How to Decide Which Behavioral Signals Are Worth Collecting at All

In the increasingly digital landscape of healthcare and beyond, organizations face a pressing challenge: determining which behavioral signals to collect, interpret, and act upon. Whether it's a National Institutes of Health (NIH) study adopting patient portal metrics or a company like MrQ applying behavioral insights to gaming platforms for responsible engagement, the question remains consistent — what signals truly matter, and how do we balance utility with respect for privacy and governance?

Understanding Behavioral Signals in Context

Behavioral signals are subtle indicators derived from user actions, clicks, pauses, or patterns in digital interactions that reveal underlying risks or health states. For example, within a remote monitoring system, multiple missed readings over days might hint at worsening adherence, while in a patient portal, repeated visits to educational resources could signal patient engagement or confusion. But these signals aren’t isolated blips; they emerge gradually through patterns that are more informative than any single event.

Behavioral Risk Appears Gradually, Not Instantaneously

One of the first tenets in deciding which signals to collect is recognizing that behavioral risk is often a slow, cumulative phenomenon rather than a sudden event. Consider MrQ, a responsible gambling platform that uses behavior tracking not to punish a single bet but to identify patterns such as increasing bet size or frequency, missed breaks, or escalating time spent gambling. This gradual appearance allows more precise interventions and fewer false alarms.

Similarly, in healthcare, the National Institutes of Health has emphasized the importance of longitudinal monitoring. For instance, interaction data from patient portals that track login frequency, message response times, and information searches can expose early signs of disengagement or health literacy challenges before patient outcomes deteriorate.

Prioritizing Patterns Over Single Events

Humans and machines alike crave clarity, but it's dangerous to draw conclusions from isolated actions. This principle is well established in regulated platforms, especially in industries like gambling or finance, where early warning systems must avoid spurious flags that generate anxiety or bind resources unnecessarily.

• Signals vs. Stories: As a healthcare UX and safety consultant, I maintain a running list of "signals vs stories" — separating raw data from interpretations. A missed appointment is a signal; assuming deliberate non-compliance without context is a story. Patterns reveal intent and risk.
• Aggregated Data Helps Avoid Misinterpretation: For example, multiple late-night logins combined with significant account changes might reliably represent distress or risk, unlike a one-off unusual action.
• Contextualizing Behavior: For remote monitoring systems, a missed reading might be caused by technical issues rather than patient choice—patterns allow us to filter those out.

Purpose Limitation and Data Minimization: The Foundation of Ethical Signal Collection

In the drive to better understand users and patients, it's tempting to collect vast troves of data "just in case" it becomes useful. However, the twin principles of purpose limitation and data minimization remind us to collect only what is relevant, adequate, and limited to the purposes defined in advance.

Purpose limitation means being explicit about why each type of behavioral data is collected. For instance, if a patient portal logs user clicks, the purpose might be to optimize user experience or identify points of confusion—not to surveil or judge patients. Any deviation undermines trust and violates governance norms.

Data minimization complements this by encouraging organizations to only collect signals they are prepared to use responsibly. Collecting excessive data adds complexity, increases privacy risks, and can dilute the focus on signals that truly matter.

Governance: Ensuring Accountability in Behavioral Signal Use

Governance frameworks are essential to oversee the responsible collection, interpretation, and action on behavioral data. This includes:

1. Human Oversight: Avoid shipping AI-driven features without clear human review paths. Machines can flag patterns but should not replace clinical judgment or ethical decision-making.
2. Evidence Standards: Use validated signals backed by research rather than solely correlations or convenience. The NIH conducts extensive validation for behavioral measures in its studies, setting a high bar.
3. Privacy by Design: Ensure data collection is proportionate and transparent, with clear consent and opt-out mechanisms where possible.
4. Regular Audits: Continuously monitor the impact of data collection on user outcomes and privacy to course-correct when harms or inefficiencies emerge.

Learning from Gambling Regulation: A Model for Healthcare?

Regulated track patient engagement metrics gambling platforms like MrQ demonstrate how early warning behavioral signals can be used effectively and ethically. These platforms monitor not just frequency but changes, context, and patterns to offer timely supportive interventions before issues escalate. Healthcare can adopt similar models for remote monitoring systems — focusing less on flagging single non-compliant acts (“missed reading”) and more on trends of disengagement where support can make a difference.. Exactly.

Case Study: The National Institutes of Health Patient Portal Behavior Monitoring

Consider an NIH-funded research project aiming to improve patient portal engagement among older adults. Rather than recording every click indiscriminately, researchers focused on specific behavioral signals:

• Frequency and timing of portal logins to evaluate consistency
• Navigation paths to identify pages that caused confusion or drop-off
• Message response times as an indicator of patient-provider communication effectiveness

Crucially, these signals were chosen for their direct relation to health outcomes and patient experience, respecting data minimization and purpose limitation. Analysis emphasized longitudinal patterns rather than snapshots, with clinical teams reviewing flagged cases to determine if patients needed additional support or education.

Practical Framework: Steps to Decide Which Behavioral Signals to Collect

Step Description Example 1. Define Clear Purpose Specify the goal of collecting the behavioral data—improving experience, early warning, research, etc. Tracking login frequency to identify disengagement risk in patient portals. 2. Assess Data Minimization Ensure data collected is the minimum required to fulfill purpose. Collecting time-stamped login events instead of full clickstreams. 3. Validate Behavioral Signals Rely on evidence-based signals linked to meaningful outcomes. Using research-backed patterns of missed monitoring correlated with hospitalization risk. 4. Prioritize Pattern Recognition Focus on trends rather than isolated incidents. Flagging repeated missed blood glucose readings over a week rather than a single missed test. 5. Embed Governance Controls Implement oversight, human review paths, and privacy mechanisms. Review flagged patient portal usage patterns by clinical staff before intervention. 6. Monitor and Reassess Regularly audit the effectiveness and privacy implications of data collection. Annual review of remote monitoring adherence flags and impact on patient trust.

Conclusion: Striking the Balance Between Insight and Intrusion

Collecting behavioral signals is a powerful tool that, done right, can transform patient engagement, risk identification, and support. However, it carries inherent challenges and responsibilities. Prioritizing purpose limitation, data minimization, and rigorous governance ensures that behavioral monitoring respects privacy, maintains trust, and generates actionable insights.

Learning from regulated industries like gambling and incorporating evidence-based practices championed by institutions like the National Institutes of Health allows healthcare providers and developers to steer clear of pitfalls such as misinterpretation and overreach.

Ultimately, the signals we choose to collect must support a compassionate, ethical, and effective digital health ecosystem — where patterns illuminate paths to support rather than punishment, and where patient autonomy and privacy lead every step of the journey.