Common Healthcare Data Management Issues … And How to Solve Them
Scott Norris
December 12, 2023
A modern data management strategy treats data as a valuable business resource. That’s because data should be managed from creation to the point when it’s no longer needed in order to support and grow the business. Data management entails collecting, organizing, and securely storing data in a way that makes it easily accessible to everyone who needs it. As organizations create, ingest, and analyze more data than ever before, especially in the healthcare field, data management strategies are essential for getting the most value from data.
Making data management processes scalable is also critical, as data volumes and the number of data sources continue to rapidly increase. Unfortunately, many organizations struggle with data management problems, such as silos that result in outdated and untrustworthy data, legacy systems that can’t easily scale, and data integration and quality issues that create barriers to using data.
When these challenges enter the healthcare industry, the impact can be significant, immediate, and costly. That’s because data volumes in healthcare are enormous and growing at a fast rate. As a result, even minor issues with data management can become major problems as processes are scaled to handle massive data volumes.
Data management best practices are essential in healthcare to ensure compliance, enable data-driven outcomes, and handle data from a myriad of sources. The data can be connected, managed, and analyzed to improve patient outcomes and lower medical costs. Here are common data management issues in healthcare—and how to solve them:
Data Silos are an Ongoing Problem
Healthcare data comes from a variety of sources, including patient healthcare records, medical notes and images, insurance companies, financial departments, operations, and more. Without proper data management processes in place, harnessing this data can get very complex, very fast.
Complexity often leads to data silos and shadow IT approaches. This happens when departments or individuals want to quickly access data, but don’t want to follow established protocols that could require IT help, so they take shortcuts. This results in islands of data that are not connected and may be outdated, inaccurate, or have other quality issues.
Breaking down silos and connecting data requires the right data platform. The platform should be scalable, have easy-to-use integration capabilities to unify data, and make data easy-to-access, without IT assistance. Making data easy discourages silos, fosters a data-driven culture that supports data management best practices, and allows all users to tap into the data they need.
Barriers to Data Integration and Quality
Many legacy systems used by healthcare organizations are not integration-friendly. They may have been built as a single-purpose solution and interoperability was not a primary concern. In today’s healthcare environment, connectivity is important to enable data sharing, automation, and visibility into the organization.
“The flow of data is as important as the flow of people,” according to FQHC Associates, which specializes in Federally Qualified Health Center (FQHC) programs. “One common issue in connected care is a lack of data standardization, in which the different platforms used by different departments are not mutually readable or easily transferable. This results in data silos, blocks productivity, and even worse, leads to misunderstandings or errors.”
Data integration—bringing together all required data from all available sources—on a single platform helps inform decision-making, delivers complete patient records, and enables healthcare data analytics. The Centers for Medicare & Medicaid Services (CMS) has mandates to prioritize interoperability—the ability for systems to “speak” to each other.
A modern platform is needed that offers simple integration and ensures data quality to give stakeholders confidence in their data. The platform must be able to integrate all needed data from anywhere, automate data profiling, and drive data quality for trusted results. Ensuring the accuracy, completeness, and consistency of healthcare data helps prevent problems, such as misdiagnosis or billing errors.
Complying with Ever-Changing Regulations
The healthcare industry is highly regulated, which requires data to be secure and meet compliance mandates. For example, patient data is sensitive and must meet regulations, such as the Health Insurance Portability and Accountability Act (HIPAA).
Non-compliance can result in stiff legal and financial penalties and loss of patient trust. Protecting patient data from breaches and unauthorized access is a constant concern, yet making data readily available to physicians when treating a patient is a must.
Regulations can be complex, vary by state, and continually evolve. This challenges healthcare organizations to ensure their data management plan is regularly updated to meet changing requirements. Implementing role-based access controls to view data, using HIPAA-compliant data management technologies, and encrypting data help with patient privacy and protection.
Similarly, data governance best practices can be used to establish clear governance policies. Best practices help ensure data is accurate, protected, and compliant. Healthcare organizations need a modern data platform capable of offering transparency into data processes to ensure they are compliant. Automating data management tasks removes the risk of human errors, while also accelerating processes.
Dealing With Duplicate Patient Records
The healthcare industry’s shift from paper-based patient records to electronic health records enabled organizations to modernize and benefit from a digital transformation. But this advancement came with a challenge—how to link a person’s data together in the same record. Too often, healthcare facilities have multiple records for the same patients due to name or address changes, errors when entering data, system migrations, healthcare mergers, or other reasons.
“One of the main challenges of healthcare data management is the complexity of managing and maintaining patient, consumer, and provider identities across the enterprise and beyond, especially as your organization grows organically and through partnerships and acquisition,” according to an article by MedCity News.
This problem increases data management complexity by having duplicate records for the same patients. Performing data cleansing can detect duplicate records and reconcile issues. Likewise, having a robust data quality management framework helps prevent the problem from occurring by establishing data processes and identifying tools that support data quality.
Delivering Trust in Healthcare Data
Many healthcare organizations struggle to optimize the full value of their data, due to a lack of data standards, poor data quality, data security issues, and ongoing delays in data delivery. All of these challenges reduce trust in data and create barriers to being a truly data-driven healthcare company.
Solving these issues and addressing common data management problems in healthcare requires a combination of technology solutions, data governance policies, and staff training. An easy-to-use data platform that solves issues for data scientists, managers, IT leaders, and others in healthcare organizations can help with data management, data visualization, and data accessibility.
For example, the Actian Data Platform gives users complete confidence in their data, improves data quality, and offers enhanced decision-making capabilities. It enables healthcare providers to:
- Connect data sources. Integrate and transform data by building or using existing APIs via easy-to-use, drag-and-drop blocks for self-service, removing the need to use intricate programming or coding languages.
- Connect to multiple applications. Create connections to applications offering a REST or SOAP API.
- Broaden access to data. Use no-code, low-code, and pro-code integration and transformation options to broaden usability across the business.
- Simplify data profiling. Profile data to identify data characteristics and anomalies, assess data quality, and determine data preparation needs for standardization.
- Improve data quality. Track data quality over time and apply rules to existing integrations to quickly identify and isolate data inconsistencies.
Actian offers a modern integration solution that handles multiple integration types, allowing organizations to benefit from the explosion of new and emerging data sources and have the scalability to handle growing data volumes. In addition, the Actian Data Platform is easy to use, allowing stakeholders across the organizations to truly understand their data, ensure HIPAA compliance, and drive desired outcomes faster.
Find out how the platform manages data seamlessly and supports advanced use cases such as generative AI by automating time-consuming data preparation tasks. Try it for free.
Additional resources
- What is the Future of Data Quality Management (DQM)?
- Data-Driven Analytics for Superior Value-Based Care and Payments Integrity
- Are You Getting the Most Out of Your Healthcare Data Platform?
