Healthcare has lengthy struggled with a paradox. We stay 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 purchasing cart weeks prematurely. But in medication, a few of the most important details about sufferers stays trapped inside static PDF recordsdata and scanned paperwork, locked away in codecs that had been by no means designed for scientific use. Nowhere is that this extra evident than within the realm of social determinants of well being (SDOH), the non-medical elements that usually dictate well being outcomes extra powerfully than any prescription.
The irony is putting. 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 usually exist as unstructured, unsearchable textual content—buried in referral notes, consumption varieties, or social work assessments saved as PDFs. For clinicians making an attempt to construct a holistic image of a affected person’s life, this implies crucial info is both hidden, inconsistently recorded, or worse, misplaced totally.
This isn’t simply an inconvenience. It’s a structural barrier to raised care. If a affected person’s chart accommodates details about their housing insecurity however a doctor by no means sees it, that perception can not inform care plans, useful resource referrals, or danger stratification fashions. The very information we have to drive higher healthcare outcomes stays functionally invisible.
An information liberation second
Fortuitously, we’re on the cusp of a significant shift. Because of advances in pure language processing (NLP), optical character recognition (OCR), and huge language fashions (LLMs), the thought of liberating information from static paperwork is now not a futuristic imaginative and prescient—it’s occurring now. These instruments can quickly scan PDFs, doctor notes, consumption varieties, and different unstructured information, changing them into structured, standardized, and usable information that integrates seamlessly into an EHR. What as soon as required guide chart evaluations, tedious information entry, or complete groups of abstractors can now be achieved in seconds.
Think about this in observe: a scanned referral letter notes {that a} affected person has restricted entry to transportation. With the appropriate NLP pipeline, that truth will be extracted, coded, and flagged straight within the EHR as a transportation-related SDOH danger. Immediately, a doctor reviewing the affected person’s chart doesn’t must comb by means of attachments—they see actionable information instantly. Extra importantly, care groups can proactively reply, whether or not by arranging telehealth visits, coordinating rides, or connecting the affected person with group assets.
This isn’t about flashy AI gimmicks. It’s about making the information clinicians have already got really accessible and actionable.
From trapped information to scientific perception
The promise of this expertise extends comfort. By breaking down information silos, healthcare organizations can:
1.   Construct a extra full image of the affected person – Structured SDOH information, drawn from beforehand inaccessible sources, gives the context wanted to deal with the entire individual, not simply the illness.
2.   Enhance care coordination – When social staff, major care physicians, specialists, and case managers all have entry to the identical enriched dataset, sufferers are much less more likely to fall by means of the cracks.
3.   Cut back administrative burden – Automating information extraction reduces the hours clinicians spend on guide information entry.
4.   Improve inhabitants well being analytics – Aggregating structured SDOH information allows well being programs to determine community-level dangers, goal interventions, and allocate assets extra successfully.
5.   Drive fairness in care – By shining a lightweight on the social limitations that disproportionately have an effect on susceptible populations, this strategy helps healthcare organizations transfer nearer to equity-driven outcomes.
The shift just isn’t hypothetical. Early adopters, like Watershed Well being, are already demonstrating how structured extraction of unstructured paperwork results in fewer missed diagnoses, extra correct danger stratification, and better affected person satisfaction.
Why that is the proper of AI in healthcare
After all, any point out of synthetic intelligence in healthcare sparks professional considerations: Will machines exchange clinicians? Will algorithms make life-or-death choices? Will affected person belief erode if expertise takes an excessive amount of of the wheel?
Right here, the reply is reassuring. Utilizing AI to unlock healthcare information just isn’t about changing judgment or scientific experience—it’s about eliminating blind spots. It doesn’t change how physicians observe medication; it ensures they observe with higher, extra full info.
That is the proper of AI software: slender, dependable, and targeted on lowering friction within the system relatively than redefining it. It isn’t diagnosing sufferers, writing prescriptions, or making moral choices. It’s merely making certain that when a doctor sits all the way down to evaluate 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 expertise overpromise and underdeliver. However on this case, the chance is simply too clear to disregard. We’ve got the instruments to unlock information 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 spend money on integrating NLP and OCR pipelines straight into their platforms. Well being programs ought to prioritize pilots that show how structured SDOH information improves care supply and value financial savings. Policymakers and payers ought to incentivize the seize and use of this information, recognizing that upstream social elements drive downstream healthcare spending.
For too lengthy, clinicians have been compelled to observe with one eye coated, missing the total image of their sufferers’ lives. By releasing SDOH and different information from their doc prisons, we are able to lastly equip suppliers with the readability they want.
That future just isn’t science fiction. It’s inside attain at present.
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 information. Unstructured paperwork ought to now not be a graveyard for crucial info. With the accountable software of AI, they’ll as an alternative grow to be a goldmine—powering higher care, driving fairness, and bettering lives.
The revolution begins not by inventing new information, however by lastly utilizing the information we have already got.
George Bosnjak is co-founder of Morph Companies, an revolutionary AI start-up firm.
Â
