We help leaders make
    more impactful decisions
    using data and evidence
    “We blend design, engineering,
    and analytics expertise to help
    you build the future.”


    We work with bio-pharmaceutical, biotechnology, medical devices startups, and companies, helping them solve significant challenges in various areas such as clinical and financial analysis, drug discovery, product launch, life cycle management, commercial strategy, valuation, and optimization of operations.

    Focus areas

    A successful value proposition for all life sciences products, including drugs, medical devices, and diagnostics, must be accompanied by product value evidence. We work with clients in all development and commercialization phases to identify market access challenges, opportunities, and evidence-generation activities to support successful outcomes.
    Our capabilities include:

    • Analyze the competitive landscape, and identify evidence requirements
    • Evaluate product attributes and opportunities for evidence generation, the strength of supporting evidence, and opportunities to fill gaps
    • Develop value proposition and key-value messages, supported by evidence and research to gain an understanding of stakeholder needs
    • Develop a detailed roadmap for evidence generation and communication of product value. Assess of clinical and economic value versus alternatives in early phases, using modeling and simulation
    • Evaluate the effectiveness of marketing activities such as medical visits or marketing campaigns
    • Personalize marketing campaigns using insights from neuroscience and behavioral science
    • Evaluate the brand value and positioning on the market and design a comprehensive communication strategy for the company, portfolio, and brand strategy
    • Understand the context of disease and patient populations such as unmet population needs, biomarkers, and genetic characteristics 

    Understanding the decision-making processes, expectations, and perceptions of the product value of payers, providers, and other key health care stakeholders is fundamental to the success of our clients’ market access strategies.  We provide comprehensive qualitative, semi-qualitative, and quantitative research capabilities — tailoring the research and participants to our clients’ specific business needs. Our capabilities include:

    • Run product positioning with relevant stakeholders
    • Assess payer landscape assessments and market segmentation
    • Develop evidence generation strategy
    • Carry out value proposition and key-value message development
    • Carry out pricing, reimbursement, and contracting strategy
    • Carry out forecasting and market planning
    • Understand in real-time the usage of treatments by patient groups (e.g., market, product share), line of therapy, geography, and other characteristics
    • Predict treatment compliance, identify the risk profile, and design patient support programs using insights from neuroscience and behavioral science
    • Measure the delivery of treatment intervention protocols and agreements in real-world settings, including safety, quality assurance

    HDI Group has developed unique methodologies that enable biopharmaceutical organizations to make robust pricing across therapeutic areas, geographies, channels, and product types. Our capabilities include: Pricing Strategies, Contracting and Tendered Business Management, Competitive Response Strategies, Copay and Other Patient Support Solutions.

    Particularly, we can help you:

    • Evaluate pricing and market access options for pipeline products
    • Create pricing models to support and evaluate key decisions
    • Develop integrated pricing strategies for launch products
    • Design and implement innovative pricing, contracting, and reimbursement arrangements
    • Partner with clients early in the product life cycle to establish the evidence strategy to support a winning value proposition for stakeholders globally
    • Develop models that forecast revenue, estimate product and portfolio value and return on investment, and that simulate the clinical and economic value
    • Review and optimize the performance of contracting and global tendered business
    • Develop best-practice pricing and bid tools and processes for contracting and tender business management
    • Conduct advanced payer analytics, including segmentation, performance tracking, and payer impact modeling
    • Develop strategic responses to branded, biosimilar, and generic competition
    • Conduct competitive-response simulations
    • Assess and develop integrated care and other novel care delivery models

    To effectively help our clients communicate the clinical, economic and humanistic value of their products to key stakeholders, we provide the following services:

    • Support reimbursement submissions and health technology assessments across global markets
    • Develop dossiers for the assessment of new technologies, such as diagnostics, including dossiers that are designed to meet policy requirements
    • Develop evidence packages for hospital-based products, including pharmaceuticals, diagnostics, and medical devices
    • Create tools for account managers and medical science liaisons to enhance communication tools for Field-Based Teams
    • Communicate to stakeholders the impact innovation can have on patients, institutions, or health care systems.
    • Synthesize available evidence into the dossiers required by health technology appraisal bodies around the world and develop a range of focused communication tools
    • Optimize price, access, and reimbursement, including the use of innovative contracting approaches with payers worldwide

    HDI Group provides the expertise you need in all clinical trial development stages usinepidemiology and industry insights. HDI Group Company augments your company’s intelligence in running clinical trials by unveiling successful initiatives worldwide, predicting enrollment, or building local capacity.

    • Design, plan and conduct clinical trials
    • Select sites, patients, and the required hospitals
    • Establish the incidence and prevalence rate
    • Collect and interpret the clinical trial data
    • Run efficacy and safety analysis of phase I-IV clinical trials
    • Post hoc analyses of clinical trials data to address emergent safety issues and secondary endpoints
    • Indirect product comparisons across clinical trials when head-to-head trials are not available
    • Carry out meta-analysis and network meta-analysis of pooled clinical trial data
    • Run personalized or precision medicine analysis to identify high-value subgroups
    • Predict and monitor trial enrollment, cost, and quality
    • Optimize patient and compliance monitoring
    • Evaluate the drug development pipeline by phase globally
    • Evaluate the findings and methodological rigor of current trials running globally
    • Identify top therapeutic focus areas, targets, and mechanisms
    • Analyze clinical trial data concerning efficacy and safety to address questions regarding the drug’s patent and the potential to make and sell a generic form before the patent’s expiration.
    • Conduct ad hoc analyses of clinical trial data to address emergent drug safety issues and to analyze secondary endpoints, such as quality-of-life measures, in the trials.

    Pharmaceutical companies need an accurate, detailed picture of patient experience and outcomes in clinical trials and the real world to develop and deliver effective drugs. However, analyzing real-world data for differentiated biomedical insight requires dealing with tremendous scale and complexity, as well as potential privacy concerns. For example, claims data is on the order of billions of rows, and genomics data can contain thousands of features per patient. HDI Group enables the integration and end-to-end analysis of internal drug development data alongside external data sources (such as PubMed, TCGA, and ChEMBL) and real-world data. We have applied innovative methodologies to epidemiologic analyses and counterfactual modeling, such as marginal structural models, where challenges exist in estimating the causal effect using these observational data (e.g., time-dependent confounding and informative censoring).

    • Understand patient population dynamics and drug outcomes with unprecedented granularity
    • Identify promising gene and biomarker combinations for clinical trial stratification and analysis
    • Demonstrate cost-effectiveness of drug therapy
    • Identify molecular correlates of drug response based on internal and external screening data
    • Run descriptive prevalence and incidence studies
    • Run pharmacovigilance and spontaneous report adverse event analyses
    • Run post-marketing drug safety epidemiology studies
    • Use registries and run active surveillance
    • Perform quality of life and patient-reported outcome studies
    • Run risk-benefit analysis
    • Run comparative studies to evaluate causality between treatments and efficacy and safety outcomes using real-world practice data
    • Analyze a drug’s patent and the potential making and selling of a generic before the patent’s expiration
    • Signal detection in social media data
    • Bring all subject matter experts onto a single, secure platform
    • Analyze preclinical, clinical, manufacturing, sales, and marketing data in a single environment for R&D and commercial purposes
    • Conduct both visual and code-based cohort analysis with problematic inclusion and exclusion criteria
    • Map complex treatment pathways over millions of prescription claims
    • Automate the reporting of population baseline characteristics and outcome metrics

    Statistical sampling plays a significant role in any study that you will ever run. Not having a large enough and reliable sample will pose questions regarding your project findings’ rigor, and worse, it could lead to misinformed budgetary decisions. To properly select a sample or evaluate the quality of an existing sample, our analyses often focus on critical questions about sample design and implementation, including:

    • What question needs to be answered?
    • What is the population of interest?
    • What level of accuracy is required, and what size sample is needed to achieve that desired accuracy level?
    • Is there a benefit to selecting a stratified sample?
    • Is the sample representative? Is it biased?
    • Were the sampling plan and data collection adequately implemented?
    • Is the selected sample sufficient to answer the question of interest?
    • Which method should be used to extrapolate from the sample to the population of interest?

    Besides helping you design the right model, HDI Group can help you create the data collection instruments. Our capabilities include:

    • Design of Vignettes instruments
    • Design of Mystery Shopper
    • Design of Standardized patients
    • Design of Patient charts
    • Design of Observation files
    • Design of Patient files
    • Administrative data
    • Literature review and meta-analysis
    • Web scrapping
    • Application programming interface
    • RPA

    Finally, we provide the resources you need to collect the highest quality data rigorously to end the full circle. Our capabilities include:

    1. Field data collection (F2F)
    2. Telephone data collection (CATI)
    3. Web-data collection (CAWI)
    4. Focus groups

    Learn about the data quality steps we take here

    Predictive Analytics, forecasting, modeling, and data visualization

    Valuing health care products and businesses has become increasingly complex. Access and reimbursement are vital factors, even in early commercial development. Forecasts must consider the decisions that payers and health technology assessment bodies are likely to make and the future of the standard of care and decisions of competitors, physicians, patients, and policymakers. 

    The HDI Group team integrates information from product profiles, landscape assessments, and stakeholder research to forecast and model engagements to provide the best forward-looking guidance for critical business decisions and strategic planning.

    • Support Early Commercial Forecasts by developing forecasts for early-phase assets before the availability of Phase 2 data
    • Support launch investments by conducting in-depth analyses
    • Product and Franchise Forecasts for Marketed Products by developing flexible, transparent models supported by relevant research
    • Support the licensing and acquisition of products by conducting analyses, including valuation and modeling, to support development decisions
    • Support portfolio and Franchise-Level Planning by evaluating markets, technology, competition, and investment options
    • Help to forecast the market demand for new products or features and the success of marketing campaigns
    • Predict high users of medical services based on their diagnostic and prescription drug use history
    • Use social media analytics to evaluate general causation between medical products and adverse events in product liability assessments
    • Examine similarities/dissimilarities of patent language published in multiple jurisdictions to determine the likelihood that a specific patent will survive a challenge
    • Efficiently analyze billions of mutual fund transactions across numerous file formats and platforms
    • Create custom analytics and dashboards that help companies develop adequate controls
    • Develop interactive platforms and web apps to help clients manage their data-intensive projects and metrics
    • Compile and manage large, complex datasets using proprietary client data as well as data from a variety of third-party data sources

    Machine learning, broadly defined, involves computer programs and algorithms that automatically improve their performance at specific tasks through experience. The primary objective of these tasks is out-of-sample performance. The algorithm is first trained on a sample of inputs for which the target output responses are known. Training enables the algorithm to learn highly complex and intricate relationships in high-dimensional data, rather than pre-imposing assumptions on how inputs and outcomes are related. The algorithm’s performance is then assessed on a different sample to determine how it would perform in the real world with new data.

    One powerful application of machine learning is natural language processing (NLP). NLP can extract useful data elements from unstructured, raw data. Using language- and grammar-specific constructs builds on a unique combination of algorithms and artificial intelligence tools to analyze, extract, and classify human communications from unstructured data such as online reviews, patent claims, physician notes in a medical file, insurance claims, and even audio recordings. As a result, it can be used to develop and implement predictive models across several sectors.

    Our capabilities include:

    • Sort through reams of filings and online product reviews to reveal which features were considered relevant to consumers in patent infringement and consumer protection cases
    • Collect, cluster, and analyze information coming from a variety of unstructured text financial sources to find relationships between specific language and conducts on the financial markets
    • Examine detailed information of online booking information, like pricing or other specific characteristics, to measure the impact of constraints (or removing restrictions) of competition on consumer welfare
    • Uncover issues that are not captured by traditional patient-reported outcome instruments, including through the use of social media and online patient data on medical conditions and their treatments
    • Efficiently conduct literature reviews, organizing large datasets of scientific articles, ranking abstracts by expected relevancy for particular research topics, and highlighting changes in research topics over time
    • Develop a method to identify and standardize medical terminologies, such as disease name, from the unstructured medical data

    Economic evaluations play a key role in informing strategic, organizational, and funding decisions. Our capabilities include:

    • Develop models of complex consumer decision making 
    • Develop, validate, adapt and assess decision-analytic models
    • Develop economic analysis alongside clinical trials
    • Develop cost of illness studies
    • Develop cost-consequence analysis
    • Develop cost-effectiveness analysis
    • Develop cost-utility analysis
    • Develop budget impact analysis/ forecast
    • Develop health economic analysis of sponsors’ product portfolio
    • Detect the potential for health economic value propositions
    • Perform analysis of sponsors’ health economics strategies and planning
    • Perform feasibility studies
    • Run evidence appraisal and synthesis
    • Offer insights into the quality of life, disability-adjusted life years, and other aspects that move beyond the clinical outcomes
    • Perform literature reviews, meta-analyses
    • Perform Instrument validation
    • Develop outcome measures and clinical scales
    • Run price sensitivity studies
    • Perform Decision trees
    • Perform Markov models
    • Perform Retrospective database analysis
    • Perform Microsimulation models

    We aim at improving clients’ ability to demand, consume and generate evidence. The range of activities can vary from light-touch support to help clients interpret results to intensive training on evaluation strategy, biostatistics, HEOR training, and hiring efforts to transition a client’s monitoring system. We can train your staff on topics related to clinical trial management, epidemiology, and statistics.


    Promotional effectiveness of TV and poster campaigns

    Our experts have supported a pharmaceutical company to understand the impact their toothpaste promotional campaign (poster and TV) had on brand awareness and self-reported behavior.

    A leading pharmaceutical manufacturer for innovative generics and OTC drugs and products. The client employs several promotional campaigns to increase awareness about its products among clients and healthcare providers.

    The poster campaign aimed to increase awareness and knowledge of the importance of oral health maintenance with clients and determine a positive behavior change. The key oral hygiene messages were: to brush with fluoride toothpaste, use a pea-sized amount of it, brush for 3 min at least twice a day, and use it, particularly when teeth have high sensitivity. The program consisted of a promotional poster being placed in all metro stations in large cities simultaneously against a national television campaign’s backdrop. The television campaign underlined the campaign’s key messages and encouraged the public to look after their teeth, share a picture of their smile, and report their success story as part of a competition.

    An experimental/ control, pre-/post-design was used for the poster intervention, taking into account reported exposure or not to the television campaign. Cities assigned to the experimental group received the poster intervention during the television campaign period. The key messages formed the criteria of the data analyses. Those who received the poster intervention are classified as experimental and those who did not as controls. Exposure to the television campaign was determined based on an affirmative response to whether they watched the TV promotional campaign. χ2analyses were conducted on the nominal level frequency data to test for differences in responses between groupings. First, differences between experimental and control group responses to key questions at baseline were tested before intervention exposure. Differences in responses between baseline and post-test were then tested for in experimental and control children separately. The post-intervention data were then separated into those who did and those who did not see television campaigns, and within each of these groups, differences were observed between treatment and control groups. The program’s impact was evaluated through a questionnaire that assessed the campaign’s key messages concerning the frequency and duration of brushing teeth, the amount and type of toothpaste, etc.

    There was a positive net effect of the poster intervention in all but one question. The percentage of people who reported using the recommended amount of toothpaste and brushing for 3 min appeared to have increased after having seen the television campaign.

    Clinical trial planning and reporting

    Our experts supported two separate phases II, randomized, double-blind, placebo-controlled trials to evaluate two candidate vaccines (Rotarix and Pneumovax) in multiple settings.

    Both companies are among the leading multinational pharmaceutical companies in the world

    • Appropriate plans and designs of the trial (e.g., Randomized? Equivalence?)
    • Background reviews and data quantification (e.g., incidence/ prevalence rates) needed to inform the design
    • Development of international-standard scientific and operational protocols
    • Selection of appropriate trial patients and hospitals
    • Train site staff to conduct trial according to Good Clinical Practice guidelines
    • Monitor progress, analyze and interpretation of the trial data 

    In phase one, following a thorough systematic review, we developed the rationales for the trials. We calculated the initial burden of the diseases in question and estimated the expected efficacy. We defined the primary objective as conducting phase II, randomized, double-blind, placebo-controlled trials to evaluate the vaccines’ immunogenicity, reactogenicity, and safety in question. We developed the scientific and operational protocols based on the available best practices globally. In phase two, we selected the trial sites, conducted a series of staff training, then coordinated and monitored the trial activities. Once the trials were completed, we helped analyze the data.

    These trials have shown that immunization with these candidate vaccines has substantial benefits to reduce respiratory and gastrointestinal illness for both mothers and infants.

    Is the treatment for depressive disorders cost-effective? Shall the authorities reimburse it, if so, by how much?

    Our experts supported a pharmaceutical company in providing evidence regarding the cost-effectiveness and budgetary impact of antidepressants to document the reimbursement decision.

    A large innovative pharmaceutical company presents globally

    To document the reimbursement decision, the company needed to show rigorous evidence regarding the drug’s cost-efficacy and budgetary impact.

    The severity of depression was measured with the Montgomery Asberg Depression Rating Scale (MADRS) and quality-adjusted life-years scale (QALY). The costs included all the direct and indirect costs per patient associated with the two procedures. The economic evaluation was conducted from a societal perspective, including costs related to intervention, unpaid work, informal care, and absenteeism from paid work. The ICER, CE plane, CE acceptability curve documented the analysis.

    The research findings allowed the healthcare provider to prove that their drugs were cost-effective when compared to

    standard care and get approval for reimbursement.

    Case study 3: 

    Unmet needs and decision triggers in HPV in Romania

    The client would like to re-engineer a specific HPV vaccine route to market. Therefore, they would like to understand better how to target its communication strategy to increase the vaccine’s uptake among girls 11-14 years old. 

    A major pharmaceutical company is active in the provision of vaccines to combat HPV infection.

    While HPV vaccines have been shown to have high efficacy and limited side effects in preventing infection with HPV roots, the vaccination and screening rates remain low. With individual factors (e.g., social and gender norms, procrastination, undervaluation of benefits) playing a decisive role in care-seeking, it is, in the end, essential to identify how the low uptake of vaccination could be reversed by understanding and designing interventions that address both physical and psychological barriers which drive demand and access to HPV vaccines.

    A cross-sectional study evaluating parents’ and general practitioners’ knowledge, attitude, and behavior towards vaccination of children (10-14 years old) against HPV infection was carried out. Notably, the study aimed at:

    • Identifying caretakers’ and health care professionals’ (HCPs) attitude, future intentions towards vaccination against HPV for themselves and their daughters
    • Identifying key features and their perceived importance that lead to the decision to choose or refuse vaccination against HPV -e.g., geographical, institutional, and individual factors      
    • Identifying the level of awareness and attitude regarding HPV vaccine administration source, protocols, Gardasil’s safety profile, therapy duration (dosage), and efficacy among HCPs and caretakers
    • Identifying HCP’s and caretakers’ preferred information and education source and self-perceived unmet needs

    The study unveiled the significant issues leading to the low vaccination uptake among boys and girls aged 10-14 years old.