The High Price of Poor Healthcare Data Management – And How to Fix it

According to a survey conducted by HIMSS, a global healthcare transformation advisor and sponsored by Arcadia, healthcare data, just like data in general, is almost everywhere. However, only 47% of it contributes to active decision-making and healthcare innovation. This figure highlights that despite vast amounts of patient records, imaging data, lab results, and real-world patient monitoring inputs, more than half of it is underutilized! 

Why? Primarily due to poor data management in healthcare, with it being a growing concern. Failing to work through these inefficiencies can have serious consequences, especially when healthcare data volumes are projected to cross 10,000 exabytes by the end of 2025.

But how can you tell if your current data management practices are not sufficient? The first step is to audit your data infrastructure. Key indicators of poor data management include:

1. When your data is incomplete or inaccurate,
2. When healthcare data isn’t easily accessible,
3. When data isn’t standardized and interoperable,
4. When data isn’t compliant with regulations like HIPAA,
5. When it has a high duplication rate,
6. When it’s outdated.

If these issues sound familiar, your healthcare data management likely needs improvement. But don’t worry; this write-up will help you understand its impact and explore proven strategies to fix it.

The Impact of Poor Data Management in Healthcare

1. Delays in Patient Care

Poorly managed healthcare data, such as that stored across fragmented systems, incomplete patient data, inaccessible medical histories, or slow data-sharing systems, can cause significant delays in diagnosing a particular condition and, ultimately, in providing treatments. 

Additionally, if this data is also interoperable between hospitals, labs, and pharmacies, doctors and other healthcare professionals will have to spend extra time searching for and filling these gaps before diagnosing and, ultimately, providing treatment.

2. Misdiagnoses and Other Medical Errors

If your healthcare data is full of inconsistencies and inaccurately filled information, it is at a higher risk of suggesting incorrect diagnoses. For instance, this could happen if test results are mislabeled, imaging is not annotated properly, or EHR systems are not in sync with others. Any decisions made after analyzing such data are bound to falter and can cause serious medical errors that can be life-threatening. 

3. Regulatory Violation and Non-Compliance Penalties

Failing to secure and manage healthcare data according to global legal standards like HIPAA and GDPR can result in hefty penalties and, in the long term, considerable reputational loss. 

Considering HIPAA, the penalties could fall in the following range (based on severity):

• Tier 1 (majorly for unintended violations): US$141 to US$35,581
• Tier 2 (reasonable causes, violations out of negligence but not wilful): US$1,424 to US$71,162
• Tier 3 (wilful negligence, however, has an intent of correction): US$14,232 to US$71,162
• Tier 4 (wilful negligence with no intent to correct): US$71,162 to US$2,134,831

The violations are categorized using several parameters; however, they often stem from outdated security measures, unencrypted data transfers, or improper access controls that leave sensitive healthcare or medical research data vulnerable to breaches.

4. Denied Insurance Claims

Healthcare institutions and providers often struggle with lost revenue due to an increasing number of health insurance claim denials. These claims are usually denied due to incorrect invoicing, errors in patient records, missing documentation, or mismatched billing codes, all of which result from disorganized data management. 

These inefficiencies, aided by the lack of or availability of sub-par data validation systems, are vulnerable to insurance fraud. Fake claims with exaggerated treatments can slip through, resulting in substantial revenue loss for both insurers and providers.  

5. Barriers to Drug Development

Poor healthcare data management can also pose a challenge in all stages of drug development, including genomic and pharmacological data mapping, clinical trials, and even approvals. The early stages struggle due to gaps in imaging (X-rays, MRIs, CT scans), drug interactions, pharmacological information, and real-world patient data from wearables and IoT devices. 

Similarly, getting it approved by regulatory bodies like the FDA (U.S.), EMA (Europe), and CDSCO (India) requires a proper healthcare data infrastructure with structured, validated, and reproducible data that can be submitted as evidence. 

Healthcare Data Challenges? Here’s How to Fix Them 

The impact of poor data management in healthcare can be profound, especially as data grows (in volume, depth, and breadth). However, specific strategies can help you address the above data management roadblocks. As all of these stem from inefficiencies in records and a lack of standardization, centralization, and secure data management solutions to compile, process, and access this data in real-time, your strategy must address each of them. 

1. Implement an In-House Data Governance Framework

To improve overall management, start by establishing a healthcare data governance framework—a set of policies, processes, and compliance standards for collecting, storing, accessing, and using healthcare data. You can do this by:

• Auditing your existing healthcare data systems. 
• Defining who all custodians, including doctors, nurses, IT teams, and compliance officers, can access it.
• Establishing healthcare data entry standards like HL7 and FHIR for EHRs.
• Restricting data access based on roles.
• Implementing processes for validating, cleaning, and deduplicating data to reduce errors in patient records.
• Conducting regular audits and monitoring your data infrastructure in real-time.

2. Centralize your Data 

Fragmented or disjointed storage is a significant challenge in accessing and analyzing healthcare data. Once you have a governance policy, centralize this data in a secure and accessible data lake, data warehouse, or a combination of both (data lakehouse). This involves:

• Consolidating/collecting data from multiple sources.
• Implementing data cleansing and indexing to remove irrelevant or inaccurate data points. 
• Validating it to ensure relevance.
• Standardizing data for better interoperability and uniformity.

It is a comprehensive process that requires considerable time and resources. This is why many organizations choose to automate the entire process using an AI-powered data management solution for healthcare, from data entry and cleansing to processing and even analysis. Such solutions utilize ETL data pipelines for processing and CI/CD automation for real-time data validation, exchange, and on-the-fly corrections.  

While AI automation is highly efficient, it still lacks nuanced understanding in many complex scenarios. For instance, AI-powered systems may falter when scanning a doctor’s shorthand notes or abbreviations, and these factors vary by region or hospital. In such situations, AI may falter and provide incorrect outcomes. This is why many organizations choose to outsource end-to-end data management services to benefit from a balance of AI automation and human expertise. 

3. Expand your Ecosystem with EHRs and Other Interoperable Systems 

With structured and automated data flows in place, other systems like EHRs can be implemented for easier access and interoperability. These systems facilitate secure data exchange, real-time access, and coordinated patient care across multiple healthcare providers, insurers, and researchers by breaking down silos. 

Additionally, these systems can be integrated with medical billing software, which helps validate the records by matching them with the final invoice (and even claims). This can streamline revenue cycles for large organizations. 

4. Use Blockchain for Data Integrity and Security

Ensure data security in your healthcare infrastructure because a lack of it can result in hefty implications. Consider this as an example—in February 2024, Change Healthcare faced a ransomware attack, exposing the data of over 100 million individuals and disrupting their claims processing activities and, ultimately, insurance payments. Estimated losses are now reported to be close to US$2.87 billion!

A reliable solution is blockchain technology, which creates a tamper-proof record of patient data and transactions. Each entry is encrypted and linked to the previous one, ensuring data cannot be altered or deleted without detection.

5. Enable Real-Time Data Analytics 

Healthcare has now become proactive, with researchers and professionals trying to prevent medical conditions from aggravating or recurring altogether. They do this by analyzing volumes of historical medical data along with real-world evidence to draw insights (patterns, commonalities, outliers, etc.). These insights enable early diagnosis, faster treatment decisions, and proactive patient monitoring. 

There are two ways to do this:

1. Use AI-powered analytics tools that make everything happen on a single platform.
2. Centralize data from public sources and integrate this repository with IoT-enabled medical devices (e.g., heart rate monitors and glucose sensors) for live data streams. 

You can find relevant data points on historical and comparative medical data from publicly available healthcare databases, such as the National Library of Medicine (NLM), PubMed, The Cancer Genome Atlas (TCGA), and DrugBank. 

This data is then used to train AI/ML models to detect anomalies, predict disease progression, and optimize treatments. 

Note: If you’re going with the second approach, your data must be AI-ready, i.e., thoroughly processed, structured, suitably annotated, and validated. You can consider outsourcing professional data quality management services for this.  

End Note

Only through organized administration, security, and interoperability can the full potential of healthcare data be realized. Additionally, with constantly growing data volumes, the need for secure and scalable data management solutions only intensifies. 

While many organizations have recognized the risks of poor data management in healthcare and are taking steps to improve, others have yet to act. If you haven’t already, now is the time to standardize procedures, automate with human oversight, and utilize real-time analytics for improved insights, efficiency, and patient care. After all, efficient data management isn’t just a necessity; it’s the foundation for proactive healthcare.