Parallel User Interface Design of a Clinical Decision-Support Application for Desktop and Pocket Pc Platforms

2002 ◽  
Vol 46 (16) ◽  
pp. 1423-1427
Author(s):  
Robert Tannen
Keyword(s):  
Decision Support ◽  
User Interface ◽  
Interface Design ◽  
Transfer Of Training ◽  
Clinical Information ◽  
Clinical Decision ◽  
User Centered Design ◽  
Design Development ◽  
Physician Decision Support ◽  
Pocket Pc

In order to increase physician acceptance and use, it is necessary for clinical information systems to better support workflow and connectivity. Towards that end, it is advantageous to develop clinical applications that support a range of platforms and mobile devices. However, differences in design/development approaches, technical limitations, and user interactivity across devices result in inconsistent features and user experiences, limiting functionality, usability, and transfer of training. In the current project, a browser-based physician decision-support and order entry prototype was developed for the Windows desktop and Pocket PC in parallel. Corresponding functionality was implemented on both platforms via an iterative, user-centered design approach that utilized components of the desktop version to create the PocketPC screens. Subsequent physician feedback demonstrated high transfer of training from the desktop version to the PocketPC. The findings from this work can be applied to other multi-platform user interface projects.

scholarly journals Utilizing a user-centered approach to develop and assess pharmacogenomic clinical decision support for thiopurine methyltransferase

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Khoa A. Nguyen ◽  
Himalaya Patel ◽  
David A. Haggstrom ◽  
Alan J. Zillich ◽  
Thomas F. Imperiale ◽  
...  
Keyword(s):  
Decision Support ◽  
User Interface ◽  
Phase I ◽  
Clinical Decision Support ◽  
Phase Ii ◽  
Usability Testing ◽  
Clinical Decision ◽  
Thiopurine Methyltransferase ◽  
User Centered Design ◽  
User Centered

Abstract Background A pharmacogenomic clinical decision support tool (PGx-CDS) for thiopurine medications can help physicians incorporate pharmacogenomic results into prescribing decisions by providing up-to-date, real-time decision support. However, the PGx-CDS user interface may introduce errors and promote alert fatigue. The objective of this study was to develop and evaluate a prototype of a PGx-CDS user interface for thiopurine medications with user-centered design methods. Methods This study had two phases: In phase I, we conducted qualitative interviews to assess providers’ information needs. Interview transcripts were analyzed through a combination of inductive and deductive qualitative analysis to develop design requirements for a PGx-CDS user interface. Using these requirements, we developed a user interface prototype and evaluated its usability (phase II). Results In total, 14 providers participated: 10 were interviewed in phase I, and seven providers completed usability testing in phase II (3 providers participated in both phases). Most (90%) participants were interested in PGx-CDS systems to help improve medication efficacy and patient safety. Interviews yielded 11 themes sorted into two main categories: 1) health care providers’ views on PGx-CDS and 2) important design features for PGx-CDS. We organized these findings into guidance for PGx-CDS content and display. Usability testing of the PGx-CDS prototype showed high provider satisfaction. Conclusion This is one of the first studies to utilize a user-centered design approach to develop and assess a PGx-CDS interface prototype for Thiopurine Methyltransferase (TPMT). This study provides guidance for the development of a PGx-CDS, and particularly for biomarkers such as TPMT.

Developing Clinical Decision Support System for Sleep Staging Tasks by Formulating Explanations from Artificial Intelligence: User-Centered Design and Evaluation. (Preprint)

2021 ◽  
Author(s):  
Jeonghwan Hwang ◽  
Taeheon Lee ◽  
Honggu Lee ◽  
Seonjeong Byun
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Design Process ◽  
Clinical Decision Support ◽  
User Study ◽  
Clinical Decision ◽  
Sleep Staging ◽  
User Centered Design ◽  
Iterative Design ◽  
User Centered

BACKGROUND Despite the unprecedented performances of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces block the adoption of these AI systems in practice. OBJECTIVE The aim of this study was to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered fashion. METHODS User needs for the system were identified during interviews with polysomnographic technicians. User observation sessions were conducted to understand the workflow of the practitioners during sleep scoring. Iterative design process was performed to ensure easy integration of the tool into clinical workflows. Then, we evaluated the system with polysomnographic technicians. We measured the improvements in sleep staging accuracies after adopting our tool and assessed qualitatively how the participants perceived and used the tool. RESULTS The user study revealed that technicians desire explanations relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of the AI predictions. Here, technicians could evaluate whether AI models properly locate and use those patterns during prediction. Based on this, information in AI models that is closely related to sleep EEG patterns was formulated and visualized during the iterative design process. Furthermore, we developed a different visualization strategy for each pattern based on the way the technicians interpreted the EEG recordings with these patterns during their workflows. Generally, the tool evaluation results from the nine polysomnographic technicians were positive. Quantitatively, technicians achieved better classification performances after reviewing the AI-generated predictions with the proposed system; classification accuracies measured with Macro-F1 scores improved from 60.20 to 62.71. Qualitatively, participants reported that the provided information from the tool effectively supported them, and they were able to develop notable adoption strategies for the tool. CONCLUSIONS Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

scholarly journals Leveraging Electronic Tablets for General Pediatric Care

2015 ◽  
Vol 06 (01) ◽  
pp. 1-15 ◽  
Author(s):  
S. McKee ◽  
T.M. Dugan ◽  
S.M. Downs ◽  
V. Anand
Keyword(s):  
Decision Support ◽  
User Interface ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Self Report ◽  
Pediatric Care ◽  
Patient Questionnaire ◽  
Health Improvement ◽  
Completion Rates

SummaryBackground: We have previously shown that a scan-able paper based interface linked to a computerized clinical decision support system (CDSS) can effectively screen patients in pediatric waiting rooms and support the physician using evidence based care guidelines at the time of clinical encounter. However, the use of scan-able paper based interface has many inherent limitations including lacking real time communication with the CDSS and being prone to human and system errors. An electronic tablet based user interface can not only overcome these limitations, but may also support advanced functionality for clinical and research use. However, use of such devices for pediatric care is not well studied in clinical settings.Objective: In this pilot study, we enhance our pediatric CDSS with an electronic tablet based user interface and evaluate it for usability as well as for changes in patient questionnaire completion rates.Methods: Child Health Improvement through Computers Leveraging Electronic Tablets or CHICLET is an electronic tablet based user interface. It is developed to augment the existing scan-able paper interface to our CDSS. For the purposes of this study, we deployed CHICLET in one outpatient pediatric clinic. Usability factors for CHICLET were evaluated via caregiver and staff surveys.Results: When compared to the scan-able paper based interface, we observed an 18% increase or 30% relative increase in question completion rates using CHICLET. This difference was statistically significant. Caregivers and staff survey results were positive for using CHICLET in clinical environment.Conclusions: Electronic tablets are a viable interface for capturing patient self-report in pediatric waiting rooms. We further hypothesize that the use of electronic tablet based interfaces will drive advances in computerized clinical decision support and create opportunities for patient engagement.Citation: Anand V, McKee S, Dugan TM, Downs SM. Leveraging electronic tablets for general pediatric care – a pilot study. Appl Clin Inf 2015; 6: 1–15http://dx.doi.org/10.4338/ACI-2014-09-RA-0071

scholarly journals Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis

2018 ◽  
Vol 38 (4) ◽  
pp. 46-54 ◽  
Author(s):  
Devida Long ◽  
Muge Capan ◽  
Susan Mascioli ◽  
Danielle Weldon ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
User Interface ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Providers

BACKGROUND Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers. OBJECTIVE To gain insight into clinical decision support systems–based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences. METHODS An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop-up format, and addressed the patient’s clinical condition rather than regulatory guidelines. CONCLUSIONS The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high-quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

scholarly journals LIANA Model Integration System - architecture, user interface design and application in MOIRA DSS

2005 ◽  
Vol 4 ◽  
pp. 9-16 ◽  
Author(s):  
D. Hofman
Keyword(s):  
Decision Support ◽  
User Interface ◽  
Interface Design ◽  
Model Integration ◽  
Data Sets ◽  
Data Set ◽  
Integration System ◽  
Rapid Construction ◽  
Environmental Decision Support Systems ◽  
Made In

Abstract. The LIANA Model Integration System is the shell application supporting model integration and user interface functionality required for the rapid construction and run-time support of the environmental decision support systems (EDSS). Internally it is constructed as the framework of C++ classes and functions covering most common tasks performed by the EDSS (such as managing of and alternative strategies, running of the chain of the models, supporting visualisation of the data with tables and graphs, keeping ranges and default values for input parameters etc.). EDSS is constructed by integration of LIANA system with the models or other applications such as GIS or MAA software. The basic requirements to the model or other application to be integrated is minimal - it should be a Windows or DOS .exe file and receive input and provide output as text files. For the user the EDSS is represented as the number of data sets describing scenario or giving results of evaluation of scenario via modelling. Internally data sets correspond to the I/O files of the models. During the integration the parameters included in each the data sets as well as specifications necessary to present the data set in GUI and export or import it to/from text file are provided with MIL_LIANA language. Visual C++ version of LIANA has been developed in the frame of MOIRA project and is used as the basis for the MOIRA Software Framework - the shell and user interface component of the MOIRA Decision Support System. At present, the usage of LIANA for the creation of a new EDSS requires changes to be made in its C++ code. The possibility to use LIANA for the new EDSS construction without extending the source code is achieved by substituting MIL_LIANA with the object-oriented LIANA language.

scholarly journals Computerized Clinical Decision Support System for Emergency Department–Initiated Buprenorphine for Opioid Use Disorder: User-Centered Design (Preprint)

2018 ◽  
Author(s):  
Jessica M Ray ◽  
Osama M Ahmed ◽  
Yauheni Solad ◽  
Matthew Maleska ◽  
Shara Martel ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Emergency Care ◽  
Clinical Decision ◽  
Opioid Use Disorder ◽  
Care Pathways ◽  
User Centered Design ◽  
Opioid Use ◽  
Prototype Development ◽  
User Centered

BACKGROUND Emergency departments (EDs) frequently care for individuals with opioid use disorder (OUD). Buprenorphine (BUP) is an effective treatment option for patients with OUD that can safely be initiated in the ED. At present, BUP is rarely initiated as a part of routine ED care. Clinical decision support (CDS) could accelerate adoption of ED-initiated BUP into routine emergency care. OBJECTIVE This study aimed to design and formatively evaluate a user-centered decision support tool for ED initiation of BUP for patients with OUD. METHODS User-centered design with iterative prototype development was used. Initial observations and interviews identified workflows and information needs. The design team and key stakeholders reviewed prototype designs to ensure accuracy. A total of 5 prototypes were evaluated and iteratively refined based on input from 26 attending and resident physicians. RESULTS Early feedback identified concerns with the initial CDS design: an alert with several screens. The timing of the alert led to quick dismissal without using the tool. User feedback on subsequent iterations informed the development of a flexible tool to support clinicians with varied levels of experience with the intervention by providing both one-click options for direct activation of care pathways and user-activated support for critical decision points. The final design resolved challenging navigation issues through targeted placement, color, and design of the decision support modules and care pathways. In final testing, users expressed that the tool could be easily learned without training and was reasonable for use during routine emergency care. CONCLUSIONS A user-centered design process helped designers to better understand users’ needs for a Web-based clinical decision tool to support ED initiation of BUP for OUD. The process identified varying needs across user experience and familiarity with the protocol, leading to a flexible design supporting both direct care pathways and user-initiated decision support.

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