Qian Yang
Aaron Steinfeld
John Zimmerman
ABSTRACT
Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of Unremarkable Computing, that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience.
- David Arnott and Graham Pervan. 2014. A critical analysis of decision support systems research revisited: the rise of design science. Journal of Information Technology 29, 4 (01 Dec 2014), 269--293.Google Scholar
Cross Ref
- Raymond L Benza, Dave P Miller, Robyn J Barst, David B Badesch, Adaani E Frost, and Michael D McGoon. 2012. An evaluation of longterm survival from time of diagnosis in pulmonary arterial hypertension from the REVEAL Registry. CHEST Journal 142, 2 (2012), 448--456.Google ScholarCross Ref
- David Coyle and Gavin Doherty. 2009. Clinical Evaluations and Collaborative Design: Developing New Technologies for Mental Healthcare Interventions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '09). ACM, New York, NY, USA, 2051--2060. Google ScholarDigital Library
- Srikant Devaraj, Sushil K Sharma, Dyan J Fausto, Sara Viernes, and Hadi Kharrazi. 2014. Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review. Journal of Business Administration Research 3, 2 (2014), p36.Google ScholarCross Ref
- Glyn Elwyn, Isabelle Scholl, Caroline Tietbohl, Mala Mann, Adrian GK Edwards, Catharine Clay, France Légaré, Trudy van der Weijden, Carmen L Lewis, Richard M Wexler, et al. 2013. "Many miles to go...": a systematic review of the implementation of patient decision support interventions into routine clinical practice. BMC medical informatics and decision making 13, Suppl 2 (2013), S14.Google Scholar
- Hidden for Anonymity During Review. 2016. Hidden for Anonymity During Review.Google Scholar
- Karine Gravel, France Légaré, and Ian D Graham. 2006. Barriers and facilitators to implementing shared decision-making in clinical practice: a systematic review of health professionals' perceptions. Implement Sci 1, 1 (2006), 16.Google ScholarCross Ref
- Monique WM Jaspers, Marian Smeulers, Hester Vermeulen, and Linda W Peute. 2011. Efects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of highquality systematic review fndings. Journal of the American Medical Informatics Association 18, 3 (2011), 327--334.Google ScholarCross Ref
- Kensaku Kawamoto, Caitlin A Houlihan, E Andrew Balas, and David F Lobach. 2005. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. Bmj 330, 7494 (2005), 765.Google Scholar
- Leah Kulp and Aleksandra Sarcevic. 2018. Design In The "Medical" Wild: Challenges Of Technology Deployment. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA '18). ACM, New York, NY, USA, Article LBW040, 6 pages. Google ScholarDigital Library
- Bill Moggridge. 2007. Designing interactions. Vol. 14. Google ScholarDigital Library
- Mark A Musen, Blackford Middleton, and Robert A Greenes. 2014. Clinical decision-support systems. In Biomedical informatics. Springer, 643--674.Google Scholar
- Annette M O'Connor, John E Wennberg, France Legare, Hilary A Llewellyn-Thomas, Benjamin W Moulton, Karen R Sepucha, Andrea G Sodano, and Jaime S King. 2007. Toward the tipping point: decision aids and informed patient choice. Health Afairs 26, 3 (2007), 716--725.Google ScholarCross Ref
- Brindha Pillay, Addie C Wootten, Helen Crowe, Niall Corcoran, Ben Tran, Patrick Bowden, Jane Crowe, and Anthony J Costello. 2016. The impact of multidisciplinary team meetings on patient assessment, management and outcomes in oncology settings: a systematic review of the literature. Cancer treatment reviews 42 (2016), 56--72.Google Scholar
- Kate Sellen, Dominic Furniss, Yunan Chen, Svetlena Taneva, Aisling Ann O'Kane, and Ann Blandford. 2014. Workshop Abstract: CHI 2019, May 4--9, 2019, Glasgow, Scotland Uk HCI Research in Healthcare: Using Theory from Evidence to Practice. In CHI '14 Extended Abstracts on Human Factors in Computing Systems (CHI EA '14). ACM, New York, NY, USA, 87--90. Google ScholarDigital Library
- Dean F Sittig, Adam Wright, Jerome A Osherof, Blackford Middleton, Jonathan M Teich, Joan S Ash, Emily Campbell, and David W Bates. 2008. Grand challenges in clinical decision support. Journal of Biomedical Informatics 41 (2008), 387--392. Google ScholarDigital Library
- Mark S. Slaughter, Francis D. Pagani, Joseph G. Rogers, Leslie W. Miller, Benjamin Sun, Stuart D. Russell, Randall C. Starling, Leway Chen, Andrew J. Boyle, Suzanne Chillcott, Robert M. Adamson, Margaret S. Blood, Margarita T. Camacho, Katherine A. Idrissi, Michael Petty, Michael Sobieski, Susan Wright, Timothy J. Myers, and David J. Farrar. 2010. Clinical management of continuous-fow left ventricular assist devices in advanced heart failure. The Journal of Heart and Lung Transplantation 29, 4, Supplement (2010), S1 -- S39. Clinical Management of Continuous-fow Left Ventricular Assist Devices in Advanced Heart Failure.Google ScholarCross Ref
- Nicole Sultanum, Michael Brudno, Daniel Wigdor, and Fanny Chevalier. 2018. More Text Please! Understanding and Supporting the Use of Visualization for Clinical Text Overview. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 422, 13 pages. Google ScholarDigital Library
- Alan R Tait, Terri Voepel-Lewis, Brian J Zikmund-Fisher, and Angela Fagerlin. 2010. The efect of format on parents' understanding of the risks and benefts of clinical research: a comparison between text, tables, and graphics. Journal of health communication 15, 5 (2010), 487--501.Google ScholarCross Ref
- Svetlena Taneva, Waxberg Sara, Goss Julian, Rossos Peter, Nicholas Emily, and Cafazzo Joseph. 2014. The Meaning of Design in Healthcare: Industry, Academia, Visual Design, Clinician, Patient and Hf Consultant Perspectives. In Proceedings of the Extended Abstracts of the 32Nd Annual ACM Conference on Human Factors in Computing Systems (CHI EA '14). ACM, New York, NY, USA, 1099--1104. Google ScholarDigital Library
- Danielle Timmermans, Bert Molewijk, Anne Stiggelbout, and Job Kievit. 2004. Diferent formats for communicating surgical risks to patients and the efect on choice of treatment. Patient education and counseling 54, 3 (2004), 255--263.Google Scholar
- Peter Tolmie, James Pycock, Tim Diggins, Allan MacLean, and Alain Karsenty. 2002. Unremarkable Computing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '02). ACM, New York, NY, USA, 399--406. Google ScholarDigital Library
- Robert L Wears and Marc Berg. 2005. Computer technology and clinical work: still waiting for Godot. Jama 293, 10 (2005), 1261--1263.Google ScholarCross Ref
- Jeremy C Wyatt and Douglas G Altman. 1995. Commentary: Prognostic models: clinically useful or quickly forgotten? Bmj 311, 7019 (1995), 1539--1541.Google ScholarCross Ref
- Qian Yang, John Zimmerman, and Aaron Steinfeld. 2015. Review of Medical Decision Support Tools : Emerging Opportunity for Interaction Design. In IASDR 2015 Interplay Proceedings.Google Scholar
- Qian Yang, John Zimmerman, Aaron Steinfeld, Lisa Carey, and James F. Antaki. 2016. Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 4477--4488. Google ScholarDigital Library
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