Five Data Aggregation Challenges in Healthcare

Chris Ingersoll
Tue 01 Nov 2022
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Healthcare organizations face many challenges as they strive to transform their organizations and remain competitive while managing patient health, complying with evolving regulations, and achieving interoperability with other healthcare organizations. In my previous blog, I discussed data aggregation in the healthcare context and why it is important. If data aggregation can lay the groundwork for advanced data strategies, then why is it so difficult? The answer to this lies in the sheer complexity of healthcare.

Let's examine a hypothetical patient, John, to illustrate the complexity.

John visits his primary care physician and pharmacy as needed. But he might change his address permanently or temporarily. He might find himself in an emergency in a hospital. While on vacation, he might choose, for instance, to visit a different pharmacy. His physician might refer him to a lab, which sends test results back to the physician, who refers him to a specialist, and so on. Moreover, any interaction between John and the healthcare providers ties into insurance companies, the government, and other entities. Thousands of data points are generated at every touchpoint, and John is just one of the millions of such cases out there. This complex movement of data unfolds various challenges for healthcare organizations to integrate data and make it accessible for further use.

As a result of so many touchpoints, keeping data up-to-date and usable is both challenging and crucial. In this blog, I will be discussing the key challenges around data aggregation that healthcare companies are grappling with today.

What Makes it Difficult to Aggregate Healthcare Data?

  1. Data Silos

    The conventional HIT infrastructure often stores data in silos to cater to departmental needs for specific solutions. The fragmentation of IT needs a technology solution that aggregates data and makes it useful. In a clinical setting, care teams manage data from a variety of disconnected electronic health record (EHR) systems, which makes it difficult to access accurate insights on patients within their network. The fragmented data stored in different standards and formats—such as images, texts, videos, EMR, and many others—presents a substantial challenge in integrating data silos and aggregating them for relevant use cases.

  2. Multiple Data Standards and Formats

    There are over 40 SDOs (standard developing organizations) operating in U.S. healthcare that are accredited by the American National Standards Institute (ANSI) and the International Organization of Standardization (ISO). These standards are separated into four different categories mainly transport (such as FHIR and CDISC), content (such as C-CDA and HL7 v2), terminology (such as ICD-10-CM and CPT), and security standards (HIPAA and GDPR). The integration of data adhering to so many standards is complex and extremely challenging for providers and payers. Additionally, proprietary and custom data formats also push healthcare organizations to implement data aggregation initiatives.

  3. Stringent Data Regulations

    Healthcare data is governed by federal laws and regulations to mandate safeguards on the access, use, and sharing of healthcare information. The HIPAA Privacy Rule sets limits on the use and sharing of patient data while the HIPAA Security Rule defines what electronic health information must be protected. Similarly in the EU, the protection of healthcare information falls under the scope of GDPR. Needless to mention, there are heavy penalties if any rule is violated. To fulfill the requirements set by these laws, an up-to-date IT infrastructure is required. Healthcare stakeholders must employ mechanisms such as administrative, physical, and technical safeguards for every device that could impact the safety of ePHI. For regulatory compliance, updating existing infrastructure or building one from scratch adds to the cost bucket.

  4. User-Generated Data

    User-generated data has become the holy grail of business for data-driven organizations. With the rise of consumerism in healthcare, wearable devices are gaining popularity and pushing organizations to integrate data generated from these devices to create personalized care journeys. The scope of wearable device data goes beyond individual care journeys to advance medical research. According to Statista, the global wearable device data traffic increased to 335 petabytes per month in 2020 from just 15 petabytes per month in 2015. The real challenge arises when data needs to be managed at scale for millions of patients making it complex to streamline and integrate data accurately and consistently. On top of this, this data is also subject to regulatory standards, and failing to adhere can rack up penalties.

  5. Manual Processes

    Healthcare organizations are looking for more than just data integration. They are also seeking actionable insights to make better decisions, increase operational efficiency, and enhance visibility into processes. The route to derive actionable insights is lengthy as IT teams are often tangled with manual processes. While large entities may be more likely to have implemented highly automated and digitized systems, other healthcare providers may still photocopy records and manually input patient data. When IT modifies any aspect of the application, there can be a cascading effect. Some companies have been unable to figure out how to get data from one application to another without writing another application around it. In many organizations, nightly jobs are run that dig into databases and replicate data in another format into another system. This kind of solution introduces a 24-hour lag, which eliminates the possibility of real-time visibility into the patient’s care journey.

  6. Data Aggregation Requires a ‘Do It All’ Approach

    Healthcare's complex business models and underlying data sets have led to equally complex applications and clinical systems, which are difficult to integrate. But once every checkbox is ticked, data integration becomes easier and paves the way for advanced analytics that can help derive actionable insights to improve clinical, financial, and operational outcomes. These checkboxes include connecting data silos, leveraging relevant formats and standards, being compliant with regulation and security, and automating data integration processes. All of these can only be achieved with the right mix of technology, tools, and processes.

    For healthcare leaders, the dilemma is to build or buy the technology that can help them integrate data in the shortest amount of time.

    Choosing the do-it-yourself route would lead to hiring skilled professionals who understand data and healthcare, which would raise costs. The “buy” option could help ease their efforts, and save time and money. Even if healthcare organizations have the required resources at their disposal to build the solution, they follow a trajectory to build their processes to aggregate data. The buy option can help eliminate all of this with plug-and-play capabilities to advanced customization. In any case, healthcare needs to do it all—connect data silos, standardize data, be compliant, and automate aggregation—to achieve accurate and timely data integration.


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Tags: Healthcare
Chris Ingersoll
Principal Architect, Innovaccer

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