A Pharmacist’s Guide to Algorithmic Bias in Clinical Tools

Clinical decision support tools have become familiar parts of Australian pharmacy practice, appearing as interaction warnings, dose calculators, antimicrobial review prompts, renal-function flags and risk alerts. Their apparent objectivity can be misleading. Algorithmic bias is a systematic pattern of errors or assumptions that produces unfair outcomes for particular groups, even when the tool’s developers have no discriminatory intent. Understanding and mitigating algorithmic bias in clinical decision support tools is therefore a patient-safety responsibility, not simply an information-technology concern.

Pharmacists often meet the consequences of these systems at the dispensing bench. A warning may be framed around population averages while failing to account for pregnancy, renal impairment, disability, cultural expectations or medicines obtained outside the prescription record. Australia’s national ePrescribing infrastructure has improved the availability of medication data, but it has also connected pharmacies to datasets shaped by historical prescribing, unequal access to care and fragmented health services. Counteracting bias requires more than trusting the software or dismissing every alert; it requires structured evaluation of the tool, its data, its workflow and its effects on patients.

Where bias enters a clinical tool

Clinical algorithms learn from records such as prescribing histories, diagnoses, pathology results, hospital admissions and dispensing claims. Bias enters when those records contain unequal access to care, outdated prescribing practices or labels that reflect social judgement rather than clinical evidence. Missing information can also be systematically distributed. Rural patients may have fewer pathology results because of distance and limited services, while people who avoid or cannot access conventional healthcare may be absent from the training population. A model can therefore treat absence of data as absence of risk.

Developers and local implementers add further assumptions when they select variables, clean records or set alert thresholds. Postcode may serve as a proxy for socioeconomic conditions, language may be used to estimate adherence, and previous prescribing may be treated as proof that a medicine is suitable. These variables can reproduce inequities hidden in earlier clinical decisions. A model trained mainly on adult data may also perform poorly for children, pregnant patients, people with severe kidney disease or groups poorly represented in research. Aggregate accuracy can conceal these subgroup failures.

Human interaction creates another layer. Automation bias encourages clinicians to accept a reassuring result without independent assessment, while excessive alerts produce alert fatigue and reflexive overrides. Overrides may then enter the data used to improve the tool, teaching the system that certain warnings can be ignored. Local customisation can create further variation by changing thresholds without retesting. Pharmacist review remains essential, but a system that makes unsafe behaviour look reasonable shifts responsibility without removing risk.

Why Australian pharmacy workflows change the risk

Australia’s geography makes local validation particularly important. A patient treated in a major hospital in Sydney or Melbourne may have a detailed electronic record, specialist input and frequent pathology, while a patient in a regional service or a remote Aboriginal community may have a sparse history and delayed results. Algorithms developed in metropolitan settings can interpret that sparse history incorrectly. Access to medicines also varies with distance, transport, income and the availability of a nearby community pharmacy, yet recommendations may be generated without considering whether the patient can realistically obtain the proposed treatment.

The Australian medicines market adds complexity because the Pharmaceutical Benefits Scheme, private prescriptions, hospital medicines and over-the-counter products operate together. People may use subsidised medicines while also purchasing paracetamol, ibuprofen, antihistamines or vitamins for the same condition. Many of these purchases never appear in the clinical record, making an adherence warning or interaction alert incomplete. Electronic repeats can create a similarly misleading picture of reliable access: a prescription history may look continuous even when supply has been interrupted because of cost, stock shortages or a move between services.

Local legal and regulatory settings shape the safeguards available to a pharmacy team. The Therapeutic Goods Act 1989 and associated regulations may apply when clinical software is a medical device, depending on its intended purpose and the claims made about it. The Privacy Act 1988 and Australian Privacy Principles govern personal and health information handled by covered organisations, while state and territory laws and professional codes may also apply. The Australian Government’s AI Ethics Principles provide valuable guidance but are not a substitute for enforceable governance. Teams building a business case can use independent analysis of algorithmic patient-safety risks to frame procurement and safety questions.

Bias across prescribing, dispensing and mental health

Prescribing support systems often assess pain, anticoagulation, antimicrobial use, psychotropic medicines or controlled-drug risk. Their outputs may appear equitable because they use the same threshold for everyone, yet the underlying evidence or prescribing history may represent only some populations. A conservative alert can direct disproportionate monitoring toward people already receiving intensive care, while another group receives false reassurance because its patterns resemble the model’s reference population. False positives create workload and may lead clinicians to bypass the system; false negatives can delay intervention.

Dispensing tools have their own blind spots. Interaction checkers depend on complete medicine lists, and adherence prompts may treat missed doses as irresponsibility rather than a consequence of cost, transport, health literacy or a need to share medicines within a household. Language barriers, intellectual disability and cultural expectations can also affect how a recommendation is received. A warning that appears in technically correct language may fail to be understood or may alienate the patient. Pharmacists can compensate for some of these failures, but repeated over-alerting weakens both safety culture and patient trust.

Mental health, addiction and suicide-prevention tools require especially careful scrutiny. Risk models may draw on crisis referrals, emergency department visits, policing data or previous diagnoses, all of which reflect both clinical need and unequal access to services. Administrative labels can be treated as clinical truth, while missing information about family support, housing, employment, trauma or cultural safety leaves important risks unidentified. False alarms can trigger coercive or stigmatising responses; false reassurance can delay help. Further reading on patient-centred tool evaluation can support the use of lived experience, clinical judgement and clear escalation pathways when automated outputs are used in these settings.

A practical bias audit for pharmacy teams

An audit should begin with the decision the tool influences, the people affected and the harm that could follow. A pharmacist, data or informatics staff member, clinical governance representative and vendor or health information technology contact should examine the same system from different perspectives. Community members, Aboriginal health professionals and mental health consumers can identify missing variables and harmful assumptions that technical review alone may overlook. Testing should use de-identified or appropriately governed data and record the software version, local configuration and patient population.

Map the decision by naming the tool, intended user, eligible patients, expected action and downstream clinical outcome for every alert or recommendation.
Inspect the inputs by checking representativeness, missing data, proxy variables, coding consistency and whether important clinical information exists outside the system.
Measure performance across relevant groups and sites using false-positive rates, false-negative rates, predictive values, alert frequency and time required to act, while avoiding unreliable conclusions from very small samples.
Test the human workflow through shadow mode or simulated cases, examining alert burden, override behaviour, usability, handoffs and the consequences of following the tool incorrectly.
Examine equity and harm by identifying who receives false reassurance, burdensome interventions, delayed treatment, additional costs or stigmatising labels, including people with limited English or health literacy.
Require change control that names a responsible owner, documents vendor updates, establishes review intervals, provides an incident-reporting route and includes criteria for suspending or reverting a tool.

Audit findings should enter the organisation’s clinical risk register rather than remain informal observations. A high alert rate for one group may reflect appropriate clinical need, poor data quality or an unusable threshold; each explanation requires different action. Conversely, few overrides may indicate trust, poor usability or staff disengagement. Governance groups should compare findings with complaints, medication incidents and patient feedback, then communicate what changed. Software updates, new patient populations and altered workflows should trigger renewed validation.

Turn scrutiny into safer pharmacy practice

At the counter, pharmacists should treat an algorithmic output as one input within a broader clinical assessment. Medication reconciliation should include over-the-counter medicines, allergies, pregnancy, renal function, adherence barriers and relevant social circumstances. When an alert conflicts with contemporary guidance or the patient’s circumstances, the pharmacist should follow the evidence, communicate with the prescriber when appropriate and document the reason for acting differently. Transparent override reasons create useful safety data; unstructured clicking does not.

Organisational controls must support those decisions. Procurement should require information about training data, validation populations, known limitations, update cycles and subgroup performance. Local policies should preserve access to clinical review, offer staff training, monitor repeated overrides and provide a non-blaming route for reporting questionable outputs. Patients should know when automated tools have contributed to their care and how they can challenge an outcome. These controls make accountability shared among vendors, health services, digital teams and pharmacists rather than concentrating it at the final check before dispensing.

Start this month with one clinical decision support tool used in your pharmacy: name an audit owner, obtain its validation summary, review a small governed sample, compare alerts and overrides across sites and relevant patient groups, and schedule a follow-up after the next software update. Record the findings, assign each corrective action and take recurring concerns through clinical governance. Choose the tool, name the owner and schedule the first audit before the next dispensing cycle.