Preethi Srinivas

VizCom: a novel workflow model for ICU clinical decision support

The Intensive Care Unit (ICU) has the highest annual mortality rate (4.4M) of any hospital unit or 25% of all clinical admissions. Studies show a relationship between clinician cognitive load and workflow, and their impact on patient safety and the subsequent occurrence of medical mishaps due to diagnostic error - in spite of advances in health information technology, e.g., bedside and clinical decision support (CDS) systems. The aim of our research is to: 1) investigate the root causes (underlying mechanisms) of ICU error related to the effects of clinical workflow: medical cognition, team communication/collaboration, and the use of diagnostic/CDS systems and 2) construct and validate a novel workflow model that supports improved clinical workflow, with goals to decrease adverse events, increase safety, and reduce intensivist time, effort, and cognitive resources. Lastly, our long-term objective is to apply data from aims one and two to design the next generation of diagnostic visualization-communication (VizCom) system that improves intensive care workflow, communication, and effectiveness in healthcare.

Clinicians as secondary users of patient-centered mobile technology in complex healthcare settings

This paper describes the preliminary research findings and prototype development of a Personal Health Record mobile application. A pilot study about patient-clinician interaction guided by common ground theory was performed. The goal of the pilot study was to gather requirements to support development of a smartphone application to be used in a future experimental study. Findings from the pilot study suggest that smartphones could be used to manage health information considered important for a successful healthcare consultation.

Supporting information management in ICU rounding a novel mobile system for managing patient-centered notes and action-items

Team rounds on patients in the hospitals intensive care unit (ICU) results in the generation of several paper-based and digital notes. Paper-based notes, although short-lived, act as translational artifacts that help organize and coordinate patient information and care. Maintaining double records of paper and digital notes can introduce several awareness and coordination problems such as contextually situating clinicians as to a patients on-going care. Based on the design requirements derived from our fieldwork, we propose a new technology, PANI (Patient-centered Notes and Information Manager). PANI is a clinical tool that integrates the use of a mobile application, paper-based artifacts, and a wearable device (such as FitBit) in one system to support the management of notes and action-items that are generated throughout a typical ICU clinical shift. In this paper, we present the functional design of PANI and our preliminary findings of a participatory study that included 15 clinician participants.

Modeling Clinical Workflow in Daily ICU Rounds to Support Task-based Patient Monitoring and Care

Of all the duties performed by the critical care team in a hospital intensive care unit (ICU), a primary duty is the morning attending rounds. During and following the rounds, the ICU team devises a 24-hour plan of action comprised of patient-centered tasks. The aim of this doctoral research is to: (1) design and evaluate a novel task management tool that addresses breakdowns in critical care workflow and (2) introduce a new task management notification tool that mitigates workflow breakdowns by identifying the nature and type of notification/alert sent to the clinical team.

A ubiquitous situation-aware data visualization dashboard to reduce ICU clinician cognitive load

Hospital intensive care unit (ICU) bedside devices and electronic medical record (EMR) technology do not yet adequately address the visualization of patient data in the context of cognitive overload and its impact on patient safety. We respond to these challenges through the design of a novel visualization dashboard for use in the ICU: MIVA 2.0 (Medical Information Visualization Assistant, v.2). MIVA 2.0 is designed to support rapid analysis and interpretation of real-time clinical data-trends and communication for clinical work and information flow. This paper describes the system design, functionality, and prior studies of MIVA 2.0.