Appl Clin Inform 2016; 07(02): 604-623
DOI: 10.4338/ACI-2015-12-RA-0182
Research Article
Schattauer GmbH

Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain

A pilot study
Don Roosan
1   Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA
,
Guilherme Del Fiol
1   Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA
2   IDEAS Center for Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84108, USA
,
Jorie Butler
2   IDEAS Center for Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84108, USA
,
Yarden Livnat
3   Scientific Computing and Imaging Institute, Department of Computer Sciences, University of Utah, 72 S Central Campus Dr, Salt Lake City, UT 84112, USA
,
Jeanmarie Mayer
2   IDEAS Center for Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84108, USA
,
Matthew Samore
1   Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA
2   IDEAS Center for Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84108, USA
,
Makoto Jones
2   IDEAS Center for Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84108, USA
,
Charlene Weir
1   Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA
2   IDEAS Center for Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84108, USA
› Author Affiliations
This project was supported by the Agency for Healthcare Research and Quality (grant R36HS023349) and Department of Veterans Affairs Research and Development (grant CRE 12–230). Dr. Islam was supported by National Library of Medicine training grant (T15-LM07124) and partially supported by Houston Veterans Affairs Health Services Research & Development Center for Innovations in Quality and Effectiveness and Safety (IQuESt).
Further Information

Correspondence to:

Don Roosan, PharmD, PhD
University of Utah
Department of Biomedical Informatics
421 Wakara Way
Ste 140
Salt Lake City
UT 84108
USA
Phone: 260-580-0903

Publication History

received: 31 December 2015

accepted: 01 May 2016

Publication Date:
16 December 2017 (online)

 

Summary

Objective

Big data or population-based information has the potential to reduce uncertainty in medicine by informing clinicians about individual patient care. The objectives of this study were: 1) to explore the feasibility of extracting and displaying population-based information from an actual clinical population’s database records, 2) to explore specific design features for improving population display, 3) to explore perceptions of population information displays, and 4) to explore the impact of population information display on cognitive outcomes.

Methods

We used the Veteran’s Affairs (VA) database to identify similar complex patients based on a similar complex patient case. Study outcomes measures were 1) preferences for population information display 2) time looking at the population display, 3) time to read the chart, and 4) appropriateness of plans with pre-and post-presentation of population data. Finally, we redesigned the population information display based on our findings from this study.

Results

The qualitative data analysis for preferences of population information display resulted in four themes: 1) trusting the big/population data can be an issue, 2) embedded analytics is necessary to explore patient similarities, 3) need for tools to control the view (overview, zoom and filter), and 4) different presentations of the population display can be beneficial to improve the display. We found that appropriateness of plans was at 60% for both groups (t9=-1.9; p=0.08), and overall time looking at the population information display was 2.3 minutes versus 3.6 minutes with experts processing information faster than non-experts (t8= -2.3, p=0.04).

Conclusion

A population database has great potential for reducing complexity and uncertainty in medicine to improve clinical care. The preferences identified for the population information display will guide future health information technology system designers for better and more intuitive display.


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Conflict of Interest

All authors declare that there are no conflicts of interest.

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Correspondence to:

Don Roosan, PharmD, PhD
University of Utah
Department of Biomedical Informatics
421 Wakara Way
Ste 140
Salt Lake City
UT 84108
USA
Phone: 260-580-0903

  • References

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  • 26 Miller A, Sanderson P. editors. Designing an information display for clinical decision making in the intensive care unit. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2003. SAGE Publications.
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  • 28 Jing X, Cimino JJ. A complementary graphical method for reducing and analyzing large data sets. Case studies demonstrating thresholds setting and selection. Methods of Information in Medicine 2014; 53 (03) 173-185 doi:10.3414/ME13–01–0075.
  • 29 Kopanitsa G, Hildebrand C, Stausberg J, Englmeier KH. Visualization of medical data based on ehr standards. Methods of Information in Medicine 2013; 52 (01) 43-50 doi:10.3414/ME12–01–0016.
  • 30 Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013; 309 (13) 1351-1352.
  • 31 Livnat Y, Gesteland P, Benuzillo J, Pettey W, Bolton D, Drews F. et al. Epinome – a novel workbench for epidemic investigation and analysis of search strategies in public health practice. AMIA Annu Symp Proc 2010; 2010: 647-651.
  • 32 Garvin JH, Duvall SL, South BR, Bray BE, Bolton D, Heavirland J. et al. Automated extraction of ejection fraction for quality measurement using regular expressions in unstructured information management architecture (uima) for heart failure. J Am Med Inform Assoc 2012; 19 (05) 859-866 doi:amiajnl-2011–000535 [pii]10.1136/amiajnl-2011–000535.
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  • 34 Wolf JR. Do it students prefer doctors who use it?. Computers in Human Behavior 2014; 35 (00) 287-294 doi:http://dx.doi.org/10.1016/j.chb.2014.03.020.
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  • 36 Wickens CD, Carswell CM. The proximity compatibility principle: Its psychological foundation and relevance to display design. Human Factors: The Journal of the Human Factors and Ergonomics Society 1995; 37 (03) 473-494.
  • 37 South BR, Shen S, Leng J, Forbush TB, DuVall SL, Chapman WW. editors. A prototype tool set to support machine-assisted annotation. Proceedings of the 2012 Workshop on Biomedical Natural Language Processing. 2012. Association for Computational Linguistics.
  • 38 Clark J. How to peer review a qualitative manuscript. Peer review in health sciences 2003; 02: 219-235.
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