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The modernization of health and pharma analytics in a post-Covid world – Five predictions

Healthcare
(Image credit: Image Credit: Lightpoet / Shutterstock)

One year ago, the Covid-19 pandemic changed the world and forced many organizations to demonstrate resilience and adaptability. Collaboration across the health and life sciences global ecosystem accelerated, resulting in new therapies and medical discoveries. At every level – from patients to cost to quality to outcomes – analytics became increasingly important to drive insights, make informed decisions and realize the value of digital transformation.

As we move into a post-pandemic world, here are five predictions for the use of analytics to modernize health and life sciences that build on the challenges faced and innovations achieved in 2020.

1. Health and life science companies will accelerate investments in digital research and engagement platforms.

While this industry has been slower to adopt digital technologies than retail or banking, many health and life sciences companies found themselves thrust into digital-first scenarios due to the risks presented by the pandemic. As a result, health systems are now taking advantage of digital transformation to improve efficiency across clinical and operational decisions – from detecting infectious disease faster to automating claims processing. And life sciences companies are modernizing analytics to change engagement strategies with health care providers and keep clinical trials operating in a decentralized model. This transformation is bound to accelerate through the rest of 2021.

In a recent survey of more than 200 pharmaceutical executives conducted by BioPharma Dive and SAS, almost 65 percent of respondents reported to be considering modernization efforts or already planning modernization efforts related to data management for clinical trials. Investments in making patient data available and conducting decentralized analysis not only opens the possibility for providing options to clinical trial sponsors, but also to governments and health systems. 

In health care, Covid-19 underscored the value of virtual care settings for convenience, safety, flexibility and expanded access to care. Virtual care technology continues to improve through integration with EMRs, interoperability within and across health systems, regulation, and reimbursement standards. We have seen – and will continue to see – unprecedented investment in these types of digital platforms and continued increases in patient demand.

2. Government health agencies will ramp up data collection to fuel analytics and policy planning systems.

Government agencies were not prepared for the global pandemic. They lacked data to make informed decisions and had antiquated processes in place to gather and disseminate timely information. Even advanced countries needed assistance with developing a platform to ingest data to help them operationalize analytics. According to SAS Medical Director Steve Kearney, “The pandemic revealed the inefficient workarounds that many agencies have used to fill gaps in data quality and completeness. Resilience for the future requires capabilities to easily connect to the data you need, whether in the cloud or on site.”

Government agencies are now reimagining what their systems should look like and how to make them more operational in the future, starting with ramping up data sources and collection processes. The pandemic highlighted a need to find a balance between privacy and public health, as well as the necessity to detect early signals about adverse events and establish more reliable disease surveillance programs. To respond, governments will invest significantly in “readiness systems.” We have learned that merely having the data is not enough – the key is applying analytics to the data to generate actionable insights. At the core, data collection processes can be optimized by newly gained insights, driving further optimization.

And last, while parts of patient privacy legislation have been temporarily neutralized in this public health emergency, we will see a real willingness to set up safe data spaces for analysis of population data, including a breakthrough of cloud facilities to provide national, secure safe zones to use health data to drive insights.

3. Supply chain management is under the microscope as the industry balances readiness and cost.

All aspects of health care and life sciences have been put under a magnifying glass during the pandemic. It became clear that supply chains for health care are crucial to ensure timely delivery of medication, PPE and vaccines. It also became evident that health care supply chains were stretched thin pre-pandemic and buckled under the new strain.

Moving forward, we will see increased investment to make supply chains more resilient and the application of real-time analytics, big data, and connected systems to make informed decisions and optimize supply chains. These “intelligent supply chains” will be equipped with the ability to forecast not only regular changes but also to quickly adapt to major shocks, such as a pandemic or geopolitical change.

Likewise, just in time supply chains are not acceptable – we instead will see “just in case” supply chains. Supply chains need to be visible and horizontally integrated with manufacturing capacity. Artificial intelligence (AI) will be used to manage inventories, generate signals across end-to-end supply chains, provide real time location analysis, and automate standard processes.

4. Digital transformation and AI will enable patient-centricity at all touchpoints.

Becoming more member centric, personalizing the patient journey and applying precision medicine has been a burning platform for providers and health plans alike. Digital transformation efforts must put the patient at the center and include all points of where a patient interacts with a health system –appointment data, laboratory, genomic, x-ray, treatment plans, etc.

Equally important for digital transformation is data management built on interoperable platforms with explainable algorithms and models that include open source. To operationalize AI and apply predictive models in clinical use, organizations must have a well-established analytics infrastructure before the next crisis hits. Established competencies in forecasting and predictive modeling are essential to know where to go for answers. Advanced analytic solutions augment this process by making models more repeatable and governed.

Large health entities are preparing for digital transformation by bringing together health plans, pharmacies, pharmacy benefits managers and clinics. They are creating Centers of Excellence to help promote and give credibility to AI in clinical use. Smaller health organizations, like the COPD Foundation, are using analytics to gain insights from data to better meet the needs of their target populations and focus community outreach and support. For health plans, converting into more member centric processes is a true transformation from the digital front door all the way through the organization.

5. Health industry leaders will expand their mission towards global health equity through innovative analytic collaborations.

To help address inequities in our health system and meet the needs of vulnerable populations, health leaders will turn to analyzing data – in new ways and from new sources – to understand community needs and optimize resources. Analytics can shine a light on the strengths and weaknesses in a health ecosystem, and increasingly will be used to discover new collaborative approaches to improving population health outcomes. Since no one is safe until every world citizen has been vaccinated and protected, we need to explore how technology can help low- and middle-income countries benefit from new virtual health models in a cost-effective manner.

During the pandemic, technology was center stage as health and social services agencies scaled up operations to help communities and optimize medical resources. We saw health systems share data and models, industry convergence, and innovative partnerships to integrate data silos across public and private entities. In the future, we will continue to see barriers broken down in data and analytic sharing towards a common purpose of improving and saving lives globally.

One of the key learnings of the pandemic is that we cannot go back to our pre-Covid-19 ways of solving health problems. We all must work together for the collective good of humanity. Technology is not the barrier to innovation: the barrier is the resistance to changing our mindset and culture to accept new ways of working.

Mark Lambrecht, Director, Global Health and Life Sciences Practice, SAS

Mark Lambrecht is the director of the SAS Global Health and Life Sciences Practice, setting the strategic direction of SAS' global health care and life sciences solutions and providing industry domain knowledge to colleagues and customers.