What Is Automated Data Transformation and How Can It Accelerate Healthcare Insights?

With each passing second, enormous amounts of data are generated by the healthcare field, currently accounting for 30% of the world’s data volume. Faster than any other industry, those in healthcare will see 36% annual growth in the output of this information by 2025. With digital products becoming integrated more and more into the lives of health consumers, data is significantly driving healthcare advances. However, there has been little success in harnessing the potential they hold to extract reliable insights. This is where the idea of an automated data transformation (ADT) platform comes in to provide a solution for the common difficulties presented by medical datasets.

Challenges of Harnessing Healthcare Data

Despite the value it presents, the vast majority of data within the healthcare industry goes entirely unused after being created. In today’s healthcare structure, patient information is fragmented and asynchronized across multiple disparate systems in multiple different formats. With this data existing as lab reports, images, and clinical notes inconsistently spread across the medical records of different providers, extracting substantial insights becomes more difficult. It’s important to note that there are also strict privacy and regulatory restrictions which must be followed before stakeholders can fully harness these datasets to their fullest potential.

Why Automated Data Transformation (ADT)?

Recognizing the frustrations of managing large volumes of disorganized data, many industry leaders are exploring automated approaches for transforming this information to gain insights. Currently, stakeholders like healthcare organizations and biopharmaceutical companies are caught in an inefficient process of spending more time cleaning datasets than extracting value from them. Despite the human mind’s creativity and capacity for problem-solving, automation is inevitably required to quickly curate, integrate, and standardize heterogeneous patient health records. Ideally, ADT should occur within a complex platform driven by artificial intelligence (AI) wherein each data point from multiple sources is anonymized and copied into a central standardized location for easy search and analysis. Such a platform provides numerous benefits to industry professionals and enables faster time to detect useful trends for driving innovation.

Advantages of Automated Data Transformation

Most significantly, adopting ADT technology tools frees organizations from the inefficient use of time and money to carry out repetitive manual tasks which result in minimal outcomes. With automation removing this challenge, healthcare, clinical research organizations (CROs), and research teams are better equipped to focus on more important goals, such as creating critical solutions to advance medical care. ADT methods also enable companies such as CROs to maintain more current health records because the technology can continuously work in the background to quickly transform incoming new data.

This ensures all analytics are performed on the most up-to-date information, resulting in smarter decisions being made in both the short term and long term. With the projected exponential rise of data output in the healthcare industry in the near future, companies must keep up by priming their data records to be ready for integration with the newest machine learning and AI techniques. Ultimately, ADT helps healthcare, CRO, and life science organizations run in a more cost-effective manner, while still increasing their business potential for discovering valuable data insights.

How Can ADT Accelerate Healthcare Insights?

Most patient data, like in electronic medical records (EMR), is too complex and inconsistently structured to allow research analysis without significant transformation first. Standardized, anonymized healthcare data transformation platforms can help deliver notable improvements in areas such as drug development, population-level analysis, healthcare delivery, and patient care quality. ADT techniques can especially be useful for EMR mining of large patient populations: clinical trial research sites can take advantage of this ability to identify potential participants; companies can evaluate the performance of certain interventions or medical devices in specialized populations; and trends in the needs and burdens of various at-risk patient populations can be detected by healthcare and clinical research organizations to ensure optimal delivery of care and resources.

Driving Modern Innovation and Problem-Solving in Research and Clinical Care

Automated data transformation processes have recently been promoting the emergence of complex AI-driven platforms for healthcare and life sciences companies. The creation of such platforms by industry technology leaders is paving the path for harnessing the full power of large volumes of unstructured patient data taken from heterogeneous sources. There is no shortage of applications in the field of healthcare to gain valuable insights for driving modern innovation and problem-solving in research and clinical care. Embracing ADT promotes more efficient use of company resources and ensures industry professionals can prioritize accelerating their growth, as well as becoming better equipped to address the needs of patients.

The Vial CRO

The Vial CRO is committed to harnessing modern technology to reimagine today’s clinical research experience. Vial offers niche to full-service, global clinical services for Phase I-IV trials. Vial’s CRO services provide faster, more efficient trial results for an affordable cost. Vial offers a fixed-fee pricing model, without the use of change orders to keep budgets from inflating. To discover how our CRO services and products are delivering faster and more cost-effective study outcomes for sponsors, visit https://vial.com.

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