The Monitoring Gap In Recovery

Outpatient and residential recovery programs are accountable for outcomes they cannot observe between visits. Periodic urinalysis confirms substance use only after metabolites are present, and self-report depends on the patient disclosing a struggle they may be actively concealing. By the time either signal fires, the relapse process is well underway. Clinicians describe relapse as a trajectory rather than an event: sleep disruption, withdrawal from routine, changes in movement and speech, and erosion of the daily behavioral rhythm that recovery had rebuilt. These precursors are observable in behavior and physiology, but no widely deployed tool watches for them continuously, and the tools that could would have to retain exactly the kind of intimate behavioral record that recovery patients, and the law, most want protected.

The biological identity layer disclosed in United States Patent Application 19/647,395 was built to maintain a continuity baseline for an individual and to detect deviation from it without storing raw signal. That is precisely the shape of the recovery monitoring problem, which is why the invention applies here as an enabling implementation rather than as a metaphor.

How The Invention Maps Onto Recovery

The application reuses the disclosed primitives directly. Four of them carry the clinical work.

The trust slope is the disclosed mechanism by which identity accumulates as a continuity record over time rather than being asserted from a single enrollment. In the recovery deployment, the same accumulated record is the patient's behavioral and physiological baseline: the typical daily rhythm, the typical movement and interaction dynamics, the typical voice characteristics, and the temporal coupling among them under stable recovery.

Stable sketching and biological hashing reduce each captured signal to a sketch and a hash through locality-sensitive hashing, with domain separation and salt rotation, so that the maintained state is a continuity model and never a recoverable recording. The system can tell that today's pattern departs from the baseline without ever holding a replayable trace of what the patient did.

Non-diagnostic state inference, disclosed as a byproduct of the same continuity validation, classifies deviation from the individual's own baseline into operationally defined categories such as elevated stress, fatigue, and impairment. In recovery, a sustained multi-dimensional deviation consistent with the patient's own pre-relapse history becomes a relapse-risk indication. Critically, the disclosed boundary is preserved: the system does not measure blood alcohol content, does not diagnose substance use disorder, and does not assess mental health. It reports that the patient has departed from their own established normal, and nothing more.

Privacy-governed disclosure means the indication is released only through the disclosed tiered credential structure. A counselor credential, a prescriber credential, and the patient themselves can each be authorized for different views, and the disclosure decision is enforced at the primitive level rather than asserted by a downstream policy that could be bypassed.

What The System Actually Reports

The output is deliberately narrow. The system emits a deviation classification grounded exclusively in the individual's own continuity baseline: a magnitude (how far the current pattern departs), a pattern (which behavioral and physiological features are deviating and in what combination), dynamics (whether the deviation is abrupt or gradual, sustained or transient), and context (whether it is explained by a known factor such as travel, illness, or a schedule change). A deviation that is large, multi-dimensional, sustained, and unexplained by context is the one that matters clinically.

Because the classification model is individualized, it calibrates to the specific patient's pre-relapse signature rather than to a population norm. One patient's relapse trajectory may present as gait and sleep degradation; another's as voice and interaction changes. The model learns each patient's own deviations rather than imposing a single template. This individualization is a disclosed property of the state-inference mechanism, not an addition invented for this use case.

Deployment Embodiments

The application supports a range of deployments rather than a single configuration:

  • Outpatient continuity monitoring, in which ambient signals from a phone and a wearable feed the trust slope between clinic visits, and a sustained deviation raises an indication routed to the patient's counselor under the patient's standing consent.
  • Patient-facing self-monitoring, in which the only credential authorized to see the indication is the patient's own, giving the individual an early private signal without any third-party disclosure.
  • Medication-assisted treatment support, in which the indication modulates contact cadence (for example, prompting an outreach call) without ever functioning as a diagnostic gate on prescribing decisions, which remain with the clinician.
  • Quorum-governed escalation, in which the disclosed quorum recovery and tiered-credential primitives require more than one authorized party to release a higher-sensitivity view, so that no single actor can unilaterally surface a patient's recovery state.
  • Cross-modal fusion deployments, in which movement, voice, and interaction-rhythm channels are fused through the disclosed cross-modal fusion so that a relapse indication rests on coherent deviation across modalities rather than a single noisy channel.

A skilled implementer can build any of these on the disclosed primitives: capture signals, reduce them to stable sketches and hashes, accumulate the trust slope, run deviation detection against the individualized baseline, classify non-diagnostically, and gate disclosure through the tiered credential structure.

Why The Privacy Posture Is Architectural

The reason this application is defensible under 42 CFR Part 2 is that the protection is a property of the data the system holds, not a promise layered on top of it. Raw behavioral and physiological observations are reduced to stable sketches and biological hashes and are never retained in recoverable form, so there is no raw recovery record to subpoena, breach, or improperly disclose from the maintained state. Salt rotation and domain separation in the hashing mean that the continuity record built for recovery monitoring cannot be cross-linked against a record built for any other purpose. Disclosure is governed at the primitive level by tiered credentials, so the question of who may learn that a patient is deviating is answered by the architecture rather than by an administrator's discretion. The non-diagnostic boundary keeps the output on the correct side of the line between a monitoring signal and a medical determination, which matters for both clinical liability and regulatory classification.

This is the inverse of a conventional monitoring database, which retains intimate raw records and then tries to protect them with access controls. Here there is no protected raw record to leak, because the disclosed primitives never create one.

Disclosure Scope

The technology underlying this application, comprising the trust-slope continuity record, biological hashing with stable sketches under domain separation and salt rotation, the individualized continuity baseline, multi-dimensional deviation detection and non-diagnostic state classification, cross-modal fusion, tiered-credential privacy-governed disclosure, and quorum recovery, is disclosed in United States Patent Application 19/647,395. This article describes an addiction recovery monitoring application of that disclosed technology. The domain framing, the clinical workflow, and the deployment scenarios are application context; every claimed capability of the underlying system traces to the cited disclosure. The scope extends to recovery monitoring deployments that detect relapse precursors as deviation from an individual's own continuity baseline, retain no raw biometric observations, keep the output non-diagnostic, and govern disclosure at the primitive level, and does not extend to any embodiment that stores raw biometric recordings, classifies against population norms, or emits a clinical or diagnostic determination.