Encyclopedia of Behavioral Medicine

Living Edition
| Editors: Marc Gellman

Consumer Health Informatics

  • Robin AustinEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6439-6_102006-1
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Synonyms

Definition

Consumer health informatics (CHI), a specialty field of health informatics, focuses on education, practice, research, and policy specifically for the health consumer.

Description

Introduction

In the last decade, there has been an increased focus on consumer or patient engagement and empowerment in healthcare. As consumers are taking a more active role in their care, health information technology (HIT), specifically consumer-directed technologies, is being used to enable ability to communicate, share information, and collaborate across health settings (Lai et al. 2017). Patients with access to consumer-focused technologies can also increase access to care, allow more control of over their health information, possibly reduce barriers to care, and assist in self-management behaviors (Knight and Shea 2014). Consumer health informatics (CHI), a specialty field of health informatics, focuses on education, practice, research, and policy specifically for the health consumer. CHI plays a major role in providing information to patients and the public, which facilities the promotion of self-care, enabling informed decision-making, promoting healthy behaviors and peer information exchange (Abaidoo and Larweh 2014). A major driver of this informatics specialty is the increasing use of consumer-directed health technologies that include mobile health application (mHealth apps), wearable sensor technology and devices, and personal health records (PHR) to name a few. In addition, specific attention is being focused on consumer-facing tools for behavior modification to improve a wide ranging of health outcomes. This paper addresses current definitions of CHI, current state of the science of CHI, and future implications to the field of CHI.

Background and Definitions

In the field of CHI, there is a strong interest in how to use technology to engage and empower consumers. In addition, advances in technology and availability of consumer-facing technologies allow unprecedented access and insight into consumer health behavior. CHI has been stated to be one of the most dynamic specialties of health informatics due to the pace of continuous developments, increase use of new technologies, and growth of data generated by consumers (Eysenbach 2000). A list of frequently used CHI definitions serves as a guide to provide context to the evolving field of CHI (Table 1).
Table 1

Definitions of consumer health informatics

Reference

Definition

Eysenbach (2000)

CHI is the branch of medical (biomedical) informatics that analyzes consumers’ needs for information, studies and implements methods of making information accessible to consumers, and models and integrates consumers’ preferences into medical information systems

Gibbons et al. (2009)

CHI as an electronic tool, technology, or system (a) primarily designed to interact with health information consumers (anyone who seeks or uses healthcare information for nonprofessional work) (b) that interacts directly with the consumer who provides personalized health information to the to the CHI system and receives personalizes health information from the tool or system and (c) in which the data, information, recommendations, or other benefits provided to the consumer may be used with a healthcare professional, but is not dependent on a healthcare professional

American Medical Informatics Association (AMIA) (2018)

CHI is the field devoted to informatics from multiple consumer or patient views. These include patient-focused informatics, health literacy, and consumer education. The focus is on information structures and processes that empower consumers to manage their own health – for example, health information literacy, consumer-friendly language, personal health records, and Internet-based strategies and resources. The shift in this view of informatics analyzes consumers’ needs for information, studies and implements methods for making information accessible to consumers, and models and integrates consumers’ preferences into health information systems

A recent review of CHI definitions recommended that future CHI definitions should be understandable and inclusive for a broad range of diverse users from experts in the field to interested consumers (Flaherty et al. 2015). The choice to use the term “consumer” rather than “patient” aligns with the expanding concept of health data to include information about people across the spectrum of sickness and in health (Evans 2016). A recent suggestion has been to replace “consumer” with “person” or “personal” as individuals may not consider themselves a “consumer” of health but as a person or individual.

State of the Science

The current state of CHI science includes a wide range of research conducted across age groups, populations, and settings with the use of various technologies, devices, and platforms. Most notably mobile health applications (mHealth apps) have increased in popularity, and as a result this technology has created a plethora of consumer or person-generated health data (PGHD). This type of data has provided insight into consumer behavior such as searching for health information and tracking of personal health data and social media activities.

Over the last decade, the increase in mobile applications and self-tracking devices has changed how consumers search and receive health information. Recent reports suggest 59% of US adults have searched health information online within the year, and one in three cell phone users have used their phones to find health information (Fox and Duggan 2012). It is important to note that this figure may even be higher now given the ubiquitous nature of Internet and mobile phone use. mHealth apps allow for the ability of continuous data monitoring and to connect with consumers anywhere and anytime (Steinhubl et al. 2016). From the perspective of health professionals, this type of technology can be used to connect, communicate, and collaborate with patients, families, and populations like never before. However, true effectiveness studies need to be conducted to understand how this technology can best change health behavior (Steinhubl et al. 2016).

The use of mHealth apps and smart technology, such as sensors, has increased the production and availability of PGHD. Studies in CHI and the use of PGHD have begun to show positive results that impact care delivery, improve patient-provider communication, and enhance health outcomes (Lai et al. 2017). In addition, there has been an overall acceptance of consumer technology and person-generated data by consumers, providers, and researchers (Lai et al. 2017). Despite acceptance and positive results, significant challenges exist, specifically the use of PGHD in the clinical setting and the ability to have this type of data accessible and usable in real time (Hsueh et al. 2017; Lai et al. 2017; Woods et al. 2016).

Consumer-facing technologies have provided knowledge of consumer interactions with the health system, and over time this may have clinical practice and policy implications. A recent review found that consumers seek information at various stages across their health journey, through differing platforms and delivery mediums, and the previous one-dimensional chronological health information methods are no longer suitable (Ramsey et al. 2017). This insight can inform health systems how and when consumers seek information and provide the ability to intervene earlier and across platforms.

Future Implications

With increasing consumer expectations and technological advances, the field of CHI will continue to expand across the health domain. One emerging trend is the quantified self-movement and health of the Internet of things (IoT). Today, 49% of the world’s population is connected online, and an estimated 8.4 billion connected things are in use worldwide (Rainie and Anderson 2017). With the proliferation and expanding science of CHI, future research should be direct toward effectiveness of CHI methods and tools for positive health outcomes.

Future CHI research should be directed toward enabling a more nimble HIT infrastructure and the ability to optimize EHRs. Currently, most person-generated data is an unstructured form making it challenging to integrate into current electronic health systems, and using data standards and standardized health terminology can greatly improve the usability of consumer-generated data (Raghupathi and Raghupathi 2014; Woods et al. 2016). In addition, time constraints and a fast-pace clinic environment to increase the need for person-generated data however it needs to be usable and interpretable in real-time. There is tremendous opportunity for CHI however, there are also ethical, legal, social, and privacy considerations. These technologies may influence behavior change for individuals, families, and populations toward optimal health, it will also be important to understand how the health system interacts with these technologies and use them in collaboration with the individual or family.

Conclusion

Emerging trends in consumer health informatics have the potential to shift healthcare delivery. Consumer-facing tools can provide necessary information to empower patients to be more active in their care and facilitate informed decision-making. There is tremendous opportunity to engage consumers and increase the ability to interact and collaborate as a health team that includes the voice of the consumer. Moving forward it will take an interdisciplinary approach to overcome challenges and barriers and generate research and create responsible policies that will benefit individuals, families, and communities.

Cross-References

References and Further Readings

  1. Abaidoo, B., & Larweh, B. T. (2014). Consumer health informatics: The application of ICT in improving patient-provider partnership for a better health care. Online Journal of Public Health Informatics, 6(2), e188.  https://doi.org/10.5210/ojphi.v6i2.4903.CrossRefPubMedPubMedCentralGoogle Scholar
  2. AMIA. (2018). Consumer health informatics. Retrieved from https://www.amia.org/applications-informatics/consumer-health-informatics
  3. Evans, B. (2016). Barbarians at the Gate: Consumer-driven health data commons and the Transformation of Citizen Science. American Journal of Law & Medicine, 70(12), 773–779.  https://doi.org/10.1097/OGX.0000000000000256.Prenatal.CrossRefGoogle Scholar
  4. Eysenbach, G. (2000). Consumer health informatics. British Medical Journal, 320(7251), 1713–1716.  https://doi.org/10.1136/bmj.320.7251.1713.CrossRefPubMedGoogle Scholar
  5. Flaherty, D., Hoffman-Goetz, L., & Arocha, J. F. (2015). What is consumer health informatics? A systematic review of published definitions. Informatics for Health & Social Care, 40(2), 91–112.  https://doi.org/10.3109/17538157.2014.907804.CrossRefGoogle Scholar
  6. Fox, S., & Duggan, M. (2012). Mobile health 2012 (p. 29). Washington, DC: Pew Internet. Retrieved from http://www.pewinternet.org/2012/11/08/mobile-health-2012/.Google Scholar
  7. Gibbons, M. C., Wilson, R. F., Samal, L., Lehman, C. U., Dickersin, K. (2009). Impact of consumer health informatics applications (Vol. 09). Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK32638/.
  8. Hsueh, P.-Y., Cheung, Y.-K., Dey, S., Kim, K. K., Martin-Sanchez, F. J., Petersen, S. K., & Wetter, T. (2017). Added value from secondary use of person generated health data in consumer health informatics. IMIA Yearbook, 26(1), 1–12.  https://doi.org/10.15265/IY-2017-009.CrossRefGoogle Scholar
  9. Knight, E. P., & Shea, K. (2014). A patient-focused framework integrating self-management and informatics. Journal of Nursing Scholarship, 46(2), 91–97.  https://doi.org/10.1111/jnu.12059.CrossRefPubMedGoogle Scholar
  10. Lai, A. M., Hsueh, P.-Y. S., Choi, Y. K., & Austin, R. R. (2017). Present and future trends in consumer health informatics and patient-generated health data. Yearbook of Medical Informatics, 26(1), 152–159.  https://doi.org/10.15265/IY-2017-016.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.  https://doi.org/10.1186/2047-2501-2-3.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Rainie, L., & Anderson, J. (2017). The internet of things connectivity binge: What are the implications? Pew Internet, (June). Retrieved from http://www.pewinternet.org/2017/06/06/the-internet-of-things-connectivity-binge-what-are-the-implications/
  13. Ramsey, I., Corsini, N., Peters, M. D. J., & Eckert, M. (2017). A rapid review of consumer health information needs and preferences. Patient Education and Counseling, 100(9), 1634–1642.  https://doi.org/10.1016/j.pec.2017.04.005.CrossRefPubMedGoogle Scholar
  14. Steinhubl, S. R., Muse, E. D., Topol, E. J., & Jolla, L. (2016). The emerging field of mobile health. Science Translation Medicine, 7(283), 1–12.  https://doi.org/10.1126/scitranslmed.aaa3487.The.CrossRefGoogle Scholar
  15. Woods, S. S., Evans, N. C., & Frisbee, K. L. (2016). Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications. Journal of the American Medical Informatics Association, 23(3), 491–495.  https://doi.org/10.1093/jamia/ocv199.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of NursingUniversity of MinnesotaMinneapolisUSA

Section editors and affiliations

  • Emily Lattie
    • 1
  1. 1.Center for Behavioral Intervention TechnologiesNorthwestern UniversityChicagoUSA