Real-time analytics enables informed decision-making, enhanced patient outcomes, and creates a competitive edge for the drug in the ever-evolving Healthcare industry. A data-driven approach across the drug lifecycle ensures that the drug launch is not just a milestone, but a stepping stone towards improved healthcare and a brighter future.

5 Benefits of a Data-Driven Drug Launch

How can Pharma leaders benefit from a data-driven launch? We’ve put together a few of our observations here:

1. Precise Patient Targeting
Begin by identifying the most promising patient segments through comprehensive data analysis. By integrating electronic health records, prescription data, and demographic information, you can pinpoint the specific patient populations that will benefit most from your drug. Tailor your messaging and outreach to address their unique needs and preferences.

2. Segmented Marketing Strategies
Develop personalized marketing strategies for each identified patient segment. Utilize commercial analytics to understand the distinct characteristics of these segments and create tailored campaigns that resonate with their concerns. This approach enhances engagement and encourages a deeper connection between patients and your product.

3. Tactical Pricing Optimization
Determine the optimal pricing strategy for your drug by analyzing market dynamics, competitor pricing, and patient affordability. Commercial analytics helps strike the right balance between maximizing revenue and ensuring accessibility. Data-driven pricing decisions also enhance negotiations with payers and reimbursement discussions.

4. Multi-channel Engagement
Leverage commercial analytics to identify the most effective communication channels for reaching healthcare professionals and patients. Analyze historical prescription patterns and physician preferences to allocate resources to the channels that yield the highest impact. This approach ensures that your message reaches the right stakeholders at the right time.

5. Continuous Performance Monitoring
The launch doesn’t end on the day of the launch — it’s a continuous process. Utilize real-time data analytics to monitor your drug’s performance in the market. Track metrics such as prescription volume, market share, and patient feedback. This information helps you adapt your strategies as needed and capitalize on emerging opportunities.

Enabling a 360-Degree View of Pharma Drug Launch with Commercial Analytics

At Tiger Analytics, we developed a Data Analytics solution, tailored to meet the specific requirements of our clients in the Pharmaceutical industry. Our Commercial Analytics engine powers a suite of data-driven analytical interventions throughout the lifecycle of a drug. It serves as a bridge between goals and actionable insights, effectively transforming raw data into strategic decisions. The solution supports pre-launch patient segmentation and provider engagement. It also aids in launch-stage payer analytics and pharmacy optimization. Lastly, it enables post-launch patient journey analysis and outcomes assessment – giving Pharma leaders a 360-degree view of the entire launch cycle.

Here’s how it works:

Pre-Launch: Setting the Stage for Success

In this stage, the goal is to lay a strong foundation for success by developing the value proposition of the drug. Clinical teams, data strategists, and market researchers collaborate to assess the drug’s commercial potential and create a strategy to realize it. To begin, comprehensive surveys and market research are conducted to gain insights into healthcare personnel (HCP) behavior, competitor analysis, patient profiles, packaging analysis, price comparison, and sales benchmarks. These analyses shape the roadmap for the drug’s performance and enable the exploration of various scenarios through forecasting exercises. Patient profiling and segmentation strategies are also implemented to devise effective marketing and engagement strategies.

From Action to Impact

To drive tangible results, at Tiger Analytics we orchestrated a series of targeted initiatives with specific outcomes:

What did we do?

• Conducted a comprehensive analysis of analog drugs in the market and performed market scoping along with other forecasting exercises to understand the potential impact of the new drug once launched.
• Analyzed survey results and developed a tool to assess the possible effectiveness of the drug in real-world scenarios.
• Formulated multiple scenario analyses to account for unprecedented events and their potential impact on the demand for the drug.

How did the solutions help?

• Provided a clear view of the expected market landscape through market sizing.
• Prepared the pharma company for unknown events through scenario analysis.
• Facilitated target adjustment and improved planning by forecasting numbers.

Launch: Strategic Payer Engagement in a Complex Landscape

During the drug launch, the focus shifts to accelerating drug adoption and reducing the time it takes to reach peak sales. At this juncture, analytics plays a crucial role in optimizing market access and stakeholder engagement (payers, prescribers, and patients). By analyzing payer data, claims information, and reimbursement policies, pharmaceutical companies gain insights for strategic decision-making, including formulary inclusion, pricing strategies, and reimbursement trends. These insights enable effective negotiations with payers, ensuring optimal coverage and patient access to the medication.

Monitoring sales and identifying early and late adopters among HCPs and patients enables targeted marketing activities and tailored promotional initiatives. This approach effectively propelled successful market penetration.

From Action to Impact

To drive tangible results, we, at Tiger Analytics, orchestrated a series of targeted initiatives with specific outcomes:

What did we do?

• Implemented a robust email marketing campaign, targeting the identified early adopter HCPs.
• Monitored HCP engagement and response to emails using advanced analytics and tracking tools.
• Leveraged predictive models to conduct real-time analysis of promotional activities, optimizing their effectiveness and making data-driven adjustments.

How did the solutions help?

• Achieved a 15% increase in HCP engagement and response rates.
• Real-time analysis led to a 10% improvement in effectiveness.

Post-Launch: Empowering Patient-Centric Care

Post-launch analytics focuses on monitoring the market and adapting to market dynamics (competition, regulations, reimbursements, etc.) to extend the drug’s lifecycle. Advanced analytics also enables understanding a patient’s journey and optimizing the person’s medication adherence. By leveraging real-world data, electronic health records, and patient-reported outcomes, pharmaceutical companies gain invaluable insights into patient behavior, adherence rates, and treatment patterns. These insights facilitate the development of personalized interventions, patient support programs, and targeted educational campaigns to enhance patient adherence and improve treatment outcomes. Additionally, continuous tracking of the medication’s performance, market share, and patient-reported outcomes enables pharmaceutical companies to make data-driven decisions, generate evidence for stakeholders, and drive ongoing improvements in patient care.

From Action to Impact

To drive tangible results, we, at Tiger Analytics, orchestrated a series of targeted initiatives with specific outcomes:

What did we do?

• Utilized real-world data and electronic health records to track patient behavior and medication adherence.
• Conducted in-depth analysis of patient-reported outcomes to gain insights into treatment patterns and efficacy.
• Developed personalized interventions and patient support programs, based on the identified patterns and behaviors.

How did the solutions help?

• Improved medication adherence by 25%.
• Achieved a 30% increase in patient satisfaction and treatment compliance.

For Pharmaceutical companies, the goal of a successful drug launch is not only about accelerating the medicine’s time to market, but it is also about ensuring patient awareness and access to life-saving drugs. By leveraging the power of data to fuel AI-enabled drug launches, we’ll continue to see better medication adherance, satisfied patients, compliance to treatments – which will ultimately lead to better health outcomes.

The post A Pharma Leader’s Guide to Driving Effective Drug Launches with Commercial Analytics appeared first on Tiger Analytics.

For a long time, Pharma companies have been seen as ‘drug providers’ with a highly transactional relationship with patients. However, with the advancements in data analytics in recent years, the Pharma industry has now adopted a radically different approach aiming to be a ‘partner in every patient’s journey at every step of the way’. The impact of this approach is most imminent in rare disease therapies where patients require continuous support and engagement (from caregivers and the drug provider). Pharma companies – via their Patient Services organization – work tirelessly to ensure that the patients get easy access to therapy, remain on therapy, and the caregiver/patient needs are taken care of.

With the help of advanced analytics and AI, it is now possible to personalize the patient’s journey and provide actionable intelligence to the Patient Services team for proactive interventions to drive optimal health outcomes. For example:

• AI can help identify key drivers of therapy discontinuation and “never-start” patients
• Turnaround time metrics can help identify any bottlenecks across the patient care continuum

Role of Advanced Analytics (AI Solutions) in the management of Patients and Caregivers

Capturing the holistic experience of a patient can be a challenging task for Pharma companies as there are multiple channels and touchpoints – Support centers, Clinics, Pharmacies, Insurance, caregivers, HCPs, Trainers, etc. For meaningful results, it is imperative to aggregate all this information that is received from the variety of data sources and create a holistic 360° view of the patient journey – to analyze the overall patient experience and inform the patient outreach strategy.

Over the past years, at Tiger Analytics we have collaborated with leading Pharma companies to answer the most pertinent questions in the Rare Disease treatment space in order to design a framework for the holistic 360° view. These questions can be categorized across the analytics complexity spectrum:

1. Descriptive Analytics

a. What is the size of the treatment program?
b. What are the discontinuation and never start rates?
c. How do they change over time?

2. Correlations /Explanative Analysis

a. What are the key drivers leading to discontinuation of therapy?
b. Is there a correlation between continued financial aid through copay programs and time on therapy?

3. What if Analysis

a. What if we decrease our copay support?
b. How elastic is the shipment metric w.r.t size of co-pay support?

4. Predictive Analytics

a. Can we predict who is most likely to drop off therapy?

5. Prescriptive Analytics

a. What are some of the actions to be taken by Field Service Managers?

A major multinational Pharma company chose us at Tiger Analytics as their AI partner to enable them to become a more Patient-centric organization. Through our bouquet of AI interventions, we helped create a 360° view of patient experience to enable the leaders in Patient Engagement & Experience teams to keep a pulse check on the various patient outreach programs and monitor their operational efficiency.

How it works

The Tiger Analytics team provides end-to-end data and AI solutions to empower the client team in answering the above complex business questions. Our solution leverages various data feeds received from multiple touchpoints – HealthCare Prescribers, Insurance providers, Pharmacies, Patient demographics, etc. These data inputs are then transformed into actionable insights where we combine our expertise in Data Engineering, Data Science, App Engineering, and BI reporting, and to further deliver to our client tangible outcomes via continuous improvements through end-user feedback.

With the help of our team of Data Engineers, Data Scientists, and industry experts we develop capabilities and roadmaps for the analytics journey for Patient Services and other associated functions. The analytics support provided by Patient Services organizations is of great benefit to Patient Service Managers, Account managers, Therapeutic Area leads, and other associated functions like Patient Engagement, Vendor Management, etc.

With the foundational pillars and analytical capabilities in place, any Pharma company can therefore make a shift from just being a drug provider, to a support system and care partner, offering patients of rare diseases and their caregivers much-needed support during their very difficult patient journey.

Source:

The future of treating rare diseases | World Economic Forum (weforum.org)

The post Patient Services: Pharma’s Answer to Supporting Patients with Rare Diseases appeared first on Tiger Analytics.

The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally. India’s digitization journey and the introduction of the Aadhaar system in 2010 – the largest biometric identity project in the world – has opened up new venues for AI and data analytics. The interlinking of Aadhaar with banking systems, the PDS, and several other transaction systems allows greater visibility, insights, and metrics that can be used to bring about improvements. Besides using these to raise the quality of lives of citizens while alleviating disparities, AI can support more proactive planning and formulation of policies and roadmaps. Industry experts concur a trigger and economic growth spurt, opining that “AI can help create almost 20 million jobs in India by 2025 and add up to $957 billion to the Indian economy by 2035.”

The current state of AI in India

The Indian government, having recently announced the “AI for All” strategy, is more driven than ever to nurture core AI skills to future-proof the workforce. This self-learning program looks to raise awareness levels about AI for every Indian citizen, be it a school student or a senior citizen. It targets meeting the demands of a rapidly emerging job market and presenting opportunities to reimagine how industries like farming, healthcare, banking, education, etc., can use technology. A few years prior, in 2018, the government had also increased its funding towards research, training, and skilling in emerging technologies by 100% as compared to 2017.

The booming interest has been reflected in the mushrooming of boutique start-ups across the country, as well. With a combined value of $555 million, it is more than double the previous year’s figure of $215 million. Interestingly, analytics-driven products and services contribute a little over 64% of this market -clocking over $355 million. In parallel, the larger enterprises are taking quantum leaps to deliver AI solutions too. Understandably, a large number of them use AI solutions to improve efficiency, scalability, and security across their existing products and services.

Current challenges of making India-centric AI

There is no doubt that AI is a catalyst for societal progress through digital inclusion. And in a country as diverse as India, this can set the country on an accelerated journey toward socio-economic progress. However, the socio, linguistic and political diversity that is India also means more complex data models that can be gainfully deployed within this landscape. For example, NLP models would have to adapt to text/language changes within just a span of a few miles! And this is just the tip of the iceberg as far as the challenges are concerned.

Let’s look at a few of them:

• The deployment and usage of AI have been (and continues to be) severely fragmented without a transparent roadmap or clear KPIs to measure success. One of the reasons is the lack of a governing body or a panel of experts to regulate, oversee and track the implementation of socio-economic AI projects at a national level. But there’s no avoiding this challenge, considering that the implications of AI policy-making on Indian societies may be irreversible.
• The demand-supply divide in India for AI skills is huge. The government initiatives such as Startup India as well as the boom in AI-focused startups have only contributed to extending this divide. The pace of getting a trained workforce to cater to the needs of the industry is accelerating but unable to keep up with the growth trajectory that the industry finds itself in. Large, traditionally run institutions are also embracing AI-driven practices having witnessed the competitive advantage it brings to the businesses. This has added to the scarcity that one faces in finding good quality talent to serve today’s demand.
• The lack of data maturity is a serious roadblock on the path to establishing India-centric AI initiatives – especially with quite a few region-focused datasets being currently unavailable. There is also a parity issue with quite a few industry giants having access to large amounts of data as compared to the government, let alone start-ups. There is also the added challenge of data quality and a single source of truth that one can use for AI model development
• Even the fiercest AI advocates would admit that its security challenges are nowhere close to being resolved. There is a need for security and compliance governance protocols to be region-specific so that unique requirements are met and yet there is a generalisability that is required to rationalize these models at the national level.
• There is also a lot of ongoing debate at a global level on defining the boundaries that ethical AI practices will need to lean on. Given India’s diversity, this is a challenge that is magnified many times over

Niche areas where AI is making an impact

Farming

The role of AI in modern agricultural practices has been transformational – this is significant given that more than half the population of India depends on farming to earn a living. In 2019-2020 alone, over $1 billion was raised to fuel agriculture-food tech start-ups in India. It has helped farmers generate steadier income by managing healthier crops, reducing the damage caused by pests, tracking soil and crop conditions, improving the supply chain, eliminating unsafe or repetitive manual labor, and more.

Healthcare

Indian healthcare systems come with their own set of challenges – from accessibility and availability to quality and poor awareness levels. But each one represents a window of opportunity for AI to be a harbinger of change. For instance, AI-enabled platforms can extend healthcare services to low-income or rural areas, train doctors and nurses, address communication gaps between patients and clinicians, etc. Government-funded projects like NITI Aayog and the National Digital Health Blueprint have also highlighted the need for digital transformation in the healthcare system.

BFSI

The pandemic has accelerated the impact of AI on the BFSI industry in India, with several key processes undergoing digital transformation. The mandatory push for contactless remote banking experience has infused a new culture of innovation in mission-critical back-end and front-end operations. A recent PwC-FICCI survey showed that the banking industry has the country’s highest AI maturity index – leading to the deployment of the top AI use cases. The survey also predicted that Indian banks would see “potential cost savings up to $447 billion by 2023.”

E-commerce

The Indian e-commerce industry has already witnessed big numbers thanks to AI-based strategies, particularly marketing. For retail brands, capturing market share is among the toughest worldwide – with customer behavior being driven by a diverse set of values and expectations. By using AI and ML technologies – backed by data science – it would be easier to tap into multiple demographics without losing the context of messaging.

Manufacturing

Traditionally, the manufacturing industry has been running with expensive and time-consuming manually driven processes. Slowly, more companies realize the impact of AI-powered automation on manufacturing use cases like assembly line production, inventory management, testing and quality assurance, etc. While still at a nascent stage, AR and VR technologies are also seeing adoption in this sector in use cases like prototyping and troubleshooting.

3 crucial data milestones to achieve in India’s AI journey

1) Unbiased data distribution

Forming India-centric datasets starts with a unified framework across the country so that no region is left uncovered. This framework needs to integrate with other systems/data repositories in a secure and seamless manner. Even private companies can share relevant datasets with government institutions to facilitate strategy and policy-making.

2) Localized data ownership

In today’s high-risk data landscape, transferring ownership of India-centric information to companies in other countries can lead to compliance and regulatory problems. Especially when dealing with industries with healthcare or public administration, it is highly advised to maintain data control within the country’s borders.

3) Data ethics and privacy

Data-centric solutions that work towards improving human lives require a thorough understanding of personal and non-personal data, matters of privacy, and infringement among others. The responsible aspect to manage this information takes the challenges beyond the realms of deployment of a mathematical solution. Building an AI mindset that raises difficult questions about ethics, policy, and law, and ensures sustainable solutions with minimized risks and negative impact is key. Plus, data privacy should continue to be a hot button topic, with an uncompromising stance on safeguarding the personal information of Indian citizens.

Final thoughts

India faces a catch-22 situation with one side of the country still holding to its age-old traditions and practices. The other side embraces technology change, be it using UPI transfers, QR codes, or even the Aarogya Setu app. But sheer size and diversity of languages, cultures, and politics dictate that AI will neither fail to find areas to cause a profound impact nor face fewer challenges while implementing it.

As mentioned earlier, the thriving startup growth adds a lot of fuel to AI’s momentum. From just 10 unicorns in India in 2018, we have grown to 38. This number is expected to increase to 62 by 2025. In 2020, AI-based Indian startups received over $835 million in funding and are propelling growth few countries can compete with. AI is a key vehicle to ring in the dawn of a new era for India-centric AI– an India which despite the diversity and complex landscape, leads the way in the effective adoption of AI.

This article was first published in Analytics India Magazine.

The post Why India-Targeted AI Matters: Exploring Opportunities and Challenges appeared first on Tiger Analytics.

State of the Private Equity (PE) industry

For starters, Private Equity (PE) firms have to work twice as hard to make sense of their data before turning them into actionable insights. This is because their client portfolios are often diverse, as is the data – spread across different industries and geographies, which limits the reusability of frameworks and processes. Furthermore, each client may have its own unique reporting format, which leads to information overflow.

Other data analytics-related challenges that PE firms have to overcome include:

• No reliable sources and poor understanding of non-traditional data
• Archaic and ineffective data management strategy
• Inability to make optimal use of various data assets
• Absence of analytics-focused functions, resources, and tools

These challenges offer a clear indication of why the adoption of data analytics in the PE industry has been low – compared to others. According to a recent study conducted by KPMG, only a few PE firms are currently exploring big data and analytics as a viable strategy, with “70% of surveyed firms still in the awareness-raising stage.”

Why PE firms need to incubate a data-first mindset

So, considering these herculean challenges, why is a data analytics strategy the need of the hour for Private Equity firms? After all, according to Gartner, “By 2025, more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics.”

First, it’s important to understand that as technology continues to skyrocket, a tremendous amount of relevant data is generated and gathered around the clock. And without leveraging data to unearth correlations and trends, they can only rely on half-truths and gut instincts. For instance, such outdated strategies can mislead firms regarding where their portfolio companies can reduce operating costs. Hence, the lack of a data analytics strategy means they can no longer remain competitive in today’s dynamic investment world.

Plus, stakeholders expect more transparency and visibility into the valuation processes. So, Private Equity firms are already under pressure to break down innovation barriers and enable seamless access and utilization of their data assets to build a decision-making culture based on actionable insights. They can also proactively identify good investment opportunities, which can significantly help grow revenue while optimizing the bandwidth of their teams by focusing on the right opportunities.

Some of the other benefits for PE firms are:

• Enriched company valuation models
• Enhanced portfolio monitoring
• Reduced dependency on financial data
• Pipeline monitoring and timely access for key event triggers
• Stronger due diligence processes

Final thoughts

The emergence of data analytics as a game-changer for Private Equity firms has caused some to adopt piecemeal solutions – hoping that it could reap low-hanging fruits. However, this could prove to be hugely ineffective because it would further decentralize the availability of data, which has been this industry’s biggest problem in the first place.

In reality, the key is for Private Equity firms to rethink how they collect data and what they can do with it – from the ground up. There’s no doubt that only by building a data-led master strategy can they make a difference in how they make key investment decisions and successfully navigate a hyper-competitive landscape.

We hope that we helped you understand the current data challenges Private Equity firms face while adopting a data analytics strategy and why it’s still a competitive differentiator. Stay tuned for the next blog in the series, in which we will shed light on how Private Equity firms can overcome these challenges.

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