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Healthcare data streams continue to increase exponentially thanks to the influx of information from sensors and medical equipment. This has made clinical data processing increasingly complex. Electronic health records, medical imaging systems and clinical research databases generate a vast amount of data, presenting a formidable challenge for healthcare professionals who need efficient data management, accuracy and security.

Today, data analytics and self-learning artificial intelligence (AI) models have revolutionized how we manage, analyze and use data across industries. The healthcare sector is one where real-time data management and analytics are making major strides.

To tackle the mounting challenges of medical data management, the industry is turning to a patient-centric, data-driven approach, where real-time data management plays a crucial role in facilitating patient services and supporting medical research.

Real-time healthcare data management uses historical and real-time data to predict trends, uncover actionable insights, drive medical advances and fuel long-term growth. Accessing and analyzing data in real time is essential for healthcare professionals to provide better patient care and to enable medical breakthroughs.

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Implemented correctly, real-time data management can yield significant benefits, including reduced treatment costs, a comprehensive understanding of patients and their conditions, and optimized workflows.

How real-time data is enhancing healthcare services

Real-time data management and analysis can significantly enhance quality of care by improving clinical workflows. Currently, the healthcare industry is mostly analyzing clinical or billing data that’s several months old to find ways to enhance future care. In contrast, real-time data empowers providers to influence the clinical encounter as it unfolds.

Healthcare organizations are embracing real-time data analytics to reduce overspending on inefficient stock management, patient care and staff deployment.

Better medical decision-making

“Leveraging real-time data can allow a clinician to ask better questions and take a complete history, write complete notes that the care team can study, and also make better clinical decisions that impact the trajectory of the patient’s health,” Dr. Shiv Rao, co-founder and CEO of medical AI platform Abridge, told VentureBeat. 

Abridge’s AI for medical conversations ambiently “listens” to doctor-patient conversations and generates draft notes and structured data in real time.

“Such data can be fed back into the EMR [electronic medical record], allowing clinicians not just to be unburdened from documentation but also [from] other aspects of their clinical workflows inside the EMR,” said Rao. 

Real-time, real-world data enables healthcare providers to take prompt, proactive measures to prevent negative health outcomes, ultimately reducing the cost of care for patients. In fact, real-time data derived from real-world data sources enhances the effectiveness of care delivery across the healthcare continuum, improving outcomes for diverse patient populations that require precise, tailored treatment.

“Leveraging real-time data impacts how precision medicine performs on real patients, with real syndromes, that need real-time interventions,” said Camille Cook, senior director of healthcare strategy at data management firm LexisNexis Risk Solutions. “By implementing real-time data, clinicians and public health professionals can exponentially improve coordinated care efforts, patient outcomes, and cost of care to the patient.”

Clinical trials and medical devices

Another application of real-time data is for clinical trial monitoring. Here real-time data is sometimes used to detect possible safety concerns among trial participants. For example, AI/ML-based early warning systems for clinical deterioration can detect abnormal vital signs that precede patient deterioration and adverse outcomes. Alerted, caregivers can promptly intervene to stabilize the patient.

“The integration of real-time data from an endoscopy camera with an AI-powered application at the point of care has opened up new possibilities for digital surgery,” David Niewolny, director of healthcare development at Nvidia, told VentureBeat.

Nvidia and Medtronic recently announced a collaboration to integrate Nvidia Holoscan, a real-time AI computing software platform for building medical devices, and Nvidia IGX, an industrial-grade hardware platform, with Medtronic’s GI Genius AI-assisted colonoscopy system, which detects early signs of colorectal cancer. 

A seamless data architecture for managing healthcare data

Effective healthcare delivery relies on a seamless, cohesive ecosystem. This ecosystem includes surgical tools, connected sensors, radiology imaging, EMRs and other applications that must work together to provide a comprehensive picture to surgeons, clinicians and interventionists. 

To ensure that these systems function effectively and efficiently, it is crucial to understand the data flow and underlying architecture. Any disruptions or gaps in the data flowing between systems can be problematic. Integration into the clinical workflow is also vital, as it is a key evaluation criterion for receiving FDA clearance for a new medical device or software-as-a-medical-device (SAMD) algorithm.

“The data must be locatable, searchable, retrievable and useful — for example, [it must be in] the right format or units. If any of these is missing, the whole data chain breaks,” said Nvidia’s Niewolny. “When the data chain is broken, data ends up in silos on multiple systems or across multiple applications, and the clinician is left with incomplete data or has to do the work to piece together a total view of a patient’s condition or status.”

Accuracy through connection

Accurate healthcare data is essential for making informed decisions that affect patient care. One key requirement for data accuracy is a connected framework that establishes clear “sources of truth,” identifying the systems with jurisdiction over specific data points. 

Inaccuracies can occur when data is re-entered unnecessarily, such as by manually inputting patient demographics when this information could be automatically pulled from an EMR. A connected data architecture is crucial for reducing these errors, streamlining the flow of information and minimizing the need for manual data entry.

“It’s important that leveraging real-time data enables workflows and solutions with the uptime that clinicians and patients deserve. This results in leveraging virtualization where appropriate, and having fault-tolerant systems so there are no single points of failure when network or power outages occur,” added Niewolny. “This is supported by having a well-thought-out data architecture and using enterprise-class solutions.”

Likewise, Brigham Hyde, co-founder and CEO of data-driven physician consultation service Atropos Health, said that a well-defined data architecture helps healthcare organizations capture, store and learn from their data securely and efficiently.

“Well-defined data architectures and sources provide additional context on the status of a patient or others like them — enabling rapid identification of patterns, trends, predictions and possible treatment plans via clinically-informed analytics technologies,” said Hyde. “These results can be used to provide more informed care, undercutting the healthcare disparities stemming from the evidence gaps for diverse patient groups.”

Controlling the flood of healthcare data

The increasing amount of collected measurements presents clinicians with a deluge of data. The hurdle is to transform this plethora of data into actionable insights. And that involves filtering the data so as to comprehend the patient’s status, and identifying trends across various data sources. 

One of the technologies to address this data overload is clinical large language models (LLMs). 

Over the past few months, AI language models like ChatGPT have made a splash. But, said Nvidia’s Niewolny, “There are specially trained healthcare AI/LLM models, like GatorTron, that can do things clinicians don’t have time to do.”

He added that such AI models can aggregate multiple data sources, including patient notes, into a consistent view, or write a clear summary of large quantities of data to provide insight into a patient’s condition.

Healthcare providers are grappling with a slew of challenges amidst the proliferation of data. They face obstacles including security concerns, standardization issues and the need for more robust tools.

Yet technology alone can’t navigate these data management hazards. 

Instead, a fundamental shift is necessary, not only in the technical realm but also in the comprehensive design and management of healthcare processes.

This, in turn, would positively impact service providers’ business models. And placing the patient at the center of the healthcare system is crucial to its effectiveness.

“Getting the data is easy; implementing the data into existing workflows for clinicians is the hardest part,” explained LexisNexis’s Cook. “Allowing for interoperability and large data exchange markets across EHR [electronic health record] vendors, imaging specialists, registry warehouses, genomics databases and real-world data vendors enhances the ability to seamlessly integrate into these existing workflows.” 

What’s next for real-time healthcare data management? 

Niewolny believes that the future of healthcare lies in personalized care, where treatments are tailored to each patient’s unique needs. As treatments and individual patients’ needs continue to evolve, real-time data will be necessary to make this shift a reality.

“Multi-modal applications will continue to become even more prevalent, leveraging all of the available data (structured and unstructured), providing clinicians with additional insights that were unavailable without real-time data,” said Niewolny. “These insights will bring us closer to the goal of personalized, precision medicine, leading to improved outcomes and patient experience.”

Likewise, Atropos Health’s Hyde says that real-time data, real-world data, and AI hold transformative potential for accelerating medical research and improving patient outcomes. 

“We look forward to a future where there is widespread use in healthcare of technologies that make learning from clinical data faster, easier and more relevant to diverse populations,” Hyde said. “The output, for hospitals, is realizing the promise of the learning health system. For patients, it’s more tailored care based on aggregated evidence from the lived experiences of patients like them. And for science, it’s a road to augmented discovery and research potential.”

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