A SEIPS Based Task-Analysis of Informational Inputs Relevant to Patient Placement Decision-Making

Abstract

Patient placement specialists (PPS) integrate their knowledge of health system capacity and individual patient needs to strategically place patients in appropriate hospital beds. Supporting patient placement decision-making is essential to improving patient throughput and outcomes; thus, we aimed to identify informational inputs and systems barriers to obtaining those informational inputs. We interviewed (n = 4) and did 8 hr of shadowing with PPS and their managers to develop a cognitive task analysis, which revealed 1 primary goal and 4 sub-goals involved in patient-placement decision-making. To match a specific patient to a bed, PPS must identify five pieces of information about the patient, for example, reason for admission. PPS then consider these inputs within the current system state, which requires monitoring four informational inputs, for example, bed availability. Understanding the information that PPS need to make effective placements is critical to designing technology and systems that support patient placement and improve patient outcomes and throughputs.