Health Equity Depends on Pragmatic Tech for Healthcare’s Digital Have-Nots

By Bevey Miner, Executive Vice President, Healthcare Strategy and Policy, Consensus Cloud Solutions
LinkedIn: Bevey Miner
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No matter how large or well-resourced a health system may be, making the right care decisions ultimately depends on its ability to exchange information with healthcare’s “digital have-nots”—organizations that weren’t eligible for EHR implementation incentives, like post-acute facilities, home health, substance use disorder clinics and assisted living facilities.

When these technology connections don’t exist, this leads to breakdowns in efficiency and communication that affect access to care, including transitions in care, leading to poor quality outcomes. Patient information pitfalls also drive up costs of care for patients and the organizations that treat them, research by the American College of Physicians (ACP) shows.

It’s an area where pragmatic solutions that leverage existing technologies to strengthen information transfer could make a substantial difference.

Increasingly, healthcare organizations are combining common tools, like digital fax, with natural language processing (NLP) and artificial intelligence (AI) software to more fully gather shareable information through intelligent data extraction. These solutions pull needed information from unstructured documents like PDFs, scans and images and send it to clinicians and staff directly within their workflows, preventing gaps in information that may need to be data entered before care can commence, resulting in delays in treatment.

An Affordable Option for Better Decision Making

When it comes to achieving health equity, one of the biggest challenges the United States faces is an inability to move data between health systems and smaller care settings to support better health outcomes.

Care transitions—which occur when a patient goes from one care setting to treatment by a different provider—often represent changes in a patient’s clinical, cognitive or functional status. When the information needed to facilitate transitions in care isn’t available when it is needed—typically because the IT systems for health systems and post-acute settings don’t talk to one another—this presents “significant challenges to optimal care transitions [that] are often further compounded by systemic socioeconomic injustices that create health and healthcare disparities for many in our society,” says Ryan D. Mire, MD, MACP, president of the ACP. Yet most post-acute providers say this information is often missing or difficult to use. About half experience delays in receipt.

Meanwhile, as the Centers for Medicare & Medicaid Services intensifies its focus on advancing health equity, reducing gaps in health status for vulnerable populations, including tribal populations, demands that providers at every point in the care continuum have access to comprehensive data. But when a tribal health organization needs to consult with an academic medical center or large system, typically, these communications include documents sent via paper fax or phone calls. This increases the chances that important details regarding a patient’s condition or health history will be missed and errors in data entry that occur when data has to be documented. It also puts health systems at risk of receiving a penalty for not communicating admission, discharge, or transfer information to the post-acute care team.

Connecting digital have-nots with NLP and AI-powered solutions that extract data from healthcare faxed, scanned or other unstructured data, including handwritten images, holds strong potential to close gaps in information exchange and, ultimately, health equity—and it’s an affordable approach. For one, it doesn’t require implementation of an EHR, which could cost more than $100,000. And, at a time when seven out of 10 hospitals and one out of three post-acute providers still rely on fax to transfer patient information, applying intelligent data extraction to digital fax moves patient care information to the people who need it most, faster. In doing so, it becomes an integral component for health equity, easing access to care by speeding information transfer.

Here’s how it works:

  • NLP and AI machine learning transforms handwritten or text data into structured data that can be easily consumed by any system.
  • This enables staff at digital have-not facilities to process documentation faster using digital integration without the need for data entry. It also avoids document traffic jams, given that “have not” facilities typically rely on fax and phone to send and receive referrals.
  • Documents spend less time in the network, and patients spend less time waiting for the right care.

Widespread adoption of intelligent data extraction could level the playing field between digital haves and have nots, ensuring that all healthcare providers have the data they need to make informed care decisions across every facet of the healthcare delivery system. It’s an affordable solution that leverages the technology most clinics and post-acute facilities possess—digital fax—to provide right-now value that improves health access, outcomes and equity.

Bridging Healthcare’s Digital Divide

In the move to advance health equity, we can’t forget that tech equity plays an essential role in ensuring equitable access to care and care expertise. “If we don’t scale [health equity initiatives] with tools, data, and consistent knowledge and methods, we will never achieve consistent outcomes because we’ll all be approaching it differently, and we won’t know if what we’re doing is right,” Mitchell Thornbrugh, Chief Information Officer and Director, Office of IT for the Indian Health Service, shared during a HIMSS 2023 panel on “Continuing the Journey to Health Equity.”

Given that lack of information exchange continues to disrupt care for half of Medicare patients who are discharged to post-acute settings, health system’s “digital haves”—namely, large health systems—must explore solutions that ease information transfer to digital have-nots. Intelligent data extraction is an important place to start.