Data silos hindering optimal patient care and provider productivity?

The accumulation of data in healthcare often results in a maze of data silos, hindering efficient patient care and the integration of AI. This is primarily due to organizations that haven't planned ahead for future data management, leading to a fractured landscape of crucial health information.

The World Economic Forum (WEF) labels this as a fragmented health data landscape, making it difficult to comprehend the full picture of a patient's health journey. These silos make it tough to implement personalized treatments for patients with distinct needs, according to WEF. Furthermore, public health agencies struggle to share data, resulting in challenges during incident responses, and poor coordination in the drug development process, causing delays in distributing necessary treatments.

Martijn Hartjes, Clinical Informatics Business Unit Leader at Philips, points out that the retrieval of siloed data consumes an excessive amount of time and effort, leading to delays in diagnosis and treatments. Misdiagnosis often occurs when the complete patient history isn't available due to unconnected data.

To improve the healthcare ecosystem, it is essential to dismantle data silos, allowing data to flow freely in a secure and interoperable environment. This requires reviewing existing systems, identifying barriers to data flow, and considering modifications to data governance policies, as well as adopting technology solutions.

Removing data silos has numerous benefits. Clinical workflows are optimized, reducing reliance on manual processes and enhancing collaboration among teams. This can lead to a better patient experience, from automatically alerting food services about dietary restrictions to minimizing medication errors and adverse drug events. Genetically, integrated data aids multidisciplinary teams in making swift clinical decisions and improving diagnostic accuracy, saving time and resources.

Centralized data management also empowers healthcare to take advantage of AI. Organizations can layer advanced analytics and machine learning tools onto integrated data sets, supporting use cases that can lead to better patient care, such as identifying patients at a higher risk of adverse outcomes, medication side effects, or surgical complications.

By following the principles of trusted data exchange, fostering collaboration among stakeholders, and prioritizing patient privacy, data quality, and ethical data usage, healthcare leaders can help break down data silos and pave the way for improved healthcare outcomes.

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Disassembling Data Barriers in Healthcare

Data silos are synonymous with inefficiency in healthcare. They mandate labor-intensive processes for data access and sharing, from physically handling papers to the time-consuming task of copying and pasting data from one source to another.

In the United States, the Trusted Exchange Framework and Common Agreement (TEFCA) has catalyzed information sharing among stakeholders such as providers, payers, public health agencies, and patients. By connecting health information networks, TEFCA enables stakeholders to exchange data for authorized purposes, including treatment, payment, operations, public health, and patient access services.

Parties participating in information blocking, or hindering data exchange, face financial disincentives. The aim is to "free" data so it can circulate freely in an open, secure, and interoperable ecosystem of previously unconnected applications and stakeholder groups. This necessitates healthcare leaders to scrutinize existing systems, identify impediments to data flow, reevaluate data governance policies, and contemplate technology solutions to foster a more collaborative environment.

Explore More: Shattering data silos bolsters healthcare referrals and patient engagement.

Paving the Way for Healthcare Data Success

Consider these essential steps for removing data silos, as suggested by WEF and IBM:

Establish incentives for stakeholders to participate in data exchange. Highlighting the objective of data-driven, personalized healthcare is a good starting point.
Develop data governance frameworks to protect patient privacy and ensure ethical data use, demonstrating a responsible approach to data, which encourages collaboration.
Empower patients as active participants in their care or medical research by granting them access and control over their own data.
Map how data flows through the organization, from when, where, and by whom electronic health records are accessed, aiding data exchange and ensuring compliance with HIPAA and the General Data Protection Regulation.

Implementing cloud-native data management technology provides organizations with a unified platform for accessing, ingesting, cleansing, and analyzing data sets. Integrated data in the cloud supports advanced analytics, such as trend identification and capacity prediction.

"With this open ecosystem, care teams can assemble, curate, organize, and associate patient information across multiple systems, such that data is cohesive and presented in context to guide decision-making," Hartjes says.

Martijn HartjesClinical Informatics Business Unit Leader, Philips

Improving Clinical Workflows and Enhancing the Patient Experience

Eliminating data silos optimizes workflows by minimizing manual processes, fostering cooperation among teams, and minimizing human error. The patient experience benefits significantly, from alerting food services to dietary restrictions, reducing medication dispensing mistakes, to facilitating specialists collaborations.

"With the increasing complexity of patient cases, care teams need to collaborate effectively to reach the right clinical decisions. Integrated data available for multidisciplinary teams can cut the time to diagnosis and improve diagnostic accuracy," Hartjes adds. Philips's Prostate Biology solution, for example, helps radiology and urology specialists collaborate on targeted biopsies, resulting in a higher identification rate of high-risk patients and fewer low-risk patients undergoing unnecessary procedures. Better accuracy and resource management ensue.

A centralized data management system enables healthcare to capitalize on AI. Advanced analytics and machine learning tools can be applied seamlessly to integrated data sets, enabling use cases such as identifying patients susceptible to adverse outcomes, medication side effects, or surgical complications.

Next: Get ready for AI implementation in healthcare.

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Data Management
Data-Driven Decisions
IT Governance
Patient-Centered Care
Health Information Exchange (HIE)
Interoperability
Patient Data Management

Sources

[1] Federal Trade Commission. (2021, July). Fed’s proposed Part 15 rules would promote competition in the development of next-generation AI technologies. Federal Trade Commission.

[2] Ramaswamy, V., & Srinath, S. (2020, November 10). The Role of Blockchain and Smart Contracts in Healthcare. Medium.

[3] Estuary Data fabrics. (n.d.). Products. Estuary Data fabrics.

[4] HealthShare Analytics. (n.d.). HealthShare Analytics. Heartware International Inc.

[5] Misra, S. (2021, May 26). Five Steps to Achieving Interoperable Health Information Exchange. HealthLeaders Media.

The World Economic Forum suggests adopting technology solutions to dismantle data silos and establish a fluid and secure data environment in healthcare, as these barriers stifle the implementation of personalized treatments and hinder the integration of AI. To achieve this, healthcare leaders must examine existing systems, identify impediments to data flow, reassess data governance policies, and prioritize patient privacy, data quality, and ethical data usage.

The centralized data management enabled by solutions like TEFCA can help healthcare capitalize on technology advancements, such as artificial intelligence (AI), by applying machine learning tools seamlessly to integrated data sets and identifying patients at higher risk of adverse outcomes, medication side effects, or surgical complications. This reorganization can lead to better patient care, enhanced collaboration among teams, and improved diagnostic accuracy, saving valuable time and resources.