These include hardened beliefs, personal style preferences, selective reception of data, decision-making and thinking flaws, and bounded rationality.
To describe the characteristics of included studies, we will extract: year of study; details of interventions and comparators; study design and source(s) of resource use, unit costs and (if applicable) effectiveness data; decision-making jurisdiction, geographical and organisational setting; analytical viewpoint; and time horizon for both costs and effects.
Without quality data, demand for data drops, data-informed decision making does not occur, and program efficiency and effectiveness will suffer (7, 26, 27).
When the quality of data is low, demand for data decreases, data-informed decision making does not occur, and program efficiency and effectiveness suffer (4, 16, 17).
Regular use of data in decision making generates demand for quality data and the reinforcement of data-informed decision making processes (4).
By demystifying how data works, data science trainings can increase manager confidence in data and increase data-driven decision making in an enterprise.
Regular use of data in decision making generates demand for quality data and the reinforcement of data-informed decision-making processes.
In order for stakeholders and decision makers to use data in decision making, they need to place value on data (41).
The higher the value data users put on data-informed decision making, the more likely they are to use data.
This can lead to incomplete data and time lags between reporting and use of data for decision making, which hinders optimal health system performance.
