We should commend Malone et al for submitting this
AHRQ-supported* study [1] for publication when a flaw in its design or
execution could be the authors’ main reason for concluding that “the current
study was not able to demonstrate a significant beneficial effect of the
educational outreach program on [the primary performance outcome measure].” This
blog’s “Back-to-School” service campaign did not exclude studies reporting
negative outcomes because these studies can potentially inform continuing education
in the health professions (CEhp) as much as positive studies can.
CEhp/CME educational proposals,
audience-generation strategies, and outcomes reports now specify relevant “target
audiences,” recognizing that not all practitioners with a certain degree,
specialty, or other professional demographic description would benefit from the
same educational activity or design. With this more recent recognition of the
importance of targeting specific clinicians and learning about their needs has
come greater recognition that many CE participants should not be included in aggregated
data. This is even truer in studies with matched pairs, where the step of
greatest importance lies in setting match criteria. On September 15th, I
discussed an opioids-education study where matching criteria were so stringent
that the authors were not able to match certain participants (physicians in the
intervention group), and these participants’ data and group assignments were handled
nicely and reported clearly in the paper [2] (see post at http://fullcirclece.blogspot.com/2015/09/eight-year-canadian-study-on-opioid.html).
Conversely, the first result
listed in this study’s abstract indicates a matching flaw for a study on
education on drug-drug interactions (DDIs): “The 2 groups were significantly
different with respect to age, profession, specialty, and geographic region.” This
finding undermines other benefits to the study, namely, that large samples (19,606
prescribers) were recruited to both groups (educational intervention vs.
control) and matched on prescribing volume. Individualized education (also
known as academic detailing) was delivered by trained pharmacists as clinical
consultants who met with prescribers to “provide one-on-one information …
promote evidence-based knowledge, create trusting relationships, and induce
practice change.” This study’s performance (behavioral) measure was a reduced
rate of prescribing potential DDIs. The prescribing of 25 clinically important,
potential DDIs increased more in the intervention group than it did in the control
group.
In conclusion, when we look at
this presumably negative finding, we are left to wonder whether the educational
intervention was not effective—or whether a better matching process might have
revealed different results on reducing potential DDIs and improving health care
quality and utilization. One could argue that with nearly 20,000 prescribers in
both samples, more matching criteria could have been applied without
sacrificing so many data points that results would be inconclusive. The study’s
design as a retrospective study could also explain recruitment and matching
practices. In social sciences research (including educational outcomes
research), a core expectation is generalizability of a sample to a population of
interest; when reasonably achieved, generalizability lets us apply findings to practical
needs and future decisions.
Recall the study conclusion
quoted above: “The current study was
not able to demonstrate a significant beneficial effect …” (emphasis added). A
secondary analysis with different pair-matching practices might yet inform national
initiatives in improving quality while reducing costs through academic
detailing, both of which help patients. Now let’s remember to thank Malone,
Liberman, and Sun for sharing their data and methods with the healthcare
quality and educational research communities in the Journal of Managed Care & Specialty Pharmacy.
* AHRQ = United States Agency
for Healthcare Research and Quality
1. Malone DC, Liberman JN, Sun D. Effect of an educational outreach program on prescribing potential drug-drug interactions. J Manag Care Pharm. 2013;19(7):549-557. http://www.ncbi.nlm.nih.gov/pubmed/23964616. [Featured Article]
2. Kahan M, Gomes T, Juurlink DN, et al. Effect of a course-based intervention and effect of medical regulation on physicians’ opioid prescribing. Can Fam Physician. 2013;59(5):e231-e239. http://www.cfp.ca/content/59/5/e231.full.pdf+html.
Free Full Text: http://www.amcp.org/JMCP/2013/September_2013/17103/1033.html
MeSH “Major” Terms: Drug Interactions; Drug Prescriptions; Education, Medical, Continuing; Health Education; Physician's Practice Patterns; Prescription Drugs/administration & dosage
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