From Data to Diagnosis: Piecing Together the Patient Puzzle with AI

The fragmented nature of patient data and administrative burdens often lead to crucial details being overlooked, impacting patient care and healthcare revenue.

Advaith from MarianaAI
December 9, 2023

Dr. Everett rushes between exam rooms, struggling to juggle a barrage of critical tasks. There are patient charts to update, labs to review, e-mails flooding her inbox. All while piecing together clues to solve a puzzle - what's causing Mrs. Smith's abdominal pain?

She barely has time to listen to the full story. As she races off, an alert pings - Mr. Kumar's sodium level is elevated but the nurse has taken care of it for now. She makes a mental note to follow up later, but her mind is already onto the next case. Details slip through the cracks. Dr. Everett does her best, but by day's end, she drained and behind on paperwork. Surely there must be a better way?

Healthcare providers today face overwhelming workloads - endlessly racing through patient visits while balancing mounds of administrative duties. Like piecing together complex puzzles, doctors must connect scattered clues - tiny signs buried across disparate medical records - to determine the root causes behind symptoms. Yet siloed data and distractions impede a comprehensive view. In the gaps, critical details go unseen.

The Need for a Better Way

Even minor oversights can profoundly impact care quality and revenues. Yet while managing numerous administrative and diagnostic tasks, clinicians are likely to miss valuable insights. For instance, a physician fails to note a patient's sulfa allergy before prescribing a new medication. An allergic reaction transpires — sending the patient back to the ER. This leads to added costs and lowered reimbursement rates for the health system under value-based care contracts. And the resulting hospitalization could have been averted by surfacing the allergy flag during that 10-minute patient visit.

Infographic: Identifying Barriers to Effective Diagnosis and Caregiving

Critical health details end up scattered across various records and systems. Doctors simply don’t have enough hours in the day to find and connect all those dots during brief visits. Maybe a specialist recommended a new medication but primary care wasn’t looped in. Or a social risk factor got buried deep in a clinic note years ago.

Details like insurance changes also fall through the cracks, leading to procedures denied or referrals needing redo. When coverage gaps go unseen, doctors inadvertently order tests or specialists no longer covered - saddling surprised patients with crushing medical debts. When patients switch to value-based care models, critical historical details impacting risk and coding also go dark. And nurses hand off summarized snippets in the rush, but key lifestyle or diet details impacting treatment plans go unseen. Doctors also face a major challenge in keeping up with the vast and growing body of medical literature, leading to knowledge gaps that impact their ability to provide the most up-to-date, evidence-based patient care.

Patients entrust their care across multiple providers over decades. But this health journey remains filled with gaps rather than told cohesively. This fragmentation leads to missed charges, billing errors, and preventable readmissions that cost hospitals millions. What if physicians had assistance surfacing patient insights, automating documentation, and codifying each encounter? Technologies like artificial intelligence (AI) offer this possibility. The application of AI is revolutionizing healthcare, nowhere more so than in predictive diagnostics. Harnessing vast data, AI models uncover early disease indicators - enabling intervention before symptoms escalate.

The Pieces Don’t Fit the Puzzle

When you can't grasp the full health story, it's impossible to provide precision care. Current healthcare AI delivers fragmented insights. Rule-based approaches rely on human-set logic, unable to assimilate the vastness of medical knowledge. Off-the-shelf ML models provide generic utilities lacking clinical nuance.

The debut of ChatGPT was a turning point for generative AI but also sparked a wave of UI wrappers aimed at questions and answers. While these AI tools deliver strong first impressions, most struggle to move beyond surface-level responses without customization grounded in medical best practices. They fail to combine medical knowledge and longitudinal patient history vital for clinical adoption.

Some other tools today depend on fine-tuning pre-built models. This isn’t sufficient because it often relies on the quality and relevance of the pre-existing data and models, which does not fully address specific, unique, or evolving needs in healthcare.

Current AI solutions tend to skim the surface without custom care. They often miss the big picture - a patient's whole health history and unique needs. And that leaves dangerous gaps in understanding. Doctors need the full story to make the right calls. AI has to properly fit that bigger, deeper view.

Other healthcare AI tools only look at pieces of patient data - often limited to a particular conversation. This gives an incomplete picture. Dangerous gaps emerge — a missed test result here, an undetected adverse drug interaction there. Like a jigsaw puzzle with crucial missing sections, the clinical picture remains obscured. Doctors need to see the whole puzzle — how all the pieces fit over time. This totality holds the key to revealing what current siloed solutions cannot.

Take for example, an elder's cognition tests may seem normal if looked at alone each year. But patterns emerge when AI reviews 10+ years of subtle decline across memory exams, brain scans, clinic notes, and family history. This allows much earlier Alzheimer's prediction.

Unlocking Precision and Accuracy Through Purpose-Built AI

No single test result gives the whole story. MarianaAI looks across all the pieces longitudinally through deep integration across EHRs, lab systems, and clinical documents. This full perspective uncovers what standalone tools miss.

We achieve this foresight through custom AI models designed for accuracy and equity. MarianaAI further identifies potential gaps that may have been overlooked, including missing RAF details that could impact care or billing. By spotlighting incomplete areas of a patient's health journey, it enables clinicians to pursue the full picture necessary for proper diagnosis, risk scoring, and a much better “care” experience. And this happens even before an encounter starts — using AI-powered patient portraits. It looks at available test results, scans and full medical histories to offer insights - not just the latest visit.

72,100+ Doctor Ipad Stock Photos, Pictures & Royalty-Free Images - iStock |  Doctor patient ipad, Doctor computer, Doctor laptop

But accurate predictions alone mean little without user trust and adoption. So MarianaAI looks beyond technical prowess, pairing predictive performance with intuitive experiences in pursuit of an empathetic clinical AI copilot focused squarely on lifting real-world provider burdens. From AI-generated documentation to instant medical coding, we automate time-intensive administrative tasks - freeing physicians to focus on patient care.

And understanding precedes trust. This begins with explainable models - real-time clinical interventions mapped to conversations help doctors easily evaluate reliability. MarianaGPT's ability to instantly analyze electronic health records and the latest medical research allows doctors to get contextual answers to patient-specific questions, bridging knowledge gaps at the point of care. By embedding natively within existing clinical workflows rather than demanding new behaviors, MarianaAI eases the learning curve. And we deliver this capability without any IT investment required, making our solutions accessible for resource-constrained practices everywhere.

In the end, our team at MarianaAI foster trust in the human-technology partnership - where physicians guide care while AI lifts the data processing load. This harmony unlocks healthcare's fullest potential.

The Vision to Distribute Capability

AI affords a competitive edge to elevate healthcare universally. But as William Gibson noted, “The future is already here, it's just not evenly distributed.” MarianaAI looks to rectify this disparity, distributing the most advanced diagnostic capabilities to providers everywhere through ethical, equitable models paired with frictionless workflow integration.

The results already speak for themselves. Yet the surface has barely been scratched. Through continuous research and vigilance, MarianaAI paves the path to an AI-powered paradigm in medicine - one that captures the richness of human health in all its diversity. The time for transformation is now.