Programme indicators a manager can defend
AI-KPI brings the indicators a tuberculosis programme already tracks into one place. Each figure keeps the numerator, denominator, and source behind it, so a manager can explain the number.
The numbers already exist
People examined, cases notified, cluster yield, and screening efficiency are usually spread across workbooks. Reconciling them takes time, and the trail behind a figure is easy to lose.
The count, and where it came from
A dashboard number is useful when someone can open it and see the arithmetic.
Indicator
Number needed to screen
People screened for each bacteriologically confirmed case.
- Numerator
- People screened in the cycle
- Denominator
- Bacteriologically confirmed cases
- Source
- Tuberculosis unit campaign register
An example of how a figure is held. Your own registers supply the values.
Ask the programme, not the spreadsheet
01
How close is this unit to its TB Mukt Bharat target?
Progress is read at the unit, not only at the district, with the target and the latest figure side by side.
02
Which clusters returned the highest yield?
Yield stays attached to the cluster that produced it, so the next screening plan can learn from the last one.
03
How efficient was screening this cycle?
People examined, cases notified, and the effort behind them stay in one view instead of three workbooks.
What does AI-KPI change about reporting?
The indicators stay the ones a programme already tracks. What changes is that they sit together, and each figure keeps the count and the source behind it.
Can a manager ask in ordinary language?
Yes. A question about a unit's progress toward its TB Mukt Bharat target can be asked in plain language, and the answer stays tied to the figures and their sources.
How does this sit with screening and laboratories?
AI-ACF decides where to screen. AI-DNO decides where samples go. AI-KPI shows whether that work changed the indicators the programme is accountable for.