Building a Composable Electronic Health Records System using Fast Healthcare Interoperability Resources (FHIR) web components (Preprint)

2021 ◽  
Author(s):  
Bolu Oluwalade ◽  
Shruti Apte ◽  
Saptarshi Purkayastha
Keyword(s):  
Structural Equation Modeling ◽  
User Interface ◽  
Electronic Health Records ◽  
Latent Variables ◽  
Structural Equation ◽  
Ease Of Use ◽  
Equation Modeling ◽  
Structured Interviews ◽  
Health Records ◽  
Electronic Health

BACKGROUND In the last decade, Electronic Health Records systems (EHRs) have been widely adopted in the United States because of the government's efforts. These efforts have led to the broad and rapid adoption of the EHR but with numerous usability and interoperability challenges. The disconnect between the clinical users' expectations from their EHRs and what is available in the EHRs is well-known. OBJECTIVE This study aimed to evaluate (1) the feasibility of a user-composable user interface (UI) for an EHR system using FHIR web components, (2) the user's ease of learning and ease of use of the assembled EHR's UI. METHODS We developed web components for the FHIR patient resource using the Polymer 3 library. The components were assembled by 19 subjects with a clinical background and past EHR use experience utilizing a drag and drop UI designer. We measured latent variables of "ease of learning" and "ease of use" through a 33-questions pre-validated survey from these subjects. Further, we conducted semi-structured interviews as per the sequential explanatory research design. The quantitative and qualitative data obtained from the survey and the semi-structured interviews were analyzed using the convergent parallel mixed method design. The quantitative data derived through the survey estimated the Structural Equation Modeling (SEM) latent variables tagged "ease of learning" and "ease of use" of the user interface. RESULTS About 88 percent (16 of 18) of the participants were confident or very confident about assembling the user components to form a user interface after watching a workflow video of the process, and 63 percent (12 of 19) of participants eventually completed the assembly process. A partial least square structural equation modeling (PLS-SEM) analysis of the survey results showed that there is a significant positive effect (p =0.00) on the ease of learning of the assembling process and on ease of use of the system and vice versa. The qualitative analysis of the interview showed that more' easy to use' and 'easy to learn' themes appeared in the participants' responses (40.1%) than the "difficult to learn," "difficult to use," "too complex," and "cumbersome" themes combined appearing in 11.2 % of the combined themes. CONCLUSIONS It seems that the non-technical users can assemble an EHR user interface without little or no technical support. Moreover, a user-composed EHR user interface will improve EHR users' experience. Finally, web components based on FHIR resources can be developed and implemented into an EHR user interface.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

Building a Model of Family Caregiving for Children with Emotional Disorders

1997 ◽  
Vol 5 (3) ◽  
pp. 138-148 ◽  
Author(s):  
Thomas P. Mcdonald ◽  
Thomas K. Gregoire ◽  
John Poertner ◽  
Theresa J. Early
Keyword(s):  
Structural Equation Modeling ◽  
Emotional Disorders ◽  
Problem Behaviors ◽  
Family Caregiving ◽  
Latent Variables ◽  
Structural Equation ◽  
Empirical Literature ◽  
Equation Modeling ◽  
Special Challenge ◽  
And Coping

In this article we describe the results of an ongoing effort to better understand the caregiving process in families of children with severe emotional problems. We make two assumptions. First, we assume that these families are essentially like other families but are faced with a special challenge in raising and caring for their special children while at the same time performing the multiple tasks and demands faced by all families. Second, we assume that public policy and programs must be supportive of the care of these children in their own homes and communities whenever possible. The purpose of this article is to present a model of family caregiving that draws broadly from available theory and empirical literature in multiple fields and to subject this model to empirical testing. We use structural equation modeling with latent variables to estimate an empirical model based on the theoretical model. Results of the model testing point to the importance of the child's external problem behaviors and the family's socioeconomic status and coping strategies as determinants of caregiver stress. Other findings highlight difficulties in measuring and modeling the complex mediating process, which includes formal and informal supports, perceptions, and coping behaviors. The use of structural equation modeling can benefit our efforts to support families by making explicit our theories about the important dimensions of this process and the relationship between these dimensions, which can then be subjected to measurement and validation.

Analisis Pengaruh Persepsi Manfaat, Persepsi Kemudahan Penggunaan dan Persepsi Risiko Terhadap Keputusan Pembelian dengan Kepercayaan Sebagai Variabel Intervening (Studi Pada Konsumen Gofood di Kota Semarang)

2020 ◽  
Vol 19 (2) ◽  
pp. 114-125
Author(s):  
Isnaeni Agustin Widhiaswara ◽  
Harry Soesanto
Keyword(s):  
Structural Equation Modeling ◽  
Perceived Risk ◽  
Structural Equation ◽  
Sem Analysis ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Purchasing Decisions ◽  
Food Purchases ◽  
The City

This study aims to analyze the effect of perceived usefulness, perceived of ease of use, perceived risk , trust and purchasing decisions for GoFood in the city of Semarang. Respondents in this study are consumers who make food purchases through GoFood. Data collected through Google Form containing questionnaires filled out by 125 respondents. The analytical method used is Structural Equation Modeling (SEM) analysis carried out with the AMOS program. The results of hypothesis testing with SEM show that: 1. Perceived usefulness has a significant effect on trust, 2. Perceived of ease of use has a significant effect on trust, 3. Perceived risk has a significant effect on trust, 4. Trust has a significant effect on purchasing decisions, 5. Perceived usefulness has a significant effect on purchasing decisions, 6. Perceived of ease of use has a significant effect on purchasing desicion, 7. Perceived risk has a significant effect on purchasing decisions.

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

scholarly journals MODEL PERSAMAAN STRUKTURAL UNTUK MENGETAHUI PERSEPSI KONSUMEN TERHADAP BUAH IMPOR

2020 ◽  
Vol 9 (3) ◽  
pp. 149
Author(s):  
I KADEK TEGUH PRADANA ◽  
NI KETUT TARI TASTRAWATI ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Sem Analysis ◽  
Consumer Perception ◽  
Equation Modeling ◽  
Consumer Knowledge

This research is aimed to determine the factors that significantly influence consumer perception in buying imported fruits using structural equation modeling (SEM) analysis. The study use 164 data obtained from questionnaire, which respondents were aged 18 years old or above, from Gianyar Regency, had bought and had felt imported fruits. The study use 4 latent variables (perception, product, personal, culture) with 19 measured variables. The results showed that consumer knowledge about imported fruits (product) and culture about the use of imported fruits in traditional ceremonies (culture) were significantly influence consumer perception about consumption of imported fruit (perception).

scholarly journals Investigasi Faktor Yang Mempengaruhi Behavioral Intention Pada Pengguna Grab Bike

2019 ◽  
Vol 3 (2) ◽  
pp. 94
Author(s):  
Sulaiman Sulaiman ◽  
Zainal Abidin ◽  
Suharno Suharno
Keyword(s):  
Structural Equation Modeling ◽  
Behavioral Intention ◽  
Perceived Risk ◽  
Structural Equation ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Perceived Ease Of Use ◽  
Software Analysis

Tujuan penelitian ini mengetahui pengaruh behavioral intention (BI) terhadap transportasi online grab bike dengan menggunakan variabel trust, perceived ease of use (PEOU), perceived risk (PR) dan perceived usefulness (PU). Sebanyak 161 orang menjadi responden dalam penelitian ini dengan cara mengisi kuesioner. Data yang diperoleh kemudian dianalisis menggunakan alat analisis Structural Equation Modeling (SEM) dengan menggunakan software analysis of moment structure (AMOS). Setelah dilakukan penelitian diperoleh hasil bahwa PR berpengaruh signifikan terhadap trust, PEOU  berpengaruh signifikan terhadap trust, PU berpengaruh signifikan terhadap trust. Begitu pula pengaruh PR signifikan terhadap behavioral intention, PEOU, PU signifikan terhadap behavioral intention, serta trust signifikan terhadap behavioral intention.

scholarly journals COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

2021 ◽  
pp. 395-405
Author(s):  
David Opeoluwa Oyewola ◽  
Emmanuel Gbenga Dada ◽  
Juliana Ngozi Ndunagu ◽  
Terrang Abubakar Umar ◽  
Akinwunmi S.A
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Support Vector ◽  
Economic Factors ◽  
Negative Effects ◽  
The Impact

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

Using Simulation to Teach Operations Management to First- and Continuing-Generation Students

2018 ◽  
Vol 5 (2) ◽  
pp. 57-72
Author(s):  
Jason M. Riley ◽  
William A. Ellegood
Keyword(s):  
Structural Equation Modeling ◽  
Operations Management ◽  
Structural Equation ◽  
First Generation ◽  
Team Leadership ◽  
Ease Of Use ◽  
Equation Modeling ◽  
First Generation Students ◽  
Pedagogical Tools ◽  
Team Interaction

Leveraging a popular operations management simulation, we examine how team interaction, students' previous software experience, simulations' ease of use, team leadership, and instructors' guidance affects students' comprehension. Respondents completed a 27-item survey designed to assess individual comprehension. To illustrate the various affects, we use structural equation modeling to compare first-generation and continuing-generation undergraduate populations. The results indicate software's ease of use and instructors' guidance affects both groups, while team interaction to comprehension is present for only first-generation students. We also find that the strength of the various relationships depends on the population of students being studied. As simulations continue to grow in popularity, educators need to recognize how these pedagogical tools affect different student populations.

Using WarpPLS in E-collaboration Studies

2010 ◽  
Vol 6 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Research Methods ◽  
Latent Variables ◽  
Structural Equation ◽  
Software Tools ◽  
Equation Modeling ◽  
Modeling Analysis ◽  
Modeling Software ◽  
S Curve ◽  
Complex Phenomena

Most relationships between variables describing natural and behavioral phenomena are nonlinear, with U-curve and S-curve relationships being particularly common. Yet, structural equation modeling software tools do not estimate coefficients of association taking nonlinear relationships between latent variables into consideration. This can lead to misleading results, particularly in multivariate and complex phenomena like those related to e-collaboration. One notable exception is WarpPLS (available from: warppls.com), a new structural equation modeling software currently available in its first release. The discussion presented in this paper contributes to the literature on e-collaboration research methods by providing a description of the main features of WarpPLS in the context of an e-collaboration study. The focus of this discussion is on the software’s features and their use and not on e-collaboration study itself. Particular emphasis is placed on the five steps through which a structural equation modeling analysis is conducted through WarpPLS.

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