Linus Chirchir

Health Data Analyst

  • Department of Orthopaedics and Trauma

Contact details

Address

Street

Institute for Regeneration and Repair
Edinburgh BioQuarter
4-5 Little France Drive

City
Edinburgh
Post code
EH16 4UU

Availability

  • Monday to Friday: 9 AM - 5 PM Hybrid Working

Biography

Background

I am a Health Data Analyst with over 13 years of experience in health information systems, healthcare research and analytics. My core competencies include cleaning and merging existing datasets, conducting analyses of routinely collected National Health Service (NHS) and administrative datasets, developing and implementing data analyses and collection systems, and contributing to manuscripts for publication in peer-reviewed journals. My mission is to leverage data science and machine learning to improve health outcomes, quality of care, and operational efficiency in the health sector.

Qualifications

2022 – 2023    Swansea University, Swansea, United Kingdom, Master of Science in Health Data Science     

2018 – 2019    United States International University - Africa, Nairobi, Kenya, Master of Business Administration (Health Leadership and Management)

2014 – 2015    Kisii University, Kisii, Kenya, Master of Information Systems              

2008 – 2012    Moi University, Eldoret, Kenya, Bachelor of Business Management (Business Information Systems Management)

Responsibilities & affiliations

2023 – Current  Health Data Analyst, University of Edinburgh, Edinburgh, United Kingdom         

2008 - 2021         Information and Communication Technology (ICT) Officer, Moi Teaching and Referral Hospital, Eldoret, Kenya

Research summary

I am a seasoned researcher at the intersection of healthcare and technology, with a strong emphasis on real-world applications that can improve patient outcomes.

Current research interests

I am particularly excited about leveraging machine learning tools and techniques for the early detection and prediction of diseases. As we enter an era where healthcare is becoming increasingly data-driven, machine learning offers an unparalleled opportunity to revolutionise diagnostics and predictive analytics. By using machine learning algorithms capable of analysing complex datasets, I aim to develop diagnostic models that are not only more accurate but also quicker in identifying diseases at an early stage. The ultimate goal is to facilitate timely interventions that could significantly improve patient prognosis and quality of life. As I move forward, I am eager to collaborate with interdisciplinary teams of healthcare professionals, data scientists, and machine learning experts to bring these transformative healthcare solutions from the lab to the bedside.

Past research interests

My published works cover a wide spectrum, including studies on the adoption of Electronic Health Records (EHRs) by nurses and the factors that influence this. I've also researched how medical facilities are using Facebook as a tool for public interaction and information sharing. I have delved into understanding what drives professional satisfaction among healthcare providers like physicians and nurses, and how this impacts patient care. I've also examined various factors such as inspirational motivation and individualized consideration that affect employee performance in healthcare settings. My master’s dissertation used machine learning to evaluate cognitive assessments for early detection of Alzheimer’s disease, indicating my commitment to applying multidisciplinary approaches to solve healthcare challenges.

Peer Reviewed Conferences

Peer Reviewed Journal Articles

Unpublished Manuscript

  • Chirchir, L. K. (2023). Evaluating Cognitive Assessments in the Progression to Early-Stage Alzheimer’s Disease Using Machine Learning. Manuscript in Preparation.
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