Definition/Introduction

Human subjects research is a highly regulated field of inquiry. The US Department of Health and Human Services (HHS) Code of Federal Regulations, Title 45, Part 46 (45 CFR 46), defines a "human subject" as “A living individual about whom an investigator (whether professional or student) conducting research:

• Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or
• Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

Research itself is defined as “a systematic investigation, including research development, testing, and evaluation, designed to develop or contribute to generalizable knowledge.”[1] Human subjects research lies at the intersection of both federal definitions and requires Institutional Review Board approval prior to initiation, regardless of the study design. Topics may include hypothesis generation, assessment of patient satisfaction, or evaluation of medications, devices, processes, or health interventions.

Scientific research may be approached through various paradigms or philosophical frameworks that determine the conduct and interpretation of investigations.[2] A consistent paradigm across basic and clinical sciences in medical education is positivism, which emphasizes duality and objectivity, separating the researcher from the study participants to minimize bias.

Research studies are generally classified into 3 types: qualitative, quantitative, and mixed-methods, the latter combining both approaches.[3] Qualitative research collects nonnumerical data to explore concepts and experiences, whereas quantitative research focuses on collecting and statistically analyzing numerical data. Additional classifications exist based on study purpose and application, such as health systems research and operational research.[4][5] The present activity focuses on the most common quantitative and qualitative research designs.

Quantitative Research

A research study may be conducted to describe variables or determine associations between test (exposure) and outcome variables related to a specific research question. Quantitative research designs are further classified as interventional or noninterventional (observational). In interventional studies, the investigator introduces an intervention or manipulation across 1 or more groups and compares outcomes to analyze variables of interest. Randomization may or may not be employed. However, randomized controlled trials are regarded as the gold standard because random allocation minimizes bias. Interventional designs are commonly used in investigations of drugs, biologics, and medical devices.

In observational or noninterventional studies, data are collected on preidentified variables without investigator-imposed interventions or influence on the study population. Common observational designs include cohort, cross-sectional, and case-control studies.

A cohort study involves longitudinal observation of a population or populations with defined exposures to determine the incidence of specific diseases or conditions over time. This design can establish temporal and potentially causal relationships between exposures and outcomes.[6] In contrast, a cross-sectional study examines a population at a single point in time and provides information such as disease prevalence. Case-control studies compare populations with a particular disease (cases) and populations without the disease (controls) to assess associations with prior risk exposures. A classic example involves comparing smoking history among patients with lung cancer and among individuals without lung cancer.

Before large-scale implementation, smaller feasibility studies—commonly referred to as "pilot studies"—refine methodology, assess practicality, and minimize potential bias. Pilot studies are a critical phase in the research process, enabling the assessment of feasibility, methodological rigor, and potential impact prior to large-scale implementation. Such studies commonly focus on elements including intervention design, study structure, trial conduct, intervention implementation, statistical analysis, and reporting.[7]

Qualitative Research

Qualitative research seeks to address open-ended questions that emerge during the investigative process. Unlike quantitative research, which focuses on measurable variables and numerical relationships, qualitative inquiry explores “how” and “why” phenomena occur. This approach aims to elucidate the underlying meanings, perceptions, and contextual factors that influence real-world experiences and behaviors. Qualitative methods may be employed independently, integrated with quantitative approaches in mixed-methods designs, or used to explain quantitative findings. Whereas quantitative studies identify statistical relationships or correlations, qualitative research provides interpretive depth by exploring the reasons underlying these associations.

Although healthcare research has traditionally emphasized quantitative methodologies, the growing recognition of qualitative research underscores its value in examining subjective aspects of care. Applications include investigations of patient experiences with healthcare services, perceptions of illness, quality-of-life assessments, and studies involving vulnerable populations.[8]

Multiple approaches are utilized in qualitative research, each offering distinct methods for exploring human experiences and social phenomena. Standard techniques include ethnography, grounded theory, phenomenology, photovoice, and narrative research.

Ethnography involves the researcher's immersion in the participants’ environment to gain insight into behaviors, interactions, and contextual factors relevant to the study's objective. On the other hand, grounded theory employs systematic observation of the population of interest to develop a theory that explains a particular phenomenon. Furthermore, phenomenology emphasizes the “lived experiences” of individuals as the basis for understanding and explaining human behavior and perception. While both grounded theory and phenomenology seek to generate conceptual understanding, grounded theory focuses on deriving theory from observed patterns. In contrast, phenomenology centers on participants’ subjective perspectives to explain a particular phenomenon.

Meanwhile, photovoice employs photographic documentation to represent and analyze the experiences of marginalized or underrepresented groups.[9] Narrative research, another key approach, exemplifies qualitative inquiry’s capacity to convey individual stories. Incorporating personal perspectives facilitates theory development by integrating real-world experiences and implications that quantitative research may overlook. Data collection methods in qualitative research include structured or semistructured interviews, focus groups, case studies, and reviews of existing records, such as medical documentation.

Mixed Methods and Triangulation Research

In certain instances, both qualitative and quantitative methods are combined within a single study design, referred to as "mixed-methods research." This approach facilitates both hypothesis generation and hypothesis testing, allowing for a more comprehensive understanding of complex research questions. The integration of numerical and nonnumerical data enhances the depth and applicability of findings by capturing both measurable outcomes and contextual insights.

For example, a cross-sectional study may identify a high prevalence of vaping among adolescents. To further elucidate the underlying motivations, a subsequent qualitative investigation, such as a focus group discussion, may be conducted. Similarly, a 2010 study demonstrated that the think-aloud technique, combined with protocol analysis, effectively described the complexity of healthcare professionals’ decision-making in critical care settings.[10]

"Triangulation research" also employs multiple methods to strengthen validity and minimize bias. Like mixed-methods designs, triangulation may incorporate both qualitative and quantitative approaches, but it can also involve various data sources, theoretical perspectives, or investigators to achieve methodological complementarity and enhance the credibility of results.[11]

Issues of Concern

A fundamental aspect of the research process involves identifying and formulating the research problem or question. An appropriately defined research problem must be ethical, researchable, significant, and feasible. Medical research aims not only to expand the scientific literature but also to deliver findings that directly benefit patients and relevant stakeholders.

Selection of the appropriate study design represents another critical consideration. Descriptive and qualitative research often precede quantitative investigations to establish a robust, meaningful, and feasible hypothesis suitable for subsequent testing.[12] Each study design entails distinct strengths and risks of bias, as reflected in the established hierarchy of research evidence.[13]

Ensuring trust and legitimacy in research constitutes an additional challenge. Misinformation, social media, politicization, and commercial interests increasingly influence the contemporary research environment. A proposed framework to strengthen trustworthiness incorporates proxies such as informed consent, anonymization, public engagement, openness, and accountability.[14]

Contemporary Frameworks for Scientific Transparency and Research Integrity

Recent frameworks emphasize scientific transparency and accountability, incorporating measures such as open data policies, protocol preregistration, anonymization of datasets, and active public engagement. The Ethics and Governance of Artificial Intelligence for Health (2021) of the World Health Organization (WHO) and the Scientific Integrity Policy Update (2023) of the National Institutes of Health (NIH) outline practical strategies to strengthen research credibility and societal trust.[15] (Source: NIH, 2024)

Regulatory Updates in Human Subjects Protection and Ethical Oversight

In 2022, Subpart A of 45 CFR 46, also known as the Common Rule, underwent significant revision, emphasizing single Institutional Review Board requirements for multisite research, the use of broad consent for secondary research involving identifiable biospecimens, and increased flexibility in electronic informed consent procedures. In October 2024, the HHS issued nonsubstantive amendments to subparts B, C, and D of 45 CFR 46. These amendments clarified protections for vulnerable populations, including pregnant women, fetuses, neonates, prisoners, and children; updated terminology distinguishing the “2018 Requirements” from the “pre-2018 Requirements” to align with the latest Common Rule provisions; and incorporated additional exemptions and technical corrections to improve regulatory clarity. (Source: HHS, 2025)

Clinical Significance

The collective significance of research studies and their findings supports both clinical and public health objectives. The discovery of novel pharmacologic agents and therapeutic modalities is primarily achieved through randomized controlled trials, which remain the standard for evaluating efficacy and safety.[16] Public health research, encompassing both medical and social sciences, employs a wide range of study designs, including qualitative studies, descriptive and analytic investigations, community-based trials, and operational research.[17] These approaches are used to examine population characteristics, explore associations between exposures and health outcomes, and evaluate the effects of interventions, thereby generating evidence to inform policy and guide stakeholders.

In clinical contexts, case reports and case series enable clinicians, surgeons, and other specialists to document and describe rare diseases or unusual presentations systematically.[18] Analytical studies may investigate associations between specific exposures and disease occurrence, while cohort studies examine outcomes among populations with defined exposure variables. Systematic reviews and meta-analyses are valuable for synthesizing previous research, with the former integrating qualitative evidence and the latter providing quantitative summaries of existing data.[19][20] Mixed-methods designs further enhance analytical depth by integrating quantitative and qualitative approaches to maximize interpretive and evidentiary value.[21]

Research is now a requirement for residency training programs across clinical specialties. Family medicine (family practice), in particular, has a broad scope for health and clinical research because of its central role in the organization, provision, and delivery of clinical services within the healthcare system.[22]

Artificial intelligence (AI) is increasingly being integrated into scientific research design and implementation to enhance efficiency and reduce the number of experimental iterations required. Recent advances have introduced large language models and AI systems capable of assisting in trial simulation, automated data extraction, and synthesis of clinical evidence.

In pharmaceutical research and development, for instance, projects often require substantial financial investment, extend over multiple years, yet still yield low approval rates for new therapeutics.[23] AI-driven algorithms can assist in designing and optimizing experimental parameters, collecting and processing data, generating and testing hypotheses, and estimating experimental uncertainty.[24]

Several challenges remain despite AI's promise, including the need for appropriate software and hardware infrastructure, acquisition of reliable datasets, data and model standardization, ethical oversight, potential bias between industry and academic priorities, costs, and cybersecurity risks.[25] However, when developed within a rigorous and moral framework, AI possesses significant potential to transform scientific workflows.

Ethical and Regulatory Oversight in Artificial Intelligence-Driven Research

The integration of AI in medical research has prompted the development of standardized reporting and ethical oversight mechanisms, including the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI) and Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) extensions, as well as the 2023 Artificial Intelligence/Machine Learning (AI/ML) Action Plan for medical research and software as a medical device of the US Food and Drug Administration (FDA).[26] (Source: FDA, 2021) Ensuring transparency, explainability, and reproducibility in AI-driven studies has become a regulatory and ethical priority, particularly for studies involving human subjects. 

Research studies, whether simple or complex and whether aided by AI or not, must adhere to systematic, methodologically sound processes to ensure scientific integrity. The research findings contribute to the advancement and application of evidence-based medicine.[27] All research must be conducted within the bounds of medical ethics, free from bias, and directed toward the welfare of patients rather than solely the advancement of science.[28]

Nursing, Allied Health, and Interprofessional Team Interventions

Interprofessional and interorganizational collaborations are fundamental components of effective healthcare systems.[29] In 2010, the WHO developed a framework for Interprofessional Education (IPE) and collaborative practice to address growing global health challenges.[30] (Source: WHO, 2010) IPE serves as the foundation for meaningful collaboration in both clinical care and research. The WHO’s Health Workforce 2030 Strategy further expands on this foundation by highlighting digital collaboration, cross-sector integration, and equitable participation as key pillars of interprofessional practice.[31]

A related concept, referred to as the "team science approach," emphasizes the integration of diverse perspectives across professions, organizations, and cultural or geographic backgrounds. This approach has become increasingly essential within healthcare systems of growing complexity.[32] A systematic review identified team science as a critical element of interprofessional research collaboration.[33]

An example of successful implementation involved an interprofessional initiative coordinated by an orthopedic trauma department in collaboration with 18 other departments, including cardiovascular and emergency medicine. The team identified a novel cluster of preoperative factors predictive of 12-month mortality among patients older than 65 years with hip fractures.[34]

Several challenges persist despite demonstrated benefits. Power dynamics between physicians, nurses, and other healthcare professionals can hinder collaboration and affect research productivity. Furthermore, the historically siloed education of healthcare professionals continues to impede the advancement of IPE. Future efforts should focus on enhancing inclusivity, promoting diversity, and building institutional capacity to strengthen collaborative research and clinical practice.[35]

Nursing, Allied Health, and Interprofessional Team Monitoring

Continued advancement in interprofessional collaboration remains essential in both clinical practice and research. A study evaluating satisfaction among members of hospital-based interprofessional teams identified several themes associated with higher satisfaction levels. In addition to expected factors such as collaboration and communication, shared leadership and innovation were also recognized as significant contributors.[36] Another study investigating barriers to enhancing palliative care research participation among nonphysician professionals identified inadequate training and mentorship as major obstacles. The nationwide survey included chaplains, nurse practitioners, pharmacists, and social workers.[37]

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Disclosure: Marlon Bayot declares no relevant financial relationships with ineligible companies.

Disclosure: Nicholas Moore declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.