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			<titleStmt><title level='a'>'Shopping Around': An Experiment in Preferences and Incentives for Placing Long-term Patients</title></titleStmt>
			<publicationStmt>
				<publisher>ACM Digital Library</publisher>
				<date>10/18/2025</date>
			</publicationStmt>
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				<bibl> 
					<idno type="par_id">10686391</idno>
					<idno type="doi">10.1145/3757529</idno>
					<title level='j'>Proceedings of the ACM on Human-Computer Interaction</title>
<idno>2573-0142</idno>
<biblScope unit="volume">9</biblScope>
<biblScope unit="issue">7</biblScope>					

					<author>Vince Bartle</author><author>Nicola Dell</author><author>Nikhil Garg</author>
				</bibl>
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			<abstract><ab><![CDATA[<p>Hospitals and care homes devote significant resources to placing post-acute patients from hospitals into long-term care. This paper describes a two-phase experiment over SMS, conducted with a hospital in Hawai'i, in which care homes express preferences, indicate availability to accept patients, and express interest in patients. In the first phase, the treatment asks care homes to reconsider their stated preferences to better support matching. The second phase measures whether resulting changes in preferences increased how often homes express interest in patients that match newly stated preferences. First, to motivate and inform experiment design, we explore factors contributing to extended hospital stays for patients, uncovering how care homes' preferences play a major role in what patients they consider accepting. Second, we conduct a 16-week randomized controlled trial with 960 homes, where we experimentally probed, via SMS messages, homes' willingness to change their preferences to improve potential patient match recommendations. We show that inducing homes to reflect on their preferences increased the number of homes who changed their preferences by over 50%: 9.8% of homes who received our treatment changed their preference compared to 6.0% of homes in the control group (p-value = 0.0421).Third, followup interviews with 22 home operators highlight how preference malleability is shaped by a combination of design constraints and on-the-ground realities, such as load-balancing existing patient rosters. Finally, we discuss implications for real-world systems like ours that must balance constrained communication with situational complexity towards improving outcomes.</p>]]></ab></abstract>
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