Path
Health Informatics Specialist
For the specialist between the health information system and the people who must act on it — reporting completeness, aggregate data structures, and the denominators a routine system does not give you.
Competencies
Routine system data structures
AdvancedWork with DHIS2-style facility-period-element aggregates and know what each level of the hierarchy can and cannot support.
Completeness and timeliness
AdvancedMeasure whether a system is reporting before measuring what it reports, and gate every downstream indicator on the answer.
Denominator reasoning
AdvancedRecognise where an administrative denominator is doing the work in a coverage figure, and say so.
Data flow design
IntermediateTrace a figure from the register it originates in to the dashboard it lands on, and find the step that breaks it.
Before you start
- Working knowledge of a health information system — DHIS2, an EMR, or a national reporting pipeline you have had to repair.
- Comfort at a command line, or the patience the first language lesson asks for.
The route through
Stage 1
Ground the numbers
State what the system's indicators mean, so a pipeline you build later transports a definition rather than a column.
Stage 2Choose one
Pick the language your team uses
Go deep in one of Python or R. Which one is usually decided by the codebase you inherit, not by the merits — and this is the stage where that stops being a limitation.
These are alternatives, not a sequence. Take the one your team already uses — the second is far cheaper to add once you have the first.
Stage 3
Make the data trustworthy
Turn a raw export into a table you would defend line by line, and hand over a cleaning log that answers the auditor's question before it is asked.
Stage 4
Put the files together
Assemble several exports into one analysis table you would defend column by column, with every join proved, every grain stated and every denominator sourced.
Stage 5
Check it like an auditor
Assess your own data before a donor does — five dimensions with measures, a recount against the source, and a report where every finding has an owner and a date.
Stage 6
Define what you report
Write indicator definitions two analysts compute the same way, defend the denominator, and set a baseline and target that survive a mid-term review.
Stage 7
Put an interval on it
Analyse a cluster survey the way its design requires — weights from the frame, a measured design effect, and an interval that says what the sample can and cannot settle.
Stage 8
Know where it came from
Read a routine reporting system as the database it is, pull an extract you can point at months later, and answer what was counted for any figure it produces.
Stage 9
Apply it to nutrition
Take the methods into one sector — WHO growth standards, SMART plausibility, the IPC phases and the Sphere performance thresholds, on a survey and a treatment register.
Stage 10
Apply it to public health
Rates with person-time denominators, treatment cascades, coverage three ways, and an outbreak line list turned into a curve, attack rates and a case fatality you can defend.
Stage 11
Apply it to WASH
The JMP service ladders and the Sphere minimums on a household survey, then a monitoring register with repeat visits that turns one functionality rate into three and bounds each against the rounds nobody drove.
Stage 12
Apply it to food security
FCS, HHS, rCSI and the livelihood coping module built from raw components and found to disagree by a factor of seven, then a price series whose seasonality dwarfs the programme effect and the evidence table that reports both.
Stage 13
Apply it to protection
The analyses you must decline to publish, alongside the ones that matter — a consent-gated referral pathway, a nineteen-point equity gap located at a specific gate, and a caseload that explains two other tables.
Stage 14
Apply it to education
Gross against net enrolment on a projected denominator, two attendance numbers twenty-six points apart, a cohort through promotion and repetition, and two assessment rounds whose instruments differ.
Stage 15
Say how sure you are
An interval on every proportion, the right test for a comparison, an effect size beside every p-value, and the count of comparisons that turns two striking schools back into noise.
Stage 16
Model more than one thing at once
A coefficient is a comparison — which one, between which units, adjusted for what. An odds ratio your reader will misread, a covariate that removes 42% of the effect, and a model that explains three per cent and settles a targeting decision.
Stage 17
Say what caused it
A seven-point gain that is entirely the school year, a comparison group imbalanced on every characteristic measured, and the minimum detectable effect that decided the answer before any data existed.
Stage 18
Put it in front of them
The mark the comparison implies, an interval that stops a ranking, a palette that already means something to this audience, and a figure generated from the dataset so the chart and the sentence cannot drift.
Stage 19
Make it rerunnable
Raw data read-only and code the only thing edited, an environment pinned so a colleague's laptop gives your numbers, checks that stop the pipeline rather than producing a plausible wrong one, and a handover a successor can act on.
Stage 20
Put it in front of the decision
A page that answers one question rather than twenty, the definition panel that stops the monthly argument, and the report whose findings, limitations and recommendation survive being read separately.
Recommended projects
How the skill is assessed
Can compute reporting completeness at facility and period level and explain why it precedes every other indicator.
EvidenceAn analysis in which completeness gates the coverage calculation rather than being reported beside it.
Can identify a coverage figure that is wrong because of its denominator rather than its numerator.
EvidenceA comparison of two rankings differing only in denominator, with the ranking movement attributed.
Can state what a routine system cannot measure and what would be needed instead.
EvidenceA limitations note distinguishing service coverage from programme coverage.
A routine health information system produces a lot of numbers and very few answers. The gap is usually structural: a facility that did not report looks identical to a facility that vaccinated nobody, and a coverage rate is only ever as good as the population estimate underneath it.
The vaccination project in this path is exactly that failure, isolated and measured. Ranking facilities on a denominator that counts unreported months moves some facilities twenty-one places, and every facility that gains rank is at 58% reporting completeness while every facility that loses rank is above 91%. The ranking was measuring reporting, not vaccination.
What this path still needs
Three courses are published: Data Analysis Foundations, Python for Programme Data and R and the Tidyverse for Programme Data. The routine-systems content this role turns on is Routine Data and DHIS2 in the Indicators and Measurement module, which is on the programme roadmap and not yet written. See the programme for its syllabus.