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Internal standards: rationale for use in a drug utilization review program

S L Huber, R A Patry

Drug Intelligence & Clinical Pharmacy

|October 1, 1981

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Standardized criteria for drug usage are difficult to approve. Developing "internal standards" allows flexibility and facilitates data comparison between institutions, enhancing drug utilization reviews.

Area of Science:

Health Services Research
Pharmacoeconomics
Clinical Informatics

Background:

Comparing drug usage data across institutions is challenging due to a lack of standardized criteria.
Physician group approval for standardized criteria presents significant hurdles.
Need for adaptable criteria to allow for therapeutic innovation in drug management.

Purpose of the Study:

To evaluate the utility of "internal standards" in facilitating the adoption and use of standardized drug usage criteria.
To assess the impact of internal standards on drug utilization review processes.
To determine if internal standards enable easier comparison of drug usage data between healthcare institutions.

Main Methods:

A retrospective drug utilization review was performed in two distinct hospital settings.

Related Experiment Videos

"Internal standards" were developed, defining acceptable deviations from standardized criteria.

Comparison of assigned internal standards with InterQual 0 percent-100 percent standards was conducted.

Main Results:

Internal standards demonstrated similarity to InterQual standards, with notable differences in monitoring parameters and duration of therapy criteria.
Preliminary findings suggest that internal standards enhance the application of standardized criteria.
The use of internal standards appears to facilitate cross-institutional data comparison.

Conclusions:

Internal standards offer a viable approach to overcome barriers in adopting standardized drug usage criteria.
This methodology promotes flexibility in drug therapy while enabling robust data comparison.
Internal standards are a promising tool for improving drug utilization reviews and healthcare data analysis.