Healthcare Data Analyst – Consultant (Remote)

Who We Are

Founded in 2021 through a collaboration between Mayo Clinic, Rally Ventures, and Commure (a General Catalyst company), Lucem Health is on a mission to revolutionize healthcare delivery with the practical application of clinical AI. We empower healthcare organizations to detect diseases at an earlier stage and identify patients who are undiagnosed, ultimately improving outcomes and optimizing limited clinical resources. We envision a future where proactive care enables problems to be detected before they become life threatening and patients get world class care, everywhere.

We are focused on helping providers deliver more proactive care by accelerating diagnosis and treatment. To do so, we’ve built a platform that connects patient data from varied sources with powerful, clinically focused AI algorithms and other advanced tools. We deliver the insights generated by these tools seamlessly into clinical workflow: to the right stakeholder, in the right place, in the right context, at the right time. We help clinicians and other stakeholders engage with, understand, trust, and adopt these tools so they see them as valuable partners that support better care and outcomes for patients.

We leverage this platform – Lucem Health Reveal – to deliver AI-enabled solutions, targeted at specific diseases, that deliver distinct and measurable clinical value propositions and financial benefits to our provider customers.

Position Overview:

Lucem Health’s Insights is an advanced analytics platform designed to help our life science partners refine provider outreach, optimize reimbursement strategies, and capture a larger share of a growing market. It leverages real-world patient data to give insight into diagnostic and treatment pathways across populations of patients.

As a Healthcare Data Analyst, you will play a critical technical role in designing and building the Insights product, working in close collaboration with Product Management and our physicians. Your expertise will be essential in gathering, analyzing, and incorporating market intelligence and direct customer feedback to refine the tool’s functionality and ensure its relevance. Through clinical research, you will compile requirements for life sciences customers supporting the development of Insights, Reveal, and other AI-driven solutions. The goal is to ensure these tools effectively address customer needs and provide meaningful clinical insights that drive market impact. You will write and maintain Metabase Reporting and custom SQL to power Life Sciences reporting and Insights features.

This position is remote. As a remote work force that meets face-to-face occasionally to do collaborative work, this position will require occasional travel to Raleigh or Charlotte, North Carolina, New York, New York, or to other cities where there is, or will be, a critical mass of Lucem employees.

This position is a contract position. We do see potential for permanent placement in the future but not at this time.

Key Responsibilities: 

Clinical and Patient Intelligence Gathering: 

• Analyze diagnostic workflows, clinical guidelines, and emerging practices within target disease areas to inform Insights feature and reporting requirements. 
• Gather and synthesize insights on patient journeys, physician workflows, and clinical decision criteria used to diagnose and manage the disease. 
• Research, document, and maintain disease definitions and clinical logic (e.g., diagnostic signals, labs, procedures, medications), serving as a subject matter expert for supported conditions. 
• Build and maintain Life Sciences reporting that characterizes diagnostic and treatment pathways, including clinically meaningful events, milestones, and timing between steps. 
• Define and maintain customer-facing metric specifications, data dictionaries, and reporting documentation to ensure consistent interpretation across stakeholders. 
• Develop and support MetaBase dashboards and/or the curated datasets that power them, ensuring metric consistency, reproducibility, and traceability to source logic. 
• Support client-facing discussions by clearly explaining reporting methodology, assumptions/limitations, and root causes of metric variance over time (e.g., logic changes, data refreshes, upstream source changes). 

Customer and Stakeholder Engagement: 

• Collaborate with key customers, clinicians, and healthcare providers to understand their diagnostic and therapeutic workflows and needs. 
• Facilitate workshops and interviews to extract requirements for disease identification, medications, and associated clinical events. 
• Lead the build-out of key explanatory analytics that provide insight into disease pathways and variation in clinician behavior using data from the EMR 

Definition of Product Features: 

• Define and prioritize key medications, blood tests, and diagnostic filters to be incorporated into the configuration of the Disease Insights product for a particular disease. 
• Identify clinical “events of interest” relevant to the disease area, such as symptom onset, critical lab thresholds, and treatment milestones. 

Technical Collaboration and Implementation: 

• Translate market intelligence into technical specifications and requirements for the development team. 
• Collaborate with data engineering teams to ensure accurate integration of data sources (e.g., EMR data, claims data, etc.). 
• Maintain and update medical coding crosswalk mappings for use within the organization.  

Product Development Support: 

• Partner with Product Management to align technical implementation with the overall product roadmap. 
• Validate prototype features with end-users and incorporate feedback into iterative development cycles. 
• Document insights and provide comprehensive reports that support strategic decision-making. 

Quality Assurance and Validation: 

• Perform validation of data outputs to ensure accuracy and relevance to clinical scenarios. 
• Develop test cases and scenarios to evaluate the effectiveness of diagnosis filters and event-based insights. 
• Troubleshoot and resolve issues related to data inaccuracies or inconsistencies. 

What You’ll Own (Core Deliverables) 

•  Disease-area configuration specs for Insights (cohort definitions, filters, event logic, code sets) 
• Metric definitions and data dictionaries for customer-facing reporting 
• Validation plans and reconciliation of customer-facing outputs (clinical sanity checks + data QA) 
• Ongoing maintenance of terminology assets (ICD-10/LOINC/NDC/CPT crosswalks) with versioning and change logs 

Knowledge Sharing: 

• Serve as a subject matter expert for disease-specific insights, supporting both internal teams and external customers. 

Qualifications: 

The ideal candidate will have: 

• Analytical Skills: Ability to synthesize large volumes of data to extract actionable insights. Proficiency in tools such as SQL.   
• Domain Knowledge: The ability to research targeted disease areas, including diagnosis processes, treatment pathways, and key laboratory and procedure tests relevant to the disease. 
• Key Tools: Jira, Confluence, Smartsheet, SharePoint, Microsoft Office, Gemini, SQL, and MetaBase 
• Data Analysis & Interpretation: Analyze large datasets to identify trends, patterns, and anomalies.  Interpret the results to provide clear, data-driven answers to complex clinical and business questions. Strong background in data analytics, clinical workflows, or healthcare technology systems. Familiarity with EMR data, clinical terminologies (e.g., ICD-10, LOINC, NDC, CPT), and diagnostic criteria. 
• Business Intelligence (BI) and Visualization: Design, build, and maintain insightful and interactive dashboards and reports using MetaBase. 
• Communication and Storytelling: Translate complex analytical findings into compelling narratives and easily understandable visualizations for clinical staff, business stakeholders, and leadership. 
• Data Integrity and Accuracy: Ensure the accuracy and integrity of all analyses. 
• Develop and implement data validation processes to maintain high-quality, reliable results. 
• Collaboration: Work closely with clinical teams, IT, and business units to understand their needs, provide analytical support, and help them make data-informed decisions.