The quality control of oral anticoagulant therapy (OAT) during the initiation and maintenance treatment is generally poor. Physicians' ordering of OAT (especially fluindione and warfarin) can be improved by dose adjustment algorithms, taking into account the results of International Normalized Ratio (INR). Reminders at the point of care, computerized or not, have been demonstrated to be effective in changing physicians prescription behavior.
However, few studies have addressed the benefit of personalized reminders versus non personalized reminders, whereas the personalized reminders require more development to access patient record data and integrate with the computerized physician order entry system.
The Hospital Information System of George Pompidou European Hospital integrates an electronic medical record, lab test and drugs order entry system. This system allows to evaluate such reminders and to consider their implementation for routine use as well as the continuous evaluation of their impact on medical practice quality indicators.
The objective of this study is to evaluate the impact of two types of reminders on overtreatment by oral anticoagulant: a simple reminder of text formatted dose adjustment table and a personalized recommendation for oral anticoagulant dose and next date of INR control, adapted to patient data. Both types of reminders appear to the physician at the moment of drug ordering.
The study is an alternating time series experiment with three 6 months periods, each one including every 2 months according to a Latin square scheme: a control period without any reminder, a period with the simple non personalized reminder, a period with personalized reminder. All patients hospitalized in departments using the computerized physician order entry system and ordered fluindione or warfarin, will be included in the study between November 2004 and May 2006.
Main outcome will be the proportion of overcoagulation, as expressed by the proportion of observation time with INR over 4.5, assuming INR change linearly. Secondary outcome is the incidence of major haemorrhagic events. Data will be collected thanks to Hospital Information Systems databases.
Data will be analyzed taking into account patient and physician clustering effect.