Colleagues, In today’s increasingly data-driven healthcare environment, your daily experience interpreting laboratory data has quietly prepared you for some of the most in-demand roles in the industry: health informatics and data analytics.

The skills you’ve developed, distinguishing normal from abnormal, recognizing patterns, understanding pre-analytical variables, ensuring quality control, are directly transferable to the growing field of healthcare data science.

The Perfect Transition You Haven’t Considered

Healthcare informatics and analytics professionals:

• Transform raw health data into actionable insights
• Design and manage clinical databases and reporting systems
• Conduct statistical analyses of patient outcomes
• Create dashboards and visualizations for clinical decision-making
• Support research through data management and analysis
• Ensure data quality and integrity across systems

Sound familiar? Many of these functions parallel what you already do with laboratory data, just at a different scale.

Why Laboratory Scientists Would Excel in Data Roles

Your scientific background provides distinct advantages:

1. Analytical Thinking: You’re trained to interpret quantitative results and recognize patterns.

2. Quality Mindset: Your understanding of quality control applies directly to data integrity.

3. Clinical Context: You understand the healthcare environment and outcomes that matter.

4. Scientific Methodology: Your experimental approach fits perfectly with data analysis.

5. Precision Orientation: Your attention to detail is invaluable in data management.

The Growing Opportunity in Africa

The African healthcare system is increasingly prioritizing data-driven decision making:

• Hospital information systems implementation is accelerating
• Disease surveillance programs require sophisticated data analysis
• Public health initiatives depend on outcomes measurement
• Research institutions seek data management expertise
• Health insurance expansion creates needs for claims analysis

Yet there remains a significant shortage of professionals who combine healthcare knowledge with data skills.

Bridging the Skills Gap

To transition into informatics and analytics, focus on developing:

• Database Fundamentals: Understanding data structures and basic SQL
• Statistical Analysis: Mastering fundamental statistical concepts and tools
• Data Visualization: Creating clear visual representations of complex information
• Programming Basics: Learning introductory Python, R, or similar languages
• Health Informatics Standards: Familiarity with relevant data standards

Many of these skills can be developed through:

• Online courses (many free or low-cost options exist)
• Self-directed projects analyzing laboratory data
• Open-source tools and communities
• Professional workshops and webinars

I’ve encountered several laboratory professionals who’ve made this transition:

• I know a medical laboratory scientist now leads data quality for a state health information exchange
• I know a former microbiologist specialist who analyzes antimicrobial resistance data for a research institution.

The common thread? They recognized how their laboratory perspective uniquely qualified them to understand healthcare data needs.

Testing the Waters: Where to Begin

If informatics and analytics interest you:

1. Start with Your Current Data: Analyze patterns in your laboratory’s test volumes or quality indicators

2. Learn Basic Tools: Become proficient with Excel and basic statistical concepts

3. Explore Free Resources: Platforms like Coursera, Khan Academy, and YouTube offer excellent introductory content

4. Join Relevant Groups: Connect with healthcare informatics professionals online

5. Apply for Hybrid Roles: Look for positions that combine laboratory and data responsibilities

A particularly effective entry point is laboratory information system (LIS) management, which leverages your laboratory knowledge while building technical skills.

The Upskilling Roadmap

For those serious about this transition, I recommend this learning sequence:

1. Foundational Skills: Excel mastery, basic statistics, data visualization principles

2. Database Fundamentals: SQL basics and database concepts

3. Analysis Tools: Introduction to analysis platforms (PowerBI, Tableau, or similar)

4. Programming Concepts: Basic programming in a data-focused language

5. Healthcare Informatics: Standards, privacy requirements, and healthcare data structures

Many laboratory scientists worry that data roles require advanced mathematics or computer science backgrounds. The reality is much more accessible: most healthcare data roles prioritize domain knowledge and analytical thinking over advanced technical skills.

I have several more roles lab professionals are super qualified for, stay tuned and please, share this Newsletter with your friends and colleagues who might benefit from it.

Written with Love,

Nancy.