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Dec 12 2023

Common Healthcare Data Management Issues … And How to Solve Them

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 Unfortunately, many organizations struggle with data management problems, such as silos that result in outdated and untrustworthy data, legacy systems that can’t easily scale, and data integration and quality issues that create barriers to using data.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 This results in islands of data that are not connected and may be outdated, inaccurate, or have other quality issues.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.

Related resources you may find useful:

The post Common Healthcare Data Management Issues … And How to Solve Them appeared first on Actian.

5 Strategies for Data Migration in Healthcare

All data-driven businesses need to migrate their data at some point, whether it’s to the cloud, a sophisticated data management system like a data warehouse, or applications. In some instances, the data migration process can entail changing the data’s format or type to make it more usable, increase performance, make it easier to store, or for other reasons.

In healthcare, data migration is the essential process of transferring data, including patient data, between systems in a secure way that meets compliance requirements, such as those set by the Health Insurance Portability and Accountability Act (HIPAA). Data migration can include moving information from a data platform or legacy system to a modern electronic health records (EHR) system that makes a patient’s medical information readily available to healthcare providers in any location.

Healthcare data is often complex, has extensive data sets, and must be secure to protect patient privacy. Here are five ways healthcare organizations can successfully migrate data:

1. Have a detailed data migration plan

This critical first step will guide and inform the entire migration. The plan should identify where healthcare data currently resides, where it needs to go, and how it’s going to get there. You’ll need to determine if this will be a full migration, which entails moving all data to a new system, like migrating on-premises data to the cloud or modernizing by moving data from a legacy patient records system to a new platform or EHR system.

Or, the migration can be done in phases over time, with the option for some data to stay in its current location or in a hybrid environment with some data in the cloud and some on-premises. The migration plan must include steps, timeframes, and responsibilities, along with identifying the tools and expertise needed to move the data. Migration tools can automate some processes for increased efficiency and to reduce the chance for manual errors.

2. Assess the data you’ll be migrating

You’ll need to identify all the sources containing the data that needs to be migrated. This includes databases, files, and applications that have healthcare data. You should consider converting paper medical records to EHRs, which allows the data to be integrated for a complete patient record that’s available whenever and wherever a healthcare provider needs it. Once you know which information will be migrated and where it’s stored, the next step is to assess the data. This step determines if the data needs to be standardized or transformed to meet the new system’s requirements.

3. Understand and follow compliance requirements

Healthcare is heavily regulated, which impacts data usage. You must ensure security and compliance when migrating healthcare data. This includes compliance with HIPAA and any other applicable local or state requirements. You may need to use data encryption processes and secure channels when transferring the data to ensure sensitive patient data, such as protected health information (PHI), is secure.

As part of your data migration plan, you’ll need to consider how data is protected when it’s stored, including in cloud storage. The plan may require boosting security measures to mitigate cybersecurity threats. Conducting a risk assessment can help identify any vulnerabilities or potential risks so you can resolve them before moving your data.

4. Ensure data is in the correct format

Data must be in the proper format for the destination location. Some healthcare systems require data to be in a particular format or structure, which could require converting the data—without losing any of the details. Ensuring data is formatted correctly entails mapping the data, which helps you determine how information in the current system corresponds to requirements for the new system. Data mapping helps make sure different systems, apps, and databases can seamlessly share data by showing the relationships of data elements in the different systems. Mapping also helps ensure data is properly transformed before the migration, allowing it to be easily ingested and integrated with other data.

5. Check for data quality issues

Any data quality problems, such as incomplete or missing information, will be migrated along with the data. That’s why it’s important to fix any problems now—correct errors, eliminate duplicate records, and make sure your data is accurate, timely, and complete before moving it. Data cleansing can give you confidence in your healthcare data. Likewise, implementing a data quality management program is one way to keep data clean and accurate. After the migration, data should be checked to ensure details were not lost or inadvertently changed in transit and to verify the data quality. Testing the data post-migration is essential to ensure it meets your usability requirements and the new system is performing properly.

Healthcare Data Requires a Comprehensive Migration Strategy

Actian can help healthcare providers and other organizations create and implement a detailed data management strategy to meet their particular needs. We can also make sure your data is secure, yet easy to use and readily available to those who need it. We’ll help you migrate data for cloud storage, data protection, healthcare data analytics, or other business goals. With the Actian Data Platform, you can easily build data pipelines to current and new data sources, and easily connect, manage, and analyze data to drive insights and prevent data silos.

Related resources you may find useful:

The post 5 Strategies for Data Migration in Healthcare appeared first on Actian.

The Crisis AI Has Created in Healthcare Data Management

Through the lens of time, the study of medicine dwarfs the age of modern technology by centuries. Historically, most medical treatments require decades of research and extensive studies before they are approved and implemented into practice. Traditionally, physicians alone have been charged with the task of making treatment decisions for patients. The healthcare industry has pivoted to evidence-based care planning, where patient treatment decisions are derived from available information during systematic reviews.

Should we be trusting Data Science tools like Artificial Intelligence (AI) and Machine Learning (ML) to make decisions related to our health?

In the first installment of this series, Algorithmic Bias: The Dark Side of Artificial Intelligence, we explored the detrimental effects of algorithmic bias and the consequences for companies that fail to practice responsible AI. Applications for Big Data processing in the healthcare and insurance industry have been found to exponentially amplify bias, which creates significant disparities related to oppressed and marginalized groups. Researchers are playing catch-up to find solutions to alleviate these disparities.

A study published by Science provided that a healthcare risk prediction algorithm, used on over 200 million people in the U.S., was found to be biased due to dependence on a faulty metric used to determine need. The algorithm was deployed to help hospitals determine risk levels for prioritizing patient care and necessary treatment plans. The study reported that African American patients tended to receive lower risk scores. African American patients also tended to pay for emergency visits for diabetes or hypertension complications.

Another study, conducted by Emory University’s Healthcare Innovations and Translational Informatics Lab, revealed that a deep learning model used in radiologic imaging, which was created to speed up the process of detecting bone fractures and lung issues like pneumonia, could pretty accurately predict the race of patients.

“In radiology, when we are looking at x-rays and MRIs to determine the presence or absence of disease or injury, a patient’s race is not relevant to that task. We call that being race agnostic: we don’t know, and don’t need to know someone’s race to detect a cancerous tumor in a CT or a bone fracture in an x-ray,” stated Judy W. Gichoya, MD, assistant professor and director of Emory’s Lab.

Bias in healthcare data management doesn’t just stop at race. These examples scratch the surface of the potential for AI to go very wrong when used in healthcare data analysis. Before using AI to make decisions, the accuracy and relevancy of datasets, their analysis, and all possible outcomes need to be studied before subjecting the public to algorithm-based decision-making in healthcare planning and treatment.

Health Data Poverty

More concerted effort and thorough research needs to be on the agendas of health organizations working with AI. A 2021 study by Lancet Digital Health defined health data poverty as: the inability for individuals, groups, or populations to benefit from a discovery or innovation due to a scarcity of data that are adequately representative.

“Health data poverty is a threat to global health that could prevent the benefits of data-driven digital health technologies from being more widely realized and might even lead to them causing harm. The time to act is now to avoid creating a digital health divide that exacerbates existing healthcare inequalities and to ensure that no one is left behind in the digital era.”

A study by the Journal of Medical Internet Research identified the catalysts to growing data disparities in health care:

Data Absenteeism: a lack of representation from underprivileged groups.

Data Chauvinism: faith in the size of data without considerations for quality and contexts.

Responsible AI in Healthcare Data Management

Being a responsible data steward in healthcare care requires a higher level of attention to dataset quality to prevent discrimination and bias. The burden of change rests on health organizations to “go beyond the current fad” to coordinate and facilitate extensive and effective strategic efforts that realistically address data-based health disparities.

Health organizations seeking to advocate for the responsible use of AI need a multi-disciplinary approach that includes

Prioritizing addressing data poverty.
Communicating with citizens transparently.
Acknowledging and working to account for the digital divide that exists for disparaged groups.
Implementing best practices for gathering data that informs health care treatment.
Working with representative datasets that support equitable provision of treatment using digital health care.
Developing internal teams for data analytics and processing reviews and audits.

To fight bias, it takes a team effort as well as a well-researched portfolio of technical tools. Instead of seeking to replace humans with computers, it would be better to facilitate an environment where they can share responsibility. Use these resources to learn more about responsible AI in health care management.

The post The Crisis AI Has Created in Healthcare Data Management appeared first on Actian.

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