The Development of the Biobank Collections Valuator as an Automated Tool to Determine the Noneconomic Value of Sample Collections

Abstract

Biobankers rely on their experience, supplemented with a variety of tools, to help establish and sustain their operations. These tools support operations, cost determination, quality management, and governance. Costing tools have often been used to determine the economic value of a single specimen or an entire collection, with the purpose of allowing researchers to recover costs when providing access to those resources. Until recently, biobank managers have focused on deriving sample value based solely on cost-model analyses. We propose an alternative way to value collections using a web-based, automated tool for biobankers to determine the noneconomic value of biospecimen collections. The tool supports fit-for-purpose determinations for collections using common attributes and defined criteria to facilitate broader sample utility, sharing, and overall sustainability in operations.

Introduction

With the advancement of basic, clinical, and epidemiological research findings and the rapid speed at which drug and device technologies are approved to combat serious illnesses, access to quality specimens should not be the rate-limiting step. Over the past decade, biobankers have recognized an opportunity to make biospecimens and related annotated data more accessible to the scientific community. Several specimen locators and catalogue tools have been implemented within government, academia, and many industries to support exposure of collections to the wider scientific community.^1,2 Unfortunately, biospecimen collections are still not being used to their full potential worldwide. ³ Utilization rates for biospecimens may be influenced by a variety of factors, including poor quality, an insufficient amount of the appropriate type of biomaterial, a lack of relevant data, limited informed consent, regulations, or access to collections unavailable to external researchers. Global efforts to mitigate the lack of access to biospecimen and data include the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles, as well as mandates from financers and biobank funders.^4–8 Based on the variety of factors that are being experienced globally, limitations of access are not the full reason for the lack of utilization of collections. This issue is the impetus to create a value tool. This article examines the methodology and input gathered in the creation of this value tool. The primary aim of the tool is determining the noneconomic value of banked biospecimen collections, from the perspective of the biobank management and researcher.

Biospecimens may be collected through a variety of means, such as through classical clinical trials research; as excess materials from clinical service; as de novo prospective collections for research; or through recruitment of participant donors in population sciences research. How biospecimens will be used initially for original research aims is as important as how they will be used for future research. Decisions for additional research use and sharing of biospecimens are often in the hands of the original researcher with smaller collections, versus governance committees with oversight of larger collections. These requests for access may be as simple as an email or as complex as a detailed application of scientific justification of proposed use. Biospecimen and data sharing often take into account ethical considerations but may not include fit-for-purpose of the collection within the calculus. Ideally, starting a fit-for-purpose collection requires thoughtful consideration in terms of processes and documentation for shareability, quality processing, storage, and secondary use.

Furthermore, biospecimen utilization can be affected by budgeting and funding (e.g., ensuring sustainability), disaster readiness, the types of biomaterials to be preserved, accreditation levels, and the quality management of biospecimens and data. In many biobanks, the director or custodian may also participate in the development of research protocols, which involve the management of informed consent for biospecimen and data collection. Even details such as biospecimen labeling, types of associated data, and any regulatory or legal limitations must be considered when planning biospecimen collections for sharing.

Financial, operational, and social components are the three pillars that contribute to the sustainability in a biobank. ⁹ Fortunately, the literature is replete with useful information for starting and operating a sustainable biobank, and particular subject matter expertise is frequently accessible through networking with local and global like-minded organizations.^10–14 Successful operations and sustainability of a biobank rely upon continuous oversight and awareness of the value of the biospecimen collections and the integrity of their associated data. Only recently has a limited discussion of the value of the biospecimen collections, or a biobank, through different lenses, been included in sustainability discussions beyond cost modeling.^15–17

Traditionally, the valuation of specimen collections has been based on qualitative or quantitative criteria suited to common activities in repository operations (e.g., site selection, collection at site, tube-preservative selection, administrative, infrastructure, labeling, requisitions, and project management), to derive monetary costs associated with the intake, processing, storing, and distributing of specimens.^18–21 While this assessment is often necessary for determining start-up and ongoing resources for the biobank from the perspective of the funders, monetary value of collections leaves out other fundamental attributes in their valuation. We previously proposed that for successful management of biobanks and future usability of the specimens, a noneconomic evaluation of sample value would be an important and useful tool during operations and maintenance of collections.^22,23 Providing a mechanism to examine scientific or noneconomic value of specimen collections is highly relevant to the biobankers and their organizations, as well as the scientific community that base their research on high-quality specimens and data.

Biobankers and/or researchers involved in the collection of the biospecimens know more in-depth details about the intrinsic nature of their collections and the associated attributes, which are part of the overall value and quality of a given specimen collection. Biobanking experts also generally agree that certain attributes of collections in a biobank are fundamental for sharing valuable specimens to further research. These attributes can be divided into operation-relevant, collection-relevant, and specimen-relevant subsets, for ease of consideration (Fig. 1). ²⁴ A collective set of such defining value criteria was one of our goals, as they are rarely assembled and maintained. The ISBER Best Practices definition for fit-for-(intended) purpose was used when referring to sample collections and data to ensure common perspectives during usability/feasibility testing. ¹²

FIG. 1.

Metadata of value-related attributes grouped by their relevant Biobanking activities.

The value criteria selected for the tool are relevant to the operations, collection, and biospecimen attributes and developed as elements that biobankers would find useful to support functional evaluation. The design of the tool promotes enhanced characterization of specimen collections in biobanks which supports collaboration and data sharing. FAIR principles provide the framework to expose biobanked collections and data to others. The ranked results with the annotation (i.e., associated data, collection methods, quality measures, etc.) from using the value tool, based on the attributes of the collections, complements FAIR principles to help researchers identify fit-for-purpose and trusted collections for their studies. The goal of this project was to develop a freely available, web-based value tool, now called the Biobank Collections Valuator (BCV), to provide noneconomic valuations that allow for operations management and ultimately, access to fit-for-purpose collections.

Tool Development and Refinement

First-generation version (EXCEL)

We undertook the development of an automated web-based tool to determine the noneconomic valuation of biobank collections. Throughout the tool’s evolution, we maintained an extensive review of the literature, which was supplemented with input from key opinion leaders, representative of the government, academic, commercial, and clinical research biobanking fields. Evidence-based biospecimen and collection attributes were identified as common across multiple types of biobanks, although we mainly focused on academic-based research biobanks managing human biospecimens. Our initial efforts determined which of these attributes contributed to the noneconomic value of biospecimen collections and to what degree of importance they may have within the operations of a typical research biobank (Table 1).

Table 1.

Common Criteria and Their Ranges Included in the First Generation (Excel) Version of the Sample Value Calculator

Criteria	Value range (lowest–highest)
Consenting	1—Consent not performed and/or subjects cannot be reconsented for broad use; 5—Subject’s consent restricts broad use; 10—Subjects are broadly consented
Access	1—Access limited to single entity (institution/owner); 5—Access limited to few entities; 10—Open access through network
Storage	1—Storage conditions not appropriate; 10—Storage conditions are appropriate
Replicates	1—Replicate samples do not exist; 5—Samples can be re-established; 10—Replicate samples exist
Annotation (data)	1—Samples not annotated or poorly annotated; 5—Samples have limited annotation; 10—Samples are extensively annotated
Deidentification/Anonymity	1—Samples need to be completely anonymized; 5—Some patient data are available; 10—Samples do not need to be anonymized and patient data are accessible
Quality check	1—Sample set quality measures unknown; 5—Sample set has limited quality measures; 10—Sample set quality is measured for fit-for-purpose use
Published research or reproducibility	1—Sample set not involved in previous research; 5—Sample set involved in limited research projects; 10—Sample set involved in extensive research projects
Recollection	1—Resources not available for recollection of existing consented subjects or consenting of new subjects; 10—Resources available for recollection of existing consented subjects or consenting of new subjects
Serial collections	1—Samples and data from a single timepoint; 5—Samples from single timepoint with longitudinal data; 10—Serial collections and longitudinal data

These criteria were used to develop the first set of ordered attributes specific to banked collections, based on impact and importance, from empirical data and published literature. We created a decision tree using the attributes from Table 1 with value scoring and weighting assigned at each decision step to guide the user (i.e., biobanker) through the first-generation tool. As part of the logic pathway, the tool used questions pertinent to collection attributes, which assigned a score with the response at each step. The decision tree algorithm then calculated a final numeric score based on the collection being evaluated by the biobanker. The scoring system used arbitrarily selected values per criterion (i.e., 1, 5, and 10), where 1 represented the collection with the lowest value, and 10 represented the biospecimen collection with the highest perceived value to the evaluator. Essentially, the higher the final calculated score, the more valuable the collection [i.e., biospecimens and data fit-for-(intended) purpose]. Using the three scoring levels allowed a spread or range to apply a noneconomic value to varied biobank collections based on the user’s input. The evaluator could add comments in the tool at each step to document the rationale for their answer choices.

This first-generation version of the value calculator tool used a composite decision tree logic on an Excel platform with embedded macros. We alpha tested the tool to make sure that the macros were performing correctly on any computer. The tool was provided to beta testers (beta1) who were recruited within the biobanking field, encompassing various expertise in academia, government, and industry. The testers had current experience with many biospecimen types, data, and biobank infrastructures (e.g., population biobanks, genetic, clinical research biorepositories, single and multiple studies, etc.). Each tester signed a ‘pledge’ or information waiver, to provide confidential feedback about the tool without sharing information and the tool with others. The tool is intended to be used over time, so that a future pass through the calculator for the same collection may yield a different total score, based on any changes to collection-specific information used as the rationale for the answer choices. Biobankers are able to compare the noneconomic value of similar collections, if the same scoring subjectivity is applied in the tool to these collections.

Results from the beta1 testing were presented to a larger cross-section of biobankers during a virtual 2021 ISBER Annual Meeting workshop. ²² The participants of the workshop represented international and domestic biobanks from industry, government, and academic sectors, each funded through varied financial sources for sustainability. These biobankers also managed multiple types of biobanks with biospecimens from human population-based, single or multiple diseases, environmental, and nonhuman collections. In addition, to showcase the use of the tool and seek feedback from workshop participants, the presentation also included two hypothetical case studies of biospecimen collections as demonstrations of the stepwise logic of the tool. Comments from the beta testers and workshop participants provided a basis to further refine the criteria used in the tool. Additional input on the revised features of the value calculator was sought during the 2022 ISBER Annual Meeting, including a roundtable discussion and a poster describing the operations-, collections-, and specimen-relevant metadata/attributes that are critical factors in successful management of biobanks.^23,24 These metadata elements are ideally stored or linked to the Biobank-Laboratory Information Management System and are critical for managing biobanks through the valuation assessment.

Refined version (biobank collections valuator)

The latest and current refinement of the value tool, now called the Biobank Collections Valuator (BCV), was optimized in the Qualtrics (https://www.qualtrics.com/) cloud-based survey platform. This platform provides flexibility with expanded tool logic using a robust skip pattern flow and enhanced practicality for the end-user. The BCV includes ten collection attributes, increased from an original seven, and also includes a larger spread of scoring criteria at each step (1, 3, 5, and 7) with a total possible valuation score of 70 (Table 2 and Fig. 2). This 4-level scoring system provided a spread of responses from users while discouraging central tendency.

Table 2.

Collection Attributes and Their Associated Criteria Used in the Biobank Collections Valuator Scoring Logic Subject to Beta 2 Testing

Attribute and definition	Criteria value elements and score
Ability to share samples • To what degree samples from the collection are available for internal or external use	1—(No) Can participants be recruited for a new collection? 3—(Access is limited) Are similar or processed samples available at the same repository or another repository? (possible split collection?) 5—(Yes) I have a retrospective collection I am willing to share within my organization 7—(Yes) I have a retrospective collection I am willing to share outside my organization
Biospecimen collection quality and fit for analytical purpose 25–27 (specimen quality) • The activity, expression, or concentration of an analyte or a group of analytes is determined by using a validated method •To be fit for analysis, samples must be collected and processed correctly from the appropriate patient and then transported and stored at the designated conditions	1—Specimens not collected/preserved correctly. Existing collection has no preanalytical testing or documentation 3—Specimens collected/preserved correctly. Existing collection has no preanalytical testing or documentation 5—Specimens collected/preserved correctly. Existing collection has limited preanalytical testing or documentation 7—Specimens collected/preserved correctly. Collection is fit for purpose per biospecimen research standards; preanalytical variations are tested; evaluation of QC markers performed; SOPs on file
Biospecimen collection quality and fit for analytical purpose12,28–30 (data quality) • Documentation of preanalytical variations between moment of collection to sample analysis • Specimen integrity and patient identification have been maintained • To what extent clinical and/or phenotypic data are available	1—Level of specimen annotation not suitable for research or of commercial value (no usable patient data or pre-analytical-processing data), no chain of custody 3—Limited suitability, partial specimen annotation, limited regarding patient data AND pre-analytical-processing data, limited chain of custody 5—Adequate suitability, specimen annotation includes more robust patient data OR pre-analytical-processing data, but not both 7—Level of specimen annotation is robust, suitable for research or personalized medicine; and specimen is of commercial value, full chain of custody history available, publications documented
Does sample data quality meet research need? 31–33 • Standardized data structure allows sample type, patient details, disease status and relevant clinical data from different biobanks to be compared and integrated • To what degree does sample data align with user expectations/needs considering factors like accuracy, completeness, consistency and timeliness	1—Data not fit for purpose 3—Few applicable data standards 5—Data standards meet the minimum standards 7—Broad fit for purpose, data standards are consistent with best practices
What degree of sharing is allowed by the informed consent? 34–39 • Legal review and adherence to HIPAA and Common Rule where applicable (9 45C.F.R. § 46.116 and 45C.F.R. § 164.508, respectively) plus sufficient inventory determine when samples can be shared or not shared	1—Informed consent and/or ethics review not documented or available; degree of sharing is unknown 3—Known restrictions from original consent are documented; (study) specific consent obtained only for immediate research; forbids any future research study, separate consent needed for new proposed research that meets applicable regulations/laws 5—Partially restricted (modified) consent allows for use in immediate research and any future research that is directly or indirectly related to the specimens, consent is waived by ethics board for specimens used under certain conditions 7—Broad consent allows for unspecified future research; broad sharing is acceptable
If sharing is allowed, to what degree do samples and data need to be (de) identified, coded or anonymized?37,40	1—Ethical and/or legal requirements and safeguards for specimen and data deidentification are not identified or in place for sharing/access 3—Ethical and/or legal requirements from majority of countries are restrictive for sample and data deidentification under limited sharing situations 5—Ethical and/or legal requirements from a few countries are considered for sample and data deidentification under particular sharing situations 7—Ethical and/or legal requirements from various countries are considered for sample and data deidentification under BROAD sharing situations
Have samples been stored appropriately based upon type and intended use?41,42 • Proper storage conditions need to be selected to reduce analyte degradation and preservation of biomarkers • Adherence to sample stability limits as determined by empirical research and/or method validation activities • Samples protected from physical damage (whether accidental or intentional) and a record of such risk or impact maintained and evaluated by the quality program	1—Does not meet basic considerations for long-term storage: conditions for existing samples are not known or known to be not suitable based on industry standards for solution composition and biospecimen type, not compliant with best practices 3—Meets a few sample storage requirements; stored at optimal condition, no temperature profile or record of suboptimal conditions during storage 5—Meets many of the best practices for storage of specimens and oversight for specimen quality: stored at optimal condition, long-term storage conditions monitored 7—Meets standards and best practices for storage of specimens and maintaining quality: long-term storage conditions monitored and documented, compliance with regulatory requirements for specimen storage and quality surveillance
What level of quality measures have been performed?42,43	1—Quality control systems not implemented, sample quality unknown or not reliable for downstream analysis 3—Sample quality suitable for limited downstream analytics 5—Sample quality suitable for most downstream analytics 7—Sample quality is suitable for all downstream analytics
Are samples scarce in current collections? 44 • New collections of specimens may be initiated due to scientific interest of a particular disease population (refer to attribute “Ability to Share Samples”)	Yes/No
Are samples rare due to specific disease occurrence?44,45	Yes/No

SOPs, standard operating procedures; QC, quality control.

FIG. 2.

An example of step-wise skip logic, scoring and decision progression through the BCV. BCV, Biobank Collections Valuator.

The BCV generates a valuation summary as a PDF file that displays each question in the decision logic along with the responses selected, free-text details the user may have added during valuation to identify the specific collection and to support their responses, as well as the final valuation score. A user may take multiple passes through the tool, generating a report for each collection evaluated. The tool is constructed in such a way that no individual responses or personal data are stored once the report is finalized and sent by email to the user. Once the valuation is completed, the user is prompted to complete a voluntary survey to provide feedback used for continuous improvement of the tool.

Beta2 testers were invited and provided with a hyperlink for the BCV tool and a set of instructions. Prior to gaining access to the BCV, the testers signed the pledge to keep the information confidential. After using the tool, beta2 testers sent their generated values along with their feedback to the team for review. This review provided additional information to allow optimization of the BCV.

The tool was submitted for the Office of Management and Budget (OMB) Paperwork Reduction Act (PRA) review and received an exemption (# PRA-I-1373) based on the 21st Century Cures Act. The PRA policy was created to avoid overburdening the public with federally sponsored data collections. The Paperwork Reduction Act (PRA) of 1995 requires that U.S. federal government agencies obtain OMB approval before requesting or collecting most types of information from the public. The PRA limited the systematic asking of questions (i.e., survey) and gathering of feedback from up to 10 customers/beta testers, per version of the tool. With the cleared PRA exemption, the tool is now open for wider access.

Results and Discussion

During the stages of development of the BCV, we collected feedback on all aspects of the tool, including relevant criteria, ordering of questions, scoring spread between answers, usability, and feasibility. Two phases of beta testing were completed in the development of the BCV. Beta testers were biobankers representative of global hospital, academia, government, and commercial health sectors. Their input was used to make valuable improvements in the tool and to collect the necessary granular data based on the user’s experience. The assessments from the distinct groups of biobankers during beta testing greatly improved the tool’s decision logic in accommodating the heterogeneity among biobank research collections. Overall feedback provided a broad consensus that this type of tool was a very helpful contribution to the operations of a biobank.

Early alpha and beta testing on the Excel platform elucidated the definitions to be used in the tool (Table 3). During the development of the tool, some attributes and definitions did not carry through to the current version of the BCV and associated glossary. With ongoing feedback of the tool, some attributes will be considered for inclusion with future versions of BCV. The glossary of terminology relevant to biobanks was especially important when adding context to the tool’s questions, reducing ambiguity, and facilitating step logic to focus user responses. When appropriate, we opted to use definitions consistent with the ISBER Best Practices (4th and then 5th editions).^12,46 In later versions of the tool, we clarified any definitions used in the glossary, within pop-up text, as needed.

Table 3.

The Glossary of Terminology Used in the First Generation Tool and Biobank Collections Valuator

Term	Definition
Access	Specimen access may be limited to investigator or institutional use only or widely accessible through a federated network. 46
Annotation	Explanatory information associated with a biospecimen. Extensively annotated samples include metadata and phenotypic data. 28
Consenting	A decision to participate in research, taken by a competent individual who has received the necessary information; who has adequately understood the information; and who, after considering the information, has arrived at a decision without having been subjected to coercion, undue influence or inducement, or intimidation. Informed consent may allow for broad use of patient biospecimens during or after the original consenting process.
De-identifying or De-linking	Involves the use of unique code(s) to link specimens and data to donors. The repository or a third-party intermediary may act as an “honest broker” and hold the link between the codes and donor specimens and data. Researchers, receiving the specimens, receive the coded specimen(s). Regulations regarding de-identification may vary in different areas and investigators are encouraged to review local, state, and national/federal regulations and laws. 46
Fit for purpose	Suitable; appropriate, of a necessary standard, for its intended use. 46 5th ed: The specimens and/or associated data meet the criteria associated with the use and are deemed applicable and appropriate for that intended use by the end-user. 12
Metadata	Data that provides context or additional information about other data.
Phenotypic data	Data consisting of clinical information regarding patients’ disease, symptoms, relevant demographic data, such as age, ethnicity and sex. This type of information is collected and stored by patient registries and biobanks.
Published Research or Reproducibility	When specimens are used for multiple research projects and results are reproducible, they are more valuable than specimens that have not been studied as extensively.
Quality check	Biospecimens have undergone preanalytical documentation of quality parameters to facilitate improved quality research and to standardize information for reporting experimental results.
Rare	Biospecimens from subjects with a clinical disease with a low prevalence in the general population (e.g., orphan diseases) 44
Recollection	The ability to collect additional specimens from the same patients in the original cohort or consenting of new patients to collect similar specimens compared to the original cohort.
Scarce	Biospecimens that are deficient in quantity or number compared with demand (e.g., difficult to arrange due to ethical or legal reasons, cultural research or motivation by researchers) 44
Serial collections	A study where repeated collections are taken over time for the same individuals.

Feedback from the discussions at ISBER^22,24 revealed a need to include questions relating to rarity and scarcity of available banked collections. We defined rarity as samples that are collected for disease that has a low prevalence in the general population (e.g., orphan diseases). Scarce biospecimens are deficient in quantity or number compared with demand, thus not available in existing accessible collections. Having these two distinct steps in the tool allows for the attribution of specific value that these collections maintain to the research community and the organization that manages the biobank. This further illustrates attributes that provide value beyond their monetary worth of the collection.

During our review of the metadata, we identified three general categories of value-related attributes common to biobanks and their associated activities: Operations-relevant, collection-relevant, and specimen-relevant (Fig. 1). Operations-relevant activities include those that are overarching in nature such as quality management, infrastructure (e.g., staffing, equipment and instrumentation, data management), and project transition (e.g., onboarding sponsored projects or study protocols, creation of required documentation, etc.). Collection-relevant activities include project management (e.g., managing key deliverables of the studies to the required milestones, etc.) and oversight for administration or control of the associated collections. The biospecimen-relevant category includes detailed activities and elements that encompass the biospecimen life cycle as well as the scientific acumen and empirical experience of the biobanker. These metadata attributes are used as the basis to answer the stepwise questions within the BCV.

The metadata attributes that are most relevant to the noneconomic valuation may not always be readily accessible in all biobanks, based on feedback from our ISBER presentations, key opinion leader discussions, and beta testing. We found that several of the beta testers noted they had to gather outside information from their research stakeholders and organization before completing an evaluation in the tool. This was an especially critical step when the biobank is a central processing, storage, and distribution site for multiple studies, where the collection and consent information may be stored outside of the biobank’s purview. As this was an important finding, we made a recommendation within the ISBER Best Practices revision (5th) that biobanks should strive to collect these metadata attributes in their information management systems to support future valuations, sharing, and potential culling.

The BCV can be a useful tool to create ranked noneconomic valuations of legacy collections in a biobank. The evaluation and ranking allow the biobankers and researchers, per governance oversight, to examine age, shareability, informed consent and its limitations, quality, storage conditions, availability of data, possible serial sampling, previous research supported, and a legacy collection’s rarity and scarcity. Understanding these legacy collection attributes can support operational planning of the biobank and its organization. The ranking can help support decisions to maintain and advertise a valuable legacy collection for use internally and potentially externally or to cull the collection that is deemed not valuable by these attributes. Additionally, the tool can provide integral information when legacy biospecimen collections are considered as additions to a biobank, through transfer within or between facilities.

It was clear from usability testing results and user feedback that there was variability in notations provided by the user when they chose answers at each step of the tool. The significance and use of the valuation results can be subjectively dependent on the thoroughness of the details that the user provided at each step of the BCV. This may make it difficult to compare different user reports within a biobank, although it may also provide insight into valuation from different perspectives (management vs. technical vs. researcher). We recommend for consistency at each timepoint, the same user generates the reports for all of the collections being ranked and compared using suggested cut-points for decision-making purposes (Fig. 3). For example, collections found to be valued within the lowest tercile (below 25) should be considered not fit for purpose and as targets of potential culling from the biobank.

FIG. 3.

Possible break-point valuations to support decision-making from BCV results.

We acknowledge that there were some limitations to our testing approach due to the PRA. The PRA constrained the numbers of primary beta testers (10 at each round) and thus made it important to develop an ISBER workshop (with ∼60 attendees) to collect wider feedback. This open event allowed us to expand our beta testing audience, without violating the PRA. The URL for the tool is now widely available at https://go.nih.gov/jB1NfyS and we encourage its use. As we collect more voluntary feedback from various biobank users of the BCV, we may create future enhancements.

The use of the noneconomic valuation ranking from using the BCV across collections in a biobank can provide an important resource to the management of a biobank. Using the ranking after a detailed review of attributes from the collections supports operational decisions, such as where to invest in new freezers for the most valuable collections, what collections to advertise to the research community due to their intrinsic noneconomic value, or which collections to secure first in a disaster.

Using the BCV with robust input from the biobanker will benefit business planning to support biobank best practices and sustainability. For example, should the valuation tool identify a biospecimen collection as unsuitable for particular research needs, the biobanker may optimize the associated collection and processing methods to ensure higher quality for future activities. The reports generated by the BCV are also useful metrics for rating biospecimen collections and their data quality as resources to advance research.^47,48 Periodic use of the BCV could be incorporated into a biobank’s standard operating procedures for operational quality management. Additionally, leveraging the BCV will inform business decisions in the biobank that are essential for its sustainability and the development of shared collections and data across the scientific community. As organizational biobanks see the utility of the BCV, it has the potential to be incorporated into formal requirements of biobank collections evaluation over time.

Summary

We created the BCV, an automated tool for biobankers and researchers, to evaluate their collections not focused on cost modelling, but rather on collection attributes. The determination of the noneconomic value of collections supports operational, ethical, fiscal, and risk management, as well as the sustainability of biobanks. Planning and compiling the described metadata types for collections support value determinations that bolster astute management and sharing to further scientific discovery.

The BCV is a decision tool, for evaluating specimen collections against a set of qualitative attributes. It provides utility to rank value collections that are banked, as well as to gauge value iteratively, particularly if the biobank uses the tool over time. The design of the BCV is interactive with a decision-making logic to support biobanks with maintaining, sharing, and culling or moving biospecimen collections. Empirical knowledge obtained using the BCV will be the basis for prioritizing collections and critical for biobank governance and business continuity. Certainly, an early understanding of the noneconomic attributes of a specimen collection will be influential for biobanks to assign a relevant value to a specimen collection and its usage, contributing to the overall sustainability of the biobank. Funders can appreciate the collections that have high noneconomic value, by providing resources directed for their support and cutting off resources for those collections that should be culled.

The BCV is versatile enough to be relevant to various research biobanks, and the resulting values generated can support ranked comparisons of collections across the biobank. This will be especially important within a federated network of biobanks to facilitate cross-institutional access of biospecimens. The tool will work best within networks to use the BCV with agreed-upon attributes for specimen and data quality to ensure the ranking results are consistent across their shared collections.

The BCV provides a framework to identify high noneconomic value/fit-for-purpose collections held in biobanks. Catalogues show collections that may be available for research outside of their organizations. The combination of these tools support enhanced “FAIRification” of collections across biobanks.

It is anticipated that artificial intelligence (AI) capabilities will change biobank data management with the promise of revolutionizing research and ultimately treatment paradigms. With AI, data from clinical care and research studies, such as phenotypic information about patients and participants, preanalytical measurements, biospecimen characteristics, and assays (e.g., -omics), should be more accessible and structured for analysis and discovery of valuable research findings. The FAIR access principles provide a framework that is amenable to generative AI models that will result in more comprehensive sets of metadata associated with biospecimen collections that are open for sharing, thus allowing easier access to the data for use in the BCV. Potentially underutilized, scarce, or rare collections that are of high noneconomic value could be located to connect researchers to these fit-for-purpose collections.

The utility of an interactive biospecimen collections value determinator has been demonstrated with input from diverse stakeholders in the biobanking field. We wish to acknowledge this ‘voice of the customer’ as it was critical to align sample collection attributes with the tool to ensure relevance, validity, and utility with repository operations. Ongoing voluntary feedback from users will continue to provide key information supporting continuous enhancements of the BCV.

Authors’ Contributions

J.A.K.: Conceptualization (lead); software/testing (equal); writing—original draft (lead); investigation (equal) formal analysis (equal); writing—review and editing (equal); methodology (lead). T.M.J.: Conceptualization (contributor); software/testing (equal); writing—original draft (supporting); investigation (equal); formal analysis (equal); writing—review and editing (equal); investigation (equal) methodology (equal). M.K.H.: Conceptualization (contributor); software/testing (equal); writing—original draft (supporting); investigation (equal); formal analysis (equal); writing—review and editing (equal); investigation (equal) methodology (equal). All co-authors have reviewed and approved the final draft article submitted to the journal.