The life sciences industry is at a crossroads with Data Analytics in Life Sciences and Healthcare. Biopharma and medical technology companies seek innovative methods to produce value and make sense of today’s wealth of data to plan for the future and remain relevant in an ever-changing business context. 

Many firms are attempting to accelerate the discovery and development of medicines by leveraging cutting-edge technology such as Artificial Intelligence (AI), Machine Learning (ML), and automation.

To be ready for the future, all types of life sciences organizations, from biopharma to Medtech, will need to identify new ways to produce value and metrics to help them make sense of today’s plethora of data. Data Analytics in Life Sciences and Healthcare organizations employing obsolete legacy on-premises and cloud database systems face significant management and security difficulties as the volume and variety of data grows.

Furthermore, legacy systems make it difficult for life sciences organizations to get the data diversity required to optimize business processes and make critical decisions. Here are significant challenges that Data Analytics in Life Sciences and Healthcare organizations face when using data to improve therapeutic and business outcomes:

Key Takeaways and Report of Data Analytics in Life Sciences and Healthcare

●     Access live third-party data without any ETL, making the data ready for rapid analysis or merging with your data.

●     Discover third-party data sets that best meet your business needs, such as anonymized prescription data, medical sales data, COVID-19 data, and demographic data.

●     Enrichment services can help you increase the quality of your first-party data by securely exchanging slices of it with suppliers.

●     According to Global News Wire Report by Precenda Research, the Life Science Analytics market was worth $7.57 billion in 2019 and is expected to be worth $18.12 billion by 2030, growing at an 8.25 percent CAGR.

●     According to the research, the growing use of big data in healthcare has increased the life science care analytics industry. In life science analytics, data standardization has become critical.

Source: Precedenceresearch

1). Top Challenges Faced By Data Analytics in Life Sciences and Healthcare

a). Data efficiency

Life sciences companies must be able to process vast amounts of data rapidly and achieve valuable conclusions. For example, efficient clinical trial data integration, validation, and mining are critical for drug development. Time-to-insight is also essential in running effective sales and marketing efforts and managing inventory management and supply chain logistics.

 However, many businesses continue to rely on slow legacy systems that exacerbate the problems caused by data silos, provide poor and inconsistent user experiences, and produce fragmented insights generated by a great deal of manual labor. Such systems are difficult to expand to handle a higher volume of data or users, which could be important when, for example, a pharmaceutical corporation needs to act rapidly during a public health crisis.

b). Performance Issues

To obtain timely and valuable insights, Data Analytics in Life Sciences and Healthcare organizations must be able to process large amounts of data rapidly and efficiently. Efficient clinical trial data integration, validation, and mining are critical for drug development. Time-to-insight is also essential in running effective sales and marketing efforts and managing inventory management and supply chain logistics.

Many businesses, however, continue to rely on old-fashioned legacy systems that create data silos, provide uneven user experiences, and provide fragmented insights after much human effort. Such systems are difficult to scale to accommodate a higher volume of data or a more significant number of users.

To handle different analytical workloads, modern analytics platforms can swiftly and efficiently analyze information from disparate sources and store it in a single spot. Teams can execute self-service analyses and access real-time data to make educated decisions. Improved performance leads to faster innovation and market time.

 c). Collaboration and data exchange

Access to a diversified and varied data source improves informed decision-making. To achieve data diversity, life sciences firms must communicate massive amounts of sensitive data with other entities, which frequently necessitates back-and-forth collaboration.

Data on therapies, patients, and lab results, for example, must be transmitted between a pharmaceutical company and a variety of partners throughout a clinical study. However, diverse legacy systems impede rapid, easy, and secure data transfer, forcing businesses to rely on manual, unsecure techniques such as FTP.

d). Capturing of Data

Data Analytics in Life Sciences and Healthcare comes from some place, but unfortunately for many healthcare providers, that does not always have perfect data governance practices. Capturing clean, complete, accurate, and adequately organized data for usage in numerous systems is a constant battle for businesses, many of which are losing.

In one recent investigation at an ophthalmology clinic, only 23.5 percent of EHR data matched patient-reported data. Patients’ EHR data did not accord when they reported having three or more eye health complaints.

Poor EHR usability, complicated workflows, and a misunderstanding of why big data is vital to capture well can all contribute to quality issues that afflict data throughout its lifecycle.

e). Compliance with regulations

Companies in the life sciences business must adhere to strict laws and quality norms, including GxP criteria, which govern processes in manufacturing, laboratories, and clinical settings to guarantee medical products are safe for consumers. Furthermore, life sciences organizations must adhere to stringent rules regarding using, storing, and disposing of sensitive data.

Source: Smartasset

f). Security ro Peak

Data security is the top issue for healthcare institutions, especially after a string of high-profile breaches, hackings, and ransomware incidents. Healthcare data is vulnerable to almost unlimited threats, from phishing assaults to malware to computers left in cabs.

The HIPAA Security Rule contains many technological precautions for enterprises that store protected health information (PHI), such as transmission security, authentication procedures, access, integrity, and auditing controls.

In reality, these measures take the form of common-sense security methods such as utilizing up-to-date anti-virus software, configuring firewalls, encrypting sensitive data, and employing multi-factor authentication.

However, even the most well-protected data center can be brought down by the fallibility of human staff members, who prefer ease over lengthy software updates and sophisticated restrictions on their access to data or software.

To prevent unscrupulous parties from causing damage, healthcare companies must frequently remind their staff members of the critical nature of data security standards and consistently check who has access to high-value data assets.

g). Data Updation

Healthcare data is not static, and most elements must be updated regularly to remain current and relevant. These updates may occur for some datasets, such as patient vital signs every few seconds. Other information, such as a person’s home address or marital status, may only change a few times in their lifetime.

Understanding considerable data volatility, or how frequently and to what extent it varies, can be difficult for organizations that do not routinely monitor their data assets.

Providers must understand which datasets require manual updating, which may be automated, how to finish this process without causing downtime for end-users, and how to ensure that changes can be performed without compromising the dataset’s quality or integrity.

When trying an update to a single element, organizations should also verify that they are not creating unneeded duplicate records, which may make it difficult for physicians to obtain critical information for patient decision-making.

h). Data integrity

Life sciences organizations must analyze a tremendous amount of real-world data in various formats to conduct R&D and clinical trials and manage day-to-day business. According to a business wire, In the United States, less than 15% of healthcare and life sciences organizations use clinical decision support technologies to manage data.

Life sciences firms waste valuable time importing, cleaning and organizing data, but old data warehouses cannot offer data in a form that allows for fast, accurate analysis and insights.

Furthermore, data is frequently separated into two silos: commercial (for data such as sales and marketing records) and regulated (for data such as clinical trials and laboratory results).

i). Care Delivery Costs

The cost of service delivery is central to the healthcare industry’s issues. Healthcare spending amounts to 18% of the US GDP. Although industry actors are attempting to improve care delivery efficiency, there is tremendous revenue pressure due to novel payment/reimbursement methods, making it challenging to maintain historical financial parity.

There is an urgent need to leverage data and analytics to uncover trends that will allow healthcare organizations to improve treatment effectiveness, minimize errors, better understand risk, reduce costs, increase operational efficiency, and capture maximum reimbursements for care delivery. Healthcare has been sluggish in adopting modern data and analytics capabilities, leaving healthcare professionals with insufficient knowledge to make decisions and affect positive change.

j). Scalability and data management

A data platform that is simple and inexpensive to administer and scale is critical to success. Legacy platforms, whether on-premises or in the cloud, can be difficult and expensive to maintain and expand. Data Analytics in Life Sciences and Healthcare wastes time managing the platform and worrying about its cost rather than making data-driven decisions.

Bottom Line

To stay ahead of the industry’s tectonic developments, today’s Data Analytics in Life Sciences and Healthcare must leverage the cloud’s strength and ability to deliver performance, speed, and flexibility.

Data from any source can be used by businesses to improve therapeutic and business outcomes for patients, customers, partners, and care providers.

They can securely govern, manage, scale, share, and exchange data, resulting in faster actionable insights in clinical trials and a shorter time to market.