Abstract
BACKGROUND: Medical Assessment Units (MAUs) have become a popular model of acute medical care to improve patient flow through timely clinical assessment and patient management. The purpose of this study was to determine the effectiveness of a consensus-derived set of clinical criteria for patient streaming from the Emergency Department (ED) to a 15-bed MAU within the highly capacity-constrained environment of a large quaternary hospital in Queensland, Australia. METHODS: Clinically coded data routinely submitted for inter-hospital benchmarking purposes was used to identify the cohort of medical admission patients presenting to the ED in February 2016 (summer) and June 2016 (winter). A retrospective review of patient medical records for this cohort was then conducted to extract MAU admission data, de-identified patient demographic data, and clinical criteria. The primary outcome was the proportion of admissions that adhered to the MAU admission criteria. RESULTS: Of the total of 540 included patients, 386 (71 %) patients were deemed to meet the MAU eligibility admission criteria. Among patients with MAU indications, 66 % were correctly transferred (95 % CI: 61 to 71) to the MAU; this estimated sensitivity was statistically significant when compared with random allocation (p-value < 0.001). Transfer outcomes for patients with contraindications were subject to higher uncertainty, with a high proportion of these patients incorrectly transferred to the MAU (73 % transferred; 95 % CI: 50 to 89 %; p-value = 0.052). CONCLUSIONS: Based on clinical criteria, approximately two-thirds of patients were appropriately transferred to the MAU; however, a larger proportion of patients were inappropriately transferred to the MAU. While clinical criteria and judgement are generally established as the process in making decisions to transfer patients to a limited-capacity MAU, our findings suggest that other contextual factors such as bed availability, time of day, and staffing mix, including discipline profile of decision-making staff during ordinary hours and after hours, may influence decisions in directing patient flow. Further research is needed to better understand the interplay of other determinants of clinician decision making behaviour to inform strategies for improving more efficient use of MAUs, and the impact this has on clinical outcomes, length of stay, and patient flow measures in MAUs.