Greg Crawley
January 1, 2025
Challenges in healthcare data transformations:
How to avoid pitfalls and adopt solutions
Healthcare data can power better patient outcomes, drive research progress, and enhance operational processes. However, turning raw information into practical insights often proves difficult. Missteps in data initiatives can lead to regulatory fines, patient safety problems, and massive financial strain. Below, we review key obstacles, notable case studies, and how AI-powered ETL services like Roboshift provide a more efficient approach.
How data efforts in healthcare can go off track
Healthcare generates enormous volumes of complex information—patient records, diagnostic images, clinical trial results, insurance claims, and more. Converting these varied data sets into cohesive, trustworthy resources isn’t straightforward. Here are some notable examples of where things went wrong:
US Department of Veterans Affairs EHR modernization (2020–present)
The Department of Veterans Affairs (VA) delayed the deployment of the Oracle Cerner electronic health record (EHR) system in 2023 to address technical issues such as system latency, patient scheduling problems, and medication management challenges. The VA aimed to replace its aging data system with a modern EHR platform, but poorly planned rollouts led to repeated delays, technical problems, and confusion among medical staff. These setbacks disrupted patient services and significantly increased project costs.
South Australia’s EPAS turmoil (2016–2021)
South Australia’s Electronic Patient Administration System (EPAS) aimed to offer a unified platform for public hospitals. The project encountered interface compatibility issues and inconsistent data handling practices, which resulted in widespread complaints from clinicians and an inflated budget. In 2021, state authorities announced a transition to a different platform, effectively drawing the troubled EPAS initiative to a close.
When data efforts in healthcare go right
Below are a few examples of where the data transformation process went well, resulting in improved care, enhanced research, or stronger operations.
Mount Sinai’s AI-driven EHR integration (2022)
Mount Sinai Health System launched a broad data consolidation plan to merge multiple EHR systems. Using AI-based algorithms, they automated the mapping of medical codes and patient identifiers across various hospitals and clinics. This approach cut down on manual data manipulation, reduced mismatches, and sped up analytics workflows, giving clinicians access to more cohesive patient records.
Cleveland Clinic’s big data overhaul with predictive analytics (2022)
Cleveland Clinic is integrating AI, data, and analytics to enhance patient care, improve operational efficiency, and advance medical research. AI is being used in surgical planning, patient data analysis, and administrative tasks like automated note-taking. The Virtual Command Center helps predict hospital capacity and optimize staffing.
University of California health system’s data integration success (2024)
Researchers across the University of California system are leveraging a unified health data resource to accelerate medical research and improve health outcomes. The data undergoes transformations through advanced data science methods and machine learning techniques to identify new treatments, speed up diagnoses, and understand the impact of social determinants on health and access to care. This enables researchers to explore new lines of inquiry and develop targeted healthcare strategies.
Common challenges in healthcare data transformations
- Disparate systems and formats
EHR software, billing applications, and lab data repositories use different structures, complicating alignment. - Data quality issues
Duplicate patient records, inconsistent coding (ICD-10, CPT, SNOMED), or missing details can distort analytics and compromise clinical choices. - Regulatory pressures
HIPAA, GDPR, and other rules impose strict guidelines on healthcare data. Mistakes invite legal trouble and damage public trust. - Time-consuming ETL processes
Extract, transform, load workflows may be prone to human error and are often a major drain on time, especially for teams with limited technical backgrounds. - Resource constraints
Staff may lack specialized data skills, and hiring or training new staff can be expensive.
Typical hurdles during healthcare data projects
Many healthcare organizations encounter significant complexity when consolidating records after mergers or acquisitions, because they must bring together large sets of incompatible EHR data. Another obstacle arises when compiling compliance reports that require data from multiple systems, each with its own standards, leading to potential inaccuracies and setbacks. In addition, population health monitoring often involves tracking chronic disease trends by gathering information from claims, pharmacy records, and clinical documentation, which can be stored in diverse formats across various platforms.
Where Roboshift fits into healthcare data work
Similar to the data transformation challenges faced in HR data, healthcare demands precise, well-defined processes. Roboshift brings modern tools to reduce complexity and help hospitals and clinics focus on patient care.
- Natural language commands
Non-technical staff can set up data workflows using plain English instructions, minimizing reliance on IT or external consultants. - Clever field mapping
Roboshift recognizes equivalent fields even when they’re named differently across systems, removing a common source of human error. - Built-in compliance safeguards
Sensitive identifiers can be automatically masked or removed, helping meet HIPAA, GDPR, and other regulatory requirements. - Real-time data checks
Mistakes such as duplicates, invalid codes, or empty fields are flagged early for correction, keeping quality high. - Faster turnaround
Automated workflows cut down lengthy processes, enabling healthcare leaders to act quickly on data-driven insights.
