HFES Conference Part 6: Health & Human Factors

The medical domain is an area where human factors research is very active. Here are some highlights from the conference.

Health Records

The following two presentations/proceedings papers examined Personal Health Records (user-maintained medical records):

Improving the user interface and adoption of online personal health records. (2009).  Peters, K. A., Green, T. F., & Shumacher, R. M.

This paper was a usability evaluation of some of the most popular PHR web apps on the web (Google Health, Microsoft Health Vault).  Thirty participants completed a variety of tasks using both systems while the number and types of errors were examined.  In addition, qualitative analysis examined adoption factors (perceived usability, utility, etc).  We’ve blogged about this study (which is also available as a white paper from UserCentric).

Examining non-critical health information seeking:  A needs analysis for personal health records. (2009).  Price, M. M., Breedlove, J., Pak, R., Muller, H., & Stronge, A.

This was in-progress work that my student and I conducted.  We were interested in the generic adoption of health IT by older consumers.  While PHR development has proceeded full-steam, there has not yet been research examining the utility of these systems for older adults–those who may benefit the most from accurate, user-maintained, always-available health information.  The study was a diary study of the kinds of health-related questions that older adults (those over age 60) have in the course of 2 weeks.  Other dimensions were number of health questions, severity, strategies used to answer the questions, and attitudes toward technology assistance.

Some papers examined health records from the perspective of the health care provider (Electronic Health Records; EHRs):

Healthcare workers’ perceptions of information in the electronic health record. (2009).  Russ, A.L., Saleem, J. J., Justice, C. F., Hagg, H., Woodbridge, P. A., Doebbeling, B. N.

The researchers were interested in understanding why EHR adoption by health care workers was not as swift as one would assume.  They conducted 14 interviews of health care workers to better understand how EHRs could be designed to improve health care work flow.  Seventeen major aspects of EHRs that prevented adoption were identified but only 5 were discussed in detail:  Customizeability (EHRs were difficult to customize or not customizeable), Prioritized (EHRs did not present ‘priority’ information; users often used external programs to add this information), Trendable (long term trends were not easily visualizeable), Locatable (classic information overload; there is just too much information and not an easy way to navigate it), and Accessible (technical issues such as login problems, network issues).  The paper outlines specific examines of “work-arounds” used by workers to overcome some of these issues.

Technology & Health

On to the more generic topic of health care IT, there was

Documentation in a medical setting:  effects of technology on perceived quality of care. (2009).  DeBlasio, J., & Walker, B. N.

imagesThis paper found that perceptions of the technology used by the health care provider significantly influenced the patient’s perceived quality of care. In their study, they had undergraduates view video clips of a doctor taking notes from a patient. The researchers manipulated the method of note-taking (mental notes, paper, PDA, desktop PC, and wearable PC) and angle of view (head-on and offset 90 degrees). The lowest quality of care condition was desktop PC. The highest QoC conditions were wearable PC or mental notes)–presumably reflecting the negative perceptions of “obvious” technology.


The nurse’s role in health care was highlighted in many papers.  Two noteworthy papers (because they both deal with prospective memory) were:

Distributed prospective memory:  an approach to understanding how nurses remember tasks. (2009).  Grundeiger, T., Sanderson, P. M., MacDougal, H. G., & Venkatesh, B.

Prospective memory in the nursing environment:  effects of type of prospective task and prospective load. (2009).  Fink, N., Pak, R., & Battisto, D.