Medical Analyst Career Path Guide

A Medical Analyst plays a crucial role at the intersection of healthcare and data science, combining medical knowledge with analytical skills to evaluate and translate complex clinical data into actionable insights. They support healthcare providers, pharmaceutical companies, insurers, and regulatory bodies to improve patient outcomes, optimize healthcare services, and facilitate evidence-based decision-making. The role demands a deep understanding of medical terminology, biostatistics, healthcare systems, and data analytics tools to extract meaningful patterns from diverse medical datasets.

12%

growth rate

$85,000

median salary

remote-friendly

📈 Market Demand

Low

High

The demand for Medical Analysts is currently high, propelled by the healthcare industry's shift towards data-driven decision making, rising adoption of electronic health records, and the increasing use of predictive analytics to improve patient outcomes and reduce costs.

🇺🇸 Annual Salary (US, USD)

60,000—110,000

Median: $85,000

Entry-Level: $67,500
Mid-Level: $85,000
Senior-Level: $102,500

Top 10% of earners in this field can expect salaries starting from $110,000+ per year, especially with specialized skills in high-demand areas.

Core Functions of the Medical Analyst Role

Medical Analysts act as vital contributors within the broader healthcare ecosystem by bridging the gap between clinical practice and data-driven insights. Utilizing their expertise in medicine, biology, and analytical methodologies, they mine electronic health records (EHRs), clinical trial results, insurance claims data, and medical imaging reports to detect trends, predict patient risks, and evaluate treatment effectiveness.

Their work directly informs evidence-based healthcare policies, patient care strategies, and medical research advancements. By analyzing large datasets, Medical Analysts help healthcare organizations reduce costs, minimize errors, and improve the quality and accessibility of care. They also help in compliance tracking and risk assessment, ensuring that healthcare providers meet regulatory standards.

The role demands collaboration across multidisciplinary teams, including doctors, nurses, IT professionals, and statisticians, requiring strong communication and teamwork skills. Medical Analysts also translate technical findings into clear reports and visualizations tailored to diverse stakeholders, enhancing informed decision-making. As healthcare systems increasingly rely on digital data and predictive analytics, the role of a Medical Analyst is expanding rapidly, becoming critical for shaping the future of medicine.

Key Responsibilities
Collecting, processing, and analyzing healthcare and clinical data from various sources such as EHRs, clinical trials, and insurance claims.
Identifying patterns and trends in patient health data to inform clinical decisions and healthcare interventions.
Developing predictive models to assess patient risk factors and forecast treatment outcomes.
Collaborating with healthcare professionals to understand clinical challenges and tailor data analyses accordingly.
Preparing detailed reports and visualizations that communicate analytical insights to non-technical stakeholders.
Ensuring compliance with healthcare data privacy laws and industry regulations such as HIPAA.
Supporting pharmaceutical research by analyzing drug efficacy and safety data.
Evaluating healthcare service performance through outcome metrics and cost-effectiveness studies.
Using statistical tools to validate the accuracy and reliability of medical data analyses.
Conducting literature reviews and integrating scientific research findings into practical analytics.
Designing and automating data workflows to streamline reporting and analysis processes.
Monitoring real-world evidence to track disease outbreaks, treatment adoption, and public health trends.
Providing insights for healthcare policy development and strategic planning.
Maintaining up-to-date knowledge on medical technology and analytics software advancements.
Training clinical staff on interpreting data reports and employing data-driven decision making.

Work Setting

Medical Analysts typically work in office settings within hospitals, healthcare organizations, pharmaceutical companies, insurance firms, or government health agencies. Their role frequently involves working at computer workstations where they manage large datasets and analytical programs. Collaboration with clinical and administrative teams is common, sometimes including meetings in hospital or research environments. The position is predominantly desk-based and may require extended periods of screen time focused on data analysis software. Depending on the employer and project scope, some Medical Analysts may also work in laboratory settings or attend clinical meetings. The job often offers a blend of independent data-focused work alongside active teamwork and interdisciplinary communication. Working hours usually align with standard office schedules, though project deadlines or emergencies, such as public health crises, can occasionally extend work hours. Remote work opportunities are emerging but traditionally are limited due to the sensitive and regulated nature of healthcare data.

Tech Stack

Electronic Health Record (EHR) Systems (e.g., Epic, Cerner)
Statistical Software (e.g., SAS, SPSS, Stata)
Data Visualization Tools (e.g., Tableau, Power BI)
Programming Languages (Python, R)
SQL and Database Management Systems
Natural Language Processing (NLP) Tools
Clinical Decision Support Systems (CDSS)
Health Information Exchange Platforms
Biostatistical Modeling Software
Machine Learning Frameworks (e.g., TensorFlow, Scikit-learn)
Cloud Computing Platforms (e.g., AWS, Azure for Healthcare)
Medical Coding Software (e.g., ICD-10, CPT tools)
Data Warehousing and ETL Tools
Regulatory Compliance Software (e.g., HIPAA compliance management)
Pharmacovigilance and Drug Safety Databases
Project Management Tools (e.g., Jira, Trello)
Microsoft Office Suite (especially Excel advanced features)
Clinical Trial Management Systems
Health Analytics Platforms (e.g., Health Catalyst, Optum)
Collaboration Software (e.g., Slack, Microsoft Teams)

Skills and Qualifications

Education Level

Typically, Medical Analysts hold at least a bachelor's degree in fields related to health sciences, life sciences, public health, biomedical informatics, or data analytics. Degrees like Bachelor of Science in Healthcare Administration, Biology, or Statistics serve as foundational stepping stones. Many employers prefer or require advanced education such as a master's degree in Public Health (MPH), Health Informatics, Biostatistics, or a related discipline.

Comprehensive understanding of medical terminology, disease pathology, healthcare delivery systems, and clinical protocols is essential. Formal education also imparts fundamental skills in biostatistics, health data management, and research methodologies. Internship or practicum experiences in healthcare settings often enhance practical skill acquisition.

Certifications, although not always mandatory, significantly augment qualifications. Credentials like Certified Health Data Analyst (CHDA), Registered Health Information Administrator (RHIA), or Certified Professional in Healthcare Quality (CPHQ) validate specialized expertise and commitment to the profession. Continuous education is critical due to the evolving landscape of healthcare technology and data analytics.

Tech Skills

Advanced proficiency in statistical analysis and interpretation
Experience with healthcare data standards (HL7, FHIR, ICD-10)
Programming in Python and R for data manipulation and modeling
Proficient use of SQL for database querying
Mastery of data visualization software like Tableau or Power BI
Knowledge of machine learning techniques applicable in healthcare
Competence in natural language processing for medical texts
Expertise in using EHR systems (e.g., Epic, Cerner)
Data cleaning, normalization, and validation
Understanding of clinical trial data structures
Familiarity with cloud computing solutions for health data
Use of project management platforms for collaborative workflows
Competence in regulatory compliance tools and protocols
Ability to automate reports and dashboards
Experience with health economics and outcomes research methods
Knowledge of pharmaceutical safety databases
Strong documentation and technical reporting skills
Proficiency in Microsoft Excel for pivot tables, macros, and data analysis
Understanding of database architecture and data warehousing
Ability to conduct literature reviews and integrate research findings

Soft Abilities

Critical thinking and problem solving
Effective communication tailored to technical and non-technical audiences
Interpersonal skills for collaborating across disciplines
Attention to detail and accuracy
Time management and prioritization
Adaptability to rapidly changing technologies and regulations
Ethical judgment and data confidentiality awareness
Curiosity and commitment to continuous learning
Presentation and storytelling with data
Teamwork and conflict resolution

Path to Medical Analyst

Becoming a Medical Analyst starts with obtaining a relevant undergraduate degree. Degrees in health sciences, biology, informatics, statistics, or public health provide foundational knowledge essential for this career. During undergraduate studies, gaining hands-on experience through internships in hospitals, research institutions, or healthcare data departments can be invaluable to understand real-world medical data complexities.

While entry-level Medical Analyst roles are accessible with a bachelor's degree, pursuing a master's—particularly in Health Informatics, Public Health, or Biostatistics—greatly enhances employability and expertise. Graduate programs often offer specialized coursework in clinical data analytics, healthcare systems, and advanced biostatistics, equipping candidates to handle more complex analytical challenges.

Complement education with industry certifications such as Certified Health Data Analyst (CHDA) or Registered Health Information Administrator (RHIA). These credentials demonstrate a commitment to the profession and mastery of healthcare data standards and regulations.

Develop proficiency in key software tools and programming languages like Python, R, SQL, Tableau, and common EHR systems used within the industry. Practical skills in data cleaning, visualization, and statistical modeling are vital for success.

Building a portfolio of projects—ranging from academic research to internship case studies—is also recommended to showcase applied expertise to employers.

Networking through professional organizations like the American Health Information Management Association (AHIMA) or Healthcare Information and Management Systems Society (HIMSS) opens doors to mentorships and job opportunities.

Finally, continuous professional development is critical given the dynamics of healthcare regulations and emerging technologies such as AI and machine learning in medicine. Staying updated through workshops, online courses, and conferences will keep one competitive and effective in the Medical Analyst role.

Required Education

An ideal educational trajectory for aspiring Medical Analysts begins with foundational coursework in biology, chemistry, statistics, and computer science at the undergraduate level. Degrees such as a Bachelor of Science in Health Informatics, Public Health, Biology, or Data Science are typically most aligned with this career path.

Graduate education frequently enhances capability and prospects. Master’s programs in Health Informatics or Biostatistics emphasize critical skills like clinical data management, clinical trial design, epidemiology, and advanced analytics methods. Core classes cover medical terminology, healthcare laws, population health, and software applications for healthcare analytics.

Numerous universities offer specialized degrees or certificates in healthcare analytics, many accessible online, broadening options for working professionals. Factors such as program accreditation and practical lab or internship components should be considered when selecting a program.

Training often involves working with real-world clinical datasets through internships or partnerships with healthcare providers. This hands-on exposure reinforces classroom learning and develops competencies in navigating complex medical datasets while ensuring ethical standards.

Professional certification further solidifies a Medical Analyst’s credentials. The Certified Health Data Analyst (CHDA) credential, offered by AHIMA, is highly regarded, covering health data management, use, and governance. The Registered Health Information Administrator (RHIA) credential focuses more on managing patient health information systems.

Continuing education plays an essential role. Medical Analysts routinely pursue workshops or advanced training modules in emerging technologies like artificial intelligence in healthcare, predictive analytics, and health informatics standards updates to remain at the forefront of the evolving industry.

Career Path Tiers

Junior Medical Analyst

Experience: 0-2 years

At the entry level, Junior Medical Analysts are primarily focused on learning the foundational aspects of medical data analysis. They assist senior analysts by cleaning datasets, conducting preliminary statistical tests, and preparing data for deeper analysis. This role emphasizes developing fluency in medical terminology and analytical tools under supervision. Junior analysts contribute to report generation and help validate data accuracy, while slowly building expertise in interpreting clinical results. Strong attention to detail and a willingness to learn the complexities of healthcare systems are critical at this stage.

Mid-Level Medical Analyst

Experience: 2-5 years

Mid-Level Medical Analysts handle more complex analytical projects with moderate independence. They design and implement statistical models, conduct in-depth research, and begin leading smaller projects or cross-functional initiatives. Responsibilities include collaborating directly with clinical teams to tailor data insights, conducting predictive analytics, and delivering presentations to diverse stakeholders. Mid-level professionals improve efficiency by automating workflows and mentoring junior colleagues. They must demonstrate robust technical skills, healthcare knowledge, and effective communication.

Senior Medical Analyst

Experience: 5-8 years

Senior Medical Analysts serve as strategic contributors responsible for guiding data-driven healthcare decisions. They oversee large-scale analytics projects involving multifaceted datasets and advanced modeling techniques such as machine learning and artificial intelligence. This role involves mentoring junior staff, shaping analytical methodologies, and influencing organizational healthcare strategies with evidence-based insights. Seniors often liaise with executive leadership and regulatory agencies, requiring excellent communication and leadership skills along with comprehensive technical and clinical expertise.

Lead Medical Analyst / Healthcare Data Scientist

Experience: 8+ years

At this pinnacle level, Lead Medical Analysts drive the vision and innovation agenda in healthcare analytics. They manage multidisciplinary teams, architect complex data solutions, and set standards for clinical data governance. Their role extends into research collaborations, policy advising, and implementation of cutting-edge technologies like predictive AI models and real-world evidence analytics. They play a critical role in influencing health policies, improving population health outcomes, and spearheading corporate or governmental healthcare initiatives.

Global Outlook

Medical Analysts are in demand worldwide, with opportunities expanding alongside global healthcare digitization and data-driven care transformation. North America, notably the United States and Canada, offers a robust market fueled by advanced healthcare infrastructure, extensive clinical research activities, and a high adoption rate of electronic health systems. Health IT and pharmaceutical hubs provide numerous roles requiring data expertise combined with clinical insight.

Europe also presents strong prospects, especially in countries with well-funded public health systems such as the United Kingdom, Germany, and the Netherlands. Increasing regulatory efforts around patient data protection (for example, GDPR) demand skilled analysts for compliance alongside data utilization. The presence of leading global pharma firms and research organizations make Europe attractive for medical analysis careers.

Emerging markets like India and Southeast Asia are rapidly developing healthcare IT sectors, creating new job openings focusing on expanding digital health records and health analytics for large populations. However, these regions may necessitate adaptability regarding regulatory environments and data infrastructure maturity.

Global health agencies, including the World Health Organization (WHO) and international NGOs, also recruit Medical Analysts to support large-scale epidemiological studies and public health interventions worldwide. Fluency in English is often required, and familiarity with region-specific health concerns or languages can enhance career mobility.

International collaboration, remote data projects, and evolving telehealth models increasingly enable Medical Analysts to contribute beyond geographic boundaries, highlighting a growing globalization of this profession.

Job Market Today

Role Challenges

Data privacy concerns remain a significant challenge in the Medical Analyst arena. Healthcare data’s sensitivity requires strict adherence to regulations like HIPAA in the US and GDPR in Europe, which can complicate access to data and limit large-scale analytics projects. Variability in data quality and format across different EHR systems adds another layer of complexity, demanding robust data cleaning and integration skills. Additionally, the rapid pace of healthcare technology advancement requires continuous learning to stay current with new analytical tools and methodologies. There is also a shortage of professionals who combine deep medical knowledge with data science proficiency, leading to competitive hiring environments. Finally, communicating complex analytical insights to non-technical healthcare providers can be a persistent hurdle, sometimes limiting the practical impact of analyses.

Growth Paths

The increasing digitization of healthcare records and the rising emphasis on personalized medicine are fueling demand for Medical Analysts globally. Health data analytics is central to improving patient outcomes, streamlining operations, and reducing costs, positioning this role at the forefront of healthcare innovation. Growth areas include predictive modeling for chronic disease management, real-world evidence aggregation for pharmaceutical research, health economics evaluation, and AI-driven diagnostics. Expansion into telemedicine, wearable data analysis, and population health management further diversifies career opportunities. Combined with increasing government and industry investments in health IT, Medical Analysts can expect rich prospects for career advancement and specialization.

Industry Trends

Healthcare data is transitioning from traditional reporting towards predictive, prescriptive, and real-time analytics, heavily influenced by advances in artificial intelligence and machine learning. Real-world evidence (RWE) analytics is shaping drug approvals and clinical guidelines, emphasizing data gathered outside controlled trials. Interoperability of medical data systems is becoming a priority, supported by evolving standards such as FHIR. Patient-generated health data (PGHD) from devices and apps is rapidly integrating into clinical decision-making processes. Ethical AI use and transparency in predictive models have become focal points due to concerns over bias and patient privacy. Telehealth growth accelerated by the COVID-19 pandemic continues to expand data sources. Furthermore, value-based care models rely increasingly on data analytics to assess and improve care quality.

A Day in the Life

Morning (9:00 AM - 12:00 PM)

Focus: Data Collection & Cleaning

Access and extract relevant clinical data from EHR and other health information systems.
Perform quality checks, data normalization, and preprocessing to ensure accuracy.
Collaborate with IT teams to resolve data integration issues.
Review and document data sources and transformations.

Afternoon (12:00 PM - 3:00 PM)

Focus: Data Analysis & Modeling

Apply statistical techniques and machine learning algorithms to analyze datasets.
Develop predictive models for patient risk stratification or treatment outcomes.
Conduct cohort analysis and comparative effectiveness studies.
Interpret findings in the clinical context.

Late Afternoon (3:00 PM - 6:00 PM)

Focus: Reporting & Collaboration

Prepare visualizations and reports tailored for clinicians, managers, and other stakeholders.
Present insights in meetings, simplifying technical concepts for diverse audiences.
Respond to feedback and refine analyses accordingly.
Plan next steps and document procedures for regulatory compliance.

Work-Life Balance & Stress

Stress Level: Moderate

Balance Rating: Good

While Medical Analysts often work within regular business hours, project deadlines and urgent clinical data requests can occasionally increase workload intensity. Balancing data-driven demands with healthcare provider expectations requires effective time management. Most organizations promote a structured office environment, enabling a sustainable work-life balance for those disciplined with task prioritization. The role’s intellectual engagement and impact on patient care often translate into high job satisfaction, mitigating stress factors.

Skill Map

This map outlines the core competencies and areas for growth in this profession, showing how foundational skills lead to specialized expertise.

Foundational Skills

The absolute essentials every Medical Analyst must master to function competently in this complex field.

Medical Terminology and Healthcare Systems Knowledge
Basic Statistics and Biostatistics
Data Cleaning and Preprocessing
Understanding of Healthcare Data Standards (e.g., HL7, ICD-10)

Analytical and Technical Skills

Specialized skills that enable the Medical Analyst to conduct comprehensive data investigations and predictive modeling.

Statistical Software Proficiency (SAS, SPSS)
Programming with Python and R
Machine Learning and AI Applications in Healthcare
SQL and Database Management
Data Visualization (Tableau, Power BI)
Natural Language Processing for Clinical Text

Professional and Soft Skills

The interpersonal, communication, and organizational skills required to thrive and collaborate effectively.

Effective Communication and Reporting
Ethical Judgment and Confidentiality
Collaboration with Clinical and IT Teams
Time Management and Prioritization
Critical Thinking and Problem Solving

Pros & Cons for Medical Analyst

✅ Pros

High demand and growing job security in a vital healthcare sector.
Opportunity to impact patient care and public health positively.
Diverse work environments including hospitals, pharmaceuticals, and government.
Continuous learning through exposure to emerging medical technologies and data science.
Satisfactory salary ranges with potential for advancement.
Interdisciplinary collaboration improves communication and teamwork skills.

❌ Cons

Handling sensitive patient data entails strict compliance and responsibility.
Data quality issues can make analysis challenging and time-consuming.
Fast-evolving technologies require ongoing skill upgrades.
Communicating complex data findings to non-technical stakeholders can be difficult.
Some roles may involve long periods of desk-based, computer-intensive work.
Regulatory and ethical constraints can limit data access and analysis scope.

Common Mistakes of Beginners

Overlooking the importance of medical terminology, leading to misinterpretation of data.
Ignoring data cleaning steps, resulting in inaccurate or unreliable analysis outcomes.
Relying on outdated software or statistical methods not appropriate for healthcare data.
Failing to consider patient privacy laws, risking compliance violations.
Not tailoring communication style to non-technical audiences, limiting impact of findings.
Attempting overly complex models without solid foundational understanding.
Neglecting continuous education and professional certification requirements.
Underestimating the interdisciplinary nature of the role and needing better teamwork skills.

Contextual Advice

Develop a strong foundation in both medical sciences and statistics before diving deep into analytics.
Seek internships or collaborative projects within healthcare settings to gain real-world exposure.
Continuously update your technical skill set, especially in programming and visualization tools.
Always prioritize data quality—clean and verify your datasets thoroughly.
Build the ability to communicate complex analytical insights clearly and effectively to diverse audiences.
Familiarize yourself thoroughly with patient privacy regulations relevant to your region.
Engage in professional organizations to network, learn, and stay informed on industry trends.
Showcase your projects and findings in a portfolio to demonstrate applied knowledge to potential employers.

Examples and Case Studies

Improving Hospital Readmission Rates with Predictive Analytics

A team of Medical Analysts partnered with a large urban hospital to analyze patient discharge data combined with demographic and clinical variables. Using machine learning models, they identified patients at high risk of readmission within 30 days and recommended targeted interventions. This initiative led to a 15% reduction in unnecessary readmissions, improved patient satisfaction, and lowered hospital costs.

Key Takeaway: Employing predictive modeling in clinical settings can drive tangible improvements in patient care and operational efficiency.

Pharmaceutical Safety Monitoring through Real-world Evidence

Medical Analysts at a pharmaceutical company utilized real-world evidence data from insurance claims and EHR databases to monitor adverse drug reactions post-launch. By integrating natural language processing and biostatistical methods, they identified rare but serious side effects earlier than traditional reporting systems, enabling faster regulatory response and risk mitigation.

Key Takeaway: Combining diverse data sources and advanced analytics strengthens drug safety surveillance capabilities.

Optimizing Resource Allocation in a Multi-Center Clinical Trial

In collaboration with clinical researchers, Medical Analysts developed a dynamic dashboard tracking patient enrollment, adverse events, and site performance across multiple trial locations. The real-time analytics facilitated adaptive resource distribution and accelerated trial completion without compromising data integrity.

Key Takeaway: Effective use of analytics tools enhances clinical trial management, reducing costs and timelines.

Portfolio Tips

Creating a compelling portfolio is essential to distinguish yourself as a Medical Analyst. Begin by showcasing a variety of projects that demonstrate your ability to handle real healthcare data responsibly. Include case studies where you performed data cleaning, applied statistical models, or developed predictive insights, emphasizing outcomes and impact. Visualizations should be clear, professional, and tailored to familiarize stakeholders with your storytelling capability through data.

Highlight collaborations across interdisciplinary teams, underlining communication skills and adaptability. Where possible, create interactive dashboards or reports to exhibit proficiency with BI tools like Tableau or Power BI.

Document your workflow steps transparently, showing ethical considerations and compliance with data privacy laws. Including sample code snippets or detailed methodology explanations can also signify strong technical competence.

Complement technical projects with any certifications or coursework in health informatics, biostatistics, and related domains.

Regularly update your portfolio to incorporate new skills, technologies, and healthcare trends. Hosting your portfolio online, such as a personal website or GitHub repository (with all sensitive data anonymized), provides accessibility to potential employers and networks.

Job Outlook & Related Roles

Growth Rate: 12%
Status: Growing much faster than average
Source: U.S. Bureau of Labor Statistics

Related Roles

Frequently Asked Questions

What educational background is best suited for a Medical Analyst role?

A solid foundation in health sciences, biology, public health, or data analytics is essential. Many professionals hold bachelor's degrees in these fields, but advanced degrees such as a Master’s in Health Informatics, Biostatistics, or Public Health can significantly enhance expertise and career prospects. Hands-on experience and certifications also play important roles.

Do Medical Analysts need programming skills?

Yes. While not all roles demand deep coding expertise, proficiency in programming languages such as Python or R is highly valuable for performing robust data analyses, automating workflows, and building predictive models. SQL skills are essential for querying large healthcare databases.

What types of data do Medical Analysts work with?

Medical Analysts handle electronic health records (EHR), clinical trial datasets, insurance claims, pharmaceutical databases, medical imaging reports, and increasingly patient-generated data from wearable devices. Handling multi-source data requires strong integration and data cleaning capabilities.

Is this career remote-friendly?

The field is evolving, but many Medical Analyst positions require secure access to healthcare systems and sensitive data, often limiting the feasibility for fully remote work. Controlled remote or hybrid arrangements are becoming more common with secure technology and proper compliance measures.

What certifications are helpful for Medical Analysts?

Certifications such as the Certified Health Data Analyst (CHDA) from AHIMA, Registered Health Information Administrator (RHIA), or Certified Professional in Healthcare Quality (CPHQ) validate expertise in health data management and quality. Additional credentials in data science or informatics can also strengthen candidacy.

What are common challenges faced by Medical Analysts?

Key challenges include dealing with poor data quality, navigating complex privacy laws, staying current with rapidly evolving technology and analytics methods, and effectively communicating insights to clinical and non-technical stakeholders.

How do Medical Analysts impact patient care?

By analyzing clinical data, Medical Analysts help identify risk factors, improve diagnoses, optimize treatment plans, and monitor healthcare outcomes. Their insights contribute to evidence-based medicine and quality improvements in healthcare delivery.

What tools should I learn to become a Medical Analyst?

Essential tools include EHR systems like Epic or Cerner, statistical softwares like SAS or SPSS, programming languages such as Python and R, database querying with SQL, and data visualization platforms like Tableau or Power BI. Familiarity with machine learning frameworks and natural language processing is increasingly helpful.

Can Medical Analysts transition into other healthcare roles?

Absolutely. Their analytical and clinical knowledge positions them well for roles in health informatics, clinical research, healthcare consulting, data science, or management roles within healthcare organizations.