MODELING RISK IN COMPLEX MEDICAL DOMAINS: UNDERSTANDING 'HIDDEN' INTERACTIONS AND VARIATIONS ACROSS DIFFERENT PHASES OF CARE

摘要

Medicine is a complex, safety-critical and highly interactive system. Despite efforts to provide safe, effective care, adverse events still occur - clinicians make diagnostic and therapeutic errors, system constraints impact the coordination and delivery of care and patients suffer unexpected complications and injuries. Our current understanding of the nature of medical adverse events, and our ability to develop durable preventative or mitigating strategies has been hampered by a somewhat out-dated and inadequate model of the clinical system in which care is delivered, particularly with respect to risk and reliability. Traditional models of clinical risk have focused almost exclusively on patient factors (e.g., co-morbidities such as cardiovascular disease or diabetes), provider factors (e.g., a surgeon’s prior experience with a specific procedure) or the specific clinical procedure being performed (e.g., the technical and anatomic aspects of a cardiac bypass procedure). Absent from such models are the interactions and inter-dependencies between different system components (staffing, instrumentation, protocols, procedures, access to and quality of information, communication modes, and scheduling cycles, system-wide volume and acuity, throughput pressures), an understanding of the reliability of such components under different operating conditions and the significance of failure during different phases of care. In an effort to improve the understanding of medical error and adverse events, we have been modelling a series of clinical processes using the ITEM Quantitative Risk Assessment System (iQRAS) for a major Harvard Medical School teaching institution. The iQRAS tools have proved particularly useful for modelling complex interactions between clinical subsystems and for modelling variations in risk across different phases of care (e.g., pre-operative planning phase, intra-operative phase, intensive care unit phase, recovery phase, post-discharge phase) using the mission-timeline approach. This paper describes the use of the iQRAS tools to perform a powerful risk assessment in one high risk clinical domain, and how the models are being generalized for application to a broad range of other healthcare settings.