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date: 24 July 2021

Anthropology and Informatics in Health Carefree

Anthropology and Informatics in Health Carefree

  • Laurie NovakLaurie NovakVanderbilt University School of Medicine
  •  and Joyce HarrisJoyce HarrisVanderbilt University Medical Center Department of Biomedical Informatics


Information technology increasingly figures into the activities of health-care workers, patients, and their informal caregivers. The growing intersection of anthropology and health informatics is reviewed, a field dedicated to the science of using data, information, and knowledge to improve human health and the delivery of health-care services. Health informatics as a discipline wrestles with complex issues of information collection, classification, and presentation to patients and working clinical personnel. Anthropologists are well-suited as collaborators in this work. Topics of collaborative work include the construction of health and illness, patient-focused research, the organization and delivery of health-care services, the design and implementation of electronic health records, and ethics, power, and surveillance. The application of technology to social roles, practices, and power relations that is inherent in health informatics provides a rich source of empirical data to advance anthropological theory and methods.


  • Applied Anthropology
  • Sociocultural Anthropology

Health Informatics and Anthropology: Disparate Fields, Mutual Interests

Information technology increasingly figures into the activities of health-care workers, patients, and their informal caregivers. Few medical or organizational activities occur without computer processing data in the background or directly interacting with the patient or worker. The result is a vast array of everyday activities that are mutually structured by people and technology. This article describes the growing intersection of anthropology and health informatics in Western medicine. The concepts and work we discuss do not represent the breadth of contributions in both fields since they are too extensive to address here. Instead, our goal is to situate anthropologists at the interface of technological and social practices related to health as we enter a new Industrial Revolution (Schwab 2017). First, we provide an overview of the field of health informatics. Then we describe subfields of anthropology that overlap significantly with health and informatics and discuss major areas of shared activity in applied and critical work. Finally, we review emerging opportunities for anthropologists interested in this area.

What Is Health Informatics?

Informatics in health care in the United States began with medical billing. Hospitals needed to capture as much information as possible about resources spent taking care of patients in order to provide itemized bills to insurance companies. Starting in the early 1900s, employers began to use insurance companies to cover employee medical expenses in the United States. As medical encounters (i.e., defined interactions with health-care organizations such as a hospitalization or a clinic appointment) were created for billing, scheduling systems were developed. Beginning in the 1960s, systems were developed to automate the process for ordering tests and procedures in hospital setting (Staggers, Thompson, and Snyder-Halpern 2001). Computer-savvy physicians saw the utility of these systems to support their work and the field of medical informatics emerged (Collen and Ball 2015). Physicians and computer scientists began developing clinical decision support systems such as alerts and notifications for physicians to improve the quality of their orders (e.g., avoiding orders for medications to which the patient was allergic, avoiding duplication of lab tests already ordered). The ostensible goals of these tools were to improve the working lives of physicians, reduce costs (i.e., redundant or unnecessary orders), and later, to improve the quality of care. By the end of the 20th century, a majority of hospitals and many clinics in the United States were using computers to document and communicate about patient care. These systems were referred to as electronic medical records (EMRs), and over time were called electronic health records (EHRs) to signify that the records contained more than just traditional medical data. Similarly, the academic field that emerged to research and develop EHR tools was initially referred to as medical informatics or biomedical informatics (BMI). BMI is an applied discipline, defined by the American Medical Informatics Association (AMIA) as “the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health.” A corollary of the definition includes this statement: “BMI, recognizing that people are the ultimate users of biomedical information, draws upon the social and behavioral sciences to inform the design and evaluation of technical solutions and the evolution of complex economic, ethical, social, educational, and organizational systems” (Kulikowski et al. 2012). Over time, the term “health informatics” has emerged, demonstrating a broader application of the concepts and tools. In this article, the terms biomedical informatics and health informatics are used synonymously.

Informatics is widely accepted to be the vehicle for delivering state-of-the-art medical evidence (Sackett 1997) to providers as they make clinical decisions and counsel patients. Optimizing and standardizing clinical decisions were key priorities in medicine at the time that BMI came to the fore in the late 20th century (Eisenberg 1986) and provided the rationale for significant investment in informatics infrastructure at the hospital level. Therefore, BMI conceptualizes physicians and other medical providers primarily as decision makers, and clinical decision support (CDS) is a cornerstone of the field (Gardner et al. 2009; Sim et al. 2001). CDS informatics interventions in EHRs (Garg et al. 2005) include innovations such as pop-up alerts (e.g., when the physician is ordering a medication to which the patient is allergic), dashboards that enable rapid review of data, and presentation of predictive risk scores for a variety of conditions or events. Development of software tools for patients and caregivers often adopts this strong focus on decision-making and decision support.

Physicians involved in the development and implementation of EHR tools have been encouraged and rewarded by the development of a career path. There are numerous research centers and academic departments focused on BMI, and the American Board of Medical Specialties recently implemented examinations for a board certification in BMI (Lehmann 2010). This infrastructure of legitimation creates a rationale and path for the provision of research funding through the National Institutes of Health and other agencies.

Figure 1. Core disciplines and domains of scholarship that link anthropology and health informatics.

Created by the author.

Major domains of 21st-century health informatics development and scholarship include (see figure 1):

Bioinformatics—using computer and information science, statistics, and biology to analyze and interpret biological data. Social assumptions are made in statistical designs and machine learning algorithms.

Clinical decision support—the application of rules and artificial intelligence to EHR data, sometimes along with other external or patient-generated (e.g., Fitbit) data, to alert health-care workers to important information. An example is the use of risk scores to identify patients at risk of specific conditions (e.g., genetic mutations, intolerance of specific medications), behavioral issues such as suicide attempts, and social determinants of health such as food insecurity.

Clinical informatics—the development and improvement of EHR technologies including vendor-based systems, visualization of data, facilitation of data input for documentation and communication (e.g., voice-activated), and clinical messaging. Practitioners specialize in informatics applications such as primary care, intensive care, pharmacy, mental health, and nursing.

Clinical research informatics—the use of informatics to facilitate clinical research, including systems for recruitment and consent of participants, data collection and analysis, and translation of findings back into clinical settings for use by providers and patients.

Global health informatics—the use of informatics technologies and principles to achieve health equity in resource-constrained communities and to improve the quality of data in international health research consortia.

Patient and caregiver engagement—the use of technology to engage patients and their caregivers in decision-making and illness management. Examples include portals enabling access to the patient’s EHR data and mobile apps that provide information and encourage adherence to treatment.

Telemedicine and Telehealth—the use of information and communications technology to diagnose and treat patients remotely. Development and scholarship in this area continue to expand, including such applications as remote ICU care and mHealth or the use of mobile apps to support health and patient care. Globally, health care organizations engaged in rapid, widespread implementation of telemedicine and telehealth for routine patient care in response to the COVID-19 pandemic.

Other Areas of Scholarship Relevant to Both Anthropology and Health Informatics

Several areas of scholarship that are not inherently limited to the health domain provide opportunities for cross-disciplinary collaboration (see figure 1):

Community engagement—Methods, including participatory research and design, of working with community members to gain input into the implementation of informatics infrastructure in health-care organizations (e.g., collecting and reporting data on the social determinants of health) and into the development of research protocols and the design of specific technology products (Veinot, Ancker, and Bakken 2019).

Computer–human interaction and usability—electronic health records (EHRs) in US health care are almost all vendor-based systems, with the most popular vendors being Cerner and Epic. These tools have been critiqued for complexity in the design of the user interface. Anthropologists have unique training in methods to reveal the role of context in the way that health-care workers (and patients) interact with EHR systems. For example, a nurse in the emergency room, a team of trainees in the ICU, and a physician in a primary care visit would all be looking at the same presentation of laboratory data in the EHR. Understanding the context of activity can aid in improving the design of systems to support that activity.

Data science—natural language processing, machine learning, and other tools that are broadly referred to as tools of artificial intelligence. These tools seek patterns in data that are generated as a by-product of other activities. Data scientists often seek to partner with qualitative researchers to understand the context of the production of the data they use and the context of the ultimate use of their data products.

Ethical, legal, and social issues (ELSI)—An active area of scholarship, this domain focuses on ELSI issues related to the privacy and use of health-related information, the impact of automation on worker and patient safety, and the risks associated with participation in biobanking (i.e., contributing genetic material to a repository controlled by a research organization or a direct-to-consumer genetic analysis company).

Implementation—the introduction of informatics tools into clinical settings, often involving the de-implementation of existing tools. Organizational culture issues related to leadership, change management, work practices, technology adoption, and resistance are central to this work.

Social Topics of Interest to Anthropology and Health Informatics

Many subfields of anthropology can contribute to a better understanding of the impact of information technology on health and health care and to improving the design of health informatics tools. Here we review selected social topics of interest to both anthropology and health informatics, summarized in Table 1.

Table 1. Social Topics of Interest to Anthropology and Health Informatics

Role of health informatics

Social topics

Role of anthropology

The construction of health and illness

Develop and implement computational infrastructure for new innovations such as predictive analytics, genome sequencing, and incorporation of social determinants of health into the electronic health record (EHR)

Ongoing (re)definition of health and illness through institutional and technological infrastructures

Ensure that patients’ and caregivers’ cultural values are reflected in institutional technology initiatives;

ensure that informatics tools support the way health-care workers think and work;

describe unequal knowledge and power relations in provider-patient communications

Patient-focused research

Design and implement patient-centric health technologies such as mobile applications for use by patients and caregivers for medical and health management

Engaging patients and caregivers in improving health and health care

Ensure that patients’ cultural values contribute to the design and development of informatics tools

The organization and delivery of health-care services

Use of International Classification of Diseases (ICD) codes, regulatory frameworks, and taxonomies to control health and health-care costs

The cultural science of infrastructure

Examine the social production of predictive algorithms, algorithmic decision-making, and institutional access to everyday activities of individuals

Continually improve safety for patients and health-care workers

Risk and uncertainty

Introduce new conceptual resources for understanding organizational risks and safety

Use of informatics tools to facilitate decision-making related to genetic and genomic testing, treatment, and data sharing

Computational genomics

Understand patients, caregivers, and health-care workers’ knowledge, beliefs, attitudes, and behaviors related to genetic and genomic testing, treatment, and data sharing, and how various organizations address these issues

Design and implementation of electronic health records

Implement clinical informatics interventions that facilitate the use of evidence in medical practice;

implement technologies that facilitate more efficient, effective delivery of care

Ongoing automation of health-care delivery

Document the implementation of new health information technologies and the relationship between people, processes, and technology;

understand the impact of health information technologies on specific roles, clinical workflow, organizational characteristics, and workplace satisfaction

Use predictive algorithms and artificial intelligence tools such as machine learning and national language processing to alert providers of potential adverse medical conditions

Predictive analytics and the presentation of risk in clinical workflow

Uncover social and cultural assumptions made in the creation of algorithms and how the (re)use of algorithmic data and resources for purposes other than those originally intended can produce and ossify social inequalities;

identify optimal ways to characterize and present risk to clinical personnel and patients in specific contexts

Use of health and health information technologies and technological objects like talking robots and those in pseudo-caregiver roles that mediate and expand the relationship between individuals, groups, and biomedical institutions

Interaction of humans and machines

Describe how technology mediates provider-patient relationships and the impact of technology on trust

Ethics, power, and surveillance

Ensure privacy of patients and providers; ensure the safe implementation of AI-based tools

Technological risks

Collaborate with computer scientists, social and legal scholars, and other stakeholders to advance new recommendations for privacy and the implications for data science research and practice in health-care settings;

contribute critical perspectives on the role and impact of technology on human autonomy

The Construction of Health and Illness

Health informatics tools, by virtue of their design, classify people (e.g., patient, caregiver, provider), states of health and illness, tasks (e.g., monitoring blood glucose), and other fundamental elements. They also implement assumptions about the roles and identities of people in the health-care industry and in everyday life, and the relations among them. Anthropological inquiry describes and critiques these systems, and medical anthropology is the most directly relevant subfield of the discipline. The Society for Medical Anthropology defines the field as one that draws upon the four fields of anthropology “to better understand those factors which influence health and well-being (broadly defined), the experience and distribution of illness, the prevention and treatment of sickness, healing processes, the social relations of therapy management, and the cultural importance and utilization of pluralistic medical systems” (Society for Medical Anthropology 2019).

Applied and critical perspectives on the cultural significance of illness (Kleinman 1988; Murphy 2001), prevention and treatment (Merrild et al. 2017), and medical pluralism (Leslie 1980) have bolstered anthropology’s previous focus on cross-cultural comparisons, international development (Escobar 1991; Wilson 1998), and applied work in hospital settings (Caudill et al. 1952). By the late 20th century, medical anthropology was officially recognized as a subfield of anthropology after the Society for Medical Anthropology was incorporated into the American Anthropological Association in 1971 and institutionalized through a PhD program in 1972 (Van Kemper 2009). As scholarship in applied medical anthropology expanded, so did interest in the study of contemporary health-care systems. Examples include examining the organization of medical care (Solimeo et al. 2017) and the role of technology (Swinglehurst, Greenhalgh, and Roberts 2012), among other areas. (See Baer, Singer, and Susser 2013; Good et al. 2010; Inhorn 2007; and Singer and Baer 2011 for volumes on the history of medical anthropology.)

Artificial intelligence, high-throughput genome sequencing, and other digital means of collecting and analyzing data are rapidly emerging resources in the diagnosis, treatment, and management of illness and disease in Western medicine. Anthropology, through applied work and theoretical and methodological rigor, can help ensure that cultural values related to illness experiences contribute to the design and development of interventions that benefit patients and support the way health-care workers think and work.

Patient-Focused Research

As health and illness are socially constructed, so is the notion of the patient. In the academic literature, people receiving health care have been conceptualized in numerous ways. The traditional role of the “patient” is changing, in some domains, to that of the “consumer.” This shift has been controversial for several decades (Reeder 1972). Proponents argue that patients are more empowered than ever with data from the internet and should see their medical providers as just another service provider in their lives. Others argue that the relationship between a provider and a patient is different from other types of services—more intimate in a variety of ways and sometimes involving existential crises and decisions. This debate is not always carried out explicitly. Instead, informatics development projects are conceived and funded often without describing the differences in knowledge and uneven power relations between patients and providers, the impact on the power relations that new forms of data (e.g., location tracking data from mobile technologies) may have, and the impact of disrupting the traditional relationships between individual providers and patients with technology (e.g., automated phone calls). Meanwhile, technology designers representing a vast array of corporate interests are developing mobile applications for use by patients and their caregivers (Payne et al. 2015). Examples include apps for tracking insulin dosing and carbohydrate counting for diabetes, pain management apps for sickle cell disease, health management apps such as MyFitnessPal, and apps specific to women’s health. Anthropologists have contributed new insights on the activities and conceptualization of everyday illness management among patients and their caregivers (Sankar and Luborsky 2003; Schoenberg 1998; Schoenberg, Amey, and Coward 1998) and can have a role in the production of patient-focused health informatics tools in several ways:

Conducting empirical research on the everyday management of chronic illness—Interpretive methods create opportunities to expand on traditional research in chronic illness self-care and adherence to therapy that focuses on individual patients, their skills (Arcia et al. 2015), and their psychological dispositions (Mulvaney et al. 2011). Anthropological methods and theories that foreground everyday practices and associated structures (Schoenberg, Amey, and Coward 1998; Schoenberg and Drew 2002; Valdez et al. 2014) provide rich content opportunities for app designers. For example, research describing how teens manage their medications reveals that temporally structured activities such as medication-taking are disrupted by changes in everyday routines such as end of school year disruptions. App designers can easily incorporate this information into the design of the app, creating a tool that better supports teens in everyday life (Novak et al. 2015).

Exploring options for computational resources that reflect the variety of places, tasks, artifacts, and actors that can structure chronic illness management—With 21st-century sensor and tracking technologies, the everyday artifacts, spaces, and actors that contribute to structuring illness self-management have become accessible to technological intervention. For example, a key artifact for a college student with a severe food allergy is the backpack in which the epinephrine injector (containing rescue medication) may be placed. Bluetooth technology can be used to ensure that the student is alerted if the backpack and the injector are separated (e.g., the injector has been left behind in the dorm). Research frameworks that allow for conceptualizing these factors can provide better design guidance that incorporates the meaning of activities to the people involved (Hartzler and Pratt 2011; Unruh et al. 2010; Valdez et al. 2015).

The Organization and Delivery of Health-Care Services

Business and industrial anthropology (Baba 1994) is an academic subdiscipline that is also strongly connected with the large community of anthropologists practicing outside of academic settings (Baba 2002). Examples of scholarship in this area include the advancement of methods and theory in understanding the relationship between technology and organizations (Baba 1995; Baba 2002; Cefkin, Thomas, and Blomberg 2007; Eaton 2011). Anthropologists working in industry have made transformational theoretical and methodological contributions in the study of technology and organizations (Suchman 1987) and design (Blomberg et al. 2017; Dourish and Bell 2007; Pink et al. 2018).

Anthropologists interested in the intersection of technology and organizations are advised to consult the vast conceptual resources produced by qualitative research on organizations (Feldman and Orlikowski 2011; Orlikowski 2007; Orlikowski and Scott 2016; Weick 1995), systems (Gantt and Nardi 1992; Holden 2012; Karsh et al. 2010; Suchman 1987), safety (Hollnagel, Woods, and Leveson 2006; Perrow 1984; Woods 1988; Woods and Dekker 2000), and design (Gunn, Otto, and Smith 2013; Miller 2017).

The Cultural Science of Infrastructure

Informatics in health care relies on a variety of culturally infused infrastructures, the most prominent being the International Classification of Diseases (World Health Organization 2019), explored extensively by Bowker and Starr (2000). Other infrastructures include clinical terminologies (Spackman, Campbell, and Côté 1997), regulatory frameworks (DHHS), and various taxonomies (Barrett, Liaw, and de Lusignan 2014; Cronin et al. 2015; Wright et al. 2011). Anthropological perspectives on infrastructures are increasingly needed as social processes and institutions become more automated (Eubanks 2018; Zuboff 1988, 2019). This research can shed light on the social production of the data used to power predictive algorithms. Data and related infrastructures previously unrelated to health (e.g., retail purchases, fast food consumption) are now being reinterpreted as relevant by organizations that seek to control health and health costs (Mooney and Pejaver 2018). Recent versions of electronic health records (EHR) technology have included modules that address the “social determinants of health” (Adler and Stead 2015). Functionality in these modules enables health-care providers to interact with local food banks, for example. This functionality enables the health-care provider to see how much and what type of food the individual has received from the food bank. This information, potentially combined with other data that could be made available with minimal regulatory accommodation (e.g., retail activity, social media activity, criminal records) could provide health-care providers or payors (i.e., the government or an insurance company) with unprecedented access to information about individuals and their activities. Institutional access to the everyday activities of individuals is a cultural phenomenon that has been accelerated by technology (Zuboff 2019) and deserves the attention of anthropologists in health settings.

Risk and Uncertainty

The field of health informatics needs new conceptual resources for understanding risk and uncertainty (Novak 2010) for application in several important domains:

Organizational risks—Health-care organizations face a daily onslaught of threats to the security of information held in the EHR and other resources (Paul, Kohno, and Klonoff 2011; Perakslis 2014). In health-care settings, trade-offs are made between having maximally secured data and providing the ease of access that care providers need when performing dynamic patient care tasks. Understanding how to make these trade-offs is complex and involves careful documentation and interpretation of clinical risks related to cybersecurity measures (e.g., requiring providers to perform multiple security steps when logging into the EHR) that vary across settings. Ultimately, security measures are implemented into the everyday lives of clinical workers, and well-theorized qualitative findings on the activities, spaces, actors, and artifacts involved could assist technology designers in creating more useful and supportive security technology.

Safety—Patients and health-care workers perform actions every day that could be construed as “unsafe” by a variety of professional safety scientists (Bates and Gawande 2003; Battles 2006). An example is in the use of barcode medication administration (BCMA). The nurse administering the medication scans barcodes on the medication and the patient’s wristband, and the computer system assesses whether the medication being administered matches one that was ordered for that time. Workarounds to that system have included printing extra wristbands and scanning those when it is difficult to access the patient’s wristband (e.g., if the patient is asleep) (Patterson et al. 2006). Understanding such trade-offs requires rigorous characterization of the activity of the clinical worker with an emic perspective. There are opportunities for convergence research (National Science Foundation 2019) with scientists from industrial engineering and other disciplines to provide actionable frameworks for the development of technology that supports the clinical practice of workers.

Genomics and Informatics

Informatics tools have facilitated the widespread implementation of genomic medicine (i.e., the use of genomic data to support decisions related to testing and treatment). DNA testing is now being used by commercial enterprises and is available for direct-to-consumer purchase. Significant opportunities for social research exist for expanding our understanding of how people make everyday decisions that have implications for their genetic privacy and how organizations are structured to manage those expectations (Hazel and Slobogin 2018). Anthropologists can contribute important insights to current debates related to genomics and technology. For example, anthropologists are equipped to offer a holistic view regarding DNA biobanking—the choice between the current environment of mandatory contribution of DNA by certain vulnerable populations (e.g., prisoners) versus mandated contributions of DNA from all members of society (Hazel et al. 2018).

The Design and Implementation of Electronic Health Records

Projects that involve the development and evaluation of clinical technologies benefit from insights that anthropologists can bring regarding study design, collection and analysis of data, and presentation of results. An anthropological perspective is particularly useful in documenting the implementation of new health information technologies, the associated impacts on specific roles, the distribution and definition of work practices, and the meaning of the work to participants (Ash et al. 2011; Hunt et al. 2017; Novak 2007). Similarly, firms that develop EHR technologies can also benefit from these insights. An example is implementation research on BCMA technology (described previously in “Risk and Uncertainty”). When the nurse scans the barcodes on the patient and the medication, the system checks to see if the medication was ordered for the patient and also checks the dose and time of the administration. Ethnographic research in two hospitals showed different strategies related to the management of “late” doses (i.e., medications scanned after the window of the “on-time” dose) one hour after the scheduled time. Late doses can occur if the patient is away (i.e., for testing or therapy in another part of the hospital), asleep, or if the nurse is busy with another patient. In one hospital, the organization strongly endorsed a rigid BCMA-driven interpretation of a late dose, even including a metric derived from the BCMA system in nurses’ annual evaluations (i.e., linking the measure to compensation). In the other hospital, the organization rejected the rigid interpretation, communicating to nurses that it was acceptable to have a late dose as long as it was documented. The assumption of management in the latter hospital was that this option would improve patient safety by removing a possible conflict in nurse goals for the nurse. Ethnographic methods can help technology designers understand the contextual factors that can produce conflicts related to good patient-care flexibility and constraints created by rigid technology design (Novak et al. 2013).

Anthropological methods are also relevant in studies on the unintended consequences of the EHR. Ash et al. (2007) used qualitative methods to document and create a taxonomy of unintended consequences of EHR implementation. Hunt et al. (2017) used ethnographic methods to describe how the adoption and use of the EHR are transforming the roles of providers and patients in unexpected ways that serve bureaucratic ends. In particular, they call out the redefinition of the physician’s role from medical expert to administrative bureaucrat based on the amount of time physicians spend entering billing codes into the patient record.

Early informatics scholars in this area emphasized “people and organizational issues” that need to be considered in informatics design and implementation, and best accessed using qualitative methods (Kaplan 2001; Lorenzi et al. 1997). Other research has focused on documenting and theorizing the relationship between technology and the everyday activities of health-care workers (Bouskill et al. 2018; Harrod et al. 2013; Manojlovich et al. 2015; Novak, Anders et al. 2012; Swinglehurst, Greenhalgh, and Roberts 2012; Unertl et al. 2009).

Anthropologists are also active in health services research related to the organization and delivery of care not specifically related to technology. This domain includes the growing area of implementation science that is focused on improving the implementation and uptake of evidence in medical practice (Lindsay et al. 2015). The field of implementation science could benefit from updated conceptual tools for understanding the mechanisms by which informatics tools impact the implementation of evidence-based medical guidelines.

Predictive Analytics and the Presentation of Risk in the Clinical Workflow

We experience data science products in our everyday lives through interactions with the internet via search algorithms. Anthropologists can help uncover social and cultural assumptions made in the creation of these algorithms. For example, anthropologists are needed to work with data scientists in developing and implementing predictive algorithms in clinical settings. Data science methods include statistical and computational methods for searching, retrieving, integrating, and displaying information from public and private large-scale sources. In clinical settings, algorithms can produce risk scores for many conditions or events, such as the risk that a given patient will have a cardiac arrest, develop a pressure ulcer, be readmitted to the hospital within thirty days of discharge, or attempt suicide. Many of these outcomes are events that medical providers were already estimating and the risk score provides additional support for their reasoning. Training programs for most health-care professionals do not emphasize probabilistic thinking or foundational statistical knowledge that enables the comparison of predictive risk scores to other sources of evidence. Identifying optimal ways to characterize risk (e.g., as “high or low,” or using a numerical score) and present it to clinical personnel and patients is a critical need, given the proliferation of these tools.

Anthropological insights are needed to understand how institutions and individual providers can best use the risk scores, especially when they are counterintuitive or have stigmatizing or other implications for the patient. Additionally, the use and reuse of data and algorithmic resources for purposes other than those originally intended are known to reproduce and further ossify social inequalities (Eubanks 2018; O’Neil 2017). Such uses of big data resources emerge out of everyday reasoning and become realized at a large scale through institutional power. Few academic disciplines are positioned as well as anthropology to provide conceptual resources for this entire trajectory.

The Interaction of Humans and Machines

Cyborg anthropology is a discipline linked to science and technology studies (STS). The ubiquitous nature of technology has challenged notions of humanity and opened new domains of inquiry for anthropologists at the intersection of medicine and technological innovation (Davis-Floyd and Dumit 2013). Feminist studies characterizing cyborgs as hybrids of humans and machines (Escobar et al. 1994; Haraway 1990) inspired the development of cyborg anthropology as a framework for understanding the relationship between humans, culture, and technology. Because health and medicine are primarily concerned with the body, clinical settings constitute an intimate space. The relationship between patient and health-care provider has traditionally been characterized by substantial trust. The patient trusts that the provider will recommend effective treatments and help them achieve health. The provider trusts that the patient will be able to enact sometimes complex medical recommendations. What happens when that trust relationship becomes mediated by technology? The information technologies and other “enchanted” (Rose 2014) technological objects that mediate and expand the relationship between individuals, groups, and biomedical institutions are already being deployed in the form of health- and illness-management apps, human-like social robots, and patient portals.

Ethics, Power, and Surveillance

Since the early days of the American Medical Informatics Association (AMIA), there has been a strong ethical, legal, and social issues (ELSI) working group. These scholars and others have conducted research and held symposia to focus attention and advance scholarship on topics such as privacy (Kaplan 2015), health information technology, vendor–provider relationships (Koppel and Kreda 2009), and ethics (Goodman 2010). Computer scientists have partnered with social and legal scholars to advance new recommendations for thinking about privacy and the implications for data science research and practice in health care, including strategies for studying the implementation of risk scores into clinical practice (McKernan, Clayton, and Walsh 2018).

The late scholar Samantha Adams was active in the field of biomedical informatics (BMI) (DeMuro, Novak, and Petersen 2018; Novak, Kuziemsky, and Kaplan 2018), contributing significantly to debates about current practices in e-health (Adams 2014, 2017; Adams and Niezen 2016). She argued that technology was not neutral, but instead plays an active role in the co-construction of knowledge. Adams used content analysis to examine a pharmaceutical company’s website that purported to crowdsource information from patients about their experiences with medications. This crowdsourcing is ostensibly the “democratization” of health-related knowledge. However, Adams showed that the site developers managed and mediated the information to ensure that the new information complemented traditional information sources and power structures. For example, dosage information was not included with patient stories about side effects because the site developers felt that dosage information would be of interest to the pharmaceutical firm but not the patients. She argued that these practices “reinforce the dominance of and hierarchy present in the traditional sources” (Adams 2014).

Other scholars have focused on surveillance of medical personnel (Fisher 2006) and individuals in everyday life (Lupton 2012). Deserving of special mention is Diana Forsythe, an anthropologist whose interests included feminist research and artificial intelligence, namely, its application in medical settings. Seeing how influential informatics could be on the cultural practices within health care, she was active in the early stages of the formation of the highly influential AMIA. She was also part of the intellectual community that blended social and computational sciences, stemming primarily from work in industry-based organizations such as the Xerox Palo Alto Research Center (PARC), Hewlett Packard, IBM, and AT&T (Nardi 1998a, 1998b; Suchman 1987; Suchman et al. 1999). Forsythe’s work blazed multiple trails for future social scientists, including examination of the cultural production of technology in artificial intelligence (Forsythe 1993) and BMI (Forsythe et al. 1992), and navigating technology design environments as an ethnographer (D. Forsythe 1999;Diana E. Forsythe 1999; Forsythe 1992). Forsythe also turned her attention to patients, exploring in a seminal paper the distinctions between medical providers and patients in the issues that concerned them (Forsythe 1996). In the development of a natural-language patient-education system for people with migraines, Forsythe found that the designers of the system consulted with doctors but not patients when building the system. Interviews revealed that the patients’ questions were very different from the questions the doctors assumed the patients would ask. This article also illustrated the power differential between physicians as strong influencers on informatics design and patients whose perspectives may not be represented in informatics tools designed specifically for their use.

Diana Forsythe’s untimely death in 1997 led to the creation of two important awards in her honor: the Diana Forsythe Prize and the Diana Forsythe Award (DFA). The prize was created in 1998 to celebrate the best book or series of published articles in the spirit of Forsythe’s feminist anthropological research on work, science, and technology, including biomedicine. It is awarded annually at the meeting of the American Anthropological Association by a committee consisting of one representative from the Society for the Anthropology of Work (SAW) and two from the Committee on the Anthropology of Science, Technology, and Computing (CASTAC). It is supported by the General Anthropology Division (GAD) and Bern Shen. The Diana Forsythe Award, sponsored by AMIA’s People and Organizational Issues Working Group, honors a paper (from the AMIA Annual Symposium or the published literature) that best exemplifies the spirit and scholarship of Diana Forsythe’s work at the intersection of informatics and the social sciences (AMIA Working Group Awards 2019). Papers honored with the DFA have been written by informatics scholars and social scientists, including scholars in Europe where social science research in informatics is published in greater volume.

Methods and Theory for Research and Design

In this context, anthropological methods include ethnography, observation, interviewing, artifact analysis, surveys, and other approaches that elicit information about practices, context, and meaning (Jordan 2007). These methods are used for both traditional research and for direct input into product design. Figure 2, adapted from the work of design researcher Liz Sanders (2008), maps selected qualitative research methods along the continuum of research and design, and also the perspective the methods take on technology users.

Figure 2. Analytical perspectives mapped to research mindset and purpose.

Adapted with permission from Elizabeth Sanders.

User-centered design or the involvement of users in iterative stages of design is a framework of interest to health-care technology designers because of the high level of dissatisfaction among electronic health record (EHR) users. Within that framework, usability testing involves formal testing procedures to evaluate the performance of a system against a set of criteria.

Practitioners from the field of human factors and ergonomics (HFE) seek to improve human performance in sociotechnical systems. HFE investigators use methods such as observation, interviewing, and surveys to understand how people work; in our experience, these researchers can be extremely productive research collaborators with anthropologists.

Participatory design and research can be a strength of people with training in anthropological methods where engagement with individuals and communities is built in to the framing of research. In these projects, inspired by Scandinavian participatory design (Gregory 2003), potential users of technology (or processes) are seen as full partners in design and research. Many current research initiatives funded through the National Institutes of Health and the Patient Centered Outcomes Research Institute feature patients and caregivers as members of the research team. In technology design, this type of work can sometimes involve the use of generative tools or the use of mock-ups or actual materials to create products or user interfaces (i.e., screen designs) in information systems.

Theoretical resources commonly used in social research related to health informatics emphasize sociotechnical systems, agency, practice, activity, and technology as an artifact or actor. Sociotechnical research has contributed significantly to theorizing knowledge and information artifacts in health care (Østerlund 2008; Osterlund and Carlile 2005). Organizational routines theory is based in structuration theory (Giddens 1984), conceptualizing routines as having two dimensions: abstract (structural) and performative (or activity), which are mutually constituting (Feldman 2003; Feldman and Orlikowski 2011). This theoretical framing is a useful resource for informatics studies in health care because it enables the articulation of the dynamics that support and constrain activity (i.e., technology limitations on medication administration), even as that activity is performed by powerful agents (e.g., nurses) who routinely implement workarounds to standardized procedures in the name of good patient care (Novak, Brooks et al. 2012). Other useful approaches come from cognitive anthropology, particularly the concept of distributed cognition (Hutchins 1995). An example is the work of Hazlehurst and McMullin who used ethnography to develop the concept of “orienting frames” that structure the everyday activities of intensive care nurses, including “being organized,” “being prepared for emergencies,” and “being responsible and accountable” (Hazlehurst and McMullen 2007). Activity theory has also been a key resource in informatics research (Nardi 1996), influencing cross-cultural studies (Byrne and Gregory 2007; Moen, Gregory, and Brennan 2007) and workflow research (Unertl 2009). European scholarship that emphasizes participatory design has been particularly influential (Dinesen et al. 2008), as is the emerging field of techno-anthropology (Borsen and Botin 2013).

Careers in Informatics for Anthropologists

As we have described throughout this article, there is a need for anthropological perspectives in health informatics. Two key domains are research and industry, and in either domain, anthropologists are encouraged to maintain a reflexive stance on their roles. Table 2 offers a list of special interest groups of professional organizations where anthropologists interested in health informatics may find colleagues.

Table 2. Professional Interest Groups with Emphasis on Social Aspects of Health Informatics


Society for Medical Anthropology: Science, Technology, and Medicine Special Interest Group (STM)

General Anthropology Division (GAD) Committee for the Anthropology of Science, Technology, and Computing (CASTAC)

National Association for the Practice of Anthropology (NAPA)

Health and biomedical informatics

American Medical Informatics Association (AMIA) Working Groups


Ethical, Legal and Social Issues WG (ESLI)


People and Organizational Issues (POI) WG


Evaluation WG

International Medical Informatics Association (IMIA) Working Groups


Organizational and Social Issues WG


Human Factors Engineering for Healthcare Informatics WG

Human factors engineering

Human Factors and Ergonomics Society (HFES) Health Care Technical Group

Management Science

Academy of Management: Health Care Management (HCM) Division


Research training programs are funded through the National Library of Medicine.1 These programs fund masters, doctoral, and postdoctoral students seeking careers in biomedical informatics (BMI). There are also opportunities for anthropologists without specific training in BMI to participate as research scientists and staff in large studies. In our experience, clinical and data science colleagues seeking expertise in qualitative methods (e.g., interviewing and focus groups) often see the benefit of a deeper engagement with social theory and ethnography when we present options. Anthropologists can be especially valuable when they are familiar with other research perspectives that are often seen as similar to anthropology and are more frequently encountered in clinical research (e.g., psychology) and technology development (e.g., human factors engineering). An example is the area of adherence research where investigators seek to identify strategies to improve patients’ adherence to treatment recommendations. An anthropological approach (discussed in more detail in the section “Patient-Focused Research”) can be contrasted with a psychological approach that focuses on the individual patient’s cognitive capacity, literacy, numeracy, motivation, etc.


Technology firms such as IBM, Motorola, Google, and Xerox, along with other corporations such as General Motors, have a long history of using the expertise of anthropologists to improve their organizations, products, and services. Anthropologists are also found in industrial design firms such as IDEO and other organizations focusing on user experience (UX) design. As the influence of industry on the delivery of health-care services grows, anthropologists can contribute at multiple levels. Design of corporate strategy can be improved through a better understanding of structural influences on everyday practices. For example, health care organizations are partnering with pharmacy chains (e.g., Walgreens) to improve care to communities that are culturally varied. As community engagement experts, anthropologists can identify ways to optimize the delivery of these services for the community through technology and other means.


Because health-care actors are both subjects and objects of anthropological inquiry, anthropologists in both research and industry should reflect on their positionality with respect to the goals of their work. This reflection can impact how methods of data collection and analysis are deployed. Anthropologists are easily embedded into clinical settings, privy to the everyday practices of health-care workers. Access to this information can create conflicts. For example, in research on the implementation of new technology for medication administration, we participated on rollout teams and sometimes intervened when it appeared that the protocol for use of the technology was being misunderstood by a novice user. This reflects the management of everyday tensions that anthropologists are familiar with: fostering rapport with informants, advocating for informants and others who lack power (e.g., nurses and patients), managing practical issues (e.g., patient safety), and acknowledging that the ideal of an “objective” (i.e., completely without bias), yet rich and informative ethnographic observation is elusive.

Further Reading

  • Bowker, Geoffrey C., and Susan Leigh Star. 2000. Sorting Things Out: Classification and Its Consequences. Cambridge, MA: MIT Press.
  • Coiera, Enrico. 2015. Guide to Health Informatics. Boca Raton, FL: CRC Press.
  • Eubanks, Virginia. 2019. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St. Martin’s Press.
  • Shortliffe, Edward H., and James J. Cimino, eds. 2006. Biomedical Informatics: Computer Applications in Health Care and Biomedicine, 3rd ed. New York: Springer Science+ Business Media.
  • Zheng, Kai, Johanna Westbrook, Thomas G. Kannampallil, and Vimla L. Patel, eds. 2019. Cognitive Informatics. Cham, Switzerland: Springer.


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  • Adams, Samantha A. 2014. “Maintaining the Collision of Accounts: Crowdsourcing Sites in Health Care as Brokers in the Co-Production of Pharmaceutical Knowledge. Information.” Communication & Society 17 (6): 657–669.
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  • 1. The website for the National Library of Medicine’s biomedical informatics and data science research training programs is at the following location.