Healthcare Data Management: Benefits, Challenges, and Best Practices

Health data management involves interpreting and handling data to support healthcare entities, professionals, and patient health. Due to the limitations of legacy systems when handling vast digital data volumes, the healthcare sector is witnessing a sharp increase in modern data management solutions that enable accurate reporting and business intelligence initiatives. 

Read on to learn more about the benefits and unique challenges of health data management.

What is Healthcare Data Management (HDM)?

Health data management, also called clinical data management or health information management, involves managing healthcare data stored in digital formats. HDM includes various data types, including electronic medical records (EMRs), electronic health records (EHRs), and scanned handwritten medical notes stored in a digital database.

Digital transformation has driven healthcare organizations to store and manage patient records electronically. Typically, these records contain patient demographic information (including age, place of residence, and sex), medication-related records (covering lab results, medications prescribed, medical consultations, and history of treatments), and administrative details (like insurance claims, billing, and reimbursement processes).

Health data management now involves integrating and analyzing that data to improve care delivery and generate insights for better medical outcomes – all while safeguarding data privacy and security.

The Benefits of Better Healthcare Data Management

Improved healthcare data management offers significant benefits, including enhanced patient care, more accurate diagnoses, and increased efficiency in the healthcare system. 

• Health Data Analytics: The data can predict patient health, leading to early treatments and more focused healthcare. Understanding health data analytics can also improve how well patients and the public fight off infectious diseases.
• Improved Patient Safety: Data management systems can lower the likelihood of medical mistakes and adverse events. Regular maintenance and updates enable easy access to accurate, real-time patient data, such as drug histories and allergies, for a better patient experience.
• Operational Efficiency: Streamlined data management processes can significantly boost productivity within healthcare organizations by automating manual tasks like data entry, appointment scheduling, and billing. Also, using EHRs will eliminate the need for administrative workload and storage costs.
• Integration with Emerging Technologies: Strong data management facilitates the integration of emerging technologies, such as AI in healthcare, by forming a medical knowledge management system that contains all healthcare data knowledge of your current system.

Overcoming the Top Challenges in Healthcare Data Management

Healthcare data management challenges involve working with stakeholders, including healthcare providers and patients. Aligning data processes and technologies to meet the requirements of all stakeholders in the healthcare value chain can sometimes be daunting due to:

1. Fragmented Sources of Data

Healthcare data may be formatted as digital data in spreadsheets or databases, visual content, digital files, scanned papers, or more sophisticated formats like DICOM for MRI scans. Hospitals, public health entities, payers, pharmacies, and patients often duplicate and collect data, then store it in various editions.

Actionable patient health data demands a single source of truth. To promote data standardization across diverse formats and EHR systems, a range of IT tools and software solutions can now automate the data collection portion of the process to eliminate duplicates. Remember that a person still needs to connect those data systems to make them accessible, exchangeable, and interoperable.

2. Data Storage Capability

The amount of data in healthcare is only growing. Patients undergo many tests and treatments throughout their lives, and the number of available medications continues to grow. Shockingly, the healthcare industry generates 30% of the world’s data. 

Managing such vast amounts of information presents significant challenges, including high costs and security risks. As a result, many healthcare organizations are moving towards advanced cloud storage options to ensure scalability, robustness, and security.

3. Data Security and Compliance

Data needs to be securely stored, maintaining its confidentiality and integrity and ensuring it is only accessible to authorized individuals. Secure data sharing is a key step towards adopting a value-based care model, which focuses on quality and transparency in billing and moves away from an inefficient fee-for-service approach. This action also protects sensitive patient information from unauthorized access that could lead to malicious use, such as ransomware attacks.

Furthermore, data protection must comply with HIPAA regulations, ensuring government access, encryption, and security standards when data is stored and transmitted. The system must also be robust, safeguarding against cyber threats to maintain the integrity and privacy of healthcare data.

Best Practices for Effective Healthcare Data Management 

Healthcare data management is known for its complexity. Here are some best practices healthcare organizations can adopt to help strategically streamline data management.

• Collect the right data 
• Interpret data
• Manage data storage growth 

1. Collect The Right Data 

Healthcare data is sourced from various databases within an integrated care system. Whether from hospital EHRs or population health platforms, proper processes and tools are vital for managing the data effectively. 

Healthcare organizations must define the potential sources for extracting valuable data. Identifying the correct data to collect helps in the later stages of setting priorities for each data type and integrating the right tools to retrieve the data needed.

• Identify Key Data Elements: Start with your healthcare organization’s goals and align the essential data elements, such as patient health outcomes, treatment efficacy, and operational performance metrics.
• Ensure Data Quality at the Source: Implement processes to ensure the data collected is accurate, complete, and entered correctly at the point of care. This might involve training staff on the importance of data accuracy and correctly inputting data.
• Leverage Technology: Use electronic health records (EHRs) and other digital tools to automate data collection where possible. This reduces the likelihood of human error and ensures a more comprehensive data collection.

2. Interpret Data

Hospital data systems extract data from various sources to make medication decisions. This process relies on data interpretation. 

• Use Advanced Analytics Tools: Deploy analytics tools that can handle large volumes of data and provide insights through predictive analytics, machine learning algorithms, and data visualization techniques.
• Cross-disciplinary Teams: Form teams comprising healthcare professionals, data scientists, and IT experts to interpret the data – and if those resources aren’t in-house, consider a technology vendor for support! This multidisciplinary approach ensures that insights are clinically relevant and actionable.
• Continuous Learning and Adaptation: Make data interpretation an ongoing process. As new data comes in and the healthcare landscape evolves, continuously refine your analysis methods to adapt to these changes.

3. Manage Data Storage Growth 

• Embrace Scalable Solutions: Opt for cloud-based storage solutions that offer scalability. As your data storage needs grow, cloud services can easily accommodate this increase without significant upfront investments in physical infrastructure.
• Regular Data Audits and Cleanup: Conduct regular audits of your data storage to identify redundant, obsolete, or trivial (ROT) information. Removing ROT data helps manage storage costs and improves system performance.
• Implement Data Lifecycle Management: Establish policies for data retention, archiving, and deletion based on regulatory requirements and the data’s ongoing value to the organization. This not only helps in managing storage growth but also ensures compliance with data protection regulations.

4. Decode Data Access Requirements

Extracted data often comes in various formats and is subject to different accessibility regulations. Healthcare organizations must understand all the data access requirements to ensure a clear data management plan.

Also, healthcare organizations can enable Healthcare Information Exchange (HIE), which allows different devices, applications, and systems to collaborate, access, and exchange medical data. Aim for a continued goal of simplifying the process of accessing and retrieving healthcare data.

5. Implement Data Management Software 

Several health information systems overlook a crucial practice – investing in data management solutions. Healthcare data management often comes with challenges, particularly during setup. Providers may need specialized tools such as system connectors or APIs to implement and operate data management practices effectively. 

Strategic investments in collaboration with data management experts or technology providers should be used to enhance your data management system processes.

A Future Outlook Of Healthcare Data Management 

Imagine a future where healthcare data supports decision-making for everyone involved in care delivery, from providers to patients. One where data facilitates various healthcare aspects and generates revenue using a secure and collaborative system.

Healthcare experts now utilize various analytical tools and techniques to interpret collected data effectively, ensuring valuable insights. This data intelligence will likely take the healthcare industry one step closer to improved treatment results, earlier detection of health risks, and more personalized treatments. 

Thanks to improved collaboration between various healthcare stakeholders, healthcare supply chains, including medicine and specialty care, can also benefit from this growth in healthcare data management. 

Emerging technologies, like artificial intelligence and machine learning, can now be integrated into various applications, devices, and cloud platforms with the ideal environment created by better healthcare data management. Also, clinical decision support systems can examine evidence-based data gathered in a healthcare management system at any point of care, whether regular or emergency.

In short, with better healthcare data management, healthcare providers can do their jobs better while patients have access to their own health information. As healthcare data systems become more securely integrated and shared among providers, we can hope for a bright future where all patients receive the quality care they rightfully deserve.

FAQs

1. What are the considerations for healthcare data storage?

Healthcare information and imaging systems are producing a vast amount of data, and here are some considerations for storing them:

• Scalability: Unstructured data like MRIs, X-rays, and CT scans is the fastest-growing type of healthcare data. As this data reaches petabytes, healthcare institutions should adopt a scalable, cost-effective storage solution.
• Compliance: Storage needs to ensure compliance with HIPAA and other healthcare industry standards, which includes role-based access controls, audit trail logging, and data encryption at rest.
• Data resiliency and protection: Health data often becomes a target for cyberattacks, posing a significant threat to providers in case of data loss or breaches. Storage systems must offer redundancy and data backup to distribute data fragments across various nodes.

2. What are some essential compliance requirements associated with healthcare data management?

Healthcare data is regulated by various laws, such as HIPAA, GDPR, and CCPA. Compliance may require data security measures, breach notifications, patient data access requests, etc. Compliance with regulations may be daunting, but it protects healthcare entities from penalties and legal issues.

3. How can healthcare organizations embrace a data-driven culture in healthcare?

Healthcare organizations must manage the data quality collected through regular audits and data checks to ensure accuracy and consistency. Regular evaluation of data quality aids in detecting mistakes or gaps and supports decision-making with reliable data.

Also, training staff on data management best practices guarantees that all staff members understand the importance of accurate and complete data and know how to collect and manage data effectively. 

Achieving Data Management Excellence with KMS Healthcare

More than ever, patients focus on health data to make decisions about their own care. As a result, today’s healthcare organizations are undergoing significant transformation in data management. Yet, obstacles in data management, such as fragmented data and non-compliance with regulations, still hinder organizational growth.

KMS Healthcare guides healthcare organizations to advance from point solutions and basic interoperability to true data readiness. We utilize our extensive expertise and scalable technology stacks to drive innovation and digital transformation in healthcare.

With 15+ years of experience in the HealthTech field, we provide solutions that enable healthcare organizations to unlock the true potential of their data. Our team of experts is ready to integrate every emerging technology like AI & ML to make your data management practices automated, streamlined, and secure. 

Contact us today and start forming a data management strategy that streamlines and delivers!

Reference

(n.d.). Health Data: A holistic approach to unlock the value of health data. Deloitte. https://www2.deloitte.com/content/dam/Deloitte/be/Documents/life-sciences-health-care/Health%20Data.pdf

(n.d.). Data Management Plan For Healthcare: Following Fair Principles And Addressing Cybersecurity Aspects. National Institute for Research and Development in Informatics. https://www.medrxiv.org/content/medrxiv/early/2023/04/24/2023.04.21.23288932.full.pdf

(n.d.). Why Healthcare Data Quality Matters In The Age Of AI. Forbes. https://www.forbes.com/sites/forbestechcouncil/2023/09/05/why-healthcare-data-quality-matters-in-the-age-of-ai/?sh=76ce7cfb3bdd