Characterizing Workflow to Inform Clinical Decision Support Systems in Nursing Homes

2018 ◽  
Vol 59 (6) ◽  
pp. 1024-1033 ◽  
Author(s):  
Mustafa Ozkaynak ◽  
Blaine Reeder ◽  
Cynthia Drake ◽  
Peter Ferrarone ◽  
Barbara Trautner ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Nursing Homes ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Staff Members ◽  
Design And Implementation ◽  
Multiple Levels

Abstract Background and Objectives Clinical decision support systems (CDSS) hold promise to influence clinician behavior at the point of care in nursing homes (NHs) and improving care delivery. However, the success of these interventions depends on their fit with workflow. The purpose of this study was to characterize workflow in NHs and identify implications of workflow for the design and implementation of CDSS in NHs. Research Design and Methods We conducted a descriptive study at 2 NHs in a metropolitan area of the Mountain West Region of the United States. We characterized clinical workflow in NHs, conducting 18 observation sessions and interviewing 15 staff members. A multilevel work model guided our data collection and framework method guided data analysis. Results The qualitative analysis revealed specific aspects of multilevel workflow in NHs: (a) individual, (b) work group/unit, (c) organization, and (d) industry levels. Data analysis also revealed several additional themes regarding workflow in NHs: centrality of ongoing relationships of staff members with the residents to care delivery in NHs, resident-centeredness of care, absence of memory aids, and impact of staff members’ preferences on work activities. We also identified workflow-related differences between the two settings. Discussion and Implications Results of this study provide a rich understanding of the characteristics of workflow in NHs at multiple levels. The design of CDSS in NHs should be informed by factors at multiple levels as well as the emergent processes and contextual factors. This understanding can allow for incorporating workflow considerations into CDSS design and implementation.

scholarly journals A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6209
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Classification Accuracy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

scholarly journals Examining Workflow in a Pediatric Emergency Department to Develop a Clinical Decision Support for an Antimicrobial Stewardship Program

2018 ◽  
Vol 09 (02) ◽  
pp. 248-260 ◽  
Author(s):  
Mustafa Ozkaynak ◽  
Danny Wu ◽  
Katia Hannah ◽  
Peter Dayan ◽  
Rakesh Mistry
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Time Allocation ◽  
Care Delivery ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Design And Implementation ◽  
Decision Points ◽  
Stewardship Program

Background Clinical decision support (CDS) embedded into the electronic health record (EHR), is a potentially powerful tool for institution of antimicrobial stewardship programs (ASPs) in emergency departments (EDs). However, design and implementation of CDS systems should be informed by the existing workflow to ensure its congruence with ED practice, which is characterized by erratic workflow, intermittent computer interactions, and variable timing of antibiotic prescription. Objective This article aims to characterize ED workflow for four provider types, to guide future design and implementation of an ED-based ASP using the EHR. Methods Workflow was systematically examined in a single, tertiary-care academic children's hospital ED. Clinicians with four roles (attending, nurse practitioner, physician assistant, resident) were observed over a 3-month period using a tablet computer-based data collection tool. Structural observations were recorded by investigators, and classified using a predetermined set of activities. Clinicians were queried regarding timing of diagnosis and disposition decision points. Results A total of 23 providers were observed for 90 hours. Sixty-four different activities were captured for a total of 6,060 times. Among these activities, nine were conducted at different frequency or time allocation across four roles. Moreover, we identified differences in sequential patterns across roles. Decision points, whereby clinicians then proceeded with treatment, were identified 127 times. The most common decision points identified were: (1) after/during examining or talking to patient or relative; (2) after talking to a specialist; and (3) after diagnostic test/image was resulted and discussed with patient/family. Conclusion The design and implementation of CDS for ASP should support clinicians in various provider roles, despite having different workflow patterns. The clinicians make their decisions about treatment at different points of overall care delivery practice; likewise, the CDS should also support decisions at different points of care.

Clinical Decision Support Systems for ‘Making It Easy to Do It Right’

2012 ◽  
pp. 324-335
Author(s):  
Anne-Marie Scheepers-Hoeks ◽  
Floor Klijn ◽  
Carolien van der Linden ◽  
Rene Grouls ◽  
Eric Ackerman ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Care Delivery System ◽  
Computer Based ◽  
Medical Guidelines

Medical guidelines and best practises are used in medicine to increase the quality of the health-care delivery system. To support implementation and application of these guidelines, clinical decision support systems (CDSS) have been developed. These systems are defined as ‘Computer-based information systems used to integrate clinical and patient information and provide support for decision-making in patient care’ (MeSH) These are integrated with so-called Electronic Health Records (EHR), which have been developed by companies and National Governmental Institutes, and are used to register and present the patient medical data. The integration of an EHR with CDSS modules will revolutionize the way medicine will be practiced. In pediatrics, as well as geriatrics, such systems might prove to be even more needed. The development, use, and maintenance of CDSS in a hospital are complex and far from trivial. This chapter focuses on several aspects and challenges of EHR’s and CDSS-modules in daily clinical practice in the hospital.

Clinical Decision Support Systems for ‘Making It Easy to Do It Right’

Data Mining ◽  
2013 ◽  
pp. 1461-1471
Author(s):  
Anne-Marie Scheepers-Hoeks ◽  
Floor Klijn ◽  
Carolien van der Linden ◽  
Rene Grouls ◽  
Eric Ackerman ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Care Delivery System ◽  
Computer Based ◽  
Medical Guidelines

Medical guidelines and best practises are used in medicine to increase the quality of the health-care delivery system. To support implementation and application of these guidelines, clinical decision support systems (CDSS) have been developed. These systems are defined as ‘Computer-based information systems used to integrate clinical and patient information and provide support for decision-making in patient care’ (MeSH) These are integrated with so-called Electronic Health Records (EHR), which have been developed by companies and National Governmental Institutes, and are used to register and present the patient medical data. The integration of an EHR with CDSS modules will revolutionize the way medicine will be practiced. In pediatrics, as well as geriatrics, such systems might prove to be even more needed. The development, use, and maintenance of CDSS in a hospital are complex and far from trivial. This chapter focuses on several aspects and challenges of EHR’s and CDSS-modules in daily clinical practice in the hospital.

scholarly journals What Makes a Difference in Patient Safety when Designing, Implementing and Evaluating Clinical Decision Support Systems?

2019 ◽  
Vol 63 (1) ◽  
pp. 693-697
Author(s):  
Mustafa Ozkaynak ◽  
Ann Bisantz ◽  
Laura Militello ◽  
Kristen Miller ◽  
Michael Rayo ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Organizational Outcomes ◽  
Future Research ◽  
Implementation Challenges ◽  
Design And Implementation ◽  
Future Research Agenda ◽  
History Of

Clinical decision support (CDS) has become an important component of all health settings. Despite a long history of research on their design and implementation, their use is still suboptimal. Unique characteristics of specific settings can require highlighting different features and design recommendations for CDS. This panel will focus on various pitfalls in designing and implementing CDS in various clinical settings and strategies to overcome these pitfalls. Each panelist will introduce their work by discussing the design and implementation challenges that prevent achieving the targeted patient and organizational outcomes. Panelists will also discuss potential approaches with their strengths and limitations to address these challenges. The panelists will then interact with audience members to solicit users’ (e.g. clinicians, patients) unfulfilled needs and limitations of existing approaches to create a broad future research agenda at the intersection of human factors approaches and theories, and CDS systems.

scholarly journals Decision Support for Diabetes in Scotland: Implementation and Evaluation of a Clinical Decision Support System

2017 ◽  
Vol 12 (2) ◽  
pp. 381-388 ◽  
Author(s):  
Nicholas Conway ◽  
Karen A. Adamson ◽  
Scott G. Cunningham ◽  
Alistair Emslie Smith ◽  
Peter Nyberg ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Workflow ◽  
Long Term Conditions ◽  
Process Measures ◽  
Patient Questionnaires

Background: Automated clinical decision support systems (CDSS) are associated with improvements in health care delivery to those with long-term conditions, including diabetes. A CDSS was introduced to two Scottish regions (combined diabetes population ~30 000) via a national diabetes electronic health record. This study aims to describe users’ reactions to the CDSS and to quantify impact on clinical processes and outcomes over two improvement cycles: December 2013 to February 2014 and August 2014 to November 2014. Methods: Feedback was sought via patient questionnaires, health care professional (HCP) focus groups, and questionnaires. Multivariable regression was used to analyze HCP SCI-Diabetes usage (with respect to CDSS message presence/absence) and case-control comparison of clinical processes/outcomes. Cases were patients whose HCP received a CDSS messages during the study period. Closely matched controls were selected from regions outside the study, following similar clinical practice (without CDSS). Clinical process measures were screening rates for diabetes-related complications. Clinical outcomes included HbA1c at 1 year. Results: The CDSS had no adverse impact on consultations. HCPs were generally positive toward CDSS and used it within normal clinical workflow. CDSS messages were generated for 5692 cases, matched to 10 667 controls. Following clinic, the probability of patients being appropriately screened for complications more than doubled for most measures. Mean HbA1c improved in cases and controls but more so in cases (–2.3 mmol/mol [–0.2%] versus –1.1 [–0.1%], P = .003). Discussion and Conclusions: The CDSS was well received; associated with improved efficiencies in working practices; and large improvements in guideline adherence. These evidence-based, early interventions can significantly reduce costly and devastating complications.

Design and Implementation of TCM Constitution Clinical Decision Support System

2014 ◽  
Vol 519-520 ◽  
pp. 1442-1446
Author(s):  
Ming Feng Zhu ◽  
Jian Qiang Du ◽  
Cheng Hua Ding
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Design And Implementation ◽  
Clinical Diagnoses ◽  
Feature Information

In this paper, a TCM constitution clinical decision support system is introduced. The features, functions, structure and working flow of this system are discussed and illustrated in detail. This system is composed of 5 modules. They are query module, investigation construction module, investigation modification module, investigation deletion module and data analysis module respectively. The property information, constitution information and tongue feature information are collected through investigation construction module. The constitution types of the testers can be automatically recognized and the prescriptions related to specific constitutions are automatically produced. Through data analysis module, specificities and sensitivities between the specific constitutions and the tongue features can be automatically obtained. The implementation of this system is of important value and guiding function during the process of TCM clinical diagnoses and treatments.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

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