • Faster classification, evaluation, and structuring of data
  • Expansion of the data foundation
  • Informed analysis and decision-making
Case Study anschauen

Large-Scale RAG System for Efficient Information Retrieval

Chemicals

Our system allows researchers to accurately search a vast amount of internal research data using natural language, enabling them to quickly obtain the necessary information.

Benefits:

  • Enhanced search quality
  • Optimal user guidance
  • Increased user trust
Case Study anschauen
Portrait of Maximilian Kreienbaum, Project Manager at HMS
Maximilian Kreienbaum
Project Manager for Life Science

Want to learn more about our life science services?

We’re happy to discuss how HMS can support your clinical analytics workflows—whether you need support for tools, platforms, or validation.
Get in touch with our team
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