Key drivers that are shaping the next generation of digital and data-driven healthcare

Data — particularly the disparate, often unstructured data generated from our daily lives, which is often relevant to health outcomes — requires new analytical tools to turn it from raw information into actionable insights. This analytical power is now becoming available and applied to new previously unused data sources in creating   new knowledge maps.

Data and digital-driven personalized healthcare experience in India

With the advent of more digital media and smart phones, many patients now have access to information at their fingertips. They are much more aware of what types of treatments are necessary. They review physicians online; they review practices and care online — there is a lot more ‘zomatification’ of healthcare in terms of experience for the patient.

Potential challenges in adopting and using digital tools in India

• Most of the information that patients have access to today is not India specific
• India is a multi-lingual country. The access to information may be restricted because of the type of language in which the information is communicated.
• Digital literacy and reach can be other barriers, for example, the urban population will be easily able to adopt the digital tools than the population residing in villages.
• A lot of different solutions for the same purpose, or solutions that do not seamlessly onboard all stakeholders involved in healthcare delivery, may cause confusion for patients and physicians.
• Patients are reading about their own symptoms, doing the triaging themselves and deciding on their own whether they need to visit physician A vs. physician B. This can overload the system in favor of specialists and superspecialists rather than generalists.

Action items to strengthen digital healthcare ecosystem in India

The first step is to make India specific information available for patients to use. Strong patient societies can play a very important role here. These societies can create websites that disseminate impartial and unbiased information to patients in multi linguistic formats. Life sciences companies can also play an important role in providing the information about diseases and drugs.

There is also a need to set up a system that incentivizes patients to go to primary care first instead of directly going to super-specialty physicians.

Building an intelligent ecosystem that can personalize healthcare insights

As the world of healthcare data continues to expand, AI offers a means to connect, combine and interrogate these data differently and unlock actionable insights. The adoption of AI into care delivery will be a continuous learning process, in which our health care algorithms continuously grow in intelligence and value. This combination of human and computational power will offer more value and power than either alone. With the power of AI, we can begin to link the huge volumes of data generated and the vast array of technological tools being developed into a comprehensive, integrated smart health system.

Technology driven transformation across the pharma value chain to improve efficiency and outcomes

The life sciences industry has already benefited from technological enhancements, and there is the promise of more to come. Drug development, clinical trials, manufacturing, and supply chain are some of the areas impacted by AI and machine learning (ML).

Research and Development (R&D): Increasing efficiency and productivity with a focus on patient centricity

Thanks to its ability to process and interpret large data sets, AI and ML can be deployed to design the right structure for drugs and make predictions around bioactivity, toxicity, and physicochemical properties. Not only will this input speed up the drug development process, but it will also help to ensure that the drugs deliver the optimal therapeutic response when they are administered to patients.

Manufacturing and supply chain: transitioning from manual processes to agile systems for delivering personalized drugs to patients

By analyzing longitudinal data, AI and ML can identify systemic issues in the pharmaceutical manufacturing process, highlight production bottlenecks, predict completion times for corrective actions, reduce the length of the batch disposition cycle, and investigate customer complaints. It can also monitor in-line manufacturing processes to ensure the safety and quality of drugs. These interventions will give life sciences companies confidence that their manufacturing processes are operating at a high standard and not putting the organization in breach of regulations. Importantly, the bottlenecks caused by the pandemic tested the resiliency of the entire supply chain ecosystem. Furthermore, life sciences companies can improve their efficiency by applying AI to their supply chain management and logistics processes, aligning production with demand and with an AI-enabled sales and operations planning process.

The ongoing revolution in personalized, patient-centric, virtual and home-based care opening up new opportunities and business models for MedTech companies

The COVID-19 crisis has expedited health care’s move away from traditional institutional channels toward home-based settings – and this momentum continues.

The past two years suggest that virtual, flexible care delivery offers benefits and improved outcomes beyond merely the ability to adapt to, and cope with, the extraordinary demands of the pandemic. Studies have shown that hospital-at-home programs can help establish a more immediate and consistent connection between patient and provider to stabilize or improve chronic conditions, prevent hospital readmission after discharge, and mitigate the development of chronic conditions among relatively healthy patients. In addition, evidence shows that remote patient monitoring can reduce readmission rates, falls and adverse events while freeing up both hospital beds and health care professionals’ time. 

In response to the opportunities offered by these new care models, MedTech companies have innovated to deliver better outcomes, improve access to underserved populations and increase detection of underdiagnosed diseases. 

There are still issues to address beyond the regulatory and reimbursement questions, including inequitable access to the digital infrastructure (such as broadband) that is required to deliver virtual care effectively. But, working with providers, patients and policymakers, the industry can address these challenges and play a key role in innovating health care delivery in ways that offer better outcomes for all stakeholders.

Need for biopharmas to create long-term growth focusing on sustainability and ESG value- led stakeholder capitalism: the case for change

The definition of success in business has expanded to mean more than higher profits or better returns. There is a growing recognition that quarterly earnings no longer accurately reflect a company’s entire value. Indeed, while those balance sheets may have captured more than 80% of a company’s value in 1975, today’s balance sheets reflect at most 50% of that corporate value.

These changes mean life science organizations must better articulate their value outside the innovative medicines they develop. In today’s world, talent, data, trust and innovation also contribute to financial success. And, as the biopharma industry continues to wrap services around products, intangible assets such as intellectual property, human capital, organizational culture, corporate governance and public trust are growing in importance. We need new reporting frameworks to measure these intangibles and drive a long-term results model.

Defining and internalizing sustainability in life sciences

One challenge life sciences companies face is deciding which of the numerous sustainability frameworks used to  measure value. Because most frameworks are not specifically designed with life sciences companies in mind, at best these models only approximate the value biopharma companies create. This value disconnect is one of the many reasons it is difficult to draw a direct line from a company’s sustainability efforts to its financial performance. Separately, the wide variety of metrics in use is also problematic. Our analysis suggests that even within a single organization, various parts of a business may use different metrics to track sustainability efforts. That variability makes it difficult to assess the impact of sustainability programs at an enterprise level, let alone compare different companies.