Benchmarking Medical Information Services Beyond the Unsolicited Requests: A phactMI Benchmarking Survey
Bibliographic record
Abstract
OBJECTIVE: Medical Information has a strategic role that extends beyond inquiry management. The Pharma Collaboration for Transparent Medical Information (phactMI™) benchmarking survey of 35 US pharmaceutical companies was conducted to describe the current landscape and future opportunities of other services Medical Information could provide. METHODS: In July 2023, an electronic survey containing 57 closed and open-ended questions was distributed to phactMI member companies. The survey questions addressed demographics, medical review, development of materials, training, patient services, insights, and awareness. RESULTS: Medical Information is a significant contributor to the medical review of promotional healthcare provider materials (51%), patient materials (52%), and non-promotional medical materials (45%). Medical Information ensures the accuracy of medical information, fact checks and validates claim accuracy. Fifty percent of the respondents are responsible for reviewing and/or contributing to Medical Affairs material for Field Medical. Additionally, Medical Information trains both Field Medical and Sales teams on the Medical Information function, and to a lesser extent, disease state information. The majority (75%) of Medical Information Departments offer patient information. The vast majority (85%) produce and identify insights. Medical Directors, Field Medical, and Scientific Communications/ Publications often receive shared insights. Fewer individuals integrate insights with Field Medical and Medical Directors. Since 2018, Medical Information activities have seen a rise in advisory board presentations, insights reporting, publications, competitive intelligence, disease state education, surveillance, pathway submission, and labelling activities. Building awareness is still an important aspect of Medical Information and most focus on the development of their Medical Information website. CONCLUSION: The essential roles and activities of Medical Information Departments support products at every stage. Medical Information participates with multiple functions in evaluating medical materials and there is a growing trend of including Medical Information in the development and review of Medical Affairs materials. Medical Information has expanded its participation in pathway submissions, publications, and labeling activities. This benchmark for Medical Information can provide a potential best practice template for activities. For the future, the three areas to prioritize are: increasing the strategic value and KPIs of Medical Information, integrating and overseeing AI technology in the insights process, and improving internal visibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".