Computer-assisted practitioner-response system for studying the use of cimetidine

Prior research has shown that drug utilization studies often rely on manual chart reviews, which can be time-consuming and incomplete. This gap motivated the development of more efficient data collection methods. It was already known that cimetidine is frequently prescribed in hospital settings. However, the extent of its use for non-FDA-approved indications remained unclear. The need to assess drug interactions and prescribing patterns was recognized but not fully addressed. Traditional methods lacked the speed and completeness of newer systems. Computerized approaches had not been widely tested for this purpose. This study aimed to bridge the gap between manual and automated data collection techniques.

The aim was to evaluate a computer-assisted practitioner-response system for assessing cimetidine use in a hospital. The specific problem was the inefficiency of conventional chart reviews in capturing prescribing reasons and drug interactions. The motivation was to streamline data collection and improve accuracy. The study focused on identifying patients receiving cimetidine during a specific period. It also aimed to determine the reasons for prescription and assess drug interactions. The goal was to compare the efficiency of the new system with traditional methods. The study targeted a real-world hospital setting to ensure practical relevance. The focus was on both data completeness and speed of information retrieval.

The study used a computerized medication-profile system to identify patients receiving cimetidine. Data collection occurred from April 1, 1981, through May 15, 1981. A practitioner-response form was placed in patient charts to capture prescribing reasons. If forms were incomplete, chart reviews were conducted for missing data. Patient demographics and drug interactions were also recorded. The system tracked duration of therapy and concurrent medications. Data were collected daily to ensure timeliness. The method combined automated identification with manual follow-up for missing information.

Cimetidine was prescribed to 10% of patients admitted during the study period. Of these, 70% of practitioner-response forms were completed. An additional 20% of prescribing reasons were obtained through chart reviews. Seventy-five percent of patients faced potential drug interactions. The most common indication was stress ulcer prevention. Only 15% of prescriptions were for FDA-approved uses. The computer-assisted system identified patients efficiently. The method proved faster than traditional chart reviews for data collection.

The computer-assisted system was an efficient method for reviewing cimetidine use. It facilitated rapid identification of patients receiving the drug. The practitioner-response form improved the speed of capturing prescribing reasons. The system outperformed conventional chart reviews in completeness. It revealed a high rate of potential drug interactions. The findings suggest the system could be useful in other drug studies. The method's efficiency supports its use in hospital drug utilization research. The results align with the authors' claim that automation enhances data collection.