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Thought Leadership

4 Reasons Why Healthcare Organizations Need Data Observability

December 8, 2022
10 Min Read

Healthcare organizations require massive amounts of information to serve their customers and deliver essential medical and research-related services. Data observability is helping healthcare leaders optimize that data and ensure it is usable.

Data observability in the healthcare industry is fueling a revolution by transforming patient care, biomedical research, and pathology. With the importance of healthcare data growing each day, it doesn’t come as a surprise that over 30% of the world’s data originates from the healthcare industry alone. The volume of healthcare data is expected to grow at a CAGR of 36% till 2025, overtaking the manufacturing, financial services, and entertainment sectors. 

Healthcare data comes in different forms, with the primary source being Electronic Health Records (EHRs), which act as an end-to-end repository of patient data. Similar to EHRs, Electronic Medical Records (EMRs) store standard medical data and clinical data that are gathered from patients. 

To reduce mounting healthcare costs and improve patient care, EHRs, EMRs, Personal Health Records (PHRs), and Medical Practice Management Software (MPM) data all work together to guide medical decisions for better outcomes. 

Data Observability in Healthcare
Data observability supports reliability across all healthcare data

Source: AKI Research

On the surface, this looks relatively easy. Patient data is collated from diverse channels and subsequently fed into big data applications to derive actionable insights. However, what we often forget is the process behind the collection of patient data. A majority of patient data comes from handwritten clinician notes and patient charts because system-based entries such as drop-downs, menus, and checkboxes fail to capture complex medical diagnostics. The handwritten data is then scanned and added to the hospital’s HIS (Healthcare Information System). Even with the advancements in Computer Vision and the help of skilled transcriptionists, the possibility for errors is high. 

With the likelihood of duplicated data, incorrect entries, and data losses, medical establishments end up with less efficient patient care outcomes, which could be disastrous as it opens them up to potential lawsuits and libel charges. Also, as the global healthcare sector begins its recovery from the financially disastrous COVID-19 outbreak, better patient care journeys help improve patient loyalty, which creates more stability in the healthcare system. 

Data observability in healthcare addresses the Medical Data Management (MDM) challenges of these key use cases:

1. Ineffective Data Capture

Ineffective data capture is a direct result of non-uniform medical data. Patient journey data comes in various formats, such as X-ray, MRI, CAT, and Omics (genealogy) data. On top of that, general healthcare establishments will be using imaging data in a format different from a specialist, adding a layer of complexity to imaging data. The lack of standardization in data capturing is what leads to unusable EHRs across establishments. 

2. Data Fragmentation 

On the road to interoperability between medical establishments, there are quite a few potholes. Healthcare organizations rely on disconnected data silos and legacy systems, which makes data sharing difficult. Even with the arrival of integrated healthcare data systems like EPIC, the backlogged job of purging redundant data, duplicated files, and inaccurate information is stop-blocked by regulatory laws (such as HIPAA). This ultimately leads to the painstaking challenge of manually verifying every single dataset. What healthcare organizations need is a roadmap toward efficient data transformation.

3. Staggering Volume

The healthcare industry generates over 19 terabytes of clinical data each year alone, excluding other healthcare data components. And each day, clinicians are adding more to the lot. With regulatory laws banning the outright deletion of patient data, managing the constantly growing volume of data becomes strenuous in terms of costs and infrastructure requirements. Data archiving has emerged as the top contender for this predicament, where inactive data is stored on a cloud archive.

4. Regulatory Compliance 

As mentioned earlier, regulatory statutes such as HIPAA serve as the primary roadblock to HealthTech innovation. Since the data collected is sensitive patient information, HIPAA mandates that this data be handled with utmost care. Hence, medical establishments need to ensure that technology vendors check every box on a long list. How data is managed, monitored, and extracted from databases comes under the purview of HIPAA regulations. 

While these pointers broadly cover the issues with healthcare data management, it gets more complicated and interconnected as we go deeper. The hard truth is that healthcare data suffers from legacy systems, siloed data storage, and non-seamless interoperability. Data flowing through the system is regularly choked, corrupted, and lost across the pipeline. 

With so much riding on the utilization and compliance of healthcare data, it only makes sense for medical establishments to invest in an end-to-end data observability solution to manage the crisis. 

The key elements of data observability are critical for the healthcare industry

Multi-layered data observability helps address the needs of an organization by going beyond the realm of just data quality in healthcare. What it does for healthcare establishments is:

- Ensures manual EHR data entries aren’t manipulated or lost due to schema drift.

- Monitor the storage, movement, and retrieval of patient data to stay within regulatory compliance (such as HIPAA).

- Ensures key medical stakeholders (clinicians, medical practitioners, and administrative staff) have access to patient data in a secure fashion.

- Manage EHR/EMR data management costs to reduce the growing financial burden on hospitals (financial operations governance). 

Go Beyond Basic Healthcare Data Quality with Data Observability

Acceldata is the market leader in enterprise data observability. With Acceldata’s multi-layered data observability solution, enterprises gain comprehensive insights into their data stack to improve data quality in healthcare, pipeline reliability, compute performance, and spend efficiency.  

Acceldata's solutions have been embraced by global enterprises, such as Oracle, PubMatic, PhonePe (Walmart), Verisk, Dun & Bradstreet, and many more. To learn more about our solutions and how we can help you take control of your data systems, get in touch with us today.

Photo by Hush Naidoo Jade Photography on Unsplash

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