Healthcare has lengthy struggled with a paradox. We dwell in an age of unprecedented digital sophistication—streaming platforms can anticipate what we need to watch earlier than we do, and on-line retailers can predict what’s in our procuring cart weeks prematurely. But in medication, a few of the most important details about sufferers stays trapped inside static PDF information and scanned paperwork, locked away in codecs that had been by no means designed for medical use. Nowhere is that this extra evident than within the realm of social determinants of well being (SDOH), the non-medical components that usually dictate well being outcomes extra powerfully than any prescription.
The irony is hanging. We all know the place somebody lives, their entry to meals and transportation, their employment standing, and even their housing stability can profoundly affect their well being trajectory. And but, even when these particulars make their method into digital well being information (EHRs), they typically exist as unstructured, unsearchable textual content—buried in referral notes, consumption kinds, or social work assessments saved as PDFs. For clinicians attempting to construct a holistic image of a affected person’s life, this implies important info is both hidden, inconsistently recorded, or worse, misplaced completely.
This isn’t simply an inconvenience. It’s a structural barrier to higher care. If a affected person’s chart comprises details about their housing insecurity however a doctor by no means sees it, that perception can not inform care plans, useful resource referrals, or threat stratification fashions. The very knowledge we have to drive higher healthcare outcomes stays functionally invisible.
A knowledge liberation second
Luckily, we’re on the cusp of a significant shift. Due to advances in pure language processing (NLP), optical character recognition (OCR), and huge language fashions (LLMs), the thought of liberating knowledge from static paperwork is not a futuristic imaginative and prescient—it’s taking place now. These instruments can quickly scan PDFs, doctor notes, consumption kinds, and different unstructured information, changing them into structured, standardized, and usable knowledge that integrates seamlessly into an EHR. What as soon as required guide chart critiques, tedious knowledge entry, or complete groups of abstractors can now be accomplished in seconds.
Think about this in follow: a scanned referral letter notes {that a} affected person has restricted entry to transportation. With the proper NLP pipeline, that reality might be extracted, coded, and flagged instantly within the EHR as a transportation-related SDOH threat. All of the sudden, a doctor reviewing the affected person’s chart doesn’t must comb by attachments—they see actionable knowledge instantly. Extra importantly, care groups can proactively reply, whether or not by arranging telehealth visits, coordinating rides, or connecting the affected person with neighborhood assets.
This isn’t about flashy AI gimmicks. It’s about making the information clinicians have already got actually accessible and actionable.
From trapped knowledge to medical perception
The promise of this know-how extends comfort. By breaking down knowledge silos, healthcare organizations can:
1. Construct a extra full image of the affected person – Structured SDOH knowledge, drawn from beforehand inaccessible sources, offers the context wanted to deal with the entire particular person, not simply the illness.
2. Enhance care coordination – When social staff, main care physicians, specialists, and case managers all have entry to the identical enriched dataset, sufferers are much less prone to fall by the cracks.
3. Scale back administrative burden – Automating knowledge extraction reduces the hours clinicians spend on guide knowledge entry.
4. Improve inhabitants well being analytics – Aggregating structured SDOH knowledge permits well being programs to establish community-level dangers, goal interventions, and allocate assets extra successfully.
5. Drive fairness in care – By shining a light-weight on the social boundaries that disproportionately have an effect on susceptible populations, this method helps healthcare organizations transfer nearer to equity-driven outcomes.
The shift shouldn’t be hypothetical. Early adopters, like Watershed Well being, are already demonstrating how structured extraction of unstructured paperwork results in fewer missed diagnoses, extra correct threat stratification, and better affected person satisfaction.
Why that is the proper of AI in healthcare
In fact, any point out of synthetic intelligence in healthcare sparks legit considerations: Will machines substitute clinicians? Will algorithms make life-or-death selections? Will affected person belief erode if know-how takes an excessive amount of of the wheel?
Right here, the reply is reassuring. Utilizing AI to unlock healthcare knowledge shouldn’t be about changing judgment or medical experience—it’s about eliminating blind spots. It doesn’t change how physicians follow medication; it ensures they follow with higher, extra full info.
That is the proper of AI software: slender, dependable, and targeted on lowering friction within the system slightly than redefining it. It isn’t diagnosing sufferers, writing prescriptions, or making moral selections. It’s merely guaranteeing that when a doctor sits right down to overview a chart, they aren’t working with partial info as a result of key particulars are locked inside a PDF attachment.
In different phrases, AI right here is an assistant, not a decider. It enhances entry to actionable info with out encroaching on the human parts of medication that sufferers worth most—empathy, belief, and judgment.
A name to motion
The healthcare trade has a protracted historical past of letting know-how overpromise and underdeliver. However on this case, the chance is simply too clear to disregard. We’ve the instruments to unlock knowledge that already exists in affected person information and put it to work for higher outcomes. The query is whether or not healthcare leaders will seize the second.
EHR distributors should embrace interoperability and put money into integrating NLP and OCR pipelines instantly into their platforms. Well being programs ought to prioritize pilots that reveal how structured SDOH knowledge improves care supply and value financial savings. Policymakers and payers ought to incentivize the seize and use of this knowledge, recognizing that upstream social components drive downstream healthcare spending.
For too lengthy, clinicians have been pressured to follow with one eye lined, missing the total image of their sufferers’ lives. By liberating SDOH and different knowledge from their doc prisons, we will lastly equip suppliers with the readability they want.
That future shouldn’t be science fiction. It’s inside attain in the present day.
If healthcare is severe about treating sufferers as complete folks and addressing the social determinants that drive well being outcomes, then we should get severe about liberating knowledge. Unstructured paperwork ought to not be a graveyard for important info. With the accountable software of AI, they’ll as an alternative develop into a goldmine—powering higher care, driving fairness, and enhancing lives.
The revolution begins not by inventing new knowledge, however by lastly utilizing the information we have already got.
George Bosnjak is co-founder of Morph Providers, an revolutionary AI start-up firm.
