The main objective of this study was to identify potential usability problems, interaction differences, advantages, and disadvantages of two versions of a nursing documentation system: PDA and Tablet PC. A comparative study of tasks completion was made between these systems. The dependent variables of this study were tasks completion time, number of tasks completed, and user satisfaction. No significant differences were found in completion time of individual tasks between both systems. Significant differences in user satisfaction ratings for the use of the stylus, weight, and portability were found. No significant differences were found in the satisfaction of the nurses with interaction and system aspects between the two nursing documentation versions. The results of the study support the conclusion that PDAs are a better alternative for supporting nursing documentation tasks at bedside than Tablet PCs.
Challenges of Summative Usability Testing in a Community Hospital Environment...David Schlossman MD
Findings of a summative scenario based ehr usability testing protocol and challenges of conducting the research in a private practice community hospital environment.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
Evaluation of Student's Perception in Using Electronic Dental Records at Riya...Dr. Faris Al-Masaari
Dentoplus, is a custom made software that is used by Riyadh Colleges of Dentistry and
Pharmacy(RCsDP) which have been in place since January 2013, The current study was
initiated in order to evaluate the electronic dental record system- Dentoplus installed in
the Colleges of Dentistry. The focus of this study was on student’s performance and
system efficiency, satisfaction level to the system as well as their perception of how
the system has impacted patient care.
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
A web/mobile decision support system to improve medical diagnosis using a com...TELKOMNIKA JOURNAL
This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score.
Challenges of Summative Usability Testing in a Community Hospital Environment...David Schlossman MD
Findings of a summative scenario based ehr usability testing protocol and challenges of conducting the research in a private practice community hospital environment.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
Evaluation of Student's Perception in Using Electronic Dental Records at Riya...Dr. Faris Al-Masaari
Dentoplus, is a custom made software that is used by Riyadh Colleges of Dentistry and
Pharmacy(RCsDP) which have been in place since January 2013, The current study was
initiated in order to evaluate the electronic dental record system- Dentoplus installed in
the Colleges of Dentistry. The focus of this study was on student’s performance and
system efficiency, satisfaction level to the system as well as their perception of how
the system has impacted patient care.
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
A web/mobile decision support system to improve medical diagnosis using a com...TELKOMNIKA JOURNAL
This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score.
Computer capture in Clinical Data Managementbhunjawa
Computer Capture is a process to collect the data in electronic form. It is very important process in Clinical Data Management, Clinical Research Industry.
Real-world patients have an average of 6 serious co-morbid conditions & take 10 medications
*Complicated patients are invariably excluded from clinical research studies, making it impossible to know what treatments work best
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
A presentation given at the Duke Margollis Health Policy meeting in 2015 and providing insights into the current challenges related to EHR data quality. Proposes a new approach - OneSource.
Next generation electronic medical records and search a test implementation i...lucenerevolution
Presented by David Piraino, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic
& Daniel Palmer, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic
Most patient specifc medical information is document oriented with varying amounts of associated meta-data. Most of pateint medical information is textual and semi-structured. Electronic Medical Record Systems (EMR) are not optimized to present the textual information to users in the most understandable ways. Present EMRs show information to the user in a reverse time oriented patient specific manner only. This talk discribes the construction and use of Solr search technologies to provide relevant historical information at the point of care while intepreting radiology images.
Radiology reports over a 4 year period were extracted from our Radiology Information System (RIS) and passed through a text processing engine to extract the results, impression, exam description, location, history, and date. Fifteen cases reported during clinical practice were used as test cases to determine if ""similar"" historical cases were found . The results were evaluated by the number of searches that returned any result in less than 3 seconds and the number of cases that illustrated the questioned diagnosis in the top 10 results returned as determined by a bone and joint radiologist. Also methods to better optimize the search results were reviewed.
An average of 7.8 out of the 10 highest rated reports showed a similar case highly related to the present case. The best search showed 10 out of 10 cases that were good examples and the lowest match search showed 2 out of 10 cases that were good examples.The talk will highlight this specific use case and the issues and advances of using Solr search technology in medicine with focus on point of care applications.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
Theera-Ampornpunt N. Article review: IT sophistication in health care - an instrument validation study among Canadian hospitals. Presented at: Health Informatics Journal Club; 2008 Oct 9; Division of Health Informatics, University of Minnesota, Twin Cities, MN. Invited speaker.
Based on Paré G, Sicotte C. Information technology sophistication in health care: an instrument validation study among Canadian hospitals. Int J Med Inform. 2001 Oct;63(3):205-23. Available from: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T7S-43P24W1-6&_user=616288&_coverDate=10%2F31%2F2001&_rdoc=6&_fmt=high&_orig=browse&_srch=doc-info(%23toc%235066%232001%23999369996%23259465%23FLA%23display%23Volume)&_cdi=5066&_sort=d&_docanchor=&_ct=9&_acct=C000032378&_version=1&_urlVersion=0&_userid=616288&md5=ee026786822e5e65c12b5fcbd430386e
Testing technology in the ‘real world’ of acute healthcare: making it work. Presented by Bernice Redley, Deakin University, Australia, at HINZ 2014, 12 November 2014, 12.22pm, Plenary Room
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
In this talk, Hector will cover some recent advances in applying machine learning to the field of healthcare. A brief overview of deep learning and its applications in healthcare such as diagnostics, care management, decision support and personalized medicine. There will be deeper dives into specific topics such as machine learning on electronic health records and analyzing EEGs.
Computer capture in Clinical Data Managementbhunjawa
Computer Capture is a process to collect the data in electronic form. It is very important process in Clinical Data Management, Clinical Research Industry.
Real-world patients have an average of 6 serious co-morbid conditions & take 10 medications
*Complicated patients are invariably excluded from clinical research studies, making it impossible to know what treatments work best
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
A presentation given at the Duke Margollis Health Policy meeting in 2015 and providing insights into the current challenges related to EHR data quality. Proposes a new approach - OneSource.
Next generation electronic medical records and search a test implementation i...lucenerevolution
Presented by David Piraino, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic
& Daniel Palmer, Chief Imaging Information Officer, Imaging Institute Cleveland Clinic, Cleveland Clinic
Most patient specifc medical information is document oriented with varying amounts of associated meta-data. Most of pateint medical information is textual and semi-structured. Electronic Medical Record Systems (EMR) are not optimized to present the textual information to users in the most understandable ways. Present EMRs show information to the user in a reverse time oriented patient specific manner only. This talk discribes the construction and use of Solr search technologies to provide relevant historical information at the point of care while intepreting radiology images.
Radiology reports over a 4 year period were extracted from our Radiology Information System (RIS) and passed through a text processing engine to extract the results, impression, exam description, location, history, and date. Fifteen cases reported during clinical practice were used as test cases to determine if ""similar"" historical cases were found . The results were evaluated by the number of searches that returned any result in less than 3 seconds and the number of cases that illustrated the questioned diagnosis in the top 10 results returned as determined by a bone and joint radiologist. Also methods to better optimize the search results were reviewed.
An average of 7.8 out of the 10 highest rated reports showed a similar case highly related to the present case. The best search showed 10 out of 10 cases that were good examples and the lowest match search showed 2 out of 10 cases that were good examples.The talk will highlight this specific use case and the issues and advances of using Solr search technology in medicine with focus on point of care applications.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
Theera-Ampornpunt N. Article review: IT sophistication in health care - an instrument validation study among Canadian hospitals. Presented at: Health Informatics Journal Club; 2008 Oct 9; Division of Health Informatics, University of Minnesota, Twin Cities, MN. Invited speaker.
Based on Paré G, Sicotte C. Information technology sophistication in health care: an instrument validation study among Canadian hospitals. Int J Med Inform. 2001 Oct;63(3):205-23. Available from: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T7S-43P24W1-6&_user=616288&_coverDate=10%2F31%2F2001&_rdoc=6&_fmt=high&_orig=browse&_srch=doc-info(%23toc%235066%232001%23999369996%23259465%23FLA%23display%23Volume)&_cdi=5066&_sort=d&_docanchor=&_ct=9&_acct=C000032378&_version=1&_urlVersion=0&_userid=616288&md5=ee026786822e5e65c12b5fcbd430386e
Testing technology in the ‘real world’ of acute healthcare: making it work. Presented by Bernice Redley, Deakin University, Australia, at HINZ 2014, 12 November 2014, 12.22pm, Plenary Room
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
In this talk, Hector will cover some recent advances in applying machine learning to the field of healthcare. A brief overview of deep learning and its applications in healthcare such as diagnostics, care management, decision support and personalized medicine. There will be deeper dives into specific topics such as machine learning on electronic health records and analyzing EEGs.
Note This assignment is for academic research pro only Thank yo.docxgabriellabre8fr
Note: This assignment is for
academic research pro
only Thank you. Due by 11 Jul @ 11 pm
I'm looking for a tutor who understanding informatics nursing and healthcare
Please address a 3-4 page citing the references below including references page in APA format. Note: Please address it from a nursing view
Design Considerations and Workarounds
When nurse informaticists are tasked with identifying the most appropriate technology to meet a specific need within a health care setting, there are many questions that must be asked.
Consider the following scenario to address assignment
Riverdale Hospital has come under recent scrutiny for their medication procedures. Many times, paper medication records are not up to date or have been misplaced. As a result, patients have increasingly received their medications at the wrong times. Though each nurse is performing to the best of his or her ability, the fast pace of the hospital has caused some to ineffectively manage patient records.
The lead nurse informaticist, Nancy, has decided that a bar code scanner could help streamline the documentation process while also improving patient quality and safety. Nancy knows that when selecting a bar coding system she must not only examine the hardware and software of the system but also consider the various human factors that can positively and/or negatively affect the outcomes of the system implementation. As such, Nancy asked three of the most reputable bar code vendors to bring sample systems to Riverdale Hospital.
In evaluating each system, Nancy role plays the process of scanning a patient’s bar code. She rolls the coding cart into the room to begin her mock demonstration. First, Nancy scans her identification card to gain access to the medication screen. To scan the patient’s bar code identifier, Nancy then pulls the medication cart to the patient so that the attached scanner reaches the bar code on the patient’s wristband. When the scan is complete, the computer displays a screen that houses the patient’s personal information. By navigating the screens, Nancy finds that she can use the computer to track medication administration. In addition, Nancy is able to view applicable vitals and medication history. As Nancy continues to examine this system, she reflects on the other hardware and software facets she should be sure to consider. She also thinks about how human factors will affect this and other vendor systems.
In this Assignment, you consider how hardware, software, and human factors can impact the implementation of an informatics system.
·
Review Chapter 30, “The Role of Technology in the Medication-Use Process,” in the course text,
Essentials of Nursing Informatics
. When examining computerized prescriber order entry (CPOE) systems and bar code-enabled technologies, what hardware, software, and human factors did the authors identify?
·
Consider how each of these factors can negatively impact patient safety and quality of care.
·
How might the.
These slides review problems with current electronic medical record (EMR) systems and makes suggestions for future improvements in design and usability. This work was sponsored by the Szollosi Healthcare Innovation Program (www.TheSHIPHome.org).
Exercises in Measurement and validity For this assignment, you.docxSANSKAR20
Exercises in Measurement and validity
For this assignment, you will be working through questions regarding measurement and validity.. Your answers should be written in complete sentences. Some of the answers may require you to show your work.
1. You have just started a new diet program. To mark your progress, you start weighing yourself three times a day. You also notice that each time you weigh yourself in a given day, the number of pounds is different. Based on the rules regarding the scales of measurement, why is it wrong to weigh yourself more than once a day?
2. Your hospital administration has received several phone complaints from patients about rude behavior from registration staff and long wait times to register in both the Dermatology and Audiology Outpatient Clinics. A decision is made to send a patient satisfaction survey to all Outpatient Clinic patients to determine overall patient satisfaction in the hospital’s Clinic setting. The survey developed uses this type of scoring: 1 = strongly disagree and 5 = strongly agree. What type of scale of measurement is this?
3. Your hospital wants to study patients readmitted within 30-days. What measures (e.g. Medicare patients only) would you recommend be included in the study (identify at least 3)? Where would you locate the data elements (e.g. admission records)?
4. Your hospital’s Pharmacy and Therapeutics Committee undertook a quality review of Medication forms from discharges in the first quarter of the year and identified the errors by 5 general categories and then calculated the percentage of the total errors by category. The results were: Dosage Form 6%, Name confusion 13%, Communication 19%, Labeling 20%, and Human Factors 42%. As the HIM Director you are a member of the P&T Committee, the Chair asks you to prepare a graphic display of the error results for Medical Staff review. What is the best choice of a graphic display to present this data to the Medical Staff? And why
a. Line Graph
b. Bar Graph
c. Pie chart
d. Data Table
5. Provide a definition and example for the following terms:
a. Content validity
b. Construct validity
c. Criterion validity
Running head: BUSINESS AND USER REQUIREMENTS DOCUMENT DRAFT 1
BUSINESS AND USER REQUIREMENTS DOCUMENT DRAFT 6
Business and User Requirements Document Draft
thanks for your Draft report on the EHR project and requirements. There are 3 main parts to cover: Sources of information, departments affected: Provide more information about the clinical departments. HIM is not the "most important" department for this system. Clean up some of the writing possible errors or misunderstandings, too. 5 /7 Methods to gather information: Glad you mentioned interviews, focus groups, and questionnaires and explained all three. 7 /7
Requirements statements:3 /6 You are not quite understanding what Requirements are yet. They are what the system must do. We will get later on in the class, onto project implementation tasks such ...
Application Evaluation Project Part 1 Evaluation Plan FocusTec.docxalfredai53p
Application: Evaluation Project Part 1: Evaluation Plan Focus
Technology increases human effectiveness. Using a lever, you can move an object several times your size. In an airplane, you can move exponentially faster than on foot. Using the Internet, you can access information much more quickly than at a library. What possibilities like this exist in the nursing field? What health information technologies can amplify your impact as a nurse far more than ever before? In this Evaluation Project, you will have the opportunity to answer these questions.
Because of the great differences between HIT systems and different goals of an evaluation, there is no one-size-fits-all evaluation plan. Different technologies require different evaluation methods. Consequently, in this part of your Evaluation Project, you will conduct research on how system implementations similar to the one you select have been previously evaluated. After exploring similar system implementations, you will select one research goal and viewpoint to use in the evaluation.
Read the following three scenarios, and select the one that is of most interest to you:
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 intended to fulfill the “Meaningful Use” requirements stipulated in the Health Information Technology for Economic and Clinical Health (HITECH) Act. As the hospital’s lead nurse informaticist, you have been tasked with planning the evaluation of the EHR implementation.
Scenario 2:
As the lead nurse informaticist in your hospital, you have been given the task of planning an evaluation for a soon-to-be launched computerized provider order entry (CPOE) system. The CPOE system is designed to replace conventional methods of placing medication, laboratory, admission, referral, and radiology orders. CPOE systems enable health care providers to electronically specify orders, rather than rely on paper prescriptions, telephone calls, and faxes. The intended goal of a CPOE system is to improve safety by ensuring that orders are easily comprehensible through the use of evidence-based order sets. In addition, the CPOE system has the potential for improving workflow by avoiding duplicate orders and reducing the steps between those who place medical orders and their recipients.
Scenario 3:
You are the lead nurse informaticist in a large urban hospital that has recently implemented a new .
Recent discovered technologies have exposed many new theories and possibilities to improve our standard of living. Medical assistance has been a major research topic in the past, many efforts were put in to simplify the process of following treatment prescriptions. This paper summarizes the work done in developing LoRa driven medical adherence system in order to improve medicine adherence for elderlies. The designed system is composed of two sections; embedded hardware device for the use of patients at home and Web application to manage all patients along with their medicines and keep track of their medicine intake history. LoRa wireless communication technology is used for connecting all embedded devices with a central gateway that manages the network. Hardware and software tests have been conducted and showed great performance in terms of LoRa network range and latency. In short, the proposed system shows promising method of improving medicine adherence.
Regulatory Concerns When Running Virtual/Paperless Clinical TrialsTarget Health, Inc.
With drug and device manufacturers and the U.S. Food and Drug Administration (FDA) supplying much of the push, so-called paperless clinical trials are gaining momentum. In this eClinical Forum webinar, Dr. Jules Mitchel, President of Target Health, facilitated the discussion on the future landscape and regulatory concerns of paperless clinical trials and clinical trial design incorporating mobile tools.
Here is our corporate profile, you will find information about all our solutions for vaccines clinical trials and also patient's programs. We have a variety of mobile and web apps that have been developed to enhance and improve your results in any clinical trial or patient care system.
India Diagnostic Labs Market: Dynamics, Key Players, and Industry Projections...Kumar Satyam
According to the TechSci Research report titled “India Diagnostic Labs Market Industry Size, Share, Trends, Competition, Opportunity, and Forecast, 2019-2029,” the India Diagnostic Labs Market was valued at USD 16,471.21 million in 2023 and is projected to grow at an impressive compound annual growth rate (CAGR) of 11.55% through 2029. This significant growth can be attributed to various factors, including collaborations and partnerships among leading companies, the expansion of diagnostic chains, and increasing accessibility to diagnostic services across the country. This comprehensive report delves into the market dynamics, recent trends, drivers, competitive landscape, and benefits of the research report, providing a detailed analysis of the India Diagnostic Labs Market.
Collaborations and Partnerships
Collaborations and partnerships among leading companies play a pivotal role in driving the growth of the India Diagnostic Labs Market. These strategic alliances allow companies to merge their expertise, strengthen their market positions, and offer innovative solutions. By combining resources, companies can enhance their research and development capabilities, expand their product portfolios, and improve their distribution networks. These collaborations also facilitate the sharing of technological advancements and best practices, contributing to the overall growth of the market.
Expansion of Diagnostic Chains
The expansion of diagnostic chains is a driving force behind the growing demand for diagnostic lab services. Diagnostic chains often establish multiple laboratories and diagnostic centers in various cities and regions, including urban and rural areas. This expanded network makes diagnostic services more accessible to a larger portion of the population, addressing healthcare disparities and reaching underserved populations. The presence of diagnostic chain facilities in multiple locations within a city or region provides convenience for patients, reducing travel time and effort. A broader network of labs often leads to reduced waiting times for appointments and sample collection, ensuring that patients receive timely and efficient diagnostic services.
Rising Prevalence of Chronic Diseases
The increasing prevalence of chronic diseases is a significant driver for the demand for diagnostic lab services. Chronic conditions such as diabetes, cardiovascular diseases, and cancer require regular monitoring and diagnostic testing for effective management. The rise in chronic diseases necessitates the use of advanced diagnostic tools and technologies, driving the growth of the diagnostic labs market. Additionally, early diagnosis and timely intervention are crucial for managing chronic diseases, further boosting the demand for diagnostic lab services.
Navigating Challenges: Mental Health, Legislation, and the Prison System in B...Guillermo Rivera
This conference will delve into the intricate intersections between mental health, legal frameworks, and the prison system in Bolivia. It aims to provide a comprehensive overview of the current challenges faced by mental health professionals working within the legislative and correctional landscapes. Topics of discussion will include the prevalence and impact of mental health issues among the incarcerated population, the effectiveness of existing mental health policies and legislation, and potential reforms to enhance the mental health support system within prisons.
Trauma Outpatient Center is a comprehensive facility dedicated to addressing mental health challenges and providing medication-assisted treatment. We offer a diverse range of services aimed at assisting individuals in overcoming addiction, mental health disorders, and related obstacles. Our team consists of seasoned professionals who are both experienced and compassionate, committed to delivering the highest standard of care to our clients. By utilizing evidence-based treatment methods, we strive to help our clients achieve their goals and lead healthier, more fulfilling lives.
Our mission is to provide a safe and supportive environment where our clients can receive the highest quality of care. We are dedicated to assisting our clients in reaching their objectives and improving their overall well-being. We prioritize our clients' needs and individualize treatment plans to ensure they receive tailored care. Our approach is rooted in evidence-based practices proven effective in treating addiction and mental health disorders.
LGBTQ+ Adults: Unique Opportunities and Inclusive Approaches to CareVITASAuthor
This webinar helps clinicians understand the unique healthcare needs of the LGBTQ+ community, primarily in relation to end-of-life care. Topics include social and cultural background and challenges, healthcare disparities, advanced care planning, and strategies for reaching the community and improving quality of care.
Global launch of the Healthy Ageing and Prevention Index 2nd wave – alongside...ILC- UK
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A Comparative Study of Nurses Accessing Electronic Patient Record Systems with PDAs and Tablet PCs
1. A Comparative Study of Nurses
Accessing Electronic Patient Record Systems
with PDAs and Tablet PCs
Gilberto Crespo Pérez
Electrical & Computer Engineering Department
University of Puerto Rico at Mayagüez
December 2005
2. Hospitals in Puerto Rico & United States keep their
patients’ records in paper form.
– Problems with information management effectiveness.
– Handwriting may produce errors due to misunderstanding of
written information.
Compromise patients’ health.
PDAs & Tablet PCs are two technological devices with
potential for accessing and capturing clinical data at
bedside.
– Allow greater speed and effectiveness to compile patient’s
information.
– Reduce the risk of errors during treatment of patients.
– Improve physicians and nurses’ effectiveness and productivity at
point-of-care, as well as the quality of patient care.
Introduction
3. A review of the literature reveals that there are
no formal studies comparing the use of PDAs
and Tablet PCs for accessing electronic patient
records.
We believe that such study could help determine
which of the two technologies fits better for
collecting and accessing clinical information at
the point-of-care.
Introduction (Cont.)
4. Previous Work
From 44,000 to 98,000 people die annually in U.S.
hospitals as a result of medical mistakes -- more than
half of them preventable [Rosenbloom03].
Trying to correct some of these errors, the Veteran’s
Administration (VA) has taken effective measures
through electronic system controls and
implementation [Rosenbloom03]
– Over a five-year test period, the medication error rate
dropped 70%
5. Previous Work (Cont.)
The Use of Portable Devices in Health Care:
E-prescribing, ordering, checking labs tests, dictation
notes among others - [Fischer03, Ying03,
Rosenbloom03, Barret02,].
Clinical Applications for Portable Devices:
Drug references, pharmacopoeias, medical
calculators, and patient trackers among others -
[Adatia03, Rosenbloom03, Choi00, Kimura03, van
der Velde01, Berner04].
6. Previous Work (Cont.)
Usability Studies of Portable Devices in Health Care:
PDA vs Laptop & PDA vs Paper Based [Rodríguez02,
Rodríguez03, Rodríguez04, Staggers00, Stausberg03].
Users performance, subjective satisfaction, and preference were
dependable variables.
Usability of Tablet PC [Narayan04, Andon04].
Physical Aspects.
No more studies related to other usability issues of Tablet PCs
have been found, which indicates that this field is in its early
stages of research.
7. Objectives
Conduct a usability study to compare the use of
PDA and Tablet PC-based applications to
support nurses’ tasks at point-of-care.
– These studies compare both systems in terms of
performance parameters:
Time to complete tasks.
Number of tasks completed.
Subjective user satisfaction.
– Identify:
Potential usability problems.
Interaction differences.
Advantages and Disadvantages.
8. Systems
Tablet PC version
– Implemented on a Gateway tablet.
– Windows XP Tablet PC OS.
– Uses a Stylus as a pointing device.
– A soft keyboard provided by OS was used for text input.
PDA version
– Implemented on a HP iPaq 5500.
– Windows Pocket PC 2003 OS.
– Uses a small stylus as a pointing device.
– A soft keyboard provided by OS was used for Text input.
9. Systems (Cont.)
The Tablet PC version
– Developed in Java.
The PDA version
– Developed in C Sharp.
Both applications use:
– MSSQL to store and retrieve the patient’s clinical
information in a database.
– The standard IEEE 802.b to communicate with the
database server.
13. Participants
20 staff nurses
– Selected on a first-come first-served basis from those that
responded a call for participation.
– Experience as staff nurses ranged from 1 to 27 years
(Mean=12.3 years).
– Experience with computers ranged from 0 to 9 years
(Mean=4.6 years).
– On average, they used computers on their job for 3.8 hours
per day.
– None of them had prior experience with the systems used
for the study or with any similar application.
– None of them had experience using PDAs or Tablet PCs.
14. Experimental Design
Participants were asked to fill out a questionnaire about
their work experience and their experience using
computers.
They were asked to sign a letter of consent.
An orientation script was read to each participant
explaining the objective of the test.
Nurses were given a short tutorial session on the graphical
system.
– 18 minutes for the PDA version.
– 13 minutes for the Tablet PC version.
– They were allowed to use the system by themselves and were
guided to practice using each of the functions of the system.
15. Half of the nurses performed the tasks first on
one system and the other half started on the other
system.
Once the participants complete all the task in
both versions, they were asked to fill a user
satisfaction questionnaire.
Experimental Design (Cont.)
16. Tasks
1. Indicate patient’s age and weight. Say them aloud.
2. Indicate the most recent registered patient’s temperature.
3. Look for the most recent nurse note and read it aloud.
4. Acknowledge any pending medication order as
administered.
5. Enter the following set of vitals signs:
Temp: 37.0o
C; BP: 130/90; Pulse: 71; RR: 18; O2 Sat: 96%.
6. Indicate the total balance of intake/output of fluids in the
last 24 hours.
7. Look for a Dr. Colón note.
8. Enter the following patient assessment information
provided.
9. Enter the following I/O information:
Intake PO: 50 ml; Output Urine: 650 ml;
17. 10. Enter the following text as a note:
Patient presents fever and a large lung mass on left upper
lobe consistent on CT with multi-focal pneumonia
11. Enter the following patient’s pain information.
Classification: Four; Location: Right frontal shoulder;
Description: Hurt; Therapy: Massage; Administered
medicine.
12. Look for most recent physician consult order and
acknowledge it as that was notified by phone.
13. Show where is indicated in the record the reason why a
dose of Roboxine was omitted.
Tasks (Cont.)
18. User Satisfaction Questionnaire
Interaction Aspects:
– Look up patient information
– Acknowledge medication orders
– Acknowledge consult order
– Enter vitals signs
– Document intake/output
– Document patient assessment
– Document pain assessment
– Read a note
– Write a note
Nurses were asked to rate on a 1 – 7 scale (1 being poor
& 7 being excellent)
19. System Aspects:
– Record organization
– Trustworthiness of information
– Precision of Information
– Accessibility of information
– System security
Physical Aspects:
– Use of the stylus
– Use of the screen keyboard
– The screen
– The weight
– The portability
Level of Satisfaction with each system
The system they would prefer for doing their
nursing documentation.
User Satisfaction Questionnaire (Cont.)
20. Statistical Analysis
Dependent variables were:
– Task completion time.
– Number of tasks completed.
– Subjective user satisfaction.
Time analysis – Paired t Test
User Satisfaction – Wilcoxon Signed Ranks
Number of Task Completed – Wilcoxon Signed
Ranks
21. Results: Overall Completion Times
656.2 667.3
191.7
215.6
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
Seconds
Mean Std. Deviation
PDA
Tablet PC
22. Dependent Sample t test did not reveal
significant difference between average
time it took the participants to complete all
the tasks on both system.
Results: Overall Completion Time (Cont.)
24. Dependent Sample t test did not reveal
significant difference in the time it took
the participants to complete each of the
tasks on both versions of the system.
Results: Completion Times for Each Task
on Both Versions of the System (Cont.)
25. Results: Number of Participants that Complete
Each Task on Each Version of the System
0
2
4
6
8
10
12
14
16
18
20
NumberofParticipants
1 2 3 4 5 6 7 8 9 10 11 12 13
Tasks
PDA
Tablet PC
26. Wilcoxon Signed Ranks did not reveal
significant difference in the total number
of tasks completed by the participants.
The participants completed an average of
12.0 tasks on the PDA and 11.7 on the
Tablet PC
Results: Number of Participants that Complete
Each Task on Each Version of the System (Cont.)
27. Results: Average Satisfaction Ratings
for Individual Interaction Aspects
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
Rating
Look up patient information
Acknowledge medication orders
Acknowledge consult order
Enter vitals signs
Document intake/output
Document patient assessment
Document pain assessment
Read a note
Write a note
InteractionAspects
PDA Tablet PC
28. Wilcoxon test did not reveal significant a
difference on each individual interaction
aspects considered.
Also, Wilcoxon Test did not reveal a
significant difference in the overall
average satisfaction rating given to the 9
interaction aspects.
Results: Average Satisfaction Ratings
for Individual Interaction Aspects (Cont.)
29. Results: Average Satisfaction Ratings
for Individual System Aspects
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
Rating
Record
organization
Trustworthiness
of information
Precision of
Information
Accessibility of
information
System
security
SystemAspects
Tablet PC
PDA
30. Wilcoxon Test did not reveal a significant
difference in the overall average
satisfaction rating given to the 5 system
aspects.
The overall average satisfaction rating for
the PDA and the Tablet PC was 6.8
Results: Average Satisfaction Ratings
for Individual System Aspects (Cont.)
31. Results: Average Satisfactions Ratings
for Individual Physical Aspects
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
Rating
Use of the stylus
Use of the screen keyboard
The screen
The weight
The portability
PhysicalAspects
Tablet PC
PDA
32. Wilcoxon Test revealed a significant difference
in overall satisfaction with the five physical
aspects considered.
Wilcoxon Test also revealed significant user
satisfaction rating for:
– The use of the Stylus
– The weight of the device
– The portability of the device
Results: Average Satisfactions Ratings
for Individual Physical Aspects (Cont.)
33. Participants were more satisfied with:
– The PDA computer (Average Rating = 6.6) than with the
Tablet PC computer (Average Rating = 5.2).
– The use of the stylus on the PDA (Average Rating = 6.7)
than on the Tablet PC (Average Rating = 6.2).
– The weight of the PDA (Average Rating = 6.5) than with
the weight of the Tablet PC (Average Rating = 4.1).
– The PDA portability (Average Rating = 6.6) than with the
Tablet PC portability (Average Rating = 3.5).
Results: Average Satisfactions Ratings
for Individual Physical Aspects (Cont.)
34. Overall Results
No significant differences were found in theNo significant differences were found in the
overall satisfaction rating given by theoverall satisfaction rating given by the
participants to each version of the system.participants to each version of the system.
However, the majority of the participantsHowever, the majority of the participants
preferred the PDA over the Tablet PC versionpreferred the PDA over the Tablet PC version
for performing their duties.for performing their duties.
35. Discussion
No significant difference was found in the overall time it
took nurses to complete tasks on both portable devices.
– It contradicts our expectations that nurses would be faster using
a larger user interface than a smaller one.
Our results are consistent with the results of the study by
Rodríguez et. al. [Rodríguez03]
– When the task of writing a note was not considered, nurses were
able to complete nursing tasks in similar overall times on the
PDA and on Laptop.
The use of a stylus, the screen size, and resolution is not
a factor in nurses’ performance.
36. Discussion (Cont.)
Nurses were as fast on a PDA as they would on a
Tablet PC.
Younger nurses tend to take shorter times in
completing tasks.
Due to the short training given to nurses to teach
them how to use the systems and the high percentage
of tasks completed:
– Results indicate that both nursing documentation systems
are very easy to learn .
– Similar results were found in the [Rodríguez03] study.
37. Discussion (Cont.)
The word “document” in tasks T4, T6, T8, and T12
seems to create confusion in some of the nurses to the
point that they did not find the way to complete the task
successfully.
Nurses expressed similar high levels of satisfaction with
the documentation activities performed in both, the PDA
and Tablet PC systems.
They were significantly more satisfied with the physical
aspects of the PDA than those of the Tablet PC
38. Discussion (Cont.)
Many of the participants (12 out of 20) expressed
discomfort in holding the Tablet PC in a standing
position during the test session.
– This result is consistent with the studies conducted by:
Michael A. Narayhan [Narayhan04]
Christopher L. Andon [Andon04]
Fourteen of the nurses indicated that they preferred
the PDA version for performing nursing
documentation tasks while only four preferred the
Tablet PC.
39. Conclusions
Our results supports that it is possible to design PDA-
based applications that allow nurses to achieve similar
performance and satisfaction levels as with a Table PC-
based.
Screen size and display resolution of the PDA are no
factors that limit nurses’ performance and satisfaction in
comparison to Tablet PCs.
Nurses were significantly more satisfied with the physical
aspects of the PDA than with those of the Table PC.
– Aspects such as the use of the stylus, the weight, and portability
are factors that influence nurses’ satisfaction with the system.
40. Conclusions (Cont.)
These applications exhibited a high degree of
learnability.
Differences on user interfaces have not affected users
satisfaction.
Usability engineering principles and guidelines
– Are critical part of the graphical user interface success.
– Proved to be powerful and important tools for measuring the
efficiency of the system.
The age seems to be a factor that affects nurses’
performance..
41. Conclusions (Cont.)
Considering that:Considering that:
– Satisfaction with the physical aspects of the systemsSatisfaction with the physical aspects of the systems
was the only dependent variable for which awas the only dependent variable for which a
significant difference was found in favor of the PDAsignificant difference was found in favor of the PDA
– That 14 out of 20 participants selected the PDA overThat 14 out of 20 participants selected the PDA over
the Tablet PCthe Tablet PC
– And the relatively low costs of the PDAAnd the relatively low costs of the PDA
We can conclude that PDAs are a better alternativeWe can conclude that PDAs are a better alternative
for supporting nursing documentation tasks atfor supporting nursing documentation tasks at
bedside than Tablet PCs.bedside than Tablet PCs.
42. Future Work
Other usability studies should be conducted:
– After the users have had a year of experience with the system.
– With physicians interacting with each version of the system used
on this research.
Application improvement:
– Incorporation of printing and supplemental language options
capabilities.
– Speech recognition functionality.
– Convert to a web based application.
44. Usability Concepts Definitions
Usability: the extent to which the intended user can meet his or her
goals using the system being tested.
Learnability: easy to learn -> time a novice user takes to complete
tasks using the system.
Efficiency: high level of productivity -> time that users with certain
expertise take to complete typical tasks.
Memorability: easy to remember.
Errors: Low error rate and easy to recover.
Satisfaction: users are subjectively satisfied -> ask users opinions.
When replies from multiple users are average, the result is objective.
-questionnaire with Likert scale.
45. General Concepts Definitions
Ethical Aspects with Human Subjects:
– Respect
– Comfortable environment
– Confidentiality
– Early success experience
46. Statistical Tools Definitions
Dependent-samples t test: used to compare the
time to complete the tasks -> same users, 2
measurements.
Wilcoxon Signed Rank test: compare differences
in user satisfaction -> dependent samples.
Correlation analysis: determine associations
between time to complete the tasks, computer
literacy, typing skills, age, and eye glasses, among
others.
Linear regression: evaluate the learning effect
between 2 systems.