How a paper chart affords collaborative and distributed small data experiments for decision making under uncertainty

Valentina Lichtner, Ela Klecun

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

This paper presents a micro-level study of affordances of a hospital paper drug chart in the clinical context of a care of the elderly. It discusses how the chart mediates the activity of prescribing pain medication under conditions of uncertainty, in a context of distributed work among different clinical roles, each with its own rules, rights and responsibilities. The chart is a multidisciplinary tool used over the course of time of a patient hospitalization. Its paper format is known to afford flexibility, more so than rigid computer systems and digital forms. Under uncertainty it would be reasonable to expect paper flexibility to support sensemaking. Yet, in this context it is its structure rather than its flexibility that affords reducing clinical uncertainty. The chart structures and solidifies messiness of pain management, by transforming patients' experiences into 'data points' (gathering small data) and building a record of a therapeutic trial. Thus, it simultaneously reduces and obfuscates uncertainty. This paper contributes to the literature on working with uncertainty in everyday practice, artifact's affordances and the role of small data (as opposed to big data) in decision making under uncertainty.

Original languageEnglish
Title of host publicationECIS 2018 Proceedings
Place of PublicationUnited States
PublisherAssociation for Information Systems
Pages1-11
Number of pages11
Publication statusPublished - 1 Jan 2018
Event26th European Conference on Information Systems, ECIS 2018 - Portsmouth, United Kingdom
Duration: 23 Jun 201828 Jun 2018

Conference

Conference26th European Conference on Information Systems, ECIS 2018
CountryUnited Kingdom
CityPortsmouth
Period23/06/1828/06/18

Keywords

  • Affordance
  • Decision making
  • Drug chart
  • Health care practice
  • Small data
  • Uncertainty

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