How to optimize the prescription of laboratory tests? Success and failures in an academic hospital

Abstract

In the context of the flat-rate reimbursements in healthcare, we reviewed physicians' behavior towards laboratory test ordering. We demonstrated how it could be improved when a specific stage of the patient management is considered. We took a multi-step approach to analyze the laboratory test orders in the context of planned laparoscopic cholecystectomy in a general teaching hospital. A reference order set was defined through a collaborative analysis between clinicians and laboratory physicians. The clinical and financial impacts were then evaluated over a period of 24 months. After the introduction of the reference order set, the number of laboratory tests per order decreased significantly for patients with cholecystitis of low severity. Above the monitoring of repeated orderings during a single stay, the major impacts were achieved by a drastic reduction of inappropriate orders, particularly in the field of bacteriology. The main effects of the order set were maintained throughout a follow-up period of 24 months. Our study demonstrated that, when considering laboratory test ordering optimization, reference order sets could achieve high levels of efficiency. To ensure high compliance to reference order sets, extensive collaboration between clinicians and laboratory physician is mandatory even if very sophisticated information systems are available.

The mean abnormal result rates of laboratory tests ordered in the emergency department: shooting percentage insights from a multi-centre study

“…Our study uncovered considerable differences in MARR scores of individual tests, and between nurse-and physicianinitiated orders. As previous studies have highlighted large numbers of tests per order [9] and physician experience [10] as factors in inappropriate lab test ordering, it is not unreasonable to infer that the correlation of these factors with MARR scores is significant. At the departmental level, MARR score data may help direct critical downstream questions, such as: (a) Are tests with low scores (close to the theoretical minimum of 5%) being ordered excessively?…”