Healthcare becomes deeply personal when the system’s fragmentation leads to life-altering outcomes. During COVID-19, my father’s doctor made what seemed like a prudent choice: postpone treatment for fluid retention to minimize virus exposure. What began as a cautious approach—understandable in a pandemic—ended up having dire consequences. By the time anyone realized how rapidly his condition was worsening, his kidneys had suffered significant damage, ultimately leading to kidney failure.
Later, despite years of regular check-ups and lab work (which hinted at possible malignancies), he was diagnosed with stage four lung cancer. Alarming as that was on its own, what stung even more was how these warning signs never coalesced into a clear intervention plan. His history as a smoker and several concerning lab results should have raised flags. Yet no one connected the dots. It was as if his care lived in separate compartments: one file at the dialysis center, another at oncology, and a third at his primary care clinic.
The Fragmentation Crisis
That disjointed experience shone a harsh light on how easily critical information can remain siloed. One specialist would note an abnormality and advise a follow-up, only for that recommendation to slip through the cracks by the time my father went to his next appointment. Each time he walked into a different office, he essentially had to start from scratch—retelling his story, hoping the right details were captured, and trusting that this piece could eventually reach the right people.
The challenges went beyond missing data. My father, who had set dialysis sessions on the same days each week, routinely found his other appointments—like oncology visits or additional lab work—piled on top of those sessions. He spent hours juggling schedules just to avoid double-booking, which was the last thing he needed while battling serious health concerns.
COVID-19 made all of this worse. The emphasis on social distancing—again, quite reasonable in itself—took away the face-to-face time that might have revealed early red flags. Without continuous, well-integrated data flow, even well-meaning advice to “stay home” inadvertently blocked us from seeing how quickly my father’s health was unraveling.
A Potential Game Changer: Subtle AI Support
Throughout this ordeal, I couldn’t help but imagine what a more seamless, data-driven healthcare system might look like. I’m not talking about robots taking over doctor visits, but rather subtle, behind-the-scenes assistance—sometimes described as “agentic workloads.” Think of these as AI systems quietly scanning medical records, cross-referencing lab results, and gently notifying doctors or nurses about unusual patterns.
AI is already proving its value in diagnostic imaging. Studies have shown that computer-vision algorithms can analyze X-rays, CT scans, and MRIs with remarkable accuracy—often matching or even surpassing human radiologists. For example, AI has been shown to detect lung nodules with greater precision, helping identify potential issues that might have been missed otherwise. This type of integration could enhance our ability to catch problems like kidney damage or lung cancer earlier, triggering quicker interventions.
Additionally, when he underwent chemotherapy, he had to wait weeks after treatment and imaging to learn whether it was effective—an excruciating delay that AI could drastically shorten by providing faster, more integrated feedback to both patients and care teams.
Ideally, this technology would work much like a vigilant assistant: it wouldn’t diagnose my father all on its own, but it could have flagged consistent changes in his kidney function and correlated them with other troubling indicators. Perhaps it would have unified those scattered bits of data—a chest X-ray here, a suspicious blood test there—so that each new piece of information triggered closer scrutiny.
Yet for all the promise AI holds, it won’t matter if patients and providers don’t trust it. If alerts and reminders are viewed as background noise—just another alarm among many in a busy clinic—then critical issues may still go unnoticed. That’s why any such system must be transparent about how it arrives at its recommendations, and it must operate continuously in tandem with real human oversight.
The Missing Thread: Continuous Care
One of the biggest challenges my father faced—beyond the clinical realities of organ failure and cancer—was navigating a disjointed care environment. Even when he saw the same doctors, he often encountered new nurses or support staff who weren’t familiar with his case. He had to become his own advocate, repeating medical histories and test results, worried that a single oversight could spell disaster.
If every practitioner had easy access to a continuous stream of up-to-date information, that weight wouldn’t have been solely on my father’s shoulders. An AI-backed platform might have served as the “single source of truth” across different hospitals, labs, and specialists. Instead of fragmented snapshots—a lab test here, a consultation there—his providers would see a holistic, evolving picture of his health. And instead of being passive recipients of siloed updates, they’d participate in a more proactive, team-based approach.
By incorporating AI, healthcare could move from isolated snapshots to a more dynamic and connected view. For example, AI systems could track trends in lab results and imaging over time, detecting subtle changes that may otherwise be overlooked. By learning from every new case, these systems continuously improve, identifying correlations across medical histories, imaging results, and lifestyle factors. This would allow for earlier interventions and more tailored care, such as flagging kidney function changes that coincide with other troubling indicators.
Why Trust Matters More Than Ever
Still, technology can only go so far without human trust and collaboration. The best data-sharing framework in the world won’t help if doctors and nurses are suspicious of AI’s findings or if patients don’t feel comfortable granting access to their health records. Some of this wariness is understandable; health information is deeply personal, and no one wants to risk privacy breaches or rely on software that might produce false alarms.
Yet, if handled properly—with robust privacy protections, clear transparency about how data is used, and consistent evidence of accuracy—AI can become a trusted ally. That trust frees up healthcare professionals to do what they do best: engage with patients, provide empathy, and make nuanced clinical judgments. Meanwhile, the AI quietly handles the complex, data-heavy tasks in the background.
Restoring the Human Element
Paradoxically, I believe that good AI could actually bring more humanity back into healthcare. Right now, many doctors and nurses are buried under administrative and repetitive tasks that eat into the time they can spend with patients. Automated systems can relieve some of that burden, ensuring that routine record checks, appointment scheduling, and cross-specialty communication flow smoothly without continuous manual follow-up.
For patients like my father, that could mean quicker recognition of red flags, fewer repeated tests, and less of the emotional toll that comes from feeling like you have to quarterback your own care. It could also open the door for more meaningful moments between patients and providers—when doctors aren’t racing against a backlog of paperwork, they can be more present and attentive.
Walking Toward a Better Future
My father’s story underscores the steep price we pay for a fragmented, often reactive healthcare system. Even though he was conscientious about his check-ups, too many critical data points floated disconnected across different facilities. By the time all those puzzle pieces came together, it was too late to prevent significant damage.
Yet this isn’t just about looking backward. If there’s a silver lining, it’s the conviction that we can do better. By embracing subtle, well-integrated AI systems, we could transform the way we handle everything from day-to-day care to life-changing diagnoses. We could move beyond isolated treatments and instead give patients a coherent support network—one that sees them as whole individuals rather than a collection of disconnected symptoms.
A Call to Rethink Care
I don’t claim to have all the answers, and I know technology can’t solve every issue in healthcare. But seeing my father’s struggle firsthand has taught me that we urgently need a more unified, trust-driven approach—one that values continuous monitoring as much as it does specialized expertise.
- Patients should have full visibility into their records, supported by AI that can highlight pressing concerns.
- Providers deserve a system that connects them with real-time data and offers gentle nudges for follow-up, not an endless overload of unrelated alerts.
- AI developers must design platforms that respect privacy, ensure transparency, and genuinely earn the confidence of medical teams.
If we can get these pieces right, tragedies like my father’s might become far less common. And then, at long last, we’d have a healthcare system that fulfills its most fundamental promise—to care for human life in a truly holistic, proactive way.