Technical transfer
Part of technical work is leaving it transferable. These are the conferences, postgraduate programmes and open initiatives where I have documented and taught the methods I use at the Ministry.
Exercises
Everything runs on open health data: real administrative records, with the mess and ambiguity that entails. No sample datasets, and no patient data.
Where I teach
2022
Postgraduate teaching in digital health strategy leadership and management.
Faculty on the Postgraduate Diploma in Digital Health Strategy Leadership and Management at Universidad Andrés Bello, aimed at professionals who will lead digital transformation inside health institutions.
2024
Data analysis material built on open health data rather than marketing case studies.
Analytics is almost always taught with examples from marketing, commercial logistics or finance. For health staff that introduces a concrete methodological friction: the procedure may be right, but the data structure looks nothing like what they handle at work.
The Hazla con Datos material was built on open health data — the ministry’s REM and DEIS series and the national open data portal — which are real administrative records, not sample sets: they carry the same coding, quality and volume problems staff face at work.
2018
Open health data science bootcamp, with all its material in a public repository.
OpenSalud LAB was created in 2018 as a citizen laboratory for open innovation in public health services. Its main output was the Health Data Science bootcamp, hosted entirely in a public GitHub repository.
The programme gathered over 100 hours of video training and more than 200 hours of practical resources, covering R programming, exploratory analysis, statistical modelling, process management and technical reproducibility, built with R-Ladies Concepción and Data UC at the Pontifical Catholic University of Chile.
Contents
The technical syllabus, from command-line tooling fundamentals through to natural language processing over clinical text.
| Module | Contents | Tooling | Clinical application |
|---|---|---|---|
| 01Foundations | Coding mindset, project path hygiene, algorithmic logic and pseudocode. | Markdown · Obsidian · WSL · Bash | Logical structuring of analytical thinking, without operational barriers to entry. |
| 02Data wrangling | Health-specific regular expressions, clinical spreadsheet processing and cleaning. | R (tidyverse) · Python · Regex | Normalising ICD-10 diagnoses and cleaning administrative health tables. |
| 03Large-scale processing | Embedded OLAP engines, data transformation schemes and SQL querying. | DuckDB · SQL · Apache Parquet · dbt | Ability to process tables of millions of care episodes on personal computers. |
| 04Statistical modelling | Biostatistics, inference, regression and survival analysis with machine learning. | R (survival, tidymodels) · Python (scikit-learn) | Estimating survival curves, proportional hazards and interpreting uncertainty. |
| 05Architecture and NLP | Natural language processing and pseudonymisation schemes. | Transformers · Docker | Entity extraction from free clinical text, and techniques for protecting confidentiality. |
Record
Peer-reviewed conferences and public-sector training venues.
2026
Official instructor for the Positron and modern data-science tooling workshops
LatinR 2026
2025
Talk and participation in the technical panels
LatinR 2025
2024
In-process databases (DuckDB) for handling big data in surgical waiting lists
LatinR 2024
2022
Technical work on programming applied to public health
useR! 2022
2021
Why public health officials should program in R
LatinR 2021
2021
Speaker at the International Telemedicine Congress
Ministry of Health of Peru
2021
Lead facilitator of the Health Data Science workshop and workshop leader in the Public Innovators Network
Laboratorio de Gobierno, Chile