Does Bio-objectification Affect Clinical Care and Research Management?

Boeckhout and Douglas (2015) consider the impact of biosample collection for clinical research within a clinical setting.¹ They examine clinical biobanking in the Netherlands between 2008 and 2013 to assess how research practices influence medical care, suggesting that bio-objectification in sample collection challenges the distinctions between research and health care provision.

Biobanking is an essential part of personalized medicine. Biosamples and patient data provide important materials for translational research, and the health care environment is a primary resource. Clinical research in this field is moving away from the search for etiology and causal agents to focus on evaluating risk and devising preventive strategies. For this to take place, researchers need to access large cohorts of material (both biosamples and data) from target populations that include healthy controls. Whereas clinical research could previously use material left over from diagnostic procedures, researchers and clinicians now look prospectively to multiple samples for –omics evaluations.

To meet the demand from the new requirements of personalized medicine, samples gathered in a clinical setting are repurposed for research. Clinical institutions must now maximize biosample value, offering up historical samples such as neonatal blood spot cards, while providing consistency in both sample quality and patient data sets through standard operating procedures and quality controls.

Boeckhout and Douglas thus identify three types of bio-objectification currently active within biobanking practices associated with a clinical care setting.

1. Combined biosampling for clinical care and research purposes: Clinical staff collect blood, tissue and biofluids for research at the same time as taking diagnostic samples.
2. Minimal data set specification: Research protocols dictate the types of samples and data to collect.
3. Identifying the patient as a regular contributor or donor: Workflows and frameworks are constructed to minimize patient burden to allow maximal research integration within routine medical care.

Although these factors are common within modern biobanking, the authors are concerned that they may breach research governance, disrupting the boundaries within biomedical practice that exist to protect the patient.

For example, they suggest that it is important to maintain the patient’s autonomy within the system, ensuring that informed consent is freely given and the right to withdraw is upheld. As procedures and research technology advance, Boeckhout and Douglas state that ethics board oversight is important, especially when introducing collection of more invasive samples, such as cerebrospinal fluid, into the minimal data set required.

The authors also discuss the ethical issues arising from maintaining the flow of information from research to clinical care. They note that governance and oversight can be complicated in clinical institutions since various departments may be involved in administering healthcare to a patient. Within these environments, without central governance and oversight, it can be difficult to assign the responsibility for relaying results, especially for incidental non-diagnostic genomics findings.

In summary, Broeckhout and Douglas suggest that clinical biobanking requires flexible management of research and healthcare provision to avoid the risk of biomedicine turning into an experimental field. They advise encouraging active patient participation and ensuring dynamic consent practices, concluding that governance in biobanking for personalized medicine research needs further exploration.

Reference
1. Boeckhout, M., and Douglas, C.M.W. (2015) “Governing the research-care divide in clinical biobanking: Dutch perspectives,” Life Sciences, Society and Policy, 11(7), doi: 10.1186/s40504-015-0025-z.