"You Have to Piece the Puzzle Together": Implications for Designing Decision Support in Intensive Care

Intensive Care Unit (ICU) professionals have to make lifesaving therapy decisions promptly under high stress and uncertainty. Clinical Decision Support Systems (CDSS) can improve the quality of healthcare by identifying complex statistical connections between patients' parameters and by rapidly presenting the statistically most promising treatment options to physicians. However, HCI aspects are rarely considered when developing CDSSs. This paper describes a field study conducted in three ICUs investigating how physicians and nurses form (volume) therapy decisions and monitor their success. Our findings reveal a continuous decision cycle in which nurses and physicians collaborate synchronously and asynchronously to provide optimal care. Furthermore, the desire to understand how a CDSS generated recommendations varies depending on the user's goals and other contextual factors such as workload. These findings show that CDSSs for ICUs need to (1) specifically facilitate collaboration and (2) support adaptation of the interface to both context and users.

[1]  Susanne Boll,et al.  Demo: Vibrotactile Alarm Display for Critical Care , 2018, PerDis.

[2]  Susanne Boll,et al.  Towards reducing alarm fatigue: peripheral light pattern design for critical care alarms , 2018, NordiCHI.

[3]  John Zimmerman,et al.  Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help , 2016, CHI.

[4]  David T. Marc,et al.  Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis , 2018, JMIR medical informatics.

[5]  Saskia Bakker,et al.  Beepless: Using Peripheral Interaction in an Intensive Care Setting , 2019, Conference on Designing Interactive Systems.

[6]  Thomas McGinn.  CDS, UX, and System Redesign – Promising Techniques and Tools to Bridge the Evidence Gap , 2015, EGEMS.

[7]  Dean A Fergusson,et al.  Association of hydroxyethyl starch administration with mortality and acute kidney injury in critically ill patients requiring volume resuscitation: a systematic review and meta-analysis. , 2013, JAMA.

[8]  J. Rodriquez,et al.  Negotiating Medical Authority: Shared Decision‐Making in the ICU , 2020 .

[9]  Kai Zacharowski,et al.  What's new in volume therapy in the intensive care unit? , 2014, Best practice & research. Clinical anaesthesiology.

[10]  M. Sheelagh T. Carpendale,et al.  An observational study on information flow during nurses' shift change , 2007, CHI.

[11]  Beth Crandall,et al.  Critical care physician cognitive task analysis: an exploratory study , 2009, Critical care.

[12]  Cecily Morrison,et al.  Electronic patient record use during ward rounds: a qualitative study of interaction between medical staff , 2008, Critical care.

[13]  Karen Holtzblatt,et al.  Contextual design , 1997, INTR.

[14]  Susanne Boll,et al.  Vibrotactile Alarm Display for Critical Care , 2018, PerDis.

[15]  Geoffrey K Lighthall,et al.  Understanding Decision Making in Critical Care , 2015, Clinical Medicine & Research.

[16]  Madhu C. Reddy,et al.  Coordinating heterogeneous work: Information and representation in medical care , 2001, ECSCW.

[17]  Gloria Mark,et al.  Designing Guidelines for Mobile Health Technology: Managing Notification Interruptions in the ICU , 2016, CHI.

[18]  J. Curtis,et al.  Shared decision-making in the ICU: value, challenges, and limitations. , 2011, American journal of respiratory and critical care medicine.

[19]  Sylvia Bereknyei,et al.  Toward shared decision making at the end of life in intensive care units: opportunities for improvement. , 2007, Archives of internal medicine.

[20]  Kim J. Vicente,et al.  Ecological interface design: theoretical foundations , 1992, IEEE Trans. Syst. Man Cybern..

[21]  Steven K. Feiner,et al.  Physician-driven management of patient progress notes in an intensive care unit , 2010, CHI.

[22]  Anne McMurray,et al.  Quality of practice in an intensive care unit (ICU): a mini-ethnographic case study. , 2009, Intensive & critical care nursing.

[23]  Wilko Heuten,et al.  Integration of multimodal alarms into Google Glass: demo , 2019, PerDis.

[24]  J. Stockman,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[25]  Jonathan M. Teich,et al.  Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.

[26]  John Zimmerman,et al.  Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes , 2019, CHI.

[27]  C Popow,et al.  Support for Fast Comprehension of ICU Data: Visualization using Metaphor Graphics , 2001, Methods of Information in Medicine.

[28]  Paul N. Bennett,et al.  Guidelines for Human-AI Interaction , 2019, CHI.

[29]  Bill Moggridge,et al.  Designing interactions , 2006 .

[30]  Gokul S. Krishnan,et al.  A Supervised Learning Approach for ICU Mortality Prediction Based on Unstructured Electrocardiogram Text Reports , 2018, NLDB.

[31]  Gernot Marx,et al.  GUIDELINES Intravascular volume therapy in adults Guidelines from the Association of the Scientific Medical Societies in Germany , 2016 .

[32]  Douglas S. Bell,et al.  Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions , 2012, J. Biomed. Informatics.

[33]  Anne Miller,et al.  The effects of clinical information presentation on physicians' and nurses' decision-making in ICUs. , 2009, Applied ergonomics.

[34]  Madhu C. Reddy,et al.  Asking questions: information needs in a surgical intensive care unit , 2002, AMIA.

[35]  Sheikh S. Abdullah,et al.  Data-Driven Activities Involving Electronic Health Records: An Activity and Task Analysis Framework for Interactive Visualization Tools , 2020, Multimodal Technol. Interact..

[36]  S. Corbridge,et al.  Enhancing clinical decision making: development of a contiguous definition and conceptual framework. , 2014, Journal of professional nursing : official journal of the American Association of Colleges of Nursing.

[37]  P. Palmieri,et al.  Performance in Nursing , 2009 .

[38]  Aldo A. Faisal,et al.  The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care , 2018, Nature Medicine.

[39]  Andreas Butz,et al.  The Expert of Oz: A Two-sided Study Paradigm for Intelligent Systems , 2020, Conference on Designing Interactive Systems.

[40]  Christophe Zemmour,et al.  Impact of a computer-assisted decision support system (CDSS) on nutrition management in critically ill hematology patients: the NUTCHOCO study (nutritional care in hematology oncologic patients and critical outcome) , 2019, Annals of Intensive Care.

[41]  Theodore J Iwashyna,et al.  ICU team composition and its association with ABCDE implementation in a quality collaborative☆ , 2018, Journal of critical care.

[42]  Madhu C. Reddy,et al.  Better managing technology-mediated interruptions in the ICU: Examining the role of patient information for improving text message notifications , 2016, AMIA.

[43]  J. Cacioppo,et al.  DISPOSITIONAL DIFFERENCES IN COGNITIVE MOTIVATION : THE LIFE AND TIMES OF INDIVIDUALS VARYING IN NEED FOR COGNITION , 1996 .

[44]  Robert Johansen,et al.  Groupware: Computer Support for Business Teams , 1988 .

[45]  R G Mark,et al.  MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring , 2002, Computers in Cardiology.

[46]  Sharoda A. Paul,et al.  Designing the Alarm Management User Experience for Patient Monitoring , 2016, CHI Extended Abstracts.

[47]  Sun Young Park,et al.  Local-universality: designing EMR to support localized informal documentation practices , 2013, CSCW.

[48]  M. Matthay,et al.  Sepsis: pathophysiology and clinical management , 2016, British Medical Journal.

[49]  Carman Neustaedter,et al.  A Collaborative Visualization Tool to Support Doctors' Shared Decision-Making on Antibiotic Prescription , 2017, CSCW Companion.

[50]  Walter Verbrugghe,et al.  Maintenance fluid therapy and fluid creep impose more significant fluid, sodium, and chloride burdens than resuscitation fluids in critically ill patients: a retrospective study in a tertiary mixed ICU population , 2018, Intensive Care Medicine.

[51]  Lauren Wilcox,et al.  "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making , 2019, Proc. ACM Hum. Comput. Interact..

[52]  K. Hofmann-Kiefer,et al.  State of the art in fluid and volume therapy , 2019, Der Anaesthesist.

[53]  F. V. van Haren,et al.  Personalised fluid resuscitation in the ICU: still a fluid concept? , 2017, Critical Care.