How Do Data Silos Impede Patient Care and Provider Efficiency?

Creating a More Open Data Ecosystem in Healthcare

Data silos in healthcare are synonymous with inefficiency. They require manual processes for accessing and sharing information — if not the literal act of putting hands on sheets of paper, then the act of copying and pasting data from one source to another.

This is labor-intensive; importing and cleansing data sets takes time, which makes real-time analysis and decision-making difficult. It’s error-prone, as nearly 1 in 5 patients report mistakes within their ambulatory visit notes. It’s costly, as it requires highly compensated clinical staff to perform clerical tasks. Finally, it means there’s no single version of the truth. One clinical note or lab result could be saved to several systems; in the absence of version control, each may be modified in its location.

In the U.S., the Trusted Exchange Framework and Common Agreement has catalyzed health information sharing among stakeholders such as providers, payers, public health agencies and patients. TEFCA connects health information networks that make it possible for stakeholders to share data for authorized exchange purposes that include treatment, payment, operations, public health, government benefits determination and individual access services. Entities face financial disincentives if they knowingly participate in information blocking, or the interference of information exchange.

The goal in breaking down data silos, as Hartjes puts it, is “liberating data” so it can flow freely in an “open, secure and interoperable” ecosystem of previously disparate applications and stakeholder groups. This requires healthcare leaders to look at the systems in place, identify barriers to data flow and consider changes to data governance policies as well as technology solutions to create a more open ecosystem.

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Setting the Stage for Healthcare Data Success

Abiding by the principles of TEFCA is one important step in removing data silos. There are several others, according to WEF and IBM, and each emphasizes helping healthcare stakeholders build trust and encourage collaboration.

• Create clear incentives for stakeholders to participate in information exchange. Emphasizing the end goal of data-driven, personalized healthcare can help here.
• Ensure that data governance frameworks protect patient privacy, ensure data quality and encourage ethical data use. This will demonstrate that data is being used responsibly, which will further encourage collaboration.
• Empower patients to access and control their own data as active participants in care delivery or medical research.
• Map how data flows through an organization, such as when, where and by whom electronic health records are accessed. Not only does this facilitate information exchange, it’s also necessary for compliance with HIPAA and the General Data Protection Regulation, both of which require an established chain of custody for patient records.

Adopting cloud-native data management technology can provide organizations with a single repository for accessing, ingesting, cleansing and analyzing data sets. Seamless data integration in the cloud supports advanced analytics, such as identifying trends and predicting capacity, and helps frontline clinicians make informed diagnostic and treatment decisions.

“With this open ecosystem, care teams can measure, harvest, organize and connect patient information across multiple systems, such that data is unified and presented in context to guide decision-making,” Hartjes says.

Eliminating data silos optimizes workflows by reducing the reliance on manual processes while enabling greater coordination among business units. This can improve the patient experience in notable ways, whether it’s automatically alerting food services about a patient’s dietary restrictions, reducing medication dispensing errors and preventable adverse drug events, or bringing specialists together.

“Patient cases are becoming more and more complex, and care teams need to effectively collaborate to make the right clinical decisions. Having integrated data available for multidisciplinary teams can reduce time to diagnosis as well as improve diagnostic precision,” Hartjes says. Philips’s prostate biology solution, for example, helps radiology and urology collaborate on targeted biopsies, which diagnose 30% more high-risk patients and 17% fewer lower-risk patients. This improves accuracy as well as resource utilization, as fewer patients undergo a procedure they don’t need.

Finally, centralized data management helps healthcare benefit from AI. Organizations can layer advanced analytics and machine learning tools onto integrated data sets in ways they cannot with disparate data sets. This supports a range of use cases that can lead to better patient care, such as identifying patients at risk of adverse outcomes, medication side effects or surgical complications.

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