Examining Front-Line Decision Making in State and Local Communicable Disease Control Programs

Front-line professionals in state and local public health agencies make important decisions for communicable disease control, under complex and challenging conditions. While front-line decisions are often routine, many have significant impact on the control or spread of serious and highly infectious diseases. Heuristics (personal rules of thumb) and biases can affect information collection, data analyses, and interventions recommended and taken.

Now more than ever, public health leaders are expected to assure effective decision-making. Recent events and the constant presence of media and Internet information about communicable diseases have heightened public awareness and concern, placing a new level of pressure and scrutiny on public health agencies. Public health agencies are expected to identify outbreaks and develop fast, informed, epidemiologically effective, and politically sensitive strategies to control outbreaks. Software support can enhance these decision-making processes.

Examining human factors and understanding the underlying cognition of decision makers is essential for developing useful and accepted decision support tools. Our research examined these factors through an innovative application of process tracing, observing responses to ambiguity in protocols and guidelines, and individual heuristics and biases used in decision-making.

We observed, through 60 interviews, how data are collected and analyzed, and processes used to make intervention decisions. For example, we observed front-line decision-making to determine if a case is urgent, if a cluster is an outbreak, and if investigation or contact tracing is needed. We examined decisions on the need to: exclude employees or children from work or school; inspect or close facilities; administer post-exposure prophylaxis, and conclusions on the overall source of a simulated outbreak.

This presentation will describe the semi-structured interview and enteric disease scenario used. We will present highlights of study results. Finally, we will discuss requirements and recommendations for decision support tools for enhancing decision-making in public health communicable disease control.