NEUROLOGY

KEY OPINION LEADERS

BETTER LIFE'S PRODUCT LAUNCH BLUEPRINT

OBJECTIVE

Strategically identify US neurology KOLs to champion Better Life's product launch, leveraging critical datasets to pinpoint influencers

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METHODOLOGY

A robust analysis integrates clinical, research, leadership, and financial data to profile key neurology KOLs, assessing their impact and network

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IMPACT

Engaging KOLs is vital for product adoption and market success, laying the groundwork for Better Life's entry and growth in the neurology sector

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EXECUTIVE SUMMARY

Clinical Trial Advocacy

KOLs ensure clinical trials are scientifically sound and relevant which boosts trial credibility and encourages wider participation

THEIR IMPACT

Product Launch and Adoption

KOLs endorse new products, speeding up market acceptance and their educational efforts help healthcare providers embrace innovations

Influence on Guidelines and Policies

KOLs shape treatment guidelines and healthcare policies and their recommendations impact therapeutic approaches and improve patient care

Insights and Expertise

KOL engagement provides access to valuable knowledge and experience through which companies gain insights into patient needs and healthcare gaps

THEIR VALUE

Strategic Influence

KOL input shapes development, marketing, and educational strategies and their expertise guides product positioning and messaging effectively

Trust and Guidance

KOL endorsement enhances credibility and assists in navigating complex healthcare landscapes, aiding effective communication of product value

WHO IS OUR CLIENT AND WHAT IS THE TASK AT HAND?

Before we dive deep, let's try to answer this question:

The mission is to harness data-driven insights to identify and engage neurology KOLs, amplifying product launch success and market penetration

Better Life is poised to disrupt the US neurology pharmaceutical market with an innovative product, amidst a landscape ripe for advanced therapeutic solutions

HOW DO WE CHOOSE OUR KOL ?

S

WHICH SOURCES?

SOURCE 1

openpaymentsdata.cms.gov/

SOURCE 2

reporter.nih.gov/

health.usnews.com/best-hospitals/rankings/neurology-and-neurosurgery

1'st Priority

2'nd Priority

Last Priority

health.usnews.com/best-hospitals/rankings/neurology-and-neurosurgery

https://clinicaltrials.gov/

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We used the data above this line.

SOURCE 3

SOURCE 4

SOURCE 5

WHY THESE SOURCES?

OPENPAYMENTSDATA.CMS.GOV/

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The Open Payments database enhances healthcare transparency by showcasing financial relationships between healthcare providers and the industry. We took a subset of the data (2022).

REPORTER.NIH.GOV/

HEALTH.USNEWS.COM/BEST-HOSPITALS/RANKINGS/NEUROLOGY-AND-NEUROSURGERY

NIH RePORTER consolidates NIH-funded research projects, outcomes, and resulting publications and patents, supporting transparency, informed decision-making, and public accountability in research funding. We took a subset of 2020 - 2024 to consider years around the date 2022.

U.S. News offers rankings and insights on top hospitals for neurology and neurosurgery, aiding in informed care choices for complex neurological issues through data and expert opinions. We found out the institutions and its rank, and attached to each profile.

TOTAL PAYMENTS

COUNT OF PUBLICATIONS

PROJECTS

COUNT

RANK OF ORGANISATION

RELATIVE CITATION RATE

CLINICAL TRIALS COUNT

METRICS IDENTIFIED

RELVANCE SCORE

DEGREE CENTRALITY

HOW TO UTILIZE THE METRICS ?

This is a decision-making tool that decomposes complex problems into a hierarchy of simpler elements, evaluated through pairwise comparisons. It combines quantitative metrics and subjective judgments to assign weights to each element, enabling informed and consistent decision-making

ANALYTIC HIERARCHY PROCESS

This matrix uses fractional values for the inverse comparisons for simplicity, though typically the exact reciprocal would be used (e.g., if A is 4 times as important as B, then B is 1/4 as important as A).

The diagonal is always 1, as each metric is equally important to itself.

COMPARISON MATRIX

COMPARISON MATRIX

Explanation of Assumptions:

• Total Payments may indicate industry engagement but are considered less indicative of academic or research merit compared to Publications or Clinical Trials.
• Clinical Trials are highly valued for their direct impact on healthcare and medical practice, often leading to high-impact publications and citations.
• Publications are fundamental to academic contributions; however, their value is moderated when considering the Relative Citation Rate, which provides context to the impact of these publications.
• Relative Citation Rate is considered extremely important as it reflects the quality and influence of research within the specific context of the field and publication year.
• Organisational Rank reflects leadership and administrative influence but is considered less directly indicative of research impact compared to metrics like Publications or Clinical Trials.
• Projects indicate practical engagement and collaboration but are weighted less compared to direct research outputs like Clinical Trials and Publications.

This matrix is an illustrative example. In practice, the specific weights and the importance of each parameter should be determined through a collaborative process involving domain experts, using a systematic approach like the Analytic Hierarchy Process (AHP) to reach a consensus.

RELEVANCE SCORE

DEGREE CENTRALITY

Degree Centrality quantifies a researcher's direct connections in their professional network, indicating their collaborative reach and centrality in the research community. It highlights:

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• Collaborative Potential: High centrality suggests strong networks, beneficial for multidisciplinary projects
• Information Spread: Central researchers can quickly disseminate new ideas and innovations.
• Influence: Reflects a researcher's visibility and engagement, marking their impact within the community

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Incorporating Degree Centrality compliments traditional academic metrics and underscores the significance of networking in research advancement

DEGREE CENTRALITY

Incorporating Degree Centrality compliments traditional academic metrics and underscores the significance of networking in research advancement

*Created using the Network X Library in Python

Summation Across Columns: For each column in the pairwise comparison matrix, we add the values. This reflects the combined influence that each criterion has when compared against all others.

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Normalization of Elements: Each element in the matrix is divided by the sum of its respective column. This adjustment ensured that the sum of each column equaled 1, standardizing the scales for comparability.

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Weight Calculation: For each row in the normalized matrix, we calculated the average of its values. These averages represented the relative weights or priorities of the criteria, indicating their importance in the decision-making context.

NORMALIZED

WEIGHTS

IMPACT

SCORE

The "Impact Score" distills a researcher's professional achievements into a single, comprehensive metric, reflecting their influence across academia and industry. By blending contributions in publications, clinical trials, and leadership with their role in collaborative projects and the relevance of their work, this score offers a nuanced view of a researcher's standing and potential in their field

We wil be using the Impact Score to find the KOL with the highest Impact

IMPACT SCORE CALCULATION

STANDARDIZATION

WEIGHTED SUM CALCULATION

WEIGHT ASSIGNMENT

INVERSION FOR RANK

ROUNDING

IMPACT SCORE CALCULATION

STANDARDIZATION

WEIGHTED SUM CALCULATION

WEIGHT ASSIGNMENT

INVERSION FOR RANK

ROUNDING

Invert rank values where necessary, so that a lower rank translates to a higher value after scaling

Use a MinMaxScaler to scale the values of each metric to a 0 to 1 range, ensuring comparability across different scales

Assign weights to each metric based on their relative importance in determining a researcher's impact, as established through methods like the Analytic Hierarchy Process (AHP)

Multiply the normalized values by their corresponding weights and sum these products for each researcher to calculate their overall "Impact Score"

Round the final "Impact Score" to a suitable number of decimal places, maintaining precision and readability.

RELEVANCE SCORE

BINNING

The "Relevance Score Classification" categorizes the alignment of a researcher's previous work with specific projects or areas of interest into three distinct tiers:

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• High: Shows strong alignment, indicating significant direct applicability.
• Medium: Reflects moderate alignment, suggesting some relevance.
• Low: Indicates minimal alignment, with potential tangential relevance only.

BENEFIT

This classification provides a quick, intuitive understanding of how closely a researcher's background matches the specific needs or goals of a project, aiding in more informed decision-making.

Academic Data Collection: Gather detailed academic data for each candidate, including publications, citations, and affiliations

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Calculation of Relevance Score: Assess the alignment of candidates' previous work with the current project

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Degree Centrality Analysis: Evaluate candidates' networks to determine their Degree Centrality, reflecting their collaborative reach and influence within professional circles

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Relevance of Non-Academic Indicators: Acknowledge the significance of Relevance Score and Degree Centrality as indicators that extend beyond traditional academic metrics

OUR PROCESS FLOW

STEP 1

DATA COLLECTION AND METRIC CALCULATION

Clearly outline the goals and necessary qualifications for the role or project, ensuring the selection criteria align with the desired outcomes

OUR PROCESS FLOW

STEP 2

DEFINE SELECTION OBJECTIVES

Establish a minimum Impact Score for candidate consideration, emphasizing its role as a primary indicator of overall contributions and influence

STEP 3

SET IMPACT SCORE THRESHOLD

Initially rank candidates based on their Impact Scores from highest to lowest, using this as the foundational layer for the selection process

OUR PROCESS FLOW

STEP 4

RANK CANDIDATES BY IMPACT SCORE

OUR PROCESS FLOW

Relevance Score Classification: Use the High, Medium, or Low relevance classification to identify candidates with expertise closely aligned with project needs

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Degree Centrality: Consider candidates' Degree Centrality to gauge their collaborative potential and network influence

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STEP 5

Incorporate Additional Indicators

OUR PROCESS FLOW

Review top candidates, particularly those with high Impact Scores, focusing on individual score components and the added context from Degree Centrality and Relevance Score Classification

STEP 6

CONDUCT CANDIDATE EVALUATION

OUR PROCESS FLOW

Make balanced decisions by weighing the comprehensive evaluations, where the Impact Score indicates overall merit, supplemented by nuanced insights from additional indicators

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Ensure selections are strategically aligned with project goals and organizational objectives

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STEP 7

FINALIZE SELECTION

SEGMENTS

High Impact Leaders

Candidates with exceptionally high Impact Scores, indicating significant contributions and influence in their field. They are often thought leaders or pioneers

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Emerging Influencers

Those with moderate to high Impact Scores, showing promise and upward trajectory in their contributions and recognition

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Specialized Contributors

Individuals with lower overall Impact Scores but high scores in specific components, indicating specialized expertise or contributions.

ARCHETYPES

Central Connectors

High Degree Centrality candidates, pivotal in networks due to their extensive collaborations. They are ideal for roles requiring broad interdisciplinary connections

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Niche Experts High Relevance Score in specific areas with moderate Degree Centrality, indicating deep, focused expertise relevant to particular projects or niches

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Influential Innovators

High scores in both Impact and Degree Centrality, marking them as influential figures who drive innovation and collaboration

WEBSITE

WEBSITE

WEBSITE

WEBSITE

WEBSITE

Give us details about your product

NAME

NEW PRODUCT

Get in touch with the KOL providers to promote

THE LONG WAIT IS OVER

Recommendations

Using our proprietary models

Extracts information from trustworthy sources

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Builds on qualitative and quantitative metrics

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Gives personalized recommendations using AI

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State of the Art Models

WIREFRAMING

PROJECTS

15

publications

133

RELATIVE CITATION RATE

5.58

NPI: 1740398494

Location: ATLANTA, GA

Degree centrality 0.085

Relvance score

0.4

Impact score

0.425

LEVEY, ALLAN I

Allan Levey, MD, PhD, is a professor in the Department of Neurology at Emory University's School of Medicine, as well as the director of Emory's Alzheimer's Disease Research Center. He has secondary faculty appointments in the Departments of Pharmacology, Psychiatry and Behavioral Sciences.

Segment: Emerging Influencers Archetype: Degree Centrality

PROJECTS

9

publications

227

RELATIVE CITATION RATE

4.9

NPI: 1649296872

Location: SAINT LOUIS, MO

Relvance score

0.2

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Impact score

0.362

BATEMAN, RANDALL J

Randall J. Bateman, MD, is the Charles F. and Joanne Knight Distinguished Professor of Neurology at Washington University School of Medicine, director of the Dominantly Inherited Alzheimer Network (DIAN) and director of the DIAN Trials Unit (DIAN-TU).

Segment: Specialized Contributor Archetype: Niche Expert

Verify the presence of all required fields: researcher names, affiliations, publication details, and project information done via merging datasets.

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Ensure comprehensive coverage of publications and projects across relevant research domains and timeframes. (We took years around 2022).

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Cross-reference researcher profiles and publications across multiple databases to identify discrepancies.

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Plan for periodic updates to the dataset to capture dynamic changes in research networks.

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Secure necessary permissions for using proprietary or sensitive data. (We scraped only public data).

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WHAT QC CHECKS DO WE RECOMMEND?

WHAT ABOUT THE ACCURACY?

VERITY?

Verifying the KOL’s selected via platforms like Linkedin and Google scholar.

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Compare the identified KOLs with authoritative rankings and databases in the field to check for alignment.

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Examine the citation impact of KOLs’ publications to assess their influence in the research community. We have done this manually by extensively going through the work of the KOL’s online.

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Looking at the results and the model we created from a business perspective to bring value to companies/clients looking for KOL’s.

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The details mentioned here may have errors and need slight improvements

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For any changes kindly email agarw402@purdue.edu

kurada@purdue.edu

streasu@purdue.edu

REMEMBER

THANK YOU!

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SOHAM

PAWAN

SIDDHANT