Journal:Design of generalized search interfaces for health informatics

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Full article title Design of generalized search interfaces for health informatics
Journal Information
Author(s) Demelo, Jonathan; Sedig, Kamran
Author affiliation(s) Western University
Primary contact Email: sedig at uwo dot ca
Editors Almada, Marta
Year published 2021
Volume and issue 12(8)
Article # 317
DOI 10.3390/info12080317
ISSN 2078-2489
Distribution license Creative Commons Attribution 4.0 International
Website https://www.mdpi.com/2078-2489/12/8/317/htm
Download https://www.mdpi.com/2078-2489/12/8/317/pdf (PDF)
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Abstract

In this paper, we investigate ontology-supported interfaces for health informatics search tasks involving large document sets. We begin by providing background on health informatics, machine learning, and ontologies. We review leading research on health informatics search tasks to help formulate high-level design criteria. We then use these criteria to examine traditional design strategies for search interfaces. To demonstrate the utility of the criteria, we apply them to the design of the ONTology-supported Search Interface (ONTSI), a demonstrative, prototype system. ONTSI allows users to plug-and-play document sets and expert-defined domain ontologies through a generalized search interface. ONTSI’s goal is to help align users’ common vocabulary with the domain-specific vocabulary of the plug-and-play document set. We describe the functioning and utility of ONTSI in health informatics search tasks through a workflow and a scenario. We conclude with a summary of ongoing evaluations, limitations, and future research.

Keywords: information search, search tasks, health informatics, interface design, ontologies, machine learning, PubMed

Introduction

Health informatics is concerned with emergent technological systems that improve the quality and availability of care, promote the sharing of knowledge, and support the performance of proactive health and wellness tasks by motivated individuals.[1] Subareas of health informatics may include medical informatics, nursing informatics, consumer informatics, cancer informatics, and pharmacy informatics, to name a few. Simply put, health informatics is concerned with harnessing technology for finding new ways to help stakeholders work with health information to be able to perform health-related tasks more effectively.

Users in the health domain are increasingly taking advantage of computer-based resources in their tasks. For instance, a 2017 Canadian survey found that 32% of respondents within their last month had used at least one mobile application for health-related tasks. Even more, those under the age of 35 are twice as likely to do so.[2] Furthermore, studies have calculated that over 58% of Americans have used tools like Google and other domain-specific tools to support their health informatics search tasks, with search being one of the most important and central tasks in most health informatics activities.[3][4]

Yet, search can be challenging, particularly for health informatics tasks that utilize large and complex document sets. For such tasks, health informatics tools may require the use of domain-specific vocabulary. Aligning with this vocabulary can be a significant challenge within health tasks, as they can involve a lexicon of intricate nomenclature, deeply layered relations, and lengthy descriptions that are misaligned with common vocabulary. For instance, one highly cited medical research paper defines the term “chromosomal instability” as “an elevated rate of chromosome mis-segregation and breakage, results in diverse chromosomal aberrations in tumor cell populations.” In this example, those unfamiliar with the defined term could find parsing its definition just as significant a challenge as the term itself.[5] Thus, when communicating across vocabularies, users may struggle to describe the requirements of their search task in a way that is understandable by health informatics tools.[6][7] To deal with this challenge, ontologies can be a valuable mediating resource in the design of user-facing interfaces of health informatics tools.[8] That is, ontologies can bridge the vocabularies of users with the vocabulary of their task and its tools. Yet, the use of ontologies in user-facing interface design is not well established. Furthermore, health informatics tools that present a generalized interface, one that can support search tasks across any number of domain vocabularies and document sets, can allow users to transfer their experience between tasks, presenting users with information-centric perspectives during their performances rather than technology-centered perspectives.[9][10] For this, there is a need to distill criteria that can guide designers during the creation of ontology-supported interfaces for health informatics search tasks involving large document sets.

The goal of this paper is to investigate the following research questions:

  • What are the criteria for the structure and design of generalized ontology-supported interfaces for health informatics search tasks involving large document sets?
  • If such criteria can be distilled, can they then be used to help create such interfaces?

In this paper, we examine health informatics, machine learning, and ontologies. We then review leading research on health informatics search tasks. From this analysis, we formulate criteria for the design of ontology-supported interfaces for health informatics search tasks involving large document sets. We then use these criteria to contrast the traditional design strategies for search interfaces. To demonstrate the utility of the criteria in design, we will use them to structure the design of a tool, ONTSI (ONTology-supported Search Interface). ONTSI allows users to plug-and-play their document sets and expert-defined ontology files to perform health informatics search tasks. We describe ONTSI through a functional workflow and an illustrative usage scenario. We conclude with a summary of ongoing evaluation efforts, future research, and our limitations.[11]

Background

In this section, we describe the concepts and terminology used when discussing ontology-supported interfaces for health informatics search tasks involving large document sets. We begin with background on health informatics. Next, we examine machine learning. We conclude with coverage of ontologies and their utility as a mediating resource for both human- and computer-facing use.

Health informatics

Health informatics is broadly concerned with emergent technological systems for improving the quality and availability of care, promoting the sharing of knowledge, and supporting the performance of proactive health and wellness practices by motivated individuals.[1] Initially, the need for expanded health and wellness services stemmed from rising population levels combined with the growing complexity of medical sciences. These issues made it challenging to maintain quality care within increasingly stressed medical systems.[12] Thus, a central objective for health informatics is the development of strategies to tackle large-scale problems that harm trained medical professionals’ ability to perform their tasks in a timely and effective manner. For instance, telehealth services allowed doctors to practice remote medicine, providing care to those without local medical services. Another early innovation was standardized electronic health care records (EHRs), where patient records were given standardized encodings to provide an increased ability to track, compare, manage, and share personal health information.[3] Some examples of current research directions are the push for stronger patient privacy, personalized medicine, and the expansion of healthcare into underserved regions and communities.[1][2][3][13][14]

The rising production and availability of health-related data has resulted in a growing number of data-intensive tasks within health. Both private and public entities like health industry companies, government bodies, and everyday citizens are turning to health informatics tools as they manage and activate their health data.[2] A growing number of health-related tasks involve searching document sets. During these tasks, the aim of the user is to use the information described within their document set to increase their understanding of a topic or concept. For example, a search task could be a practitioner searching the EHRs of their patients, a member of the general public using public materials for their general health concerns, or a researcher performing a literature review.[12][15][16] In general, a search task involves the generation of a query based on an information-seeking objective. The computation systems of these tools then use this query to map and extract relevant documents out from the document set.[15] Powerful technologies like machine learning are increasingly being integrated within tools to help perform rapid and automated computation on document sets.[4] Yet, when taking advantage of these technologies, designers must be mindful of human factors when generating the user-facing interfaces of their tools, as a task cannot be performed effectively without direction from an empowered user.[12]


References

  1. ↑ 1.0 1.1 1.2 Wickramasinghe, Nilmini (2019/08). "Essential Considerations for Successful Consumer Health Informatics Solutions" (in en). Yearbook of Medical Informatics 28 (01): 158–164. doi:10.1055/s-0039-1677909. ISSN 0943-4747. PMC PMC6697544. PMID 31419828. http://www.thieme-connect.de/DOI/DOI?10.1055/s-0039-1677909. 
  2. ↑ 2.0 2.1 2.2 Canadian Medical Association (2018). "The Future of Technology in Health and Health Care: A Primer". Canadian Medical Association. Archived from the original on 30 April 2019. https://web.archive.org/web/20190430220959/https://www.cma.ca/sites/default/files/pdf/health-advocacy/activity/2018-08-15-future-technology-health-care-e.pdf. 
  3. ↑ 3.0 3.1 3.2 Demiris, G. (2016). "Consumer Health Informatics: Past, Present, and Future of a Rapidly Evolving Domain" (in en). Yearbook of Medical Informatics 25 (S 01): S42–S47. doi:10.15265/IYS-2016-s005. ISSN 0943-4747. PMC PMC5171509. PMID 27199196. http://www.thieme-connect.de/DOI/DOI?10.15265/IYS-2016-s005. 
  4. ↑ 4.0 4.1 Zuccon, G.; Koopman, B. (2014). Goeuriot, L.; Jones, G.J.F.; Kelly, L. et al.. ed. "Integrating understandability in the evaluation of consumer health search engines". Proceedings of the Medical Information Retrieval Workshop at SIGIR 2014 (MedIR@SIGIR 2014) 1276: 32–35. http://ceur-ws.org/Vol-1276/. 
  5. ↑ Chinese Journal of Cancer (1 December 2017). "The 150 most important questions in cancer research and clinical oncology series: questions 67–75: Edited by Chinese Journal of Cancer" (in en). Chinese Journal of Cancer 36 (1): 86, s40880–017–0254-z. doi:10.1186/s40880-017-0254-z. ISSN 1944-446X. PMC PMC5664810. PMID 29092716. https://cancercommun.biomedcentral.com/articles/10.1186/s40880-017-0254-z. 
  6. ↑ Mehta, N.; Pandit, A. (1 June 2018). "Concurrence of big data analytics and healthcare: A systematic review" (in en). International Journal of Medical Informatics 114: 57–65. doi:10.1016/j.ijmedinf.2018.03.013. ISSN 1386-5056. https://www.sciencedirect.com/science/article/abs/pii/S1386505618302466. 
  7. ↑ Thiébaut, Rodolphe; Cossin, Sébastien; Informatics, Section Editors for the IMIA Yearbook Section on Public Health and Epidemiology (2019/08). "Artificial Intelligence for Surveillance in Public Health" (in en). Yearbook of Medical Informatics 28 (01): 232–234. doi:10.1055/s-0039-1677939. ISSN 0943-4747. PMC PMC6697516. PMID 31419837. http://www.thieme-connect.de/DOI/DOI?10.1055/s-0039-1677939. 
  8. ↑ Saleemi, M.M.; Rodríguez, N.D.; Lilius, J.; Porres, I. (2011). "A Framework for Context-aware Applications for Smart Spaces". In Balandin, Sergey; Koucheryavy, Yevgeni; Hu, Honglin et al.. Smart Spaces and Next Generation Wired/Wireless Networking. Lecture notes in computer science. Heidelberg: Springer. pp. 14–25. ISBN 978-3-642-22874-2. OCLC 844916767. https://www.worldcat.org/title/mediawiki/oclc/844916767. 
  9. ↑ Gibson, C. J.; Dixon, B. E.; Abrams, K. (2015). "Convergent evolution of health information management and health informatics" (in en). Applied Clinical Informatics 06 (01): 163–184. doi:10.4338/ACI-2014-09-RA-0077. ISSN 1869-0327. PMC PMC4377568. PMID 25848421. http://www.thieme-connect.de/DOI/DOI?10.4338/ACI-2014-09-RA-0077. 
  10. ↑ Fang, Ruogu; Pouyanfar, Samira; Yang, Yimin; Chen, Shu-Ching; Iyengar, S. S. (14 June 2016). "Computational Health Informatics in the Big Data Age: A Survey". ACM Computing Surveys 49 (1): 12:1–12:36. doi:10.1145/2932707. ISSN 0360-0300. https://doi.org/10.1145/2932707. 
  11. ↑ Köhler, Sebastian; Carmody, Leigh; Vasilevsky, Nicole; Jacobsen, Julius O B; Danis, Daniel; Gourdine, Jean-Philippe; Gargano, Michael; Harris, Nomi L et al. (8 January 2019). "Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources". Nucleic Acids Research 47 (D1): D1018–D1027. doi:10.1093/nar/gky1105. ISSN 0305-1048. PMC PMC6324074. PMID 30476213. https://doi.org/10.1093/nar/gky1105. 
  12. ↑ 12.0 12.1 12.2 Carayon, Pascale; Hoonakker, Peter (2019/08). "Human Factors and Usability for Health Information Technology: Old and New Challenges" (in en). Yearbook of Medical Informatics 28 (01): 071–077. doi:10.1055/s-0039-1677907. ISSN 0943-4747. PMC PMC6697515. PMID 31419818. http://www.thieme-connect.de/DOI/DOI?10.1055/s-0039-1677907. 
  13. ↑ Gamache, Roland; Kharrazi, Hadi; Weiner, Jonathan P. (2018/08). "Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities" (in en). Yearbook of Medical Informatics 27 (01): 199–206. doi:10.1055/s-0038-1667081. ISSN 0943-4747. PMC PMC6115205. PMID 30157524. http://www.thieme-connect.de/DOI/DOI?10.1055/s-0038-1667081. 
  14. ↑ Brewer, LaPrincess C.; Fortuna, Karen L.; Jones, Clarence; Walker, Robert; Hayes, Sharonne N.; Patten, Christi A.; Cooper, Lisa A. (14 January 2020). "Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health" (in EN). JMIR mHealth and uHealth 8 (1): e14512. doi:10.2196/14512. PMC PMC6996775. PMID 31934874. https://mhealth.jmir.org/2020/1/e14512. 
  15. ↑ 15.0 15.1 Wu, Charley M.; Meder, Björn; Filimon, Flavia; Nelson, Jonathan D. (1 August 2017). "Asking better questions: How presentation formats influence information search." (in en). Journal of Experimental Psychology: Learning, Memory, and Cognition 43 (8): 1274–1297. doi:10.1037/xlm0000374. ISSN 1939-1285. http://doi.apa.org/getdoi.cfm?doi=10.1037/xlm0000374. 
  16. ↑ Talbot, Justin; Lee, Bongshin; Kapoor, Ashish; Tan, Desney S. (4 April 2009). "EnsembleMatrix: Interactive visualization to support machine learning with multiple classifiers" (in en). Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston MA USA: ACM): 1283–1292. doi:10.1145/1518701.1518895. ISBN 978-1-60558-246-7. https://dl.acm.org/doi/10.1145/1518701.1518895. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. Some grammar and punctuation was cleaned up to improve readability. In some cases important information was missing from the references, and that information was added.

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