Clinical Outcome Assessments (COAs) are designed to capture how treatments affect patients’ symptoms, functioning, and wellbeing. While patient-reported outcomes (PROs) dominate discussion, many COAs also involve clinicians, caregivers, or independent assessors. These observers provide critical evaluations, whether rating mobility, assessing psychiatric status, or guiding structured interviews.

But clinicians and observers are human. Their judgments, behaviours, and expectations can systematically shape results. These clinician- and observer-related biases can creep in through subtle cues, knowledge of treatment allocation, or differences in how interventions are delivered. Like patient-related biases, they do not produce random noise; they skew findings in systematic directions, threatening the validity of clinical evidence.

This article examines four major clinician- and observer-related biases in COAs: assessor bias, interviewer bias, performance bias, and outcome bias. It explores how they arise, their impact on trial outcomes, and strategies to mitigate them.

Assessor Bias (Detection Bias)

Assessor bias occurs when outcome assessors, aware of treatment allocation, unconsciously rate outcomes differently between groups. This is especially problematic for subjective outcomes such as mobility, mood, or quality of life.

• Positive inflation: Assessors who know a patient is receiving the active intervention may interpret ambiguous signs as improvement.

• Negative deflation: Conversely, patients in the control group may be rated less favourably, even with similar behaviours.

Evidence: A meta-analysis found that non-blinded assessors systematically exaggerated treatment effects compared to blinded assessors, particularly in trials of subjective outcomes (ScienceDirect, 2014; Wiley, 2014).

Example: In physiotherapy trials, therapists aware of allocation rated patients as more mobile and functional, even when objective measures showed no difference.

Impact: Exaggerates treatment efficacy, undermining reliability and comparability.

Mitigation:

• Use blinded assessors wherever possible (BMJ, 2011).

• Employ objective measures (e.g., gait sensors, actigraphy) alongside ratings.

• Introduce independent adjudication committees to validate ratings (NIH, 2019).

  
  

Interviewer Bias

Interviewers conducting structured or semi-structured assessments can inadvertently influence patient responses through tone, body language, phrasing, or even demographic characteristics.

• Tone effects: Patients disclose more to warm, empathetic interviewers than to stern or rushed ones.

• Characteristic effects: Gender, age, and perceived authority influence disclosure of sensitive outcomes such as sexual functioning, depression, or substance use.

Evidence: Studies show interviewer-related differences in reported pain and self-efficacy, with responses systematically shaped by interviewer style and characteristics (Oxford Academic, 2022; NIH, 2025).

Example: In a face-to-face survey, older patients underreported pain intensity when interviewed by younger staff, possibly due to generational expectations of stoicism.

Impact: Introduces variability unrelated to treatment—data reflect interviewer style as much as patient condition.

Mitigation:

• Use standardised scripts and neutral phrasing.

• Train interviewers to avoid leading questions or expressive reactions.

• When feasible, allow self-administered questionnaires to reduce interpersonal influence.

Performance Bias

Performance bias arises when study staff treat participants differently depending on treatment allocation, especially in unblinded or open-label trials. This is less about rating outcomes and more about how interventions are delivered.

• Extra encouragement: Staff may unconsciously give more attention, reassurance, or guidance to patients in the treatment arm.

• Reduced support: Control arm participants may receive less attention, indirectly worsening their outcomes.

Evidence: Non-blinded trial staff often behave differently toward participants depending on group assignment, creating confounding effects (NHMRC, 2019; Anju Software, 2020).

Example: In behavioural intervention trials, staff enthusiasm for the “new” program enhanced adherence and outcomes. These are effects that are attributable as much to enthusiasm as to the intervention.

Impact: Inflates differences between arms, making the intervention appear more effective than it truly is.

Mitigation:

• Standardise protocols for staff–participant interactions.

• Blind staff delivering interventions where possible or ensure they interact equally across arms.

• Use fidelity checks and audits to monitor deviations.

Outcome Bias

Outcome bias occurs when judgments about clinical actions are shaped by the eventual patient outcome, rather than by the quality of the decision in context. Although often studied in patient safety, it affects COAs when retrospective evaluations of treatment or care are influenced by results.

• Hindsight distortion: If a patient improves, assessors may retrospectively judge prior treatment decisions as “appropriate,” even if risky at the time.

• Negativity bias: If a patient deteriorates, actions may be judged harshly, regardless of initial rationale.

Evidence: Research shows that both clinicians and laypeople evaluate identical decisions differently depending on whether the patient recovered or declined (BMJ Qual Saf, 2025; University of Cambridge, 2024).

Example: In oncology trials, aggressive interventions may be judged as “good care” if patients survive, but “poor care” if they do not, regardless of the evidence at the decision point.

Impact: Distorts assessments of treatment appropriateness and clinician behaviour, complicating outcome interpretation.

Mitigation:

• Emphasise prospective, predefined criteria for evaluating decisions.

• Train assessors to separate process from outcome when making judgments.

• Use blinded committees to review cases without knowledge of final outcomes.

  
  

Summary

Clinician- and observer-related biases arise not from ill intent but from the reality that humans interpret, interact, and behave in ways that are rarely neutral.

• Assessor bias inflates outcomes when assessors are unblinded.

• Interviewer bias skews patient responses through subtle interpersonal cues.

• Performance bias creates unequal treatment across arms.

• Outcome bias distorts retrospective judgments based on final results.

Together, these biases systematically shape COA data. They can amplify treatment effects, mask harms, or inject interpersonal variability that has nothing to do with the intervention itself.

Implications for COA Design and Interpretation

Addressing clinician- and observer-related biases requires rigorous safeguards:

1. Blinding: Keep assessors, interviewers, and staff unaware of allocation wherever possible (BMJ, 2011; Wiley, 2024).

2. Standardisation: Train staff to follow scripts, apply consistent encouragement, and avoid leading cues.

3. Independent assessment: Use external, blinded adjudicators for subjective outcomes (NIH, 2019).

4. Objective complement: Pair subjective ratings with objective measures (e.g., wearable sensors).

5. Process evaluation: Monitor fidelity of delivery to detect and correct performance bias.

These strategies do not eliminate bias, but they reduce its magnitude and visibility, allowing more accurate estimation of treatment effects.

Conclusion

Clinicians and observers are indispensable to COAs, but their judgments are not free of bias. Assessor, interviewer, performance, and outcome biases systematically shape results, sometimes subtly, sometimes dramatically.

• Non-blinded assessors inflate outcomes.

• Interviewers influence disclosure.

• Staff deliver interventions unevenly.

• Outcomes retroactively colour judgments.

These biases remind us that clinical trials are human enterprises. Recognising them is not about discrediting clinicians, but about designing systems that buffer human influence with blinding, training, standardisation, and objective measures.

Just as patient biases must be acknowledged, so too must clinician and observer biases. Only then can COAs yield data that truly reflect treatment effects, not human expectations or interpersonal dynamics.

Thank you for reading,

Mark Gibson

Clermont-Ferrand, France

September 2025

References

Anju Software (2020). Preventing systemic bias in clinical trials.

BMJ (2011). Bias in clinical trials. The BMJ.

BMJ Quality & Safety (2025). Investigators are human too: outcome bias and perceptions of culpability.

NHMRC (2019). Assessing risk of bias.

NIH (2019). Assessing risk of bias of individual studies in systematic reviews.

NIH (2025). Uncovering potential interviewer-related biases in self-efficacy assessments.

Oxford Academic (2022). Interviewer effects in a survey examining pain intensity.

ScienceDirect (2014). Empirical evidence of observer bias in randomized clinical trials.

University of Cambridge (2024). Outcome bias and perceptions of individual culpability.

Wiley Online Library (2014). Assessment bias in clinical trials.

Wiley Online Library (2024). The impact of blinding on estimated treatment effects in randomized clinical trials.