How Keigo Higashino’s Mysteries Mirror Health Informatics Insights

A Writer Who Understood Human Complexity

When news broke that Keigo Higashino had died of colon cancer at 68, readers around the world paused. He wrote 106 books, many of which sat on the shelves of teenagers in the 2000s and 2010s. His stories weren't just puzzles; they were studies of how people behave under pressure, how secrets fester, and how institutions fail individuals.

That focus on the human element is also what makes his work oddly relevant to health informatics. At first glance, a Japanese mystery novelist seems far removed from electronic health records or clinical decision support. But Higashino’s recurring themes—fragmented identities, unreliable narratives, the gap between data and truth—map neatly onto the challenges we face when we digitize healthcare.

When the Record Lies: Lessons from Malice

In Malice, a bestselling author is murdered, and the killer’s confession seems airtight. The detective, Kyochiro Kaga, realizes the written account is a carefully constructed fiction. The murderer uses journal entries to mislead, not to reveal.

Health records are also narratives. They’re written by busy clinicians, transcribed from patient memories, and filtered through billing codes. They contain omissions, biases, and occasional outright errors. A study from the U.S. found that nearly 1 in 10 medication orders in hospitals is incomplete or incorrect. That’s not malice—just the friction of moving information between humans and systems.

But Higashino’s lesson stands: we must treat any record with skepticism. In health informatics, that means building systems that flag inconsistencies, allow patients to review their own data, and encourage clinicians to question what they see—not just accept it.

Identity and Data: The Body as a Container

Secret tells of a woman whose soul lives on in her daughter’s body. The husband must navigate a relationship where the exterior doesn’t match the interior. It’s a metaphor for how health data often separates from the person it represents.

In practice, patient identity is a tangle of identifiers—name, birth date, social security number, insurance ID. Mismatches lead to duplicate records, misattributed lab results, and even wrong-site surgeries. The Office of the National Coordinator for Health IT has spent years trying to standardize patient matching, yet a 2018 survey found that 1 in 5 hospitals still can’t accurately match patients across different systems.

Higashino’s story reminds us that identity is more than a label. It’s the continuity of experience. Health informatics must preserve that continuity, even when data moves between clinics, apps, and wearables.

The Dark Side of Data: White Night and Surveillance

White Night follows two characters bound by a childhood crime, living under false identities for decades. They never walk in the sun together. The novel is a slow-burn exploration of how surveillance and hidden pasts shape lives.

That’s a fitting lens for health data’s darker potential. Wearables and apps collect continuous streams of biometric data—heart rate, sleep patterns, location. Insurers have already used fitness trackers to adjust premiums, sometimes without clear consent. In 2019, a UK-based company faced backlash for sharing genetic data with a pharmaceutical partner without explicit opt-in.

Privacy isn’t just about keeping data secret; it’s about giving people control over how their information is used. Higashino’s characters are trapped by secrets. Patients should never feel trapped by their own health data.

Systemic Flaws and Algorithmic Bias

In Journey Under the Midnight Sun, a detective spends 19 years chasing a case that the system initially botched. The police were too quick to close it, too reliant on surface-level clues. That’s a critique of institutional inertia—something health IT knows well.

Algorithms in healthcare can inherit biases from the data they’re trained on. A 2019 study in Science found that a widely used algorithm underestimated the health needs of Black patients because it relied on cost as a proxy for illness. The algorithm’s designers didn’t intend harm; they just didn’t question the data.

Like Higashino’s detective, we need to keep digging. That means auditing algorithms, diversifying training datasets, and making sure that the people affected by decisions have a seat at the table.

Humanity in the Machine

Higashino’s later works, like The Devotion of Suspect X, show that even the most logical mind—a physicist, no less—can be driven by emotion. The scientist’s rationality masks a deep, almost self-destructive love.

Health informatics often prizes efficiency and objectivity. But care is fundamentally human. A patient portal that’s hard to use, or an alert that interrupts a clinician’s flow, adds friction to a relationship already under stress. The best systems are those that fade into the background, letting humans connect.

Higashino’s legacy is a reminder that data is never the whole story. It’s a starting point for understanding—not a substitute for judgment.

What We Can Learn

Keigo Higashino wrote mysteries, but they were never just about whodunit. They were about why people do what they do, and how systems shape those choices. Health informatics has the same challenge: to design tools that support human dignity, not undermine it.

As we build the next generation of health technologies—AI diagnostics, predictive analytics, patient-generated health data—we’d do well to remember his lessons. Question what the records say. Protect the person behind the data. And never let the convenience of a system override the complexity of a life.

His books will be read for decades. The questions they raise—about trust, identity, and justice—are timeless. For those of us in health informatics, they’re not just interesting stories; they’re cautionary tales.