In many countries, the health care sector is entering into a time of unprecedented change. Electronic
Medical Record (EMR) has been introduced into healthcare organizations in order to incorporate better
use of technology, to aid decision making, and to facilitate the search for medical solution. This needs
those professionals in healthcare organizations to be in the process of changing from the use of paper to
maintain medical records into computerized medical recordkeeping opportunities. However, the adoption
of these electronic medical records systems has been slow throughout the healthcare field. The critical
users are physicians which play an important role to success of health information technology including
Electronic Medical Record systems. As a result user adoption is necessary in order to understand the
benefits of an EMR. Therefore, in the current paper, a model of ranking factors of micro-level in EMRs
adoption was developed. Surveys distributed to physicians as this study’s respondent in two private
hospitals in Malaysia. The findings indicate that physicians have a high perception means for the
technology and showed that EMR would increase physician’s performance regarding to decision making.
They have been and continue to be positively motivated to adopt and use the system. The relevant factors
according to micro-level perspective prioritized and ranked by using the Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS). The aim of ranking and using this approach is to
investigate which factors are more important in EMRs adoption from the micro-level perspectives. The
results of performing TOPSIS is as a novelty which assist health information systems (HIS) success and
also healthcare organizations to motivate their users in accepting of new technology.
Ranking the micro level critical factors of electronic medical records adopti...hiij
In many countries, the health care sector is entering into a time of unprecedented change. Electronic
Medical Record (EMR) has been introduced into healthcare organizations in order to incorporate better
use of technology, to aid decision making, and to facilitate the search for medical solution. This needs
those professionals in healthcare organizations to be in the process of changing from the use of paper to
maintain medical records into computerized medical recordkeeping opportunities. However, the adoption
of these electronic medical records systems has been slow throughout the healthcare field. The critical
users are physicians which play an important role to success of health information technology including
Electronic Medical Record systems. As a result user adoption is necessary in order to understand the
benefits of an EMR. Therefore, in the current paper, a model of ranking factors of micro-level in EMRs
adoption was developed. Surveys distributed to physicians as this study’s respondent in two private
hospitals in Malaysia. The findings indicate that physicians have a high perception means for the
technology and showed that EMR would increase physician’s performance regarding to decision making.
They have been and continue to be positively motivated to adopt and use the system. The relevant factors
according to micro-level perspective prioritized and ranked by using the Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS). The aim of ranking and using this approach is to
investigate which factors are more important in EMRs adoption from the micro-level perspectives. The
results of performing TOPSIS is as a novelty which assist health information systems (HIS) success and
also healthcare organizations to motivate their users in accepting of new technology.
Health institution requires quality data and information management to function effectively and efficiently. It is an understatement to say that many organizations, institutions or government agencies have become critically dependent on the use of database system for their successes especially in the hospital. This work aims at developing an improved hospital information management system using a function-based approach. An efficient HIMS that can be used to manage patient information and its administration is presented in this work. This is with the goal of eradicating the problem of improper data keeping, inaccurate reports, wastage of time in storing, processing and retrieving information faced by the existing hospital information system in order to improve the overall efficiency of the health institution. The system was developed with Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), Hypertext Preprocessor (PHP), and My Structured Query Language (MySQL). The new system was tested using data collected from Renewal Clinic, Ibadan, Nigeria was used as case study were the data for the research was collected and the system was tested. The system provides a vital platform of information storage and retrieval in hospitals.
Due to the information systems objectives, and to avoid duplication and to help improve care quality and
reduce cost, it is necessary to conduct continuous evaluation to determine how to achieve these goals. This
study was performed using evaluation indices of hospital Information systems (HIS) in selected hospitals of
Iran. In this article organizational and server components of hospital information systems in selected
hospitals are being assessed.
This research is a descriptive cross – sectional study. The study population consisted of the information
system of Shohaday Tajrish, Khatamolanbiya, Imam Khomeini and Milad Hospital. Data collecting tools
were checklist of hospital information system Evaluation Index, which completed with direct observation
and interviews with users. Data analyzed by statistics software SPSS, and presented as statistical tables
and graphs.
In the studied hospitals, although the most of the organizational components subgroups and hospital
information system server components has been set up and used but pharmacy information system, decision
support systems, communication services and telemedicine services hadn`t been set up fully in the
hospitals. Currently most subtypes of organizational components and hospital information system server
components were fully in the designed software and considering all fields in 5 hospitals.
Ranking the micro level critical factors of electronic medical records adopti...hiij
In many countries, the health care sector is entering into a time of unprecedented change. Electronic
Medical Record (EMR) has been introduced into healthcare organizations in order to incorporate better
use of technology, to aid decision making, and to facilitate the search for medical solution. This needs
those professionals in healthcare organizations to be in the process of changing from the use of paper to
maintain medical records into computerized medical recordkeeping opportunities. However, the adoption
of these electronic medical records systems has been slow throughout the healthcare field. The critical
users are physicians which play an important role to success of health information technology including
Electronic Medical Record systems. As a result user adoption is necessary in order to understand the
benefits of an EMR. Therefore, in the current paper, a model of ranking factors of micro-level in EMRs
adoption was developed. Surveys distributed to physicians as this study’s respondent in two private
hospitals in Malaysia. The findings indicate that physicians have a high perception means for the
technology and showed that EMR would increase physician’s performance regarding to decision making.
They have been and continue to be positively motivated to adopt and use the system. The relevant factors
according to micro-level perspective prioritized and ranked by using the Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS). The aim of ranking and using this approach is to
investigate which factors are more important in EMRs adoption from the micro-level perspectives. The
results of performing TOPSIS is as a novelty which assist health information systems (HIS) success and
also healthcare organizations to motivate their users in accepting of new technology.
Health institution requires quality data and information management to function effectively and efficiently. It is an understatement to say that many organizations, institutions or government agencies have become critically dependent on the use of database system for their successes especially in the hospital. This work aims at developing an improved hospital information management system using a function-based approach. An efficient HIMS that can be used to manage patient information and its administration is presented in this work. This is with the goal of eradicating the problem of improper data keeping, inaccurate reports, wastage of time in storing, processing and retrieving information faced by the existing hospital information system in order to improve the overall efficiency of the health institution. The system was developed with Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), Hypertext Preprocessor (PHP), and My Structured Query Language (MySQL). The new system was tested using data collected from Renewal Clinic, Ibadan, Nigeria was used as case study were the data for the research was collected and the system was tested. The system provides a vital platform of information storage and retrieval in hospitals.
Due to the information systems objectives, and to avoid duplication and to help improve care quality and
reduce cost, it is necessary to conduct continuous evaluation to determine how to achieve these goals. This
study was performed using evaluation indices of hospital Information systems (HIS) in selected hospitals of
Iran. In this article organizational and server components of hospital information systems in selected
hospitals are being assessed.
This research is a descriptive cross – sectional study. The study population consisted of the information
system of Shohaday Tajrish, Khatamolanbiya, Imam Khomeini and Milad Hospital. Data collecting tools
were checklist of hospital information system Evaluation Index, which completed with direct observation
and interviews with users. Data analyzed by statistics software SPSS, and presented as statistical tables
and graphs.
In the studied hospitals, although the most of the organizational components subgroups and hospital
information system server components has been set up and used but pharmacy information system, decision
support systems, communication services and telemedicine services hadn`t been set up fully in the
hospitals. Currently most subtypes of organizational components and hospital information system server
components were fully in the designed software and considering all fields in 5 hospitals.
Cis evaluation final_presentation, nur 3563 sol1SBU
An overview of a Computer Information System (CIS) and considerations that need to be taken with implementing an Electronic Health Record (EHR) in a healthcare setting.
Clinical Information Systems and Electronic Health Records (October 18, 2021)Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 18, 2021
IT in Pharmaceutical Error Prevention - Bioinformatics for PharmacyAnnisa Hayatunnufus
Part of our assignment in which we have to make a paperwork to elaborate the use of IT in order to prevent pharmaceutical errors.
Created by: Annisa Hayatunnufus
Bachelor of Pharmacy
Management & Science University
Medication Administration Errors at Children's University Hospitals: Nurses P...iosrjce
Medication administration errors(MAE) can threaten patient outcomes and are a dimension of
patient safety directly linked to nursing care. Children are particularly vulnerable to medication errors because
of their unique physiology and developmental needs.
Aims: The present study aims to examine types, stages and causes of medication errors. Barriers of medication
administration errors reporting and its facilitator at pediatric University hospitals from nurses point of view.
Methods: A descriptive study was conducted in Pediatric intensive care units, medical, surgical and urology
ward of children's university hospital at Mansoura University, intensive care units, kidney dialysis at
Abouelrash pediatric hospital and general wards of Elmonaira at Cairo University Hospitals. 80 nurses were
included in the study after fulfilling the criteria of selection. A structured interview questionnaire that consists
of four sections was used.
Results: The highest types of medication errors as reported by studied nurses occurred when the medication is
delivered by the wrong route, the highest stage of medication errors done by nurses was missing of medication
then patient monitoring and administration and the highest cause of medication errors was due to heavy
workload. The results of this study indicated that the strongest perceived barriers to medication administration
errors reporting were fear from consequences of reporting, then managerial factor and then the process of
reporting from the nurse's viewpoint. The nurses agree that identifying benefits of reporting followed agree that
feeling safe about working environment, and agree that good professional relationship with physicians was the
most facilitating factors of reporting medication errors.
Conclusions: It was concluded that medication errors result from interrelated factors, the strongest perceived
barriers to medication administration errors reporting were fear from consequences of reporting, and good
relationship with nurse managers and physicians were the most facilitators of reporting medication errors.
Recommendation: The study recommended that the assessment of medication errors should be done
periodically and in- service training program about medication administrations should be applied
Cis evaluation final_presentation, nur 3563 sol1SBU
An overview of a Computer Information System (CIS) and considerations that need to be taken with implementing an Electronic Health Record (EHR) in a healthcare setting.
Clinical Information Systems and Electronic Health Records (October 18, 2021)Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 18, 2021
IT in Pharmaceutical Error Prevention - Bioinformatics for PharmacyAnnisa Hayatunnufus
Part of our assignment in which we have to make a paperwork to elaborate the use of IT in order to prevent pharmaceutical errors.
Created by: Annisa Hayatunnufus
Bachelor of Pharmacy
Management & Science University
Medication Administration Errors at Children's University Hospitals: Nurses P...iosrjce
Medication administration errors(MAE) can threaten patient outcomes and are a dimension of
patient safety directly linked to nursing care. Children are particularly vulnerable to medication errors because
of their unique physiology and developmental needs.
Aims: The present study aims to examine types, stages and causes of medication errors. Barriers of medication
administration errors reporting and its facilitator at pediatric University hospitals from nurses point of view.
Methods: A descriptive study was conducted in Pediatric intensive care units, medical, surgical and urology
ward of children's university hospital at Mansoura University, intensive care units, kidney dialysis at
Abouelrash pediatric hospital and general wards of Elmonaira at Cairo University Hospitals. 80 nurses were
included in the study after fulfilling the criteria of selection. A structured interview questionnaire that consists
of four sections was used.
Results: The highest types of medication errors as reported by studied nurses occurred when the medication is
delivered by the wrong route, the highest stage of medication errors done by nurses was missing of medication
then patient monitoring and administration and the highest cause of medication errors was due to heavy
workload. The results of this study indicated that the strongest perceived barriers to medication administration
errors reporting were fear from consequences of reporting, then managerial factor and then the process of
reporting from the nurse's viewpoint. The nurses agree that identifying benefits of reporting followed agree that
feeling safe about working environment, and agree that good professional relationship with physicians was the
most facilitating factors of reporting medication errors.
Conclusions: It was concluded that medication errors result from interrelated factors, the strongest perceived
barriers to medication administration errors reporting were fear from consequences of reporting, and good
relationship with nurse managers and physicians were the most facilitators of reporting medication errors.
Recommendation: The study recommended that the assessment of medication errors should be done
periodically and in- service training program about medication administrations should be applied
Running Head EVALUATION PLAN FOCUSEVALUATION PLAN FOCUS 1.docxcowinhelen
Running Head: EVALUATION PLAN FOCUS
EVALUATION PLAN FOCUS 1
Evaluation Plan Focus
Student Name
University Affiliations
Date
Professor
Scenario 1:
Your hospital is implementing a new unified acute and ambulatory Electronic Health Record (EHR) system through which patient care documentation will occur. Interdisciplinary assessment forms (including nursing), clinical decision support, and medical notes will be documented in this system. The implementation of the system is anticipated to improve the hospital’s performance in a multitude of areas. In particular, it is hoped that the use of the EHR system will reduce the rate of patient safety events, improve the quality of care, deter sentinel events, reduce patient readmissions, and impact spending. The implementation of the EHR system is also
Introduction
Evaluation plan involves an integral part regarding a grant suggestion providing information aimed at improving a project during the development and implementation. I will participate in the assessment of the scenario system in throughout the project. The scenario includes the hospital that is implementing the new unified as well as the Ambulatory EHR (Electronic Health Record) system that enhances the documentation of patient care. The purpose of the paper is explaining the selected scenario one, explanation of the reasons for selecting it, and summarizing of the research findings on the similar HIT implementations. More so, there is a description of the evaluation viewpoint, and goal guiding the assessment plan and same rationale.
HIT System Selected
The new system to be implemented has various modules that contain interdisciplinary assessment forms, medical notes, and clinical decision support where their documentation is guaranteed. The implementation of the unified system will enhance improved performance of the hospital in several departments. The new EHR system becomes of great importance to the hospital since there is a reduction of medical errors, reduction of the rate of the safety events of each patient, improving the quality of healthcare, deterrence of sentinel events, reduced patients readmissions as well as impact spending. Another reason for choosing the scenario is that the new system will enhance while fulfilling the requirements of meaningful use as stipulated in the HITECH (Health Information Technology for Economic and Clinical Health) Act. Therefore, the need for evaluation regarding the EHR implementation becomes paramount since it will help to identify the associated risks while adjusting the modules required when offering the medication services to the patients (Lanham, Leykum & McDaniel, 2012).
Summary of Research Findings on Similar HIT Implementations
Several evaluations are analogous to the HIT system implementation of the unified system with related differences regarding the outcomes based on the primary goals. For instance, some of the implemented systems fail to meet one hundred percent ...
Proposed Framework For Electronic Clinical Record Information Systemijcsa
This research paper is drawn from an ongoing, large-scale project of implementing Electronic Clinical Record (ECR). The overall aim in this study is to develop a deeper understanding of the socio-technical aspects of the complexities and challenges emerging from the implementation of the ECR, and in particular to study how to manage a gradual transition to digital record. We have proposed ECR conceptual mode. The end result of our research was a collection of ideas / surveys, and field work that clinical institutions and medical informatics must consider to ensure that patients and clinics do not lose long-term access to ECR and technology continually progress. Results of our study identified the need for more research in this particular area as no definitive solution to long-term access to electronic clinical records was revealed. Additionally, the research findings highlighted the fact that a few medical institutions may actually be concerned about long-term access to electronic records.
COMPETIVENESS AND PERFORMANCE COMPETIVENESS AN.docxdonnajames55
COMPETIVENESS AND PERFORMANCE
COMPETIVENESS AND PERFORMANCE
Competiveness and Performance Effectiveness for Health Care IT Systems
Teresa Pride
Strayer University
February 25, 2018
Dr. Renita Ellis
HSA 315 Health Information Systems
Information technology (IT) has undergone significant improvements thereby requiring organizations to integrate these technologies to remain competitive. Considerably, health institutions are striving to improve primary care delivery, a facet that can be attained by incorporating the use of IT in service delivery. Information technology systems enable these institutions to streamline their processes based on its ability to improve the communication aspect of the system. Enhanced communication between employees and management simplify the organization’s operations as information can be shared in real time. Notably, healthcare institutions have priorities that must be addressed adequately to ensure that both the patients and stakeholders are satisfied with the organization’s service delivery system. Based on this, the institutions ought to retain IT management personnel to sustain its processes. This paper discusses the responsibilities and characteristics of the Chief Information and Chief Technology Officers. Additionally, the paper outlines how technologies can be used to improve healthcare processes, approaches to prevent misuse of information by upholding data privacy, strategies for organizations to train providers in using IT, and best practices for efficient IT alignment with strategic planning initiatives.
Characteristics and Roles of a CTO and a CIO
Often people confuse the two important senior leadership roles within the health care organization, the Chief Information Officer and Chief Technology Officer (Wager, Lee and Glaser 2013). Despite this, they have distinct tasks that must be performed to ensure the smooth running of the healthcare organization. Primarily, the CTO ensures that the institution’s services are tailored to meet the needs of the consumers. Notably, technological innovations are continually evolving thereby the organizations should retain an individual who is conversant with the new changes whenever they occur. In this regard, the CTO has the responsibility of advising top-level executives on strategic decisions regarding technology (Stephens, Ledbetter, Mitra & Ford, 2011). Additionally, the CTO identifies, evaluates and examines high return and high-risk IT systems with the potential of its application within the organization. As a result, the CTO must assess and monitor technologies for use in better service delivery in the future. The CTO’s focus is the client whereby he/she uses technology to foster collaboration amid suppliers and management to promote the organization’s services (Stephens, Ledbetter, Mitra & Ford, 2011). As the CTO works in the external environment, he/she can comprehend what is working in other institutions and apply it within the organization.
This article introduces health care managers to the theories and philosophies of John Kotter and William Bridges, 2 leaders in the evolving field of change management. For Kotter, change has both an emotional and situational component, and methods for managing each are expressed in his 8-step model (developing urgency, building a guiding team, creating a vision, communicating for buy-in, enabling action, creating short-term wins, don't let up, and making it stick). Bridges deals with change at a more granular, individual level, suggesting that change within a health care organization means that individuals must transition from one identity to a new identity when they are involved in a process of change. According to Bridges, transitions occur in 3 steps: endings, the neutral zone, and beginnings. The major steps and important concepts within the models of each are addressed, and examples are provided to demonstrate how health care managers can actualize the models within their health care organizations.
Health Information Systems Utilization: A Comparison of Extent and Magnitude ...AJHSSR Journal
ABSTRACT: Health information systems (HISs) are critical tools that have been widely adopted and
implemented in healthcare settings around the world, intending to improve the quality of healthcare services
(OHSs) delivered. However, it is the extent and magnitude of HISs utilization that seem to guarantee
improvement in the quality of health care. The study explored the extent to which HISs have been utilized in
selected public and private health facilities (PPHFs) in Dar es Salaam, Tanzania, and the determinants of its
utilization. A descriptive cross-sectional design was employed to collect data using the Kobo Collect survey tool
from 140 respondents and 12 key informants. Descriptive statistics (frequencies and percentages), Inferential
statistics (Pearson chi-square tests), and Linear regression analyses were employed to analyse data. The analysis
revealed that private ownership has a higher utilization rate of HIS (61.4%) compared to public ownership
(38.6%). Moreover, perceived ease of use and perceived usefulness were significant predictors of actual use of
the system, suggesting that users who found the system easy to use and useful were more likely to use it. In
conclusion, the utilization of HIS in Tanzania seems to be influenced by various factors, including ownership
type.
KEYWORDS: Determinants of Health Information Systems utilization, Health Information Systems, Health
Information Systems Utilization, Private Health Facilities, Public Health Facilities
Running head INITIAL PLAN DEVELOPMENT1INITIAL PLAN DEVELOPM.docxjeanettehully
Running head: INITIAL PLAN DEVELOPMENT 1
INITIAL PLAN DEVELOPMENT 2
Initial Plan Development
Nicholas Calhoun
Foundations of Project Management
South University
January 28, 2020
Initial Plan Development
The statement of need: Write a brief description of the chosen organization and discuss the background information associated with the problems that need to be solved.
Due to the passing of the Affordable Care Act ten years ago, there has been an increased demand for medical services in the United States. This has led to medical institutions serving increased patient numbers. Consequently, physicians, clinical staff and healthcare providers have had difficulties in managing the medical records of patients manually. This created the need for organization in the process of documentation and retrieval of patient records. The Electronic Medical Record, EMR, system comes in and its implementation and acquisitions would facilitate the following. The retrieval, management and capturing of patient data such as medical history, lab results, and demographic data (Jawhari, Keenan, Zakus, Ludwick, Isaac, Saleh, Hayward, 2016). Research indicates that an effective EMR system can centralize and acquire crucial patient information efficiently leading to enhanced service delivery.
EMR offers the ability to incur improvements on patient safety through the provision of timely and consistent care to patients in performance and through compliance with clinical regulations and standards, and the avoidance of duplicates. It can also lead to lower health care costs and improved efficiencies by promoting single patient records entailing integrated information. This would ensure improved coordination and continuity of care by reducing redundant tests and waste.
The goals and objectives of the project: Identify project goals and the underlying objectives. Quantify the measurable performance expectations of the project plan to determine whether it meets the planned objectives. Performance should be defined in terms of:
· The product or process specification
An EMR is an electronic version of a paper-based record-keeping system. It is a computer-based system for retrieving, organizing and storing patient information and is expected to improve the safety and quality of healthcare tremendously. An EMR can entail information such as the following. Research and education that would be accessible from various departments of a hospital institution under confidentiality, patient privacy and the protection of security. A plan of care, vital signs, patient progress assessment, hospitalization, surgical and medical history, test and laboratory results, allergies, past and present medication history (Morris, Sheehan, Lamichahane, Zimbro, Morgan, Bharadwaj, 2019). Others include insurance information, person to be notified in case of an emergency, gender, date of birth, complete address and full names.
· The total budget at completion of the plan
The budget for ...
Technology Acceptance Model for Mobile Health SystemsIOSR Journals
Abstract: The purpose of this paper is to explore the factors that influence health-related consumer’s
acceptance to use the mobile technology as a tool for receiving healthcare services. Based on technology
acceptance model (TAM), this paper provides a better understanding of antecedent of key acceptance constructs
(e.g. intention to use, perceived usefulness, perceived ease of use). The proposed research model and hypotheses
validated and tested with data collected from 302 Egyptians and Yemenis patients, health professionals, and
general health users. The results are analyzed using a number of statistical techniques including partial least
squares. The key findings obtaining from the results of the three surveyed stakeholders reveal that: (1) ninety
percent are indented to use mobile health services. (2) While intention to use has greatly influenced by
perceived usefulness, the impact of perceived ease of use varies. (3) Perceived value, perceived ease of use and
portability factors are significantly affect perceived usefulness. (4) Self-efficacy and technology anxiety have a
great impact on perceived ease of use. (5) The impact of the rest of the suggested factors ranged from medium,
low, and insignificant. The research made an in-depth exploration and examination of the factors that influence
user’s intention to use mobile health services focusing on technological, cultural, organizational, political, and
social aspects whereas most of the previous studies considered only one or two aspects together. The proposed
model can be applied to assess mobile health user’s acceptance, thereby help mobile health developers and
providers to develop better mobile health applications that meet the needs of the potential users.
Keywords: Intention to use, Mobile health, portability, Resistance to change, Technology anxiety, Technology
acceptance model
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
HEALTH DISPARITIES: DIFFERENCES IN VETERAN AND NON-VETERAN POPULATIONS USING ...hiij
Introduction: This study investigated self-reported health status, health screenings, vision problems, and
vaccination rates among veteran and non-veteran groups to uncover health disparities that are critical for
informed health system planning for veteran populations.
Methods: Using public-use data from the National Health Interview Survey (2015-2018), this study adopts
an ecologic cross-sectional approach to conduct an in-depth analysis and visualization of the data assisted
by Generative AI, specifically ChatGPT-4. This integration of advanced AI tools with traditional
epidemiological principles enables systematic data management, analysis, and visualization, offering a
nuanced understanding of health dynamics across demographic segments and highlighting disparities
essential for veteran health system planning.
Findings: Disparities in self-reports of health outcomes, health screenings, vision problems, and
vaccination rates were identified, emphasizing the need for targeted interventions and policy adjustments.
Conclusion: Insights from this study could inform health system planning, using epidemiological data
assessment to suggest enhancements for veteran healthcare delivery. These findings highlight the value of
integrating Generative AI with epidemiological analysis in shaping public health policy and health
planning.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
AUTOMATIC AND NON-INVASIVE CONTINUOUS GLUCOSE MONITORING IN PAEDIATRIC PATIENTShiij
Glycated haemoglobin does not allow you to highlight the effects that food choices, physical activity and
medications have on your glycaemic control day by day. The best way to monitor and keep track of the
immediate effects that these have on your blood sugar levels is self-monitoring, therefore the use of a
glucometer. Thanks to this tool you have the possibility to promptly receive information that helps you to
intervene in the most appropriate way, bringing or keeping your blood sugar levels as close as possible to
the reference values indicated by your doctor. Currently, blood glucose meters are used to measure and
control blood glucose. Diabetes is a fairly complex disease and it is important for those who suffer from it
to check their blood sugar (blood sugar) periodically throughout the day to prevent dangerous
complications. Many children newly diagnosed with diabetes and their families may face unique challenges
when dealing with the everyday management of diabetes, including treatments, adapting to dietary
changes, and the routine monitoring of blood glucose. Many questions may also arise when selecting a
blood glucose meter for paediatric patients. With current blood glucose meters, even with multiple daily
self-tests, high and low blood glucose levels may not be detected. Key factors that may be considered when
selecting a meter include accuracy of the meter; size of the meter; small sample size required for testing;
ease of use and easy-to-follow testing procedure; ability for alternate testing sites; quick testing time and
availability of results; ease of portability to allow testing at school and during leisure time; easyto- read
numbers on display; memory options; cost of meter and supplies. In this study we will show a new
automatic portable, non-invasive device and painless for the daily continuous monitoring (24 hours a day)
of blood glucose in paediatric patients.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
The Proposed Guidelines for Cloud Computing Migration for South African Rural...hiij
It is now overdue for the hospitals in South African rural areas to implement cloud computing technologies in order to access patient data quickly in an emergency. Sometimes medical practitioners take time to attend patients due to the unavailability of kept records, leading to either a loss of time or the reassembling of processes to recapture lost patient files. However, there are few studies that highlight challenges faced by rural hospitals but they do not recommend strategies on how they can migrate to cloud computing. The purpose of this paper was to review recent papers about the critical factors that influence South African hospitals in adopting cloud computing. The contribution of the study is to lay out the importance of cloud computing in the health sectors and to suggest guidelines that South African rural hospitals can follow in order to successfully relocate into cloud computing.The existing literature revealed that Hospitals may enhance their record-keeping procedures and conduct business more effectively with the help of the cloud computing. In conclusion, if hospitals in South African rural areas is to fully benefit from cloud-based records management systems, challenges relating to data storage, privacy, security, and the digital divide must be overcome.
SUPPORTING LARGE-SCALE NUTRITION ANALYSIS BASED ON DIETARY SURVEY DATAhiij
While online survey systems facilitate the collection on copious records on diet, exercise and other healthrelated data, scientists and other public health experts typically must download data from those systems
into external tools for conducting statistical analyses. A more convenient approach would enable
researchers to perform analyses online, without the need to coordinate additional analysis tools. This
paper presents a system illustrating such an approach, using as a testbed the WAVE project, which is a 5-
year childhood obesity prevention initiative being conducted at Oregon State University by health scientists
utilizing a web application called WavePipe. This web application has enabled health scientists to create
studies, enrol subjects, collect physical activity data, and collect nutritional data through online surveys.
This paper presents a new sub-system that enables health scientists to analyse and visualize nutritional
profiles based on large quantities of 24-hour dietary recall records for sub-groups of study subjects over
any desired period of time. In addition, the sub-system enables scientists to enter new food information
from food composition databases to build a comprehensive food profile. Interview feedback from novice
health science researchers using the new functionality indicated that it provided a usable interface and
generated high receptiveness to using the system in practice.
AN EHEALTH ADOPTION FRAMEWORK FOR DEVELOPING COUNTRIES: A SYSTEMATIC REVIEWhiij
#Health #clinic #education #StaySafe #pharmacy #healthylifestyle
call for papers..!
-----------------------------
Health Informatics: An International Journal (HIIJ)
ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print)
Here's where you can reach us : hiij@aircconline.com
visit us on : https://airccse.org/journal/hiij/index.html
**************
published articles..!
AN EHEALTH ADOPTION FRAMEWORK FOR
DEVELOPING COUNTRIES: A SYSTEMATIC REVIEW
https://aircconline.com/hiij/V10N3/10321hiij01.pdf
GENDER DISPARITYOF TUBERCULOSISBURDENIN LOW-AND MIDDLE-INCOME COUNTRIES: A SY...hiij
The tuberculosis burden is higher in the population from low- and middle-income countries (LMICs) and
differently affects gender. This review explored risk factors that determine gender disparity in tuberculosis
in LMICs. The research design was a systematic review. Three databases; Google Scholar, PubMed, and
HINARI provided 69 eligible papers.The synthesized data were coded, grouped and written in a descriptive
narrative style. HIV-TB co-infected women had a higher risk of mortality than TB-HIV-infected men. The
risk of Vitamin-D deficiency-induced tuberculosis was higher in women than in men. Lymph node TB,
breast TB, and cutaneous and abdominal TB occurred commonly in women whereas pleuritis, miliary TB,
meningeal TB, pleural TB and bone and joint TB were common in men. Employed men had higher contact
with tuberculosis patients and an increased chance of getting the disease. Migrant women were more likely
to develop tuberculosis than migrant men. The TB programmers and policymakers should balance the
different gaps of gender in TB-related activities and consider more appropriate approaches to be genderbased and have equal access to every TB-associated healthcare.
BRIEF COMMUNICATIONS DATA HYGIENE: IMPORTANT STEP IN DECISIONMAKING WITH IMPL...hiij
Medical and health data that have been entered into an electronic data system in real-time cannot be
assumed to be accurate and of high quality without verification. The adoption of the electronic health
record (EHR) by many countries to the support care and treatment of patients illustrates the importance of
high quality data that can be shared for efficient patient care and the operation of healthcare systems.
This brief communication provides a high-level overview of an EHR system and practices related to high
data quality and data hygiene that could contribute to the analysis and interpretation of EHR data for use
in patient care and healthcare system administration.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?
RANKING THE MICRO LEVEL CRITICAL FACTORS OF ELECTRONIC MEDICAL RECORDS ADOPTION USING TOPSIS METHOD
1. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
DOI: 10.5121/hiij.2013.2402 19
RANKING THE MICRO LEVEL CRITICAL FACTORS
OF ELECTRONIC MEDICAL RECORDS ADOPTION
USING TOPSIS METHOD
Hossein Ahmadi1
, Maryam Salahshour Rad2
, Mehrbakhsh Nilashi3,*
, Othman
Ibrahim4
, Alireza Almaee5
1,2,3,4
Faculty of Computing, Universiti Technologi Malaysia, Johor, Malaysia
5
Department of Management, Rasht Payame Noor University, Rasht, Iran
ABSTRACT
In many countries, the health care sector is entering into a time of unprecedented change. Electronic
Medical Record (EMR) has been introduced into healthcare organizations in order to incorporate better
use of technology, to aid decision making, and to facilitate the search for medical solution. This needs
those professionals in healthcare organizations to be in the process of changing from the use of paper to
maintain medical records into computerized medical recordkeeping opportunities. However, the adoption
of these electronic medical records systems has been slow throughout the healthcare field. The critical
users are physicians which play an important role to success of health information technology including
Electronic Medical Record systems. As a result user adoption is necessary in order to understand the
benefits of an EMR. Therefore, in the current paper, a model of ranking factors of micro-level in EMRs
adoption was developed. Surveys distributed to physicians as this study’s respondent in two private
hospitals in Malaysia. The findings indicate that physicians have a high perception means for the
technology and showed that EMR would increase physician’s performance regarding to decision making.
They have been and continue to be positively motivated to adopt and use the system. The relevant factors
according to micro-level perspective prioritized and ranked by using the Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS). The aim of ranking and using this approach is to
investigate which factors are more important in EMRs adoption from the micro-level perspectives. The
results of performing TOPSIS is as a novelty which assist health information systems (HIS) success and
also healthcare organizations to motivate their users in accepting of new technology.
KEYWORDS
EMRs, Adoption, TOPSIS, Micro-Level Adoption Factors
1. INTRODUCTION
The health care industry’s growing adoption of Electronic Medical Records (EMR) is becoming a
new perspective on the role of healthcare professionals. Information technology has been proved
to be as an imperative element in the administration of healthcare [34]. In particular, some private
hospitals in Malaysia are adopting information systems that offer more accurate and timely
information concerning patient care [5]. By utilizing information technology hospitals are capable
to retain documentation of their daily transactions such as in data storage, retrieving and
communication. Currently, the midst of a landmark shift in record keeping, with driving for
electronic medical records well in progress [6]. An EMR system was introduced as a way to make
possible a centralized patient information repository. For many purposes EMR is utilized
2. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
20
including administration, patient care, quality improvement, research, and reimbursement [35].
These applications need knowledge of the underlying quality of the data within the EMR so as to
avoid misinterpretation [35]. EMRs would remedy the intrinsic flaws of the conventional paper
system through improvements in accessibility, efficiency, quality of data capture and cost saving.
As a result, an EMR system should be able to appropriately capturing, processing and storing
information and also should be compatible with other related systems [6]. It affects the quality
outputs in health care provider which users by using the patient information can be able to make
decisions. By increasing the accuracy of patient information, it is possible to less likely that they
face large differences in errors and consequently decreases the marginal revenue from quality
growing [6].
In relation with EMR, the concept of clinical system places reduction of medical error into the
wider context of quality of care and safety by giving a framework to evaluate and assess the
structure, process and outcomes of care. The purpose of this paper is to describe the factors that
have more priority in affecting EMR to adopt in private hospitals in Malaysia. The critical
elements of this paper include HIS quality, use and net benefits with their sub-factors.
The remainder of this paper is structured as follows. Section 1 describes the EMR and gives an
overview of this research. The section 2 introduces the proposed research model. In Section 3, we
explain the research methodology step by step. Section 4 and 5 are allocated to the background
mathematical of The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
and data collection, respectively. Finally, we present the results of TOPSIS and conclusions in
sections 6 and 7, respectively.
2. PROPOSED RESEARCH MODEL
The physician adoption model provides a conceptual model to identify the factors that have more
influence on health information systems (HIS) success. It extends Info way Benefits Evaluation
(BE) Framework [18] (adapted from the DeLone and McLean information system success model
[9] which Thereafter, [12] in his study review developed Clinical Adoption (CA) framework
based on three dimensions. The framework comprised of micro, meso and macro-level
dimensions. Each dimension has its own factors and sub-factors which could affect physicians in
EMR adoption. In this study it has been focused on micro-level factors. Physician adoption model
at the micro-level explains HIS success related to HIS quality, use and net benefits. HIS quality
divided in information, system and service quality respectively; use covers HIS usage and
satisfaction; net benefits covers care quality, access, and productivity. The physician adoption
model was developed with a range of HIS in mind, including EMRs. In this review, we examined
EMR and its success in health centre thru the lens of the physician adoption model. EMR
adoption has been described and influence on physician practice, according to evaluation
measures utilized in the studies. In regarding of Factors that have been caused to this impact, it
has been described as the reasons cited that could explain the adoption and effect. Hence, in this
study we have concentrated on Micro level factors that affects on EMR adoption. At the end the
proposed model has been developed and shown in Figure 1.
It has been required for system quality to sustain high quality health service delivery that meets
the request of the people. System quality affects the quality of care by capturing, transferring,
storing, managing and displaying medical information. In growing the quality of these processes,
the system should give higher quality (12).
System quality factors included the availability of templates [2], interface design [6, 12], Newby
[36] and technical performance (e.g. speed and reliability) [24, 35].
3. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
21
Information quality plays a critical role in hospital. The organization, accuracy, completeness and
accessibility of the patient record are the sub factors of information quality [1, 6, 11, 12, 13, 24,
27].
Service quality is a comparison of expectations with performance. Health care provider with high
service quality will meet patient needs whilst remaining economically competitive. Improved
quality may increase economic competitiveness. If patients who have not been satisfied with
preferred hospitals can deliver quality services, they would look for the services elsewhere. Thus
it is imperative to inquire patients in a straight line about the perceived quality of services
provided by the country’s hospitals [31]. Service quality factors included training and technical
support [32, 38] system backup and unexpected downtime [31].
Electronic medical records usage can differ depending on how they utilize it and who the user is.
Electronic medical records would assist to advance the quality of medical care given to patients.
Removing the traditional paper records are denied by Many doctors and office-based physicians
[22]. Factors in EMR usage covers its intent (e.g. quality improvement versus record keeping)
[21], actual strategies for optimal use, ease of use [11, 29] and usage patterns that appeared
gradually [17]. The relevant factors of interaction included patient-physician encounters like
patients’ ability to schedule appointments [10], the kind of consult (e.g. psychological) [3, 22,
23], and consult room layout [22].
Figure 1. TOPSIS Framework of Physician Adoption Model in Micro-Level
Net benefits, care quality factors covered patient safety [14], care effectiveness [17], quality
improvement [27] and guideline compliance [8, 36]. Productivity factors covered care efficiency
[14, 33], coordination [35], and net cost including billing, staffing and maintenance costs [2, 27,
24, 31].
4. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
22
Micro-level factors that found in previous research which has an effect on EMR adoption and
effect were shown (See Table 1).
Table 1.Micro -level factors that influenced EMR success
HIS quality HIS quality sub-factors References
System quality
Template [2, 22]
Design/performance [6, 12, 21, 24, 28, , 30, 35, 37]
Information
quality
Access [6]
Content [1, 6, 11, 12, 13, 24, 27, 28, 33, 35, 37]
Service quality
Support [32, 38, 39]
Downtime [31, 38]
HIS Use HIS quality Sub-factors References
Use
Use strategies [3]
Use pattern [17]
Use intention [21]
Satisfaction Ease of use [11, 29]
Interaction [3, 6, 22, 23]
Net benefits Net benefits Sub-factors References
Care quality
Safety [14]
Effectiveness [17]
Quality improvement [27]
Guidelines [8, 27, 36]
Productivity
Care coordination [2, 35]
Efficiency [16, 4, 14, 33]
Net cost [2, 27]
Cost Savings/Profits [27]
Maintenance cost [31]
Access
Communication [6]
Patient acceptance [4]
Patient choice [10]
3. RESEARCH METHODOLOGY
Researcher covered the topic of Electronic Medical Record adoptions shown that EMR are being
accepted by private hospital of Malaysia. A quantitative, survey-based research study was
performed and was analysed to explaining the factors that have an effect on EMR adoption. The
two hospitals have been chosen to conduct this research. Survey distributed to 150 physicians
who had experience using EMRs. 90 physicians fulfilled the questionnaire in this study and the
rest did not complete the survey study because of their time constrain. The survey contains
number of questions that were design to capture information about the constructs in the research
model. The questions that measured were HIS quality, HIS use and net benefits besides their sub-
factors. TOPSIS was use to obtain the ranking of these factors. Figure 2 contains a description of
each step in this study.
5. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
23
Figure 2. Research methodology
4. MATHEMATICAL BACKGROUND OF TOPSIS
TOPSIS is one of the famous classical Multi-Criteria Decision Making (MCDM) method, which
was initiated for the first time by Hwang and Yoon [40] that shall be used with both normal
numbers and fuzzy numbers [41, 42]. Furthermore, TOPSIS is more applicable in that limited
subjective input is required from decision makers. The only subjective input required is weights.
The TOPSIS procedure is shown in Figure 3 in five main steps.
Figure 3. Procedure of TOPSIS method
6. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
24
Using entropy method, objective weights were calculated. The following equation calculates
entropy measure of every index.
[ ]
=
=∀
⇒−= ∑ =
)m(Ln
1
K
n,...2,1
)n(LnnKE
jm
1i
ijijj (1)
The degree of divergence dj of the intrinsic information of each criterion C (j= 1, 2, …, n) may
be calculated as
jj E1d −= (2)
The value dj represents the inherent contrast intensity of cj. The higher the dj is, the more
important the criterion cj is for the problem. The objective weight for each criterion can be
obtained. Accordingly, the normalized weights of indexes may be calculated as
∑ =
=
n
1k
k
j
j
d
d
W
(3)
5. DATA COLLECTION
The primary data in this study were collected through questionnaire that distributed to the
physicians through web based questionnaire who have some experiences in using EMR. For this
study, a number of respondents, were approximately 150 (n=150) physicians. Sixty percent (60%)
of the respondents provided answers to all the questions in the instrument.
The first section comprise of information on respondent demographic profile, eightsections on the
independent variable namely, system quality, information quality, service quality, use,
satisfaction, care quality, productivity and access. Five options (index) ranked by 1-5 (1= very
low important 2=low important 3=moderately important 4= high important 5= very high
important) were used for the raised questions.
7. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
25
Table 2. The respondents’ demographic profile
Aspects Category Respondents (n) Respondents (%)
Gender Male 75 75%
Female 25 25%
Age 26-33
34-50
51-65
20
45
85
13.4%
30%
56.6%
Years of electronic medical
records experience
1-5 54 56.8%
6-10 15 15.8%
Over 10 3 3.2%
Medical specialization Generalist 68 67%
Specialist 34 33%
Table 2 provides the respondents’ demographic profile. About seventy five percent of physicians
were male and twenty five percent were female, generalist and specialist physicians in with one to
five years of experience with Electronic Medical Records technology.
6. RESULTS OF TOPSIS
In this section, we provide the results of TOPSIS for ranking the factors presented in the TOPSIS
Framework of physician adoption model in micro-level. According to the Figure 1, the aim of
applying TOPSIS is to rank the 23 factors to show the importance of these factors in EMRs
adoption in micro-level.
In addition, based on five steps of TOPSIS shown in Figure 3 and formulas presented in
equations 1, 2 and 3, we calculated the weights of five indices as following:
m
ij ij
i
E k (n ln(n )) .
=
= − = −∑1
1
3 26
m
ij ij
i
E k (n ln(n )) .
=
= − = −∑2
1
4 13
m
ij ij
i
E k (n ln(n )) .
=
= − = −∑3
1
2 68
m
ij ij
i
E k (n ln(n )) .
=
= − = −∑4
1
2 29
m
ij ij
i
E k (n ln(n )) .
=
= − = −∑1
5 3 25
Thus, using Entropy method, the weights are obtained as:
w 0.236=1
w 0.220=2
w 0.178=1
w 0.168=4
w 0.196=5
where
1=++++⇒=∑ 543211 wwwwwwi
9. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
27
Table 4.Negative ideal
Min Vi1 Min Vi2 Min Vi3 Min Vi4 Min Vi5
0 0.022 0.014 0 0.014
As shown in the Table 3 and Table 4, we selected the maximum and the minimum of each
column of matrix V as positive and negative ideals. Thus, A+ and A- denote all the maximum and
minimum numbers of each column of matrix V.
For step 4 of TOPSIS procedure, we calculate the distance i from positive ideal as following:
2
1
2
1
})({IdealpositivefromiDistance +− −= ∑ = jj ij
vv
Table 5 presents the distance i from positive ideal for 23 factors. In this table, the square of
difference between distance between max point and each point ideal are provided.
Table 5. Distance i from positive ideal
2
)( +− jij
vv 2
)( +− jij
vv 2
)( +− jij
vv 2
)( +− jij
vv 2
)( +− jij
vv
0.00000 0.02413 0.02965 0.00802 0.04299
0.00504 0.07089 0.02965 0.00000 0.02768
0.00000 0.07089 0.02965 0.03630 0.00000
0.00000 0.07089 0.02965 0.02357 0.00962
0.00000 0.07089 0.02965 0.00802 0.02768
0.00504 0.07089 0.00000 0.02357 0.02768
0.00504 0.00000 0.02965 0.04162 0.00000
0.00000 0.07089 0.02965 0.00802 0.02768
0.00504 0.11077 0.00000 0.00000 0.04299
0.00504 0.11077 0.02965 0.01339 0.02768
0.00000 0.11077 0.00000 0.03630 0.00962
0.00504 0.11077 0.04614 0.00000 0.00000
0.00504 0.07089 0.02965 0.02357 0.00000
0.00504 0.07089 0.02965 0.02357 0.00000
0.00000 0.11077 0.04614 0.00802 0.00000
0.00000 0.07089 0.02965 0.00802 0.02768
0.00000 0.07089 0.02965 0.02357 0.00962
0.00000 0.07089 0.02965 0.00802 0.02768
0.00504 0.07089 0.00000 0.02357 0.02768
0.00000 0.11077 0.00000 0.03630 0.00962
0.00504 0.11077 0.04614 0.00000 0.00000
0.00504 0.07089 0.00000 0.02357 0.02768
0.00000 0.00000 0.02965 0.04162 0.00000
Similar to distance i from positive ideal, we calculate the distance i from negative ideal as
following:
10. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
28
2
12
1
IdealnegativefromiDistance })({ −=
−= ∑ − jj ij
vv
Table 6 presents the distance i from negative ideal for 23 factors. In this table, the square of
difference between distance between min point and each point are provided.
Table 6. Distance i from negative ideal
2
)( +− jij
vv 2
)( +− jij
vv 2
)( +− jij
vv 2
)( +− jij
vv 2
)( +− jij
vv
0.0051 0.0316 0.0018 0.0131 0.0000
0.0000 0.0045 0.0018 0.0415 0.0017
0.0051 0.0045 0.0018 0.0002 0.0426
0.0051 0.0045 0.0018 0.0025 0.0119
0.0051 0.0045 0.0018 0.0131 0.0017
0.0000 0.0045 0.0462 0.0025 0.0017
0.0000 0.1109 0.0018 0.0000 0.0426
0.0051 0.0045 0.0018 0.0131 0.0017
0.0000 0.0000 0.0462 0.0415 0.0000
0.0000 0.0000 0.0018 0.1022 0.0017
0.0051 0.0000 0.0462 0.0002 0.0119
0.0000 0.0000 0.0000 0.0415 0.0426
0.0000 0.0045 0.0018 0.0025 0.0426
0.0000 0.0045 0.0018 0.0025 0.0426
0.0051 0.0000 0.0000 0.0131 0.0426
0.0051 0.0045 0.0018 0.0131 0.0017
0.0051 0.0045 0.0018 0.0025 0.0119
0.0051 0.0045 0.0018 0.0131 0.0017
0.0000 0.0045 0.0462 0.0025 0.0017
0.0051 0.0000 0.0462 0.0002 0.0119
0.0000 0.0000 0.0000 0.0415 0.0426
0.0000 0.0045 0.0462 0.0025 0.0017
0.0000 0.1109 0.0018 0.0000 0.0426
In the next step of TOPSIS, we calculate the sum of id+ and id- as presented in Table 7.
11. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
29
Table 7. Sum of positive ideal and negative ideal
From the Table 7, di+ and di- stand for distance i from positive ideal and di- that stands for
distance i from negative ideal, respectively. In the last step we rank 23 factors by calculating the
distance between Ai and ideal solution as following:
micli
dd
d
cli ,...2,110
11
1
=≤≤
+
= +−
−
(6)
Finally, in Table 8, we present the ranking of factors in the micro-level of ERMs adoption. The
ranking in this table demonstrates that based on physicians’ perception, ten important factors in
micro level of electronic medical records adoption are patient choice, use strategies, ease of use,
use intention, safety, communication, template, downtime and cost savings/profits. In addition,
according to the ranking presented in Table 8, the patient choice is ranked with a high priority and
this confirms the result of work developed by Dennison et al., 2006. They showed that enhanced
patient choice of appointment date and time significantly enhances the electronic surgical referral
system can improve efficiency. Thus, it is important for adopter of EMRs that patient choice can
play important role in their goals, mission and vision.
12. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
30
Table 8. Final ranking of factors in the micro-level of ERMs adoption
7. CONCLUSION
The current study was done to develop the body of research related to technology adoption inside
a professional environment in context of hospitals in private sector which could be applied in
regard to public sector. This study has focused on micro-level factors which influence on EMR
adoption and effect which is based on [12]. The limitation of the study confined the physicians
who have not yet adopted the EMR or stop using this technology. The findings of the present
study were used to address the adoption and effect of electronic medical records technology
within the physician community in private hospitals in Malaysian. Physicians had very high
perception means for the technology and showed that EMR would increase physician’s
performance. They have been and continue to be positively motivated to adopt and use the
system. The TOPSIS Framework of Physician Adoption Model in Micro-Level, factors, finding
discussed in this research give the essential components to make sense of EMR in the private
hospitals.
REFERENCES
[1] Baron, R. J. (2007)."Quality improvement with an electronic health record: achievable, but not
automatic." Annals of Internal Medicine 147(8): 549-552.
[2] Bolger-Harris, H., et al. (2008). "Using computer based templates for chronic disease management."
Australian family physician 37(4): 285.
13. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
31
[3] Booth, N., Robinson, P., & Kohannejad, J. (2004). Identification of high-quality consultation practice
in primary care: the effects of computer use on doctor-patient rapport. Informatics in primary care,
12(2), 75-83.
[4] Cauldwell, M. R., Beattie, C. E., Cox, B. M., Denby, W. J., Ede-Golightly, J. A., & Linton, F. L.
(2007). The impact of electronic patient records on workflow in general practice. Health informatics
journal, 13(2), 155-160.
[5] Chee, H. L., &Barraclough, S. (2007). Health care in Malaysia: the dynamics of provision, financing
and access: Routledge.
[6] Christensen, T. and A. Grimsmo (2008). "Expectations for the next generation of electronic patient
records in primary care: a triangulated study." Informatics in primary care 16(1): 21-28.
[7] Christian G. D. (2002). Electronic Medical Records Mark a Landmark Shift in Record Keeping.
[8] de Jong, J. D., Groenewegen, P. P., Spreeuwenberg, P., Westert, G. P., & de Bakker, D. H. (2009).
Do decision support systems influence variation in prescription? BMC health services research, 9(1),
20.
[9] DeLone, W. H. and E. R. McLean (1992). "Information systems success: the quest for the dependent
variable." Information Systems Research 3(1): 60-95.
[10] Dennison, J., Eisen, S., Towers, M., & Clark, C. I. (2006). An effective electronic surgical referral
system. Annals of the Royal College of Surgeons of England, 88(6), 554.
[11] Frank, O. R., Litt, J. C., & Beilby, J. J. (2004). Opportunistic electronic reminders: improving
performance of preventive care in general practice. Australian family physician, 33(1-2), 87-90.
[12] Hamilton, W. T., Round, A. P., Sharp, D., & Peters, T. J. (2003). The quality of record keeping in
primary care: a comparison of computerised, paper and hybrid systems. The British Journal of
General Practice, 53(497), 929.
[13] Hippisley-Cox, J., Pringle, M., Cater, R., Wynn, A., Hammersley, V., Coupland, C., . . . Johnson, C.
(2003). The electronic patient record in primary care—regression or progression? A cross sectional
study. Bmj, 326(7404), 1439-1443.
[14] Hollingworth, W., Devine, E. B., Hansen, R. N., Lawless, N. M., Comstock, B. A., Wilson-Norton, J.
L., . . . Sullivan, S. D. (2007). The Impact of e-Prescribing on Prescriber and Staff Time in
Ambulatory Care Clinics: A Time–Motion Study. Journal of the American Medical Informatics
Association, 14(6), 722-730.
[15] Hwang, C.L., Yoon, K., ‘‘Multiple Attributes Decision Making Methods and Applications’’,
Springer, BerlinHeidelberg, 1981.
[16] Keshavjee, K., Troyan, S., Holbrook, A., & VanderMolen, D. (2001). Measuring the success of
electronic medical record implementation using electronic and survey data. Paper presented at the
Proceedings of the AMIA Symposium.
[17] Kinn, J. W., O’Toole, M. F., Rowley, S. M., Marek, J. C., Bufalino, V. J., & Brown, A. S. (2001).
Effectiveness of the electronic medical record in cholesterol management in patients with coronary
artery disease (Virtual Lipid Clinic). The American journal of cardiology, 88(2), 163-165.
[18] Lau, F., Hagens, S., & Muttitt, S. (2006). A proposed benefits evaluation framework for health
information systems in Canada. Healthcare quarterly (Toronto, Ont.), 10(1), 112-116, 118.
[19] Lau, F., Price, M., & Keshavjee, K. (2011). From benefits evaluation to clinical adoption: making
sense of health information system success in Canada. Healthc Q, 14(1), 39-45.
[20] Lau, F., Price, M., Boyd, J., Partridge, C., Bell, H., &Raworth, R. (2012). Impact of electronic
medical record on physician practice in office settings: a systematic review. BMC medical
informatics and decision makin.12(1), 10.
[21] Linder, J. A., Ma, J., Bates, D. W., Middleton, B., & Stafford, R. S. (2007). Electronic health record
use and the quality of ambulatory care in the United States. Archives of Internal Medicine, 167(13),
1400-1405.
[22] Ludwick, D. and J. Doucette (2009). "Primary care physicians' experience with electronic medical
records: barriers to implementation in a fee-for-service environment." International Journal of
Telemedicine and Applications 2009: 2.
[23] Margalit, R. S., Roter, D., Dunevant, M. A., Larson, S., & Reis, S. (2006). Electronic medical record
use and physician–patient communication: an observational study of Israeli primary care encounters.
Patient education and counseling, 61(1), 134-141.
[24] Martens, J. D., van der Weijden, T., Severens, J. L., de Clercq, P. A., De Bruijn, D., Kester, A. D., &
Winkens, R. A. (2007). The effect of computer reminders on GPs’ prescribing behaviour: a cluster-
randomised trial. International Journal of Medical Informatics, 76, S403-S416.
14. Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
32
[25] Miller, R. H., Sim, I., & Newman, J. (2004). Electronic medical records in solo/small groups: a
qualitative study of physician user types. Studies in health technology and informatics, 107(Pt 1), 658.
[26] Miller, R. H., Sim, I., & Newman, J. (2004). Electronic medical records in solo/small groups: a
qualitative study of physician user types. Studies in health technology and informatics, 107(Pt 1), 658.
[27] Miller, R. H., West, C., Brown, T. M., Sim, I., & Ganchoff, C. (2005). The value of electronic health
records in solo or small group practices. Health Affairs, 24(5), 1127-1137.
[28] Mitchell, E., McConnahie, A., & Sullivan, F. (2003). Consultation computer use to improve
management of chronic disease in general practice: a before and after study. Informatics in primary
care, 11(2), 61-68.
[29] Montgomery, A. A., Fahey, T., Peters, T. J., MacIntosh, C., & Sharp, D. J. (2000). Evaluation of
computer based clinical decision support system and risk chart for management of hypertension in
primary care: randomised controlled trial. Bmj, 320(7236), 686-690.
[30] Newby, D. A., Fryer, J. L., & Henry, D. A. (2003). Effect of computerised prescribing on use of
antibiotics. Medical journal of Australia, 178(5), 210-213. Onconomics, 21:4.
[31] Randeree, E. (2007). "Exploring physician adoption of EMRs: a multi-case analysis." Journal of
medical systems 31(6): 489-496.
[32] Robinson, A. (2003). "Information technology creeps into rural general practice." Australian Health
Review 26(1): 131-137.
[33] Schade, C. P., Sullivan, F. M., De Lusignan, S., & Madeley, J. (2006). e-Prescribing, efficiency,
quality: lessons from the computerization of UK family practice. Journal of the American Medical
Informatics Association, 13(5), 470-475.
[34] Sood, S. P., Nwabueze, S. N., Mbarika, V. W. A., Prakash, N., Chatterjee, S., Ray, P., & Mishra, S.
(2008). Electronic medical records: a review comparing the challenges in developed and developing
countries. Paper presented at the Hawaii International Conference on System Sciences, Proceedings
of the 41st Annual.
[35] Tamblyn, R., Huang, A., Perreault, R., Jacques, A., Roy, D., Hanley, J., . . . Laprise, R. (2003). The
medical office of the 21st century (MOXXI): effectiveness of computerized decision-making support
in reducing inappropriate prescribing in primary care. Canadian Medical Association Journal, 169(6),
549-556.
[36] Tamblyn, R., Huang, A., Taylor, L., Kawasumi, Y., Bartlett, G., Grad, R., . . . Perreault, R. (2008). A
randomized trial of the effectiveness of on-demand versus computer-triggered drug decision support
in primary care. Journal of the American Medical Informatics Association, 15(4), 430-438.
[37] Vainiomäki, S., Kuusela, M., Vainiomäki, P., & Rautava, P. (2008). The quality of electronic patient
records in Finnish primary healthcare needs to be improved. Scandinavian journal of primary health
care, 26(2), 117-122.
[38] Wager, K. A., Lee, F. W., White, A. W., Ward, D. M., & Ornstein, S. M. (2000). Impact of an
electronic medical record system on community-based primary care practices. The Journal of the
American Board of Family Practice, 13(5), 338-348.
[39] Wells, S., Furness, S., Rafter, N., Horn, E., Whittaker, R., Stewart, A., Bramley, D. (2008). Integrated
electronic decision support increases cardiovascular disease risk assessment four fold in routine
primary care practice. European Journal of Cardiovascular Prevention & Rehabilitation, 15(2), 173-
178.
[40] Hwang, C.L. and K. Yoon, 1981. Multiple Attributes Decision Making Methods and Applications.
Springer, Berlin Heidelberg.
[41] Nilashi, M., Bagherifard, K., Ibrahim, O., Janahmadi, N., & Ebrahimi, L. Ranking Parameters on
Quality of Online Shopping Websites Using Multi-Criteria Method. Research Journal of Applied
Sciences, 4(21): 4380-4396.
[42] M. Nilashi, K. Bagherifard, O. Ibrahim, N. Janahmadi and H. Alizadeh, “Multi-criteria approach to
the evaluation of Malaysian government portal.” Journal of Theoretical and Applied Information
Technology, vol. 40, no. 2, pp. 194-201, 2012.