Healthcare organizations face many challenges as they strive to transform their organizations and remain competitive while managing patient health, complying with evolving regulations, achieving interoperability, and delivering holistic and patient-centric care.
This requires integrating massive amounts of of data from myriad disparate clinical and non-clinical sources—both inside andbeyond the health system’s four walls—such as EHRs, claims systems, pharma, labs, devices, CRMS, consumers, SDoH and industry data (ADT, etc.), and more, with each having unique data attributes. A lack of data aggregation capabilities slows the output and ROI of providers’ health IT investments and curbs innovation.
This leads to a fragmented view of the patient's health history and can have a negative impact on care quality: duplicated tests, misdiagnoses, ineffective treatments, high readmissions, overutilization, and the inability to track the health insights of the provider’s larger population. A lack of data aggregation also makes it difficult for analysts to stratify populations for risk and targeted interventions, for researchers to access comprehensive data for studies, and for the IT organization, developers, and innovators to create new solutions for stakeholders.
But if data aggregation can lay the groundwork for advanced data strategies, 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 this 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 (or in the ED for a non-emergent condition). While on vacation, he might have to 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. Everywhere John goes on his journey, thousands of data points are generated and left behind at every touchpoint. And John is just one of tens of millions of consumers out there.
This complex generation of non-unified, fragmented data presents enormous challenges for healthcare organizations that want to integrate John’s data from all of his touchpoints across the care continuum, centralize it, 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, in an increasingly value-based world, crucial. Indeed, here are the nine primary challenges around data aggregation that healthcare companies are grappling with today, and guidance on how to solve them.
The conventional HIT infrastructure nearly always 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. Fragmented data silos occur when medical data is stored in separate, unconnected systems, making it difficult to share information between providers within their networks. Teams working on this data operate independently, leading to the development of multiple data sources, systems, and frameworks, which impedes coordination and communication across the healthcare organization.
This results in issues such as double data entry, redundancy, inconsistency in generating insights for better patient care, and barriers to information sharing between departments (e.g., batch reporting, consolidation of manual spreadsheets, lack of real-time reporting and insights, etc.). Indeed, siloed data could be the prime barrier to delivering patient-centered care. It also increases administrative overhead and inefficiencies in care delivery, leading to higher costs for both patients and providers.
In a clinical setting, care teams have to manage data from a variety of disconnected electronic health record (EHR) systems, making it difficult to gain and access complete, accurate insights on patients in their network. Moreover, the fragmented data is stored using different standards and formats—such as images, texts, videos, EMR, and many others—adding to the substantial complexity of integrating data silos and aggregating them for relevant use cases.
There are over 40 SDOs (standard developing organizations) operating in US 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:
The integration of data that adheres to so many standards is a daunting task for providers and payers. In addition, proprietary and custom data formats used by HIT vendors or internally bu the organization’s IT team further complicates the normalization required to integrate systems and make them, ideally, interoperable to support data aggregation initiatives.
The scarcity of data science and integration experts and skilled IT personnel is a growing problem. Healthcare data aggregation requires individuals who are well-versed in both IT and healthcare standards, and also have the leadership skills and vision to succeed at such a complex, mission-critical initiative. However, it’s difficult for decision-makers to find and recruit talent who can work on similar initiatives across broad business use cases and deliver them on time and on (or under) budget. In addition, CIOs are burdened with working on disparate IT systems, applications, and vendors, complicating their roles. These factors can burn out IT teams that also need to tend to the organization’s fundamental needs, such as enabling their clinician workforce to ensure they’re capable of providing good care, and supporting the financial and other operational functions of the business.
Healthcare in the US is under increasing pressure to improve data privacy and security. This is due to the high volume of sensitive personal information, such as medical records, that are collected and stored by healthcare providers. To address these concerns, the US government implemented strict regulations aimed at protecting patient privacy.
The HIPAA is, of course, the most well-known of these regulations, setting standards for protecting the confidentiality and security of medical information. Additionally, the European Union's GDPR also applies to US healthcare organizations that process the personal data of EU citizens. These and other regulations require providers to take robust measures to secure personal information and be transparent about their data collection and usage practices.
Regulatory compliance is essential for maintaining trust in healthcare, protecting patient privacy, and avoiding substantial fines for failing to do so (not to mention the bad publicity that can hurt a healthcare organization’s reputation among consumers). Data must be protected from illegal access and tampering, but also securely shared under specific circumstances.
Balancing these two concerns can be tough, as providers must trust their integration initiatives and ensure that any information exchange will uphold and protect the patient’s data rights. Fulfilling these regulatory requirements requires compliant, and often certified, IT systems. Building such a system from scratch or updating existing systems adds to the IT cost, not to mention the risk of not getting compliance right.
Integrating healthcare data effectively is a complex and costly process, and many organizations struggle to secure the necessary funding and expert resources to carry it out themselves. This, combined with an inadequate roadmap, results in cost overruns and increases the chances of failure in achieving the organizational goals on time.
Cutting down investments will also slow innovations that are crucial to unlocking value from healthcare data. This will negatively affect the effectiveness of care and impede the ability to improve financial and business outcomes over time.
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. Accomplishing this requires substantial effort to standardize and normalize data inputs, monitor and constantly improve data quality, and harmonize data into an enterprise data model on a centralized data warehouse.
In addition to the complexity, IT teams are burdened with manual processes. It can be as simple as ensuring that you have selected the right data files for data integration that are ready for further use. These issues hamper the effectiveness of in-house solutions, eliminating the possibility of real-time visibility into the patient’s care journey.
The route to derive actionable insights is lengthy, as IT teams are often tangled up with manual processes. While large entities might be more likely to have implemented highly automated and digitized systems, other healthcare providers may still photocopy records and manually input patient data. And 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 yet another application around it! In many organizations, nightly batch jobs are run that dig into databases and replicate data in another format into another system. And even then, the data often finds its way into manually created and maintained Excel spreadsheets.
This piecemeal approach introduces a 24-hour (or longer) lag, which eliminates the possibility of real-time visibility into the patient’s care journey and the ability to act on those insights at the point of care.
Every second, an exponential amount of healthcare data is generated and needs to be mined for valuable insights. Today, approximately 30% of the world’s data volume is generated by the healthcare industry. With a single patient generating nearly 80 megabytes of data each year in imaging and EMR data, according to 2017 estimates, RBC Capital Market projects that “by 2025, the compound annual growth rate of data for healthcare will reach 36%.”
On top of that, with the growing popularity of wearables, user-generated data is also exploding. According to Statista, global wearable device data traffic increased to 335 petabytes per month in 2020, up from 15 petabytes per month in 2015. Managing, integrating, and streamlining this data for developing consumer-focused care journeys is complex—especially when most of the data can be of inconsistent quality and accuracy.
More often than not, the people who process a given set of data aren’t the same people who need and make use of the data. Important details are often missed in the transition, and data ownership can be unclear. When organizations plan on doing integration themselves from the ground up, they need clarity about who will use the data, where it will be used, how and what data transformations will be required, and who will own it. Not having a holistic approach to data handling and governance can lead to data inconsistency, poor data quality, and serious issues in activating the data for insights. This necessitates the need for a well-planned data aggregation strategy.
User-generated data has become the holy grail of business for data-driven organizations. With the rise of consumerism in healthcare, wearable devices, as well as smart medtech 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. 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. In addition, this data is also subject to regulatory standards, and failing to comply can result in severe penalties.
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 when every mission-critical checkbox is ticked, data integration becomes easier and paves the way for advanced analytics that can help derive actionable insights to improve clinical, financial, operational, and experiential outcomes.
These checkboxes include connecting data silos, leveraging relevant formats and standards, being compliant with regulations and security, and automating data integration processes. All of these can only be achieved with the right mix of people (experts), technology, tools, and processes.
For healthcare leaders, the dilemma is whether to build or buy the technology that can help them integrate data in the shortest amount of time.Choosing the do-it-yourself route leads to hiring skilled professionals who understand data and healthcare, which is likely the most costly route. The “buy” option could help ease their efforts, and save time and money. Even when 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 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.
Innovaccer is at the forefront of helping health systems move beyond point solutions and basic interoperability to true data readiness, using a scalable platform that accelerates innovation and digital transformation in healthcare.
Innovaccer’s #1 Best-in-KLAS data platform enables organizations to achieve data aggregation in a matter of weeks vs. months or even years, and helps solve their most complex data challenges to drive better business outcomes. The platform rapidly establishes the highest possible level of interoperability between legacy, mission-critical, and other healthcare IT systems; aggregates and unifies (cleans) the data from multiple sources; “hydrates” the data to enable any application to use it; “activates” the data to make it useable in integrated workflows; and lastly, “harmonizes” the data to ensure it’s synchronized across all relevant systems and care settings.
Learn more about or get a demo to see how the Innovaccer platform can help your organization solve your data aggregation challenges. You can also contact us for a free consultation to discuss your issues with EHR integration and patient data in healthcare.