Translate

Showing posts with label Pharmacoeconomics. Show all posts
Showing posts with label Pharmacoeconomics. Show all posts

Monday, July 13, 2020

DRUG UTILIZATION REVIEW



Drug utilization review (DUR) programs have been defined as “structured, ongoing initiatives that interpret patterns of drug use in relation to predetermined criteria, and attempt to prevent or minimize inappropriate prescribing.”
DUR programs differ from drug utilization studies, which are time-limited investigations that measure drug use, but do not necessarily assess appropriateness or attempt to change practice.
Recently, the use of clinical decision support within computerized prescriber order entry (CPOE) programs has risen dramatically. The use of such programs to improve prescribing can be considered a form of prospective DUR in which prescribers are the targets of interventions
Generally, the DUR process involves comparing actual behavior to explicit, prospectively established standards, referred to as criteria. For example, a commonly used criterion is that patients should not receive more than one non steroidal anti-inflammatory agent at any one time. Criteria have been developed to identify the following types of problems: drug–drug interactions, drug–disease interactions, drug–age interactions, drug–allergy interactions, use of too high or too low a dose, duplication of therapeutic class, excessive duration of therapy, obtaining prescription refills sooner or later than should be needed, failure to prescribe a known effective agent in patients with certain conditions, abuse of psychoactive medications, and use of a more costly agent when a less costly agent is available. After developing criteria, the next step in the DUR process is to measure adherence to explicit criteria by examining individual-level data. Instances in which medication use does not agree with criteria are called exceptions.
Next, interventions are implemented where appropriate, often following an implicit review. Although the general model for DUR does not require that practitioners be made aware of individual exceptions occurring in their patients (that is, interventions can be made based on aggregate rather than individual findings), this step usually involves alerting the physician and/or pharmacy of record as to the occurrence of the specific exception.

There are different settings in which the DUR model is applied.

Outpatient retrospective DUR programs use
computerized administrative data (i.e., pharmacy and medical claims data maintained for billing and other administrative purposes) to identify exceptions that are then reviewed by a physician or pharmacist, or by a committee of health professionals, and result in an intervention (e.g., a mailed alert letter to the physician). The alert letter typically describes the DUR program and the criterion, and provides literature references supporting the criterion and a patient profile demonstrating that the criterion was violated.



Meta Ananlysis


Meta –analysis is a method which can be used to combine the results of two or more studies. The first Meta –analysis performed by karlpearson in 1904
Meta –analysis may conveniently be defined as a quantitative method of pooling information from independent studies concerning a single theme in order to draw conclusion.

Meta –analysis does not simply involve averaging the results of the individual studies, but requires a statistical method which combines the result whilst taking into account the size of the studies. Thus Meta –analysis is the statistical analysis of a large collection of analysis results for the purpose of integrating the findings.

Meta –analysis can provide researchers with single pooled results to answer whether treatment A is more beneficial than treatment B. This pooled result is usually more precise than the result from the individual studies. The precision with each of these studies calculates the treatment effect depends on many factors including the number of people in the study. Generally as the number of people increases in a study the precision of the treatment effect will increase.

Therefore by statistical combining the all the sample size together from the individual studies, the precision of our pooled result for the treatment effect can be improved.

Meta – analysis increases power. By combining the results from the smaller studies using Meta– analysis we can increase the overall power of the analysis. Therefore, the results from a Meta – analysis will usually have more power than the results from the individual studies.

STEPS TO PERFORM META – ANALYSIS

The theoretical  relationship of interest
Collect the population of studies that provide data on the relationship.
Code the studies and compute the effect sizes
Examine the distribution of effect sizes and analyze the impact of moderating variables
Interpret and report the results.

SUMMARY
 Meta – analysis leads to a shift of emphasis from single studies to multiple studies. It is performed with assistance of computer data bases (Microsoft access, paradox) and statistical software (DSTAT, SAS). The most common use of Meta – analysis has been in quantitative literature review. A valid meta – analysis however, require careful planning in the protocol stage as for any other research.