Addressing the challenges of healthcare data transformation with automation

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Data

Addressing the challenges of healthcare data transformation with automation

By Calum Yacoubian, M.D.

Organizations continue to recognize the value of the large volumes of data they have collected and their potential for improving the quality, safety, and continuity of care. To utilize the data effectively and appropriately realize these benefits, organizations must overcome the challenges associated with transforming the data for use in healthcare. Automation can help stakeholders address these challenges and use real world data (RWD), both efficiently and economically.

Main challenges of healthcare data transformation

The first step in overcoming these challenges is to identify them. For lifescience companies and healthcare institutions the primary data management difficulties are:

  1. Large volumes of healthcare data exist, of which up to 80% is unstructured. The data originates from heterogeneous sources and comes in a wide range of formats, including physical documentation.
  2. Large quantities of the data available were not collected for the purpose of analysis, meaning organizations must spend up to 80% of their efforts manually cleaning and standardizing the data.
  3. The data contain a significant number of variables and reference ontologies, making integration and unification more labor-intensive and time-consuming to achieve.
  4. Evolving regulatory requirements impact the use of data in compliance with privacy standards (such as outlined in the Health Insurance Portability and Accountability Act of 1996 (HIPAA)). Understanding and applying the correct measures to maintain appropriate privacy is complex, but essential.

Automated approach to solving these challenges

To overcome these challenges, organizations often turn to traditional manual and time-consuming methods. Automated (either fully or partially), multi-functional data transformation is emerging as a powerful way to help healthcare organizations and life science companies make sense of their data. Technology enables data managers to render healthcare data non-identifiable and to structure and normalize those data across multiple sources simultaneously, in a matter of a few hours.

A statistical, evidence-based approach to privacy protections that help gain new insights from your most sensitive data while alleviating privacy concerns throughout the data lifecycle, aligned with General Data Protection Regulations (GDPR) and HIPAA standards, allows practitioners to stay ahead of changing requirements. Extensive, multi-country-specific ontologies automate conversion to a common data model (CDM) – such as in observational medical outcomes partnership (OMOP). Natural Language Processing transforms unstructured and semi-structured clinical data to normalized structured data, that can be incorporated into this CDM. This results in fit-for-purpose, analysis-ready data at scale, significantly improving the quality and consistency of insights and increasing regulatory compliance.

Identifying opportunities for data automation

With data automation, organizations can focus on generating insights instead of manually cleaning the data. Using this advanced technology, healthcare organizations can structure, normalize, and index population health data. By creating a complete record of a patient’s medical history, quality of care improves, and costs are reduced. This 360-degree view enables providers to identify at-risk patients earlier and develop more accurate predictions about disease onset.

Automated anonymization, alongside reidentification risk assessment ensures robust data privacy with statistical proof of nonidentification while delivering rich insights. This enables data insights to be gathered across broader data sources, and shared among a wider group of healthcare stakeholders – in turn delivering better patient outcomes and improved economic efficiency, while newly discovered analytics-based insights can bolster clinical care and enhance R&D.

Benefits of automating healthcare data

The role of technology to automate healthcare data transformation is pivotal in ensuring the tangible value of growing volumes of healthcare data can be realized. Reliable and repeatable technology processes can reduce the time, cost, and effort involved in turning data to insight – from informing quality improvement programs, to conducting post-market surveillance and other RWD activities. While these areas for impact of automated health data transformation are broad, the following are three key opportunities for adopting this approach:

  • Improving quality of care: By better capturing, structuring and normalizing clinical data, providers can better identify care gaps and improve quality of treatment and outcomes
  • Improving real world studies: With highly accurate and curated data made available in deidentified formats – more data and data insights can be shared in real world studies – enabling powerful evidence generation
  • Shift to preventative medicine: With a more complete view across a population – providers can drive more predictive disease models and identify at-risk patients to be more proactively managed

With increasing volumes and complexity of healthcare data, now is the time for organizations to look for technologies to automate their data transformation in order to accelerate insights, reduce manual workload and improve patient outcomes.

Calum Yacoubian, IQVIA Calum Yacoubian, M.D. is director of healthcare, real world solutions, IQVIA.