Digital Biomarkers in Australian Aged Care: Predicting Health Deterioration Before Crisis Occurs

July 24, 2026

Many serious health events in aged care do not begin with a sudden crisis. They begin with small changes that are easy to miss: a slower walking speed, more time spent in bed, reduced appetite, disrupted sleep, fewer social interactions, increasing confusion, subtle changes in speech or a gradual decline in everyday activity.

Digital biomarkers offer a new way to recognise these changes earlier.

A digital biomarker is an objective, measurable indicator of health, function or behaviour captured through digital technology. It may be generated by a wearable device, smartphone, smart-home sensor, connected medical device, voice system, mobility monitor or digital care platform.

The value of digital biomarkers lies in their ability to reveal patterns over time. A single data point may mean very little. A sustained departure from a person’s normal pattern may indicate that support needs are changing before a traditional clinical threshold is reached.

The wider Australia Social Care and Community Services Knowledge Hub explores how data, technology, housing, workforce practice and governance can combine to create more proactive and sustainable models of care.

Digital biomarkers should not replace conversation, observation or clinical judgement. Their purpose is to strengthen early recognition and support timely, proportionate intervention while the older person still has greater choice and more options available.

What Is a Digital Biomarker?

A biomarker is a measurable sign that provides information about a biological, functional or health-related process.

Traditional biomarkers include:

blood pressure;
heart rate;
blood glucose;
body temperature;
oxygen saturation;
weight;
laboratory results;
respiratory rate; and
clinical assessment scores.

Digital biomarkers extend this concept by using technology to measure health, function and behaviour continuously or repeatedly.

Examples may include:

changes in walking speed;
stride length;
balance variation;
time spent moving;
sleep disruption;
frequency of leaving the home;
changes in voice;
typing speed;
phone-use patterns;
social interaction;
meal preparation activity;
bathroom-use patterns;
medication-device use;
heart-rate variability;
night-time movement;
changes in routine;
frequency of falls or near falls;
changes in breathing patterns;
reduced participation in rehabilitation; and
increasing reliance on support.

Digital Biomarkers and Predictive Care

Traditional care systems often respond after deterioration becomes visible through:

a fall;
an emergency department presentation;
a medication error;
acute confusion;
infection;
carer breakdown;
significant weight loss;
hospital admission;
a safeguarding incident; or
sudden loss of independence.

Digital biomarkers can support a more predictive model by identifying the conditions that often precede these events.

For example:

reduced movement may precede deconditioning;
night-time restlessness may precede delirium or falls;
changes in gait may precede a mobility crisis;
declining social activity may precede depression;
increased bathroom use may indicate infection or medication effects;
changes in speech may indicate neurological or cognitive change;
reduced meal preparation may indicate fatigue, pain or declining executive function;
increasing time at home may indicate fear of falling; and
missed medication interactions may indicate worsening cognition or confusion.

The aim is not to predict the future with certainty. It is to identify changing probability early enough for useful action.

From Population Thresholds to Personal Baselines

Many health systems rely on standard thresholds.

Examples include:

a defined blood pressure level;
a set oxygen-saturation threshold;
a particular number of falls;
a standard mobility score;
a fixed weight-loss percentage;
a universal activity target; or
a standard warning score.

These thresholds remain important, but they may miss meaningful change in an individual.

Digital biomarkers allow care teams to compare the person with their own normal pattern.

A person may usually:

walk for twenty minutes each morning;
leave home five days each week;
sleep continuously for six hours;
prepare two meals each day;
use the bathroom once overnight;
respond quickly to messages;
participate in a weekly exercise group; and
maintain a stable walking pace.

A sustained change from this pattern may be more informative than whether the person crosses a general population threshold.

Why Baselines Matter

Individual baselines help avoid two common problems.

Under-recognition occurs when deterioration is missed because the person remains within a broad population range.

Over-escalation occurs when a normal pattern for the person is incorrectly treated as abnormal.

Baselines should be:

established over a meaningful period;
reviewed after major health events;
adjusted following rehabilitation;
interpreted alongside the person’s account;
updated after medication changes;
reviewed when routines change;
sensitive to seasonal variation;
adjusted for travel or family visits;
reviewed following bereavement or major life events; and
used flexibly rather than treated as fixed truth.

Sources of Digital Biomarker Data

Digital biomarker information may come from several technologies.

These include:

smart watches;
fitness trackers;
wearable medical devices;
smartphones;
connected blood-pressure monitors;
continuous glucose monitors;
smart scales;
smart beds;
movement sensors;
door sensors;
smart appliances;
voice assistants;
digital medication systems;
telehealth platforms;
rehabilitation technology;
electronic care records;
remote monitoring platforms;
environmental sensors;
connected hearing devices; and
smart-home systems.

The strongest models do not rely on one device or one measurement. They bring together several signals and interpret them within the person’s wider context.

Mobility as a Digital Biomarker

Mobility is one of the most valuable areas for digital biomarker development.

Relevant measures may include:

walking speed;
stride length;
step symmetry;
turning speed;
postural sway;
number of daily steps;
time spent standing;
frequency of sitting and rising;
movement between rooms;
use of stairs;
distance travelled outside the home;
hesitation before movement;
changes in mobility-aid use;
near-fall patterns; and
reduced participation in usual activities.

Mobility deterioration may reflect:

pain;
infection;
medication effects;
dehydration;
muscle weakness;
fear of falling;
neurological change;
cardiovascular deterioration;
poor footwear;
environmental barriers;
fatigue;
low mood;
reduced confidence; or
early functional decline.

Digital data should therefore trigger investigation rather than an automatic diagnosis.

Gait Change and Falls Risk

Falls may be preceded by subtle changes in gait.

Potential warning signs include:

shorter steps;
uneven pace;
slower turns;
increased shuffling;
longer pauses before standing;
reduced foot clearance;
greater side-to-side movement;
increased reliance on furniture;
hesitation near thresholds or steps;
reduced outdoor walking;
more frequent near falls; and
increased fatigue after short distances.

These patterns may be identified through wearables, smart insoles, movement sensors, rehabilitation systems or connected mobility aids.

Care teams should review whether the change relates to the person, the environment, equipment or the support system.

Operational Scenario One: Predicting a Falls Crisis

Context: An older person living at home uses a wearable activity monitor and several unobtrusive movement sensors. Over ten days, the system identifies slower walking, longer periods in the bedroom and more frequent night-time movement.

Step 1 – Validating the signal: The care coordinator checks device use, connectivity, recent routine changes and whether the pattern is sustained.

Step 2 – Speaking with the person: The person reports dizziness when standing and increasing anxiety about walking to the bathroom at night.

Step 3 – Coordinating assessment: A nurse reviews hydration, blood pressure and medication. An occupational therapist assesses lighting, flooring and the route to the bathroom.

Step 4 – Acting before crisis: Medication timing is adjusted, hydration support is strengthened, night lighting is improved and a temporary increase in home-support visits is introduced.

Step 5 – Monitoring the outcome: The person’s dizziness reduces, walking speed improves and night-time activity returns towards baseline without a fall or hospital attendance.

The digital biomarker does not predict a fall with certainty. It identifies a changing pattern early enough for preventative action.

Activity Patterns as Digital Biomarkers

Daily activity can provide a broad picture of function and wellbeing.

Relevant patterns may include:

time spent active;
time spent sedentary;
movement between rooms;
frequency of leaving the home;
use of the kitchen;
participation in exercise;
time spent in bed;
household-task activity;
community participation;
engagement with rehabilitation;
changes in regular routines;
reduced response to prompts; and
increasing dependence on others.

Reduced activity may indicate:

pain;
infection;
fatigue;
depression;
fear of falling;
breathlessness;
social isolation;
medication effects;
cognitive change;
loss of confidence;
poor weather;
bereavement;
transport problems; or
changes in family support.

Context remains essential. Reduced outdoor activity during a heatwave may be protective rather than concerning.

Sleep as a Digital Biomarker

Sleep can be affected by physical, psychological, environmental and social factors.

Digital systems may estimate:

sleep duration;
sleep interruptions;
time awake overnight;
restlessness;
night-time wandering;
breathing variation;
heart-rate variation;
sleep timing;
daytime sleeping;
bed-entry and bed-exit patterns;
frequency of bathroom visits; and
changes in sleep routine.

Changes may relate to:

pain;
infection;
anxiety;
depression;
medication;
nocturia;
sleep apnoea;
breathlessness;
delirium;
cognitive decline;
environmental noise;
room temperature;
carer disturbance; or
changes in daily activity.

Sleep information should support discussion and assessment rather than produce automatic clinical conclusions.

Night-Time Movement and Emerging Risk

Night-time patterns may reveal changes before they are apparent during daytime visits.

Examples include:

increasing bathroom visits;
frequent bed exits;
long periods standing;
wandering through the home;
repeated kitchen use;
failure to return to bed;
unusually early waking;
movement towards external doors;
reduced sleep duration;
prolonged inactivity after standing; and
changes in normal route or behaviour.

These changes may indicate:

urinary infection;
pain;
delirium;
constipation;
medication effects;
anxiety;
breathing difficulty;
temperature discomfort;
cognitive change;
increased falls risk; or
unmet support needs.

Heart Rate and Heart-Rate Variability

Wearables may record heart rate continuously or at intervals.

Changes may reflect:

infection;
dehydration;
stress;
pain;
arrhythmia;
medication effects;
reduced fitness;
sleep disturbance;
physical exertion;
anxiety;
fever;
cardiovascular deterioration; or
recovery following illness.

Heart-rate variability measures the variation in time between heartbeats. It is being explored as a potential indicator of stress, recovery, autonomic function and changing health.

It should be interpreted cautiously because readings may be affected by:

device quality;
movement;
medication;
fitness;
age;
sleep;
alcohol;
caffeine;
acute illness;
emotional stress; and
sensor contact.

Respiratory Patterns

Digital respiratory biomarkers may include:

breathing rate;
oxygen-saturation trends;
breathing variation during sleep;
cough frequency;
speech interruption caused by breathlessness;
activity-related oxygen change;
night-time breathing disturbance;
changes in exercise tolerance; and
increased recovery time after movement.

These measures may support people living with:

chronic obstructive pulmonary disease;
heart failure;
sleep apnoea;
neuromuscular conditions;
post-infection fatigue;
complex multimorbidity;
recent hospital discharge; or
reduced mobility.

Digital readings should be reviewed alongside symptoms, clinical history and the person’s normal baseline.

Voice as a Digital Biomarker

Voice contains information about physical, neurological, cognitive and emotional health.

Digital systems may analyse changes in:

speech speed;
pause length;
volume;
clarity;
word choice;
breathlessness;
pitch;
response time;
sentence complexity;
hesitation;
slurring;
emotional tone;
repetition; and
conversation duration.

Potential applications include earlier recognition of:

stroke;
respiratory deterioration;
Parkinsonian change;
depression;
cognitive decline;
fatigue;
delirium;
medication effects;
social withdrawal; and
changes in neurological function.

Voice analysis is highly sensitive and should not be introduced without clear consent, transparency and evidence of accuracy.

Speech Change and Urgent Escalation

Some speech changes may require urgent response.

Examples include:

sudden slurring;
difficulty finding words;
new confusion;
inability to form a sentence;
marked breathlessness while speaking;
unexpected weakness in voice;
sudden change in comprehension; and
loss of normal responsiveness.

Digital systems should not delay emergency action where symptoms suggest stroke, acute respiratory distress or another serious event.

Typing and Device Interaction

Changes in how a person uses a smartphone or tablet may provide information about motor, cognitive or emotional health.

Potential indicators include:

slower typing;
increased errors;
difficulty unlocking the device;
reduced messaging;
missed calls;
repeated navigation mistakes;
reduced use of familiar applications;
longer response times;
changes in touch pressure;
repeated forgotten passwords;
unusual contact patterns; and
difficulty completing routine digital tasks.

These changes may reflect:

tremor;
pain;
visual deterioration;
fatigue;
cognitive change;
low mood;
social withdrawal;
acute illness;
medication effects;
loss of confidence; or
simple frustration with technology.

Personal device use is private. Monitoring these patterns requires strong justification and informed consent.

Digital Biomarkers of Social Connection

Social isolation may not always be visible during scheduled care visits.

Potential digital indicators include:

reduced phone calls;
fewer messages;
less time outside the home;
reduced attendance at regular activities;
declining engagement with video calls;
fewer visits detected by agreed smart-home systems;
reduced interaction with community platforms;
changes in speech frequency;
more time spent inactive; and
loss of usual routines.

These indicators should not be treated as proof of loneliness.

A person may choose solitude, communicate through non-digital methods or temporarily change their routine.

The appropriate response is respectful conversation, not automatic intervention.

Digital Biomarkers of Mood and Wellbeing

Emerging systems may identify patterns associated with mood through:

activity level;
sleep;
voice;
social interaction;
app use;
mobility;
response time;
participation in routine activities;
facial expression where video is used; and
changes in self-reported wellbeing.

Potential warning signs may include:

persistent reduction in activity;
withdrawal from usual contact;
significant sleep change;
reduced speech;
loss of interest in routine activities;
increasing time in bed;
reduced self-care;
declining rehabilitation participation; and
changes in eating or meal preparation.

Digital information should support sensitive assessment and access to appropriate support. It should not label a person or make mental health decisions automatically.

Cognitive Change

Digital biomarkers may help identify gradual cognitive change through patterns such as:

repeated missed prompts;
difficulty following familiar routines;
changes in navigation;
unusual night-time activity;
declining device use;
frequent errors in everyday tasks;
changes in speech;
reduced medication adherence;
repeated door opening;
changes in meal preparation;
difficulty responding to calls;
more frequent requests for assistance;
changes in financial or shopping activity; and
reduced community participation.

These indicators may have many explanations, including:

infection;
delirium;
depression;
hearing loss;
vision loss;
fatigue;
medication effects;
pain;
grief;
poor sleep;
stress;
digital exclusion; or
changes in family support.

Digital patterns should therefore lead to comprehensive assessment rather than premature conclusions.

Medication Behaviour as a Digital Biomarker

Connected medication systems may provide information about:

missed prompts;
late medication access;
repeated attempts to open a dispenser;
changes in routine;
failure to acknowledge reminders;
medication accessed at unusual times;
increasing need for support;
refusal patterns;
frequent technical assistance; and
possible confusion about doses.

These patterns may indicate:

cognitive change;
medication side effects;
poor understanding;
visual or dexterity problems;
changes in sleep;
low mood;
acute illness;
loss of routine;
device difficulty; or
intentional choice not to take medication.

A digital system should not assume non-adherence without speaking with the person and reviewing the context.

Nutrition and Meal Preparation

Smart-home and connected-device data may identify changes in:

fridge opening;
kettle use;
cooker use;
meal-delivery interaction;
kitchen movement;
shopping activity;
weight;
meal timing;
fluid intake where recorded; and
use of dining spaces.

Reduced food preparation may indicate:

fatigue;
pain;
depression;
cognitive change;
poor appetite;
infection;
financial difficulty;
swallowing problems;
reduced dexterity;
equipment failure;
loss of confidence;
social isolation; or
temporary change in routine.

Weight as a Longitudinal Digital Biomarker

Connected scales can provide regular weight trends without relying on manual recording.

Weight change may support earlier recognition of:

malnutrition;
fluid retention;
heart failure;
dehydration;
medication effects;
reduced appetite;
swallowing difficulty;
poor dentition;
depression;
acute illness;
recovery following hospital treatment; or
changes in mobility and activity.

Weight should be interpreted alongside:

food intake;
fluid intake;
symptoms;
medication;
oedema;
continence;
clothing;
time of measurement;
scale accuracy;
ability to stand safely; and
the person’s normal pattern.

Bathroom Patterns

Bathroom-related digital biomarkers may include:

frequency of visits;
night-time use;
duration;
changes in usual timing;
repeated short visits;
prolonged inactivity;
increasing urgency;
changes in continence-device data;
movement difficulty; and
failure to return to usual activity.

Changes may relate to:

urinary infection;
constipation;
diabetes;
medication;
prostate problems;
mobility decline;
pain;
cognitive change;
dehydration;
sleep disruption; or
increased falls risk.

Bathroom monitoring is highly sensitive and should use the least intrusive technology capable of supporting the agreed outcome.

Environmental Context

Digital biomarkers should not be interpreted without considering the environment.

Relevant environmental information may include:

indoor temperature;
humidity;
air quality;
lighting;
noise;
weather;
heatwave conditions;
power interruptions;
housing accessibility;
equipment failure;
local transport disruption;
community emergencies;
visitors;
family absence; and
changes in care-worker schedules.

For example, reduced movement may reflect illness, but it may also reflect extreme heat, a broken lift or unsafe outdoor conditions.

Combining Multiple Signals

A single digital biomarker may be weak or ambiguous.

Multiple signals can create a clearer picture.

For example, the combination of:

reduced activity;
increased heart rate;
poor sleep;
reduced kitchen use;
more frequent bathroom visits; and
slower speech

may justify earlier assessment than any one change considered alone.

Multimodal analysis can improve sensitivity, but it also increases complexity, privacy risk and the possibility of incorrect conclusions.

Signal, Pattern and Context

Safe interpretation requires three elements:

Signal

What has changed?

Pattern

Is the change sustained, repeated or linked with other indicators?

Context

What is happening in the person’s health, routine, environment and support network?

Without context, even accurate data may lead to poor decisions.

Thresholds and Predictive Scores

Digital systems may use:

fixed thresholds;
personal baselines;
trend analysis;
combined risk scores;
machine-learning models;
rule-based alerts;
rate-of-change indicators;
anomaly detection;
clinical decision-support tools; and
population comparison.

Providers should understand:

how the score is calculated;
which data sources are used;
which populations were used to develop it;
how often it produces false alerts;
which events it may miss;
how thresholds are adjusted;
whether clinicians can override it;
how the person’s preferences are incorporated;
how the model changes after software updates; and
what action each score should trigger.

Prediction Is Not Certainty

Predictive systems estimate probability.

They cannot guarantee:

that deterioration will occur;
that a fall will happen;
that hospital admission will be prevented;
that an infection is present;
that cognitive decline is developing;
that a person is lonely;
that medication has been taken;
that a normal reading means the person is well; or
that an abnormal pattern has one clear cause.

Responsible care systems use prediction to support judgement, not replace it.

Clinical Interpretation and Human Review

Digital biomarker systems should support professional judgement rather than operate as autonomous decision-makers.

Human review is essential because the same pattern may have several explanations.

For example, reduced activity may indicate:

infection;
pain;
low mood;
fear of falling;
fatigue;
poor weather;
a family visit;
equipment failure;
a disrupted routine;
temporary choice; or
technical loss of data.

Safe interpretation requires:

knowledge of the person’s baseline;
understanding of current health conditions;
awareness of medication changes;
review of symptoms;
consideration of environmental factors;
checking device reliability;
speaking with the person;
clinical escalation where needed;
documentation of the decision; and
review of the outcome.

Who Should Review Digital Biomarker Alerts?

Responsibility should reflect the seriousness and purpose of the information.

Possible reviewers may include:

the older person;
a family member where agreed;
a monitoring centre;
a home-support worker;
a care coordinator;
a registered nurse;
a general practitioner;
an allied health professional;
a pharmacist;
a specialist clinician;
a residential aged care team; or
a multidisciplinary care team.

Providers should define:

which alerts each role can review;
which alerts require clinical interpretation;
which alerts require urgent escalation;
how out-of-hours coverage works;
what happens if the primary reviewer is unavailable;
how alerts are acknowledged;
how decisions are recorded;
when the person is contacted;
when family members are informed; and
how unresolved concerns are escalated.

Response Pathways

A digital biomarker has little value if no reliable response follows.

Response pathways should distinguish between:

technical alerts;
low-priority trend changes;
moderate clinical concerns;
urgent deterioration;
emergency symptoms;
safeguarding concerns;
loss of contact;
device non-use;
family-carer strain; and
system-wide failure.

Possible responses may include:

checking device function;
speaking with the person;
reviewing recent care notes;
arranging a home visit;
contacting a family member;
seeking nursing advice;
requesting a general practitioner review;
arranging allied health assessment;
reviewing medication;
increasing support temporarily;
initiating emergency response;
raising a safeguarding concern; or
changing the monitoring plan.

Operational Scenario Two: Early Recognition of Cognitive Change

Context: An older person living independently uses a connected medication dispenser, smartphone reminders and simple home sensors. Over several weeks, the system records repeated missed medication prompts, reduced kitchen activity and more frequent opening of the front door during the night.

Step 1 – Checking technical causes: The provider confirms that the dispenser, sensors and smartphone are working correctly and that the person has not changed routine intentionally.

Step 2 – Speaking with the person: A care worker notices that the person is more uncertain about the day and has difficulty explaining why the medication was missed.

Step 3 – Arranging assessment: A nurse reviews infection risk, hydration, medication and recent health changes. The general practitioner is contacted for further assessment.

Step 4 – Strengthening support: Temporary medication assistance, additional evening contact and improved night lighting are introduced while investigations continue.

Step 5 – Reviewing longer-term needs: The care plan is updated through supported decision-making, with the person involved in decisions about monitoring, family access and future support.

The digital pattern supports earlier recognition, but the response remains grounded in conversation, clinical assessment and the person’s rights.

Clinical Escalation Thresholds

Thresholds should be specific enough to support consistent action but flexible enough to reflect individual variation.

They may consider:

the magnitude of change;
the speed of change;
the number of affected indicators;
whether symptoms are present;
the person’s clinical history;
recent discharge;
known deterioration pathways;
the consequences of delayed response;
device accuracy;
the person’s normal baseline;
time of day;
availability of support; and
the person’s advance care preferences.

Thresholds should be reviewed after:

hospital admission;
new diagnosis;
medication change;
rehabilitation;
major improvement;
significant deterioration;
change of living environment;
change in family support;
device replacement;
software updates; or
repeated false alerts.

Alert Fatigue

Predictive systems can generate large numbers of alerts, especially when several devices are combined.

Alert fatigue may arise where:

thresholds are too sensitive;
the same change triggers multiple alerts;
personal baselines are poorly configured;
technical faults are interpreted as deterioration;
alerts are not prioritised;
resolved alerts remain active;
workers receive notifications outside their role;
family members receive excessive updates;
the system cannot distinguish urgent from routine changes; or
monitoring continues after it is no longer useful.

Controls may include:

severity categories;
personalised thresholds;
combined alerts;
suppression of known false triggers;
clear ownership;
automatic closure after verified resolution;
periodic threshold review;
removal of low-value measures;
monitoring of response performance;
worker feedback; and
clinical oversight.

False Positives

A false positive occurs when a system identifies a risk that is not present.

Consequences may include:

unnecessary anxiety;
avoidable clinical visits;
ambulance attendance;
increased workload;
family stress;
loss of confidence in the system;
disruption of ordinary life;
unnecessary restriction;
incorrect care-plan changes; and
increased cost.

False positives may be caused by:

poor device fit;
incomplete baseline data;
temporary routine change;
sensor movement;
connectivity problems;
algorithm limitations;
environmental variation;
shared devices;
incorrect user profiles;
software updates; or
overly sensitive thresholds.

False Negatives

A false negative occurs when deterioration is present but the system does not identify it.

This may happen because:

the person is not using the device;
the device is poorly positioned;
data is missing;
the relevant change is not being measured;
the algorithm was not trained on similar people;
the person’s baseline is inaccurate;
the condition progresses without affecting monitored indicators;
the threshold is too high;
connectivity fails;
the system misclassifies the pattern; or
workers assume that no alert means no concern.

People should still be encouraged to report symptoms, and workers should continue ordinary observation and assessment.

Data Quality and Reliability

Digital biomarker analysis depends on reliable data.

Data quality may be affected by:

missing readings;
incorrect timestamps;
poor connectivity;
battery failure;
device removal;
incorrect fitting;
sensor drift;
software faults;
duplicate data;
incorrect identity matching;
manual entry errors;
changes in device model;
inconsistent use;
environmental interference;
shared equipment; and
unrecorded changes in routine.

Providers should establish controls for:

device validation;
baseline completeness;
missing-data alerts;
identity verification;
calibration where required;
wear-time monitoring;
manual confirmation;
software-version control;
data reconciliation;
fault reporting;
quality audits; and
clinical review of unexpected patterns.

Missing Data Is Also Information

A sudden absence of data may itself be significant.

Missing data may indicate:

device failure;
battery depletion;
connectivity loss;
the person removing the device;
distress or discomfort;
hospital admission;
a change in routine;
declining cognition;
loss of digital confidence;
family interference;
technical disengagement; or
intentional withdrawal of consent.

The response should begin by establishing the reason rather than automatically treating missing data as non-compliance.

Algorithmic Bias

Predictive models may perform differently across groups.

Bias may arise where development data underrepresents:

older people with multiple conditions;
Aboriginal and Torres Strait Islander peoples;
people with disability;
people with darker skin tones;
people using mobility aids;
people living in rural and remote areas;
people with cognitive impairment;
people from culturally and linguistically diverse communities;
people with atypical movement patterns;
people with sensory impairment;
people with limited digital access; or
people receiving complex end-of-life care.

Bias can lead to:

missed deterioration;
excessive false alerts;
inappropriate risk classification;
unequal access to intervention;
over-monitoring of some groups;
under-support of others;
loss of trust;
inaccurate funding decisions; and
widening health inequalities.

Questions for Technology Suppliers

Providers should ask suppliers:

Which populations were represented in development and validation?
How many older people were included?
Were people with disability represented?
Was the system tested across different skin tones?
How does it perform for people using mobility aids?
What are the false-positive and false-negative rates?
How does performance vary by age, sex, location or condition?
How are missing data handled?
How often is the model updated?
Can providers see why an alert was generated?
Can clinicians override the system?
How are bias concerns investigated?
Will model changes be communicated before release?
Can historical performance be audited?
What independent evidence supports the claims?

Explainability

Workers and clinicians should be able to understand why a predictive alert has been generated.

Useful explanations may include:

which indicators changed;
how far they moved from baseline;
how long the change persisted;
which combination triggered the alert;
how confident the system is;
whether data is missing;
which thresholds were applied;
what action is recommended;
what alternative explanations may exist; and
which information should be checked manually.

A risk score without explanation can encourage blind acceptance or complete rejection.

Automation Bias

Automation bias occurs when people place too much confidence in a digital recommendation because it appears objective or sophisticated.

Examples include:

ignoring symptoms because the risk score is low;
escalating unnecessarily because the score is high;
failing to question poor-quality data;
assuming the algorithm is more accurate than the person;
overlooking cultural or environmental context;
continuing ineffective monitoring because the platform recommends it;
using predictions to reduce human contact; or
treating an alert as a diagnosis.

Controls should include:

training in system limitations;
clinical override;
symptom-led escalation;
audit of decisions;
review of disagreements between people and systems;
clear documentation of professional judgement;
independent validation; and
regular analysis of outcomes.

Consent and Informed Choice

Digital biomarker monitoring may reveal more than the person expects.

A movement sensor introduced for falls prevention may also show:

sleep patterns;
bathroom use;
visitors;
time spent in bed;
when the person leaves home;
reduced meal preparation;
changes in routine;
social activity; and
possible health deterioration.

Consent should therefore explain:

which data is collected;
what patterns may be inferred;
how often monitoring occurs;
who reviews the information;
which alerts may be generated;
who may be contacted;
whether family members have access;
how long information is retained;
whether the model learns from the data;
what happens if consent is withdrawn;
which alternatives are available; and
what the technology cannot guarantee.

Ongoing Consent

Consent should be reviewed when:

new sensors are introduced;
new data sources are combined;
a new algorithm is activated;
location data is added;
family access changes;
the system begins making predictions;
the person’s cognition changes;
the supplier changes;
data is used for research;
monitoring becomes more intensive;
the purpose changes; or
the person expresses discomfort.

Supported Decision-Making

Some people may need support to understand predictive monitoring.

Helpful approaches include:

plain-language explanations;
visual examples;
demonstrating the sensors;
showing a sample alert;
explaining possible responses;
offering a time-limited trial;
using preferred-language information;
involving a trusted supporter;
explaining non-digital alternatives;
checking understanding over time;
allowing selected functions to be paused; and
recording preferences accessibly.

Privacy and Data Minimisation

Digital biomarker systems can create detailed profiles of private life.

Providers should collect only what is necessary for the agreed purpose.

Data-minimisation questions include:

Does this information support a defined care outcome?
Will anyone act on it?
Could a less intrusive measure achieve the same result?
Does monitoring need to be continuous?
Does raw data need to be retained?
Can information be summarised instead?
Does precise location need to be recorded?
Who genuinely needs access?
How long should information be stored?
Can the person pause monitoring?
Can historical information be deleted?
Does the supplier use data for product development?
Is de-identification effective?
Will data be shared with third parties?

Secondary Use of Data

Digital biomarker information may be valuable for:

service improvement;
population planning;
research;
product development;
commissioning;
risk modelling;
workforce planning;
quality assurance;
funding analysis; and
public health.

Secondary use requires clear governance.

Questions should include:

Was the person informed?
Is additional consent required?
Can the data be re-identified?
Who benefits from the use?
Will findings return value to participants?
Can commercial partners access the information?
How are communities represented?
How long will data be retained?
Can the person opt out?
Who approves research use?
How is Aboriginal data sovereignty respected?

Cyber Security

Digital biomarker systems may connect wearables, home sensors, mobile applications, cloud platforms, care records and clinical dashboards.

Cyber risks may include:

weak passwords;
shared accounts;
unpatched devices;
insecure home networks;
lost phones;
unauthorised family access;
supplier remote access;
compromised cloud platforms;
insecure application programming interfaces;
malicious software;
phishing;
data interception;
outdated operating systems;
continued staff access after leaving; and
manipulation of sensor data.

Controls should include:

strong authentication;
role-based access;
encryption;
device inventories;
secure updates;
access reviews;
supplier-security assurance;
monitoring for unusual activity;
secure data export;
incident-response planning;
remote locking or deletion;
segmentation of systems;
backup arrangements; and
secure disposal.

Interoperability

Digital biomarker information may need to move between:

home-support providers;
residential aged care;
general practice;
hospitals;
pharmacy;
allied health;
specialist services;
monitoring centres;
care coordinators;
family carers;
community health services; and
emergency responders.

Poor interoperability may result in:

multiple dashboards;
duplicate data entry;
conflicting alerts;
missed information;
manual copying;
delayed escalation;
unclear ownership;
lost historical trends;
increased workload;
poor transition between providers; and
difficulty evaluating outcomes.

Integration With Care Planning

Digital biomarker information should influence care only through accountable care-planning processes.

Care plans should clarify:

which indicators are monitored;
the purpose of monitoring;
the person’s baseline;
the agreed thresholds;
who reviews alerts;
how the person is contacted;
which actions may follow;
how family members are involved;
what happens outside working hours;
how consent is recorded;
how false alerts are managed;
how the system is reviewed;
when monitoring should stop; and
which non-digital supports remain in place.

Workforce Capability

Digital biomarker programmes require more than technical installation.

Workers may need to:

explain monitoring;
support informed consent;
fit devices;
check sensors;
identify missing data;
review trends;
recognise urgent symptoms;
interpret alerts within role boundaries;
document decisions;
contact clinicians;
support family understanding;
recognise privacy concerns;
report faults;
challenge inaccurate recommendations;
support withdrawal from monitoring; and
contribute to evaluation.

Training Requirements

Training may include:

the purpose of digital biomarkers;
device limitations;
baseline interpretation;
alert categories;
symptom-led escalation;
clinical boundaries;
consent;
privacy;
data quality;
algorithmic bias;
automation bias;
technical troubleshooting;
cyber security;
safeguarding;
incident reporting;
business continuity;
supported decision-making;
cultural safety; and
documentation.

Competence should be assessed through realistic scenarios, not assumed from attendance alone.

Role Redesign

Predictive care may change how workers allocate time.

Potential changes include:

more proactive contact;
earlier multidisciplinary review;
targeted home visits;
greater remote monitoring;
new digital support roles;
more data interpretation;
less routine observation where safe;
faster escalation;
greater responsibility for validating alerts;
new clinical coordination functions; and
increased need for technical support.

Technology should not simply add dashboard work to already stretched teams.

Implementation should remove low-value duplication and ensure that sufficient time exists to act on important signals.

Family and Unpaid Carers

Family members may support predictive care by:

helping with device use;
responding to agreed alerts;
providing contextual information;
supporting appointments;
recognising routine changes;
reporting technical faults;
encouraging rehabilitation;
supporting digital confidence; and
participating in review.

However, family involvement can become unsafe where:

responsibility is transferred without agreement;
alerts are excessive;
family members lack clinical knowledge;
location tracking becomes intrusive;
monitoring increases anxiety;
one relative receives information against the person’s wishes;
there is no backup when the family member is unavailable;
family interpretation conflicts with clinical advice; or
technology is used to control the person’s choices.

Safeguarding

Digital biomarker systems can reveal or create safeguarding risks.

Potential concerns include:

covert monitoring;
unauthorised location tracking;
family members controlling access to data;
ignoring alerts deliberately;
removing sensors to conceal neglect;
using data to pressure the person;
financial exploitation through subscriptions;
technology-enabled stalking;
withholding non-digital support;
falsifying device-use records;
sharing passwords;
unauthorised research use;
discriminatory risk scoring; and
using predictions to justify unnecessary restriction.

Safeguarding procedures should recognise digital abuse and provide confidential routes for raising concerns.

Positive Risk Enablement

Digital biomarkers can support positive risk-taking where they help a person remain active, independent and connected with proportionate safeguards.

Examples include:

continuing to live alone;
walking outdoors;
using public transport;
returning home after hospital treatment;
managing a long-term condition;
reducing restrictive supervision;
participating in rehabilitation;
travelling;
returning to community activity; and
reducing dependence on family carers.

The Positive Risk-Taking Planner can support providers to balance autonomy, foreseeable risk, early-warning information, agreed safeguards and contingency planning.

The purpose of prediction should be to preserve opportunity, not to create a more restrictive life.

Digital Biomarkers in Residential Aged Care

Residential settings may use digital biomarkers to support:

falls prevention;
night-time monitoring;
infection recognition;
mobility review;
nutrition;
hydration;
sleep;
pressure-injury prevention;
medication review;
rehabilitation;
behavioural support;
clinical deterioration;
staff prioritisation; and
emergency response.

Shared environments create additional governance challenges around:

resident identification;
sensor overlap;
visitor privacy;
device sharing;
alert volume;
staff response capacity;
cleaning;
handover;
night staffing;
data integration;
consent;
room changes;
temporary residents; and
system-wide outages.

Digital Biomarkers in Home Support

Home-support models may use digital biomarkers to support:

ageing in place;
post-discharge recovery;
falls prevention;
rehabilitation;
medication management;
carer support;
early recognition of deterioration;
reduced unnecessary travel;
remote clinical input;
heatwave response;
rural service delivery; and
better targeting of visits.

Home implementation should consider:

internet access;
power reliability;
housing design;
privacy of other household members;
pets;
visitors;
shared devices;
family access;
charging;
technical support;
emergency response distance;
repair time;
affordability; and
non-digital fallback.

Rural and Remote Care

Digital biomarkers may help reduce geographic barriers by enabling:

remote monitoring;
earlier specialist advice;
fewer avoidable journeys;
support after hospital discharge;
rehabilitation follow-up;
local workforce prioritisation;
heat-risk monitoring;
better coordination with distant services;
earlier identification of decline; and
support for people remaining in community.

However, rural implementation may be weakened by:

poor mobile coverage;
unstable power;
long repair times;
limited technical support;
small workforce capacity;
distance from emergency services;
high equipment costs;
limited supplier presence;
extreme weather; and
lack of culturally appropriate implementation.

Aboriginal and Torres Strait Islander Data Governance

Digital biomarker programmes involving Aboriginal and Torres Strait Islander peoples should be developed through partnership with communities and Aboriginal community-controlled organisations.

Important considerations include:

community-defined priorities;
cultural safety;
data sovereignty;
local ownership of decisions;
connection to Country;
family and kinship roles;
language;
historical mistrust of surveillance;
who can access information;
where data is stored;
how findings are interpreted;
whether benefits return to community;
local technical capability;
community-controlled governance; and
the right to reject monitoring.

Technology should strengthen local control and capability rather than extract information without meaningful benefit.

Culturally and Linguistically Diverse Communities

Digital biomarker systems should account for:

preferred language;
health literacy;
communication style;
family roles;
privacy expectations;
cultural attitudes to monitoring;
religious routines;
gender preferences;
different patterns of social activity;
community participation;
digital access;
device stigma;
translation quality; and
trusted routes for support.

An algorithm may misinterpret culturally normal behaviour as withdrawal, risk or deterioration if context is not considered.

Digital Exclusion

Predictive care may widen inequalities where access depends on:

a compatible smartphone;
stable internet;
digital confidence;
English literacy;
financial capacity;
reliable electricity;
family support;
accessible devices;
technical assistance;
urban infrastructure; or
ability to manage passwords and applications.

Inclusive implementation may require:

loan devices;
provider-funded connectivity;
standalone sensors;
home installation;
face-to-face training;
multilingual support;
community digital workers;
simple interfaces;
non-digital alternatives;
replacement programmes;
local repair arrangements; and
ongoing practical assistance.

Procurement and Evidence

Procurement should test whether digital biomarker products deliver reliable and meaningful value.

Questions should include:

What outcome is the system intended to improve?
What evidence supports the biomarker?
Was the product tested with older people?
What are the known limitations?
How accurate are the sensors?
What are the false-alert rates?
How are personal baselines created?
Can users understand why an alert occurred?
Can clinicians override recommendations?
How is bias monitored?
What connectivity is required?
Can the system operate offline?
How are updates controlled?
What data is collected?
Where is information stored?
Can data be exported?
What technical support is available?
How quickly are faults resolved?
What happens if the supplier fails?
What is the full lifetime cost?

Total Cost of Ownership

The full cost may include:

sensors;
wearables;
installation;
mobile connectivity;
software licences;
cloud storage;
monitoring services;
clinical review;
integration;
training;
technical support;
maintenance;
replacement devices;
batteries;
cyber-security assurance;
data-governance work;
staff response time;
evaluation;
contract exit; and
transition to another supplier.

A low device price may be misleading if the system creates high subscription, staffing or integration costs.

Supplier Lock-In

Predictive care platforms may create dependency where:

data cannot be exported;
algorithms are proprietary;
devices work only with one platform;
historic baselines cannot be transferred;
subscription costs rise;
integration is restricted;
support depends on overseas teams;
contracts are difficult to terminate;
models cannot be independently audited;
updates are imposed automatically; or
derived data remains controlled by the supplier.

Contracts should include data portability, audit rights, transition support, secure deletion, model-change notification and practical exit arrangements.

Governance and Executive Accountability

Digital biomarker programmes require clear accountability across clinical, operational, digital, information-governance and safeguarding functions.

Boards and executive teams should understand:

which digital biomarkers are in use;
which people are being monitored;
which decisions are influenced by predictive data;
who reviews alerts;
how clinical escalation works;
which suppliers process information;
how consent is managed;
how bias is assessed;
how incidents are investigated;
how continuity is maintained during outages;
how outcomes are measured; and
when technologies should be withdrawn.

Responsibility may be shared across the provider, technology supplier, monitoring centre, clinician, care coordinator, family member and older person. These responsibilities should be documented rather than assumed.

A supplier may provide the platform, but the provider remains responsible for how digital biomarker information is used within care, risk and clinical decision-making.

Risk Classification

Not every digital biomarker system requires the same level of oversight.

Risk classification should consider:

whether the system influences clinical decisions;
whether alerts may trigger emergency response;
whether the model predicts serious deterioration;
whether location or intimate activity is monitored;
whether failure could cause immediate harm;
whether the person depends on the system while living alone;
whether the technology is used after hospital discharge;
whether information is combined across several sources;
whether the person can recognise system failure;
whether the model is independently validated;
whether a safe manual alternative remains available; and
whether the system affects access to services or funding.

Higher-risk systems may require:

formal clinical approval;
documented inclusion and exclusion criteria;
enhanced consent;
independent validation;
algorithmic-impact assessment;
clear clinical override;
frequent review;
tested escalation pathways;
supplier audit rights;
board-level reporting;
business-continuity testing; and
specific withdrawal thresholds.

Digital Biomarker Risk Registers

Relevant risks may include:

missed deterioration;
false alerts;
incorrect prediction;
automation bias;
poor-quality baseline data;
missing data;
device failure;
connectivity loss;
battery depletion;
misidentification of the person;
algorithmic bias;
unexplained model changes;
privacy breach;
covert monitoring;
unauthorised family access;
inappropriate clinical reliance;
failure to act on alerts;
alert fatigue;
supplier failure;
loss of historical baselines;
cyber attack;
inequitable access;
continuing monitoring after consent is withdrawn;
using information beyond the original purpose;
withholding non-digital support; and
poor integration with care planning.

Each risk should have a named owner, current controls, review date, indicator and escalation threshold.

Board Assurance Questions

Useful questions include:

Which outcomes are digital biomarkers intended to improve?
How many people are being monitored?
What proportion have current informed consent?
How many alerts are generated?
How many alerts lead to meaningful action?
What are the false-positive and false-negative rates?
How many urgent alerts are missed?
How quickly are alerts reviewed?
How often is data unavailable?
Which groups experience poorer system performance?
How many people stop using the technology?
What incidents and near misses have occurred?
How often are clinical staff overriding recommendations?
Are workers confident in interpreting alerts?
Do older people understand how predictions are made?
Are families carrying unplanned responsibility?
How is supplier performance monitored?
What cyber-security weaknesses remain?
Are services becoming more proactive?
Are hospital admissions, falls or crises reducing?
Are inequalities improving or widening?
What is the full cost of the programme?
Which systems should be withdrawn?

The Governance Maturity Assessment can help providers examine whether leadership, risk ownership, clinical oversight, information governance, supplier assurance and improvement systems are mature enough for predictive care.

Quality and Performance Dashboards

A digital biomarker dashboard should combine technical, clinical, operational, equity and person-centred measures.

Potential indicators include:

number of active users;
number of active devices and sensors;
percentage of users with current consent;
data completeness;
missing-data frequency;
connectivity failures;
battery failures;
device faults;
alert volume;
alerts by severity;
false-positive rate;
false-negative rate;
missed-alert rate;
average review time;
average response time;
clinical escalations;
emergency responses;
incidents and near misses;
complaints;
withdrawn consent;
device abandonment;
worker competency;
family-carer impact;
falls;
hospital presentations;
hospital admissions;
length of hospital stay;
mobility outcomes;
rehabilitation participation;
medication outcomes;
user confidence;
equity by geography and population group;
supplier performance;
cost per user;
cost per meaningful intervention; and
avoidable crisis reduction.

The Quality Dashboard Builder can support providers to create balanced assurance across safety, effectiveness, experience, workforce, technology and equity.

Leading and Lagging Indicators

Lagging indicators show that deterioration or system failure has already occurred.

Examples include:

hospital admission after a missed alert;
injury following unrecognised mobility decline;
medication harm;
delayed treatment of infection;
privacy breach;
cyber incident;
complaint about intrusive monitoring;
family-carer breakdown;
incorrect restriction based on prediction;
supplier outage causing loss of monitoring; and
inequitable outcomes between groups.

Leading indicators may reveal emerging weakness before harm occurs.

Examples include:

increasing missing data;
rising false-alert rates;
declining device use;
longer alert-review times;
unresolved software faults;
increasing manual workarounds;
overdue consent reviews;
workers unable to explain risk scores;
repeated family complaints;
rising differences in performance across population groups;
unreviewed model updates;
declining confidence in the system;
high levels of clinical override;
increasing supplier-response delays;
greater alert volume without more useful intervention; and
technology being used outside its approved purpose.

Strong governance uses leading indicators to intervene before technical, clinical or operational drift causes harm.

Incident Reporting

Digital biomarker incidents should be reported even where no injury occurs.

Examples include:

a deterioration alert not being transmitted;
a risk score being linked to the wrong person;
a system generating repeated inaccurate predictions;
a family member receiving information without permission;
a software update changing thresholds without notice;
a worker relying on a normal score despite serious symptoms;
an urgent alert not being reviewed;
a model performing poorly for a particular population group;
missing data being mistaken for normal activity;
a person being restricted unnecessarily because of a prediction;
a supplier using information beyond the agreed purpose;
monitoring continuing after consent is withdrawn;
a platform outage affecting multiple users;
cyber compromise of sensor data;
clinical action being delayed while staff investigate a dashboard; and
a person becoming distressed by predictive monitoring.

Incident Investigation

Investigations should examine the whole system rather than focusing only on the person who last reviewed the alert.

Review should consider:

device function;
sensor accuracy;
baseline quality;
missing data;
algorithm design;
thresholds;
model updates;
alert presentation;
worker training;
clinical workload;
response pathways;
care planning;
consent;
family involvement;
handover;
interoperability;
supplier performance;
cyber security;
business continuity;
population bias;
professional override; and
governance decisions.

Where one incident reveals a shared algorithm, device or supplier weakness, the provider should review all affected users immediately.

Operational Scenario Three: Respiratory Deterioration Missed by a Predictive Platform

Context: An older person with chronic respiratory disease is monitored through a wearable and connected oxygen-saturation device. The platform continues to display a low-risk score despite increasing breathlessness reported during home-support visits.

Step 1 – Responding to symptoms: The worker follows the clinical escalation pathway and seeks urgent nursing review rather than relying on the low score.

Step 2 – Investigating the discrepancy: The nurse finds that several overnight readings were missing and that the model treated the absence of data as neutral.

Step 3 – Assessing wider exposure: Other users on the same platform are reviewed to identify whether missing data could suppress risk scores elsewhere.

Step 4 – Correcting the system: The provider requires missing data to trigger a technical warning and prevents low-risk classifications where data completeness falls below an agreed threshold.

Step 5 – Strengthening practice: Training is updated to reinforce that symptoms and professional judgement override predictive scores.

The incident demonstrates why no-alert or low-risk status should never be treated as proof that the person is well.

Business Continuity

Digital biomarker systems should be included in continuity and emergency planning.

Plans should address:

power outages;
mobile-network failure;
internet loss;
cloud-platform outage;
cyber attack;
software failure;
device recall;
supplier closure;
loss of monitoring-centre capacity;
extreme heat;
bushfire;
flood;
cyclone;
evacuation;
staff shortages;
failure of data integration;
loss of historical baselines;
unavailable replacement devices; and
simultaneous disruption affecting many users.

Providers should know:

which people depend most heavily on predictive monitoring;
which systems support critical decisions;
how outages will be detected;
who initiates welfare checks;
which clinical reviews should continue manually;
how alternative communication will work;
how high-risk people will be prioritised;
how families will be informed;
how temporary paper or telephone processes will operate;
how data will be reconciled after recovery;
how safe restoration will be confirmed; and
how lessons will be recorded.

Testing Continuity Arrangements

Exercises may include:

loss of all wearable data overnight;
a predictive platform becoming unavailable for forty-eight hours;
a cyber incident affecting alert accuracy;
failure of a monitoring centre;
a model update producing excessive urgent alerts;
a supplier ceasing operation;
a regional network outage;
loss of historical baseline data;
an evacuation affecting multiple monitored users;
extreme heat disrupting power and connectivity; and
simultaneous staff shortages and system failure.

Testing should confirm that alternative arrangements are practical, understood and sufficiently resourced.

Clinical Governance

Clinical governance should define:

which digital biomarkers are approved for clinical use;
which measures remain advisory;
which readings require confirmation;
who interprets alerts;
which thresholds trigger action;
how symptoms override system recommendations;
how baselines are established;
how medication changes are reflected;
how data enters the clinical record;
how responsibility transfers between services;
how abnormal trends are followed up;
how model changes are validated;
how disagreement between clinicians and algorithms is handled;
how incidents are reviewed; and
when monitoring should stop.

Evaluation and Outcomes

Evaluation should test whether digital biomarkers create meaningful improvement rather than simply more information.

Questions may include:

Was deterioration identified earlier?
Did the person receive timely assessment?
Were falls reduced?
Were avoidable hospital presentations reduced?
Did hospital discharge become safer?
Did rehabilitation improve?
Did independence increase?
Did the person feel more secure?
Did the person feel intruded upon?
Did family-carer stress reduce?
Were alerts manageable?
Did workers find the information useful?
Were interventions proportionate?
Did false alerts create unnecessary activity?
Did any groups experience poorer performance?
Were benefits sustained?
Did costs remain affordable?
Was human contact preserved?
Did the technology prevent crisis or merely document it earlier?

The older person’s own experience should remain central to evaluation.

Co-Design

Older people, families, workers, clinicians and communities should help shape digital biomarker programmes.

Co-design may include:

choosing which outcomes matter;
deciding which data is acceptable;
testing consent materials;
reviewing alert wording;
setting privacy preferences;
designing response pathways;
testing dashboards;
reviewing cultural implications;
identifying non-digital alternatives;
participating in supplier selection;
defining meaningful outcomes;
reviewing complaints;
testing rural functionality;
assessing whether predictions are understandable; and
deciding when monitoring should stop.

People who decline predictive monitoring should also be heard. Refusal may reveal concerns about privacy, surveillance, trust, cultural safety or the burden of technology.

A Phased Implementation Roadmap

Phase One – Define the Outcome

Identify the specific deterioration, independence, rehabilitation, safety or care-coordination outcome the programme is intended to improve.

Phase Two – Assess the Evidence

Review whether the proposed digital biomarker is valid, relevant and tested with comparable older people.

Phase Three – Co-Design the Model

Involve older people, families, workers, clinicians and communities in defining acceptable monitoring and response.

Phase Four – Classify Risk

Assess the consequences of missed alerts, false predictions, bias, privacy loss, system failure and inappropriate reliance.

Phase Five – Establish Baselines

Collect sufficient information to understand the person’s usual pattern while accounting for health, routine and environment.

Phase Six – Build Consent and Data Controls

Define what is collected, what may be inferred, who can access it, how long it is retained and how the person can withdraw.

Phase Seven – Design the Response Pathway

Clarify who reviews alerts, expected response times, escalation thresholds and out-of-hours arrangements.

Phase Eight – Train and Test

Prepare workers, clinicians, older people and families, then test devices, alerts, integration and continuity arrangements.

Phase Nine – Pilot and Evaluate

Use a limited pilot with diverse participants, clear outcomes, stop criteria and close review of unintended effects.

Phase Ten – Scale Responsibly

Expand only where the organisation can sustain clinical review, response capacity, supplier assurance, affordability, equity and continuous governance.

Common Pitfalls

Common weaknesses include:

collecting data before defining the outcome;
assuming that prediction provides certainty;
using population thresholds without personal baselines;
failing to account for missing data;
allowing alerts to accumulate without clear ownership;
treating low-risk scores as proof that the person is well;
ignoring false-positive and false-negative rates;
failing to explain how alerts are generated;
overlooking algorithmic bias;
weak consent for inferred information;
using data beyond the original purpose;
placing unplanned responsibility on families;
adding dashboards without workforce capacity;
using prediction to reduce human contact;
failing to integrate information into care planning;
poor cyber-security controls;
weak supplier exit arrangements;
excluding people without digital access;
underestimating rural implementation challenges;
failing to test business continuity;
measuring alert volume instead of outcomes;
continuing ineffective monitoring;
using prediction to justify unnecessary restriction; and
failing to listen when the person disagrees with the data.

The Future of Digital Biomarkers in Australian Aged Care

Digital biomarkers are likely to become more continuous, multimodal and personalised.

Future developments may include:

more accurate gait and falls-risk analysis;
voice-based detection of respiratory and neurological change;
smart clothing that measures movement and physiology;
contactless sleep and breathing assessment;
hydration and nutrition biomarkers;
predictive infection monitoring;
more advanced cognitive-change indicators;
integrated home, wearable and clinical data;
real-time personal baselines;
adaptive rehabilitation monitoring;
consumer-controlled data permissions;
federated learning that reduces central data collection;
greater use of digital twins;
earlier identification of carer strain;
more local processing within devices;
improved rural and satellite connectivity;
better interoperability with health and aged care records;
explainable predictive models;
stronger bias-monitoring tools; and
community-controlled data governance.

The strongest future systems will not simply identify that risk is increasing. They will help explain why, suggest proportionate options and support the person and care team to decide what should happen next.

Protecting Human Values

Digital biomarker programmes should strengthen values that remain central to aged care.

These include:

dignity;
choice;
privacy;
trust;
cultural safety;
independence;
ordinary risk;
human contact;
compassion;
control over personal information;
the right to challenge a prediction;
the right to refuse monitoring;
the right to receive non-digital support; and
the right to be treated as more than a data profile.

Responsible innovation should ask not only what can be predicted, but whether prediction is necessary, fair, understandable and beneficial.

Conclusion

Digital biomarkers could help Australian aged care move from reactive crisis management towards earlier, more personalised and preventive support.

They can reveal changes in mobility, sleep, activity, cognition, speech, medication behaviour, nutrition and social participation before deterioration becomes obvious through a fall, hospital admission or loss of independence.

These benefits are not automatic.

Digital biomarkers can also create false reassurance, excessive alerts, privacy intrusion, algorithmic bias, workforce burden and inappropriate restriction if they are implemented without strong governance.

Effective implementation requires:

clear person-centred outcomes;
valid and reliable measures;
personal baselines;
human interpretation;
symptom-led escalation;
informed and ongoing consent;
data minimisation;
clinical governance;
algorithmic transparency;
bias monitoring;
cyber security;
workforce competence;
family-role clarity;
supplier assurance;
business continuity;
equitable access;
co-design;
outcome evaluation; and
accountable leadership.

The future of predictive aged care should not be judged by how accurately systems classify risk in isolation. It should be judged by whether older people receive earlier, kinder and more proportionate support while retaining choice, privacy, dignity and control over their own lives.