Abstract
The recent trend of e-detailing in the pharmaceutical industry aims to increase the effectiveness of promotion of prescription products to physicians at a less expensive way than traditional detailing. In the proposed promotion response model, the effect of e-detailing on new prescriptions is accounted for in the presence of traditional face-to-face detailing and a host of product-specific factors. The model is calibrated on 21 ethical pharmaceutical products in six diverse therapeutic categories over a period of two years using datasets from two industrial sources. We estimate our model once at the aggregate level and once using a fixed-effects methodology to account for unobserved heterogeneity across products. We find that prescription product (Rx) manufacturers appear to benefit from increasing both e-detailing and traditional detailing. Our findings also lead us to conclude that there is room for improving the synergy between the two types of detailing.
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Notes
Throughout the paper, we use the word “physician” to refer to physicians and all health care professionals detailed by the pharmaceutical sales representatives.
Please note that SDI Health, LLC, recently acquired Verispan, LLC, that was, in turn, formerly known as Scott-Levin, Inc.
We thank an anonymous reviewer for suggesting this model.
We keep the therapeutic states and the prescription products confidential at the vendors’ requests.
For Category 1, 19 months of data exist for each of the 6 products in this category. For Category 2, 19 months of data exist for 2 of the 3 products; the 3rd product has 17 months of data. For Category 3, 19 months of data exist for each of the 3 products. For Category 4, 19 months of data exist for each of the 5 products in this category. For Category 5, 19 months of data exist for each of the 3 products in this category. For Category 6, 19 months of data exist for the one product in this category. The total number of observations is 397.
The difference in the magnitude of the two promotion coefficients is possibly due to the difference in the magnitudes of the two promotion variables. The standardized coefficients are 0.384, 0.509, and -0.513 for e-detailing, traditional detailing, and interaction, respectively. (The standardized coefficients are results of regressions where each variable is re-constructed to have mean 0 and variance 1.)
We thank an anonymous reviewer for this explanation.
The views summarized here are obtained by telephone conversations with executives at SDI Health, LLC, and a third pharmaceutical consulting company, different from our data suppliers.
If the omitted variable (for example, samples) is positively correlated with the dependent variable (in this case, unit Rx sales) and also with the included variables (in this case, 2 forms of detailing), then the coefficients of the included variables are subject to an upward bias.
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Acknowledgements
We thank Verispan LLC and IMS Health, Inc. for the use of their data sources. We thank seminar participants at Slippery Rock University for their comments. The usual disclaimer applies.
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Gönül, F.F., Carter, F.J. Impact of e-detailing on the number of new prescriptions. Health Care Manag Sci 13, 101–111 (2010). https://doi.org/10.1007/s10729-009-9110-2
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DOI: https://doi.org/10.1007/s10729-009-9110-2