Bring Your Own Algorithm: Patients as Architects of Their Own Care

Introduction: What is “Bring Your Own Algorithm”?

Healthcare has long been built on a one-way model: doctors prescribe, patients comply. But the digital era has cracked that open. Wearables, connected monitors, and home diagnostics now stream millions of data points each day. The question is no longer whether patients can generate data — it’s whether they can use it.

“Bring Your Own Algorithm” (BYOA) is the emerging answer. It describes a new frontier where patients don’t just wear sensors, but actively choose algorithms to interpret the signals those devices produce. Instead of waiting for a physician or health system to deliver insights, patients bring their own analytic tools into the room — much like bringing your own medication list or health history, but this time in the form of an AI model.

The significance is profound. For the first time, individuals managing diabetes, epilepsy, heart disease, or other chronic conditions can run real-time analyses on their own lives. They can test hypotheses (“Does poor sleep trigger my seizures?”), see predictions (“Is my glucose trending toward a crash?”), or forecast risks (“Am I entering a high arrhythmia window?”). It’s a shift that transforms patients from passive recipients of information into active interpreters and decision-makers.

This is not science fiction. Early patient-led communities have already created open-source insulin dosing systems, seizure risk calculators, and predictive dashboards. Regulators and clinicians may not yet have caught up, but the momentum is clear: patients want — and increasingly demand — to bring their own algorithm into the exam room.

Let's dig deeper

1. The Problem Hook: What if we could eliminate waiting for the system to catch up?

What if we could eliminate the months-long lag between how patients feel and how the system responds? For people with chronic illness, life is lived moment to moment: a spike in glucose overnight, a tremor before a seizure, a subtle drift in heart rhythm that precedes an arrhythmia. Yet the traditional clinical model only “checks in” every 3–6 months — or only intervenes after a crisis. This latency is costly: lost quality of life, preventable hospitalizations, and avoidable risks.

Bring Your Own Algorithm (BYOA) challenges that paradigm. It imagines a world where patients download, select, and run algorithmic tools on their own data streams (from wearables, implantables, home devices) to detect emergent risks and assist decisions in real time. Instead of passively trusting external systems, patients become co-pilots of their own health.

2. The AI Magic Question: What if AI could see patterns we can’t?

The real magic lies not in flashy dashboards but in subtle signals. AI can pick up precursors that escape human notice. Examples already emerging:

• In epilepsy, “forecasting” models aim not to say “you will have a seizure,” but to assign real-time risk in the next minutes or hours. A recent “future-guided” deep learning approach improved seizure prediction performance by up to 44.8 % relative to baseline models.
• In Type 1 diabetes, the open-source OpenAPS system (born from patient communities) automates insulin basal dosing by combining CGM (continuous glucose monitor) data and patient-established parameters. It is used in thousands of real-world person-hours.
• New research in epilepsy shows that EEG data and autonomic signals often exhibit multidien (multi-day) and circadian cycles. Algorithms that detect those cycles can forecast seizure risk in longer windows, not just minutes ahead.

These examples show AI bridging the gap between raw signals and personalized alerts. With BYOA, patients could select among forecast models (for seizure, glucose, arrhythmia) that suit their thresholds, tolerances, and personal rhythms.

3. The Impact Test: If this worked perfectly, who would be heroes?

If BYOA works at scale, the heroes will be everyday people, not just technologists.

• A person with drug-resistant epilepsy who gains forewarning of high-risk windows and preempts injury or status epilepticus.
• A parent of a child with brittle diabetes who uses algorithmic dosing to reduce glycemic variability and nocturnal hypoglycemia.
• A heart patient who senses perturbations in arrhythmia risk ahead of overt symptoms and alerts their cardiologist proactively.

Clinicians also gain: they don’t need to parse raw logs of hundreds of days of data; they receive curated alerts from patient-chosen models. Healthcare systems gain fewer preventable admissions, fewer readmissions, and smoother transitions from acute to home settings.

In that world, patients aren’t data consumers — they’re data curators and innovators. They choose which algorithmic models to trust. They test, tune, and iterate. They become the unexpected heroes of precision health.

4. The Reality Check: What’s the simplest version that would still be amazing?

We should not wait for fully autonomous AI systems. The “minimum viable BYOA” is far more modest:

1. A secure marketplace (or library) of vetted algorithms, each documented in transparency (inputs, risk, limitations).
2. A simple UI: patient picks one algorithm to run over their data (e.g. “predict next 30-minute risk of seizure” or “trend glucose variability over next 4 hours”).
3. Alerts delivered when risk crosses a patient-set threshold: “your glucose trend is accelerating upward; consider a small correction now.”
4. Seamless export: patients can choose to share the algorithm’s outputs with their clinician or upload into their EHR.

Even a single alert that is accurate 80 % of the time would transform decision-making, reduce anxiety, and allow preventive action. It doesn’t require perfect models, only better ones than the current blind spots.

Cargo to solve: safety, validation, regulatory guardrails, model drift detection, and clinical integration. But the core user experience is simple: choose, run, act.

5. The Excitement Factor: Would people fight to get access to this?

Yes — and in many cases, they already are. The patient community around diabetes pioneered this ethos with hashtags like #WeAreNotWaiting. People hack insulin pumps, assemble DIY closed loops (OpenAPS, Loop, AndroidAPS) — not because engineers asked them to, but because commercial solutions lagged.

In epilepsy, patients and researchers publish open seizure forecasting trial results, crowdsourcing validation. The appetite is there: those with high stakes won’t passively wait for top-down solutions.

Would people fight to get access? Absolutely. With real algorithms that deliver preventive signals, BYOA becomes less “nice to have” and more a basic right for people managing complex conditions. The energy to adopt — even if imperfect at first — is strong.

Governance & The Way Forward

To avoid chaos, BYOA must be scaffolded by good governance:

• Evaluation & certification: Third-party or community review of algorithm performance, bias, failure modes.
• Interoperability standards: So patient-selected algorithms can plug into EHRs, clinician dashboards, and device APIs.
• Guardrails & transparency: Algorithm provenance, versioning, fallbacks, and patient safety thresholds.
• Clinical collaboration: Clinicians must have visibility (not control) of algorithm outputs; patients control when and what to share.

A Call to Innovators & Patients

BYOA is not a distant fantasy. The building blocks are already in motion — open insulin loops, seizure forecasting prototypes, wearable biometrics. What remains is assembling safe frameworks, marketplaces, clinical pathways, and patient adoption strategies.

We stand at a crossroads: either healthcare continues to demand that patients wait for “approved” models, or we dignify patients as co-creators of health intelligence. The next time someone asks, “Would they fight to get this?” we’ll already know the answer.