Healthcare has lengthy struggled with a paradox. We stay in an age of unprecedented digital sophistication—streaming platforms can anticipate what we wish to watch earlier than we do, and on-line retailers can predict what’s in our purchasing cart weeks upfront. But in medication, a number 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 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 typically exist as unstructured, unsearchable textual content—buried in referral notes, consumption varieties, or social work assessments saved as PDFs. For clinicians attempting to construct a holistic image of a affected person’s life, this implies vital data is both hidden, inconsistently recorded, or worse, misplaced totally.
This isn’t simply an inconvenience. It’s a structural barrier to higher 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 threat 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 serious shift. Due to advances in pure language processing (NLP), optical character recognition (OCR), and enormous language fashions (LLMs), the thought of liberating information from static paperwork is 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 handbook chart evaluations, tedious information entry, or total 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 best NLP pipeline, that truth will be extracted, coded, and flagged immediately within the EHR as a transportation-related SDOH threat. Immediately, a doctor reviewing the affected person’s chart doesn’t must comb via 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 sources.
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 medical perception
The promise of this know-how 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, supplies the context wanted to deal with the entire individual, not simply the illness.
2. Enhance care coordination – When social employees, major care physicians, specialists, and case managers all have entry to the identical enriched dataset, sufferers are much less prone to fall via the cracks.
3. Cut back administrative burden – Automating information extraction reduces the hours clinicians spend on handbook information entry.
4. Improve inhabitants well being analytics – Aggregating structured SDOH information permits well being methods to establish community-level dangers, goal interventions, and allocate sources 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 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 threat stratification, and better affected person satisfaction.
Why that is the correct of AI in healthcare
After all, any point out of synthetic intelligence in healthcare sparks reputable issues: 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 information just isn’t about changing judgment or medical experience—it’s about eliminating blind spots. It doesn’t change how physicians observe medication; it ensures they observe with higher, extra full data.
That is the correct of AI utility: slender, dependable, and centered on decreasing friction within the system relatively than redefining it. It isn’t diagnosing sufferers, writing prescriptions, or making moral selections. It’s merely guaranteeing that when a doctor sits all the way down to evaluation a chart, they aren’t working with partial data 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 data 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 an extended historical past of letting know-how overpromise and underdeliver. However on this case, the chance is just too clear to disregard. Now we have 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 put money into integrating NLP and OCR pipelines immediately into their platforms. Well being methods ought to prioritize pilots that reveal 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 lined, 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 critical about treating sufferers as entire folks and addressing the social determinants that drive well being outcomes, then we should get critical about liberating information. Unstructured paperwork ought to not be a graveyard for vital data. With the accountable utility of AI, they will as an alternative change into a goldmine—powering higher care, driving fairness, and enhancing 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 Providers, an modern AI start-up firm.
