Patients, frontline healthcare workers, and healthcare leaders are all too familiar with the headaches of gathering, accessing, and sharing healthcare information. Although the industry has come a long way in recent years, persistent difficulties with big data continue to impact every corner of the industry and have far-reaching effects.
This blog will explore at an introductory level some of today’s challenges with data sharing and integration across the healthcare industry as well as highlight innovative ways some organizations are seeking to bridge the gap.
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# From Provider to Patient: Data Sharing Challenges
# The Impact on Providers
For hospitals and health systems that have moved beyond paper health records (a few remain), many organizations use more than one Electronic Medical Record (EMR) or Electronic Health Record (EHR) system to store patient health information. This means providers may have to double or triple their efforts to capture notes from patient encounters, further increasing the risk of inaccurate, duplicative, or out-of-date data. Compounding this problem is that many of these systems don’t talk to each other or freely share information. Non-interoperability of EMR and EHR systems disrupts information sharing between providers in the same health system, those from outside health systems, and with public health agencies. This was a major issue highlighted during the Covid-19 pandemic with the barriers to efficient contact tracing.
Further adding to this challenge is the fact that the vast amount of health data is unstructured, prohibiting physicians from easily finding insights to make patient care decisions and communicating that with other providers.
# The Impact on Patients
Numerous studies point to the challenges patients face regarding insufficient health data sharing, including a recent one by Carta Healthcare. They found that nearly 8 out of 10 patients had to tell their provider the same health history each time they went in for an appointment. Survey findings also indicated that patient satisfaction scores decline due to inaccurate and inaccessible medical records. This adds to frustration when their doctors can’t provide health predictions based on other patient’s health outcomes, simply because the data isn’t readily available or shared.
In another study by AHIMA, respondents reported that about 4 out of 5 healthcare organizations collect data on Social Determinants of Health (SDOH). However, they face challenges including lack of standardization, insufficient training, and limited use of SDOH data to communicate between health providers and community-based referral organizations. This impedes patients from receiving personalized, effective care based on their unique SDOH.
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# Regulations on Data Sharing Aren’t Clear
For providers and patients alike, there is the final issue of government regulation and rules for exchanging healthcare data. When the 21st Century Cares Act was signed in 2016, it upleveled previous regulations surrounding health data exchange to cover electronic data. The new rules prohibit information blocking and delays on the part of healthcare providers but do very little to specify what data needs to be accessible to patients.
More recently, the Centers for Medicare & Medicaid Services has introduced rules on interoperability for health information portals where patients can access their health records, but most guidance is vague and does not define the extent of health information that must be uploaded to these portals. Gaps and gray areas in these regulations are burdensome to patients. It makes it difficult to point to any one standard or rule for gathering health information from one portal and transferring it to another if the other provider uses a different system and is part of another organization.
# Bridging the Gap: Innovative Ways to Strengthen Healthcare Data Sharing and Integration
The data sharing problem in healthcare is vast, costly, and has negative impacts on patients, providers, and policymakers. This all points to the need for structured, centralized, accurate, and portable data. Organizations, public and private, are attempting to overcome these challenges with innovation across the U.S. Here are some examples of leading initiatives in the journey to mature healthcare data sharing.
# California Data Exchange Framework (DEF): Expanding Data Sharing Across Entities in the Golden State
The most populated state in the U.S., with one-third of its residents on Medicaid, is creating the first of its kind, statewide data-sharing agreement to accelerate and expand health data exchange among healthcare entities, government agencies, and social service programs. The agreement represents a public approach to making health data more accessible and insightful through mutual agreements to share health information.
Organizations, like Sutter Health, can get safe access to data from other organizations that can help them enhance care quality and enable providers to deliver top-tier patient care to Californians. The foundation of DEF is supported by guiding principles that include advancing health equity, making data available to drive decisions and outcomes, promoting individual data access, and most importantly, setting standards for secure, private, and transparent data exchange.
Although over 1,400 organizations have already signed the agreement, others across the state have until January 31, 2026, to fully implement the agreement to share data statewide. This will set a strong example for other states across the US to enhance safe data exchange.
# Graphite Health Platform: Frictionless Data Exchange
With a goal to enable a secure, open marketplace for facilitating the distribution of digital health tools for health systems, leading organizations including Intermountain Healthcare, Presbyterian Healthcare Services, and SSM Health partnered up to launch Graphite Health. This platform seeks to create the first comprehensive digital ecosystem for exchanging health data between organizations.
One component of the platform is to use a common data language built off of the Fast Healthcare Interoperability Resources (FHIR) framework, solving data translation difficulties and enabling plug-and-play digital applications. The platform promises to lead to convenience, higher quality care, lower costs, and overall improved efficiency for health systems seeking to evaluate and implement new technology.
Graphite anchors its success in partnership amongst healthcare stakeholders, something that can drive healthcare data interoperability and efficiencies.
# Boston Children’s Hospital: Pioneering Patient-Centered Data Access
Years before the U.S. Department of Health and Human Services finalized a rule to give patients digital access to their health records from any provider and enhance population-wide data exchange, leaders from Boston Children’s Hospital called on the federal government. They asked them to enact regulations around common, open-source digital information platforms that would help solve the issue of health information being trapped inside proprietary health IT systems, making it difficult for patients to obtain copies of their medical history.
Even earlier, in the 1990s, Boston Children’s Hospital was at the forefront of making patient-controlled health information systems available. Working with the MIT Lab for Computer Science, they proposed one of the first patient-centric record systems. Although these aren’t universally available tools today, Boston Children’s Hospital is a great example of putting the patient at the center of data accessibility and standardizing tools that enable interoperability.
The examples mentioned above highlight some innovative steps public and private institutions are taking across the U.S. to improve healthcare data access, use, and sharing. These improvements may happen locally, such as a health system using the HIMSS Adoption Model for Analytics Maturity to advance their data competencies, or at larger state levels. It should also be noted that these stakeholders are addressing consistent gaps that are quintessential to U.S. healthcare data: interoperability, standardized data criteria and language, and accessibility.
At Propeller, we have partnered with clients on addressing their own healthcare data integration and sharing challenges. For example, one of our clients sought to build value-based care capabilities through technology implementation but required outside support for development and adoption. Our team built the technical foundation and aligned on data integration requirements to add structure, enable external data consumption, produce dashboards, and identify critical quality measures. In the landscape of shifting payment models, Propeller helped integrate millions of claims data into a new calculation, established processes to improve patient experience, and developed important data dashboards for monitoring and learning.
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# Harnessing Data Across Industries
Across industry portfolios at Propeller, our clients in the retail and technology sectors face similar challenges to those in healthcare, proving that healthcare leaders are not alone in their mission to strengthen data integration and that cross-industry learning can lead to success. Here are three real-world data challenges we’ve helped clients solve that could be applied to the healthcare industry.
# 1. Establishing Data Governance Practices
A key component of maintaining data is prescribing who has access as well as how it’s structured, secured, and shared. Having a strong data governance plan in place not only ensures data is secure and standardized but also how and who it can be shared with. Propeller has helped clients across industries stand up data governance models, including a non-profit organization that sought to protect itself, its employees, and its user’s data. After conducting a thorough current state assessment, our team developed a data cleaning and transformation program to organize all of their data and make it easier to share and gain insights.
# 2. Maturing Data Management
Elsewhere, we’ve seen retail clients struggle with maturing practices for data management and using insights to target audiences with specific marketing. At one retailer, we supported the move to a new merchandise financial planning system that accelerated their maturity to make business decisions, manage inventory, and oversee product distribution. This was all accomplished through stronger data management capabilities. Zooming out, as other large retailers, such as Walmart and Amazon break into the healthcare industry, they bring vast experience in data management that could have positive spillover effects in the healthcare industry. More established healthcare retailers, such as Walgreens and CVS, sit at the intersection of retail and healthcare and are able to harness data for their consumers.
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# 3. Using Data Insights to Drive Personalization
In retail, personalization is a driving force for many data and analytics strategies that seek to transform consumer experiences. Retail and technology companies can personalize marketing and other services to each consumer based on the vast amount of data available. At one specialty consumer product client, we helped them leverage customer data to develop and test new products, as well as increase the customer lifetime value through personalized experiences that would make customers less likely to switch to competitors.
# Conclusion
The challenges around data sharing in healthcare impact so many facets of care delivery. Difficulty with sharing health information across organizations can delay care for patients and make them less likely to refer their providers to others. And healthcare organizations that use multiple electronic health systems must navigate complex compatibility issues with other systems inside and outside of their organizations, further complicating providers’ abilities to pull insights that could lead to breakthrough care decisions. These are just some of the many issues healthcare frontline workers, providers, policymakers, and patients face every day.
Despite the challenges, we know organizations are striving to fix the problem across the industry. There are lessons we can learn from other industries, such as retail and technology, around data protection, storage, and access that can lead to success in the overall mission to use data to deliver stronger care, reduce human harm, and improve health outcomes.
