KAMAS Health 2.0 Presentation
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Kinect Abnormal Motion Assessment Score presentation from Health 2.0. See http://motionassessment.com for more.
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Towards an Adverse Event Reporting Ontology
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Real Time Location Systems in Healthcare
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The We Care A Lot Organization helps burn victims through treatment and pain management. It aims to improve patient care and outcomes using new technologies like virtual therapy and telehealth tools. The organization needs fundraising software to streamline operations and a new virtual system to better link patients to services and manage wounds. Adopting updated technologies could enhance the system, simplify management, lower costs and help more victims.
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Philip Bourne discusses the opportunities for data science in addressing diabetes. Data science involves using diverse digital data to ask and answer relevant questions, arriving at statistically significant conclusions not otherwise possible. It also involves sharing findings in a way that can improve lives. Diabetes is well-suited for data science approaches due to increasing data from genomics, wearables, electronic health records, and predictive modeling successes. However, data science must be done carefully with input from experts to account for confounders and ensure accurate outcomes for complex health issues like diabetes.
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Debra Sanford has over 10 years of experience in healthcare IT, most recently as an IT project manager and systems administrator. She enjoys using her technical and clinical skills to implement and improve healthcare systems and processes. She has a background in medical technology and extensive experience managing IT projects, applications, servers, and clinical systems.
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1) Doctors are open to using patient-generated health data from fitness trackers and connected devices to inform diagnostic decisions, and many want this data integrated into electronic medical records.
2) Data handling and security is a major concern for doctors, as is establishing defined and certified technical standards for connected health devices.
3) Doctors see potential for patient data to be harnessed in multiple ways, such as reducing unnecessary emergency room visits through remote monitoring of chronic conditions.
Big Data to Artificial Intelligence in Healthcare
Big data in healthcare is studied because electronic health data sets are large, complex and growing. They contain 90% unstructured data that will increase 25 times over the next decade. Examples of artificial intelligence in healthcare include IBM Watson which provides evidence-based treatment options to oncologists, Medical Sieve which assists with clinical decision making in radiology and cardiology, and an app from AiCure supported by NIH that uses a smartphone's camera to confirm patients are adhering to their prescriptions. Deep Genomics also aims to identify patterns in genetic data to inform doctors about the effects of genetic variations at a cellular level. Overall, big data and AI can help make the right healthcare decisions for patients.
Using Big Data to Personalize the Healthcare Experience in Cancer, Genomics a...
1. The document discusses how big data is being used to personalize healthcare experiences through genomics, cancer/clinical trials research, and mobile health applications.
2. It provides examples of companies in each area using big data to analyze genomes, personalize cancer treatments, and develop health-focused mobile apps.
3. The main bottlenecks slowing progress are identified as issues of data interoperability, sharing, and privacy concerns across genomics, clinical research, and mobile health.
Paul Coplan, VP, Johnson & Johnson_mHealth Israel
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- Types of Real-World Data Sources (Complaints like MAUDE, Eudramed and Company Databases, Hospital Databases, Electronic Health Records, Claims, Registries, Patient surveys, Surgeon surveys, PROs, Patient Preferences, wearables, sensors, social media, Surgical videos, device generated data, radiographic images)
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- J&J Med Device Epidemiology & Real-World Data Sciences
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a. Use of RWE is important to benefit patients globally and enhance the safety and innovation of medical devices
b. Regulators are interested in using RWE for regulatory decisions but data quality and evidence needs to be regulatory grade
c. NEST has been a useful forum to advance the use of RWE for regulatory decisions in the US
d. RWE can be used for safety assessments, regulatory decisions, comparative effectiveness research, and R&D of products
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Was ist angesagt?
Digital pathology transforms tissue diagnostics
The document discusses how Birmingham Heartlands Hospital in the United Kingdom has implemented digital pathology solutions from Roche to improve workflows and collaboration. The hospital scans over 175,000 slides annually and pathologists can now access virtual slides instantly for consultations, tumor boards, and teaching. This streamlines processes and saves time compared to retrieving physical slides. The digital tools also enhance education and quality assurance.
Submit20your20 powerpoint20file20here barota10_attempt_2012-12-04-22-03-37_pa...
This document explores barriers to implementing electronic medical records (EMRs) in primary care practices. It identifies the main barriers as financial cost, issues with technology, the time investment required, concerns over patient privacy, and potential negative impacts on patient-physician interactions. The document provides details on each of these barriers and recommends ways to address them, such as through government funding, improved technical support, protecting privacy under HIPAA, and optimizing EMR use during patient visits.
Digital Pathology streamlines tissue diagnostics and helps protect patient sa...
Digital Pathology streamlines tissue diagnostics and helps protect patient sa...Roche Tissue Diagnostics
RZ Tienen Hospital became one of the first hospitals in Belgium, and one of the first in Europe, to implement and integrate digital pathology into daily operations. They have transformed their AP lab and are now able to manage caseloads remotely, facility MDT oncology meetings and enhance collaboration with colleagues. The benefits of digital pathology have been well recognized and RZ Tienen expects continued growth in this area.
ESSA_2016_FC_Poster_FINAL
The document summarizes the process of implementing an integrated electronic health record (IEHR) system in a university exercise physiology teaching clinic. Key steps included procuring practice management software with IEHR capabilities, developing condition-specific protocols, designing clinical interfaces, and configuring the system for data entry via questionnaires and during consultations. Interviews found that staff and students perceived more advantages than disadvantages to adopting IEHR, such as improved patient care, progress tracking, and the ability to engage in research. The new system aims to enhance student learning and patient outcomes by allowing access to health information and progress data.
I. Glenn Cohen, "Big Data, AI, and Health Care, Legal and Ethical Issues"
I. Glenn Cohen, "Big Data, AI, and Health Care, Legal and Ethical Issues" The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics
February 8, 2019, Black-Box Medicine: Legal and Ethical Issues: A Health Policy and Bioethics Consortium
Black-box medicine—the use of opaque computational models to make care decisions—has the potential to shape health care by improving and aiding many medical tasks. For example, IBM Watson for Oncology is a machine-learning system that intends to help clinicians quickly identify essential information in patients’ medical records and explore treatment options for 13 cancers. However, it has only recently emerged that the recommendations Watson for Oncology gave for cancer treatments were “often incorrect” and that IBM kept this defect secret for over a year. What are the ethical and legal issues of black-box medicine? When do algorithms operate like a “black box“? How can we ensure that artificial intelligence technologies deliver what they promise?The We Care A Lot Organization
The We Care A Lot Organization helps burn victims through treatment and pain management. It aims to improve patient care and outcomes using new technologies like virtual therapy and telehealth tools. The organization needs fundraising software to streamline operations and a new virtual system to better link patients to services and manage wounds. Adopting updated technologies could enhance the system, simplify management, lower costs and help more victims.
Memotex tmh2011
Johns Hopkins University partnered with MEMOTEXT to conduct a study called the Automated Dosing Reminder Study that tested whether automated reminders linked to a personal health record could improve adherence to daily glaucoma medications. The study involved 428 glaucoma patients who received reminders by SMS or IVR calls and were monitored electronically for 3 months. Preliminary results found that automated reminders significantly improved medication adherence by 16% compared to the control group, demonstrating their potential as a practical way to increase patient adherence. Lessons from the study suggested developing methods to predict non-adherent patients who could benefit most from reminder interventions.
charles vincent collaborative launch
Is healthcare getting safer? Professor Charles Vincent - Patient safety lead, Oxford AHSN
Presentation from the Patient Safety Collaborative launch event held in London on 14 October 2014
More information at http://www.nhsiq.nhs.uk/improvement-programmes/patient-safety/patient-safety-collaboratives.aspx
Predictive Analytics and Machine Learning for Healthcare - Diabetes
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
Nist tech beat for january 7, 2015
The National Cybersecurity Center of Excellence (NCCoE) at the National Institute of Standards and Technology is inviting feedback on a draft project to address cybersecurity challenges with wireless infusion pumps in hospitals. The project aims to identify security risks posed by connecting medical devices to networks and define solutions to protect the devices from malware or hacking. The NCCoE is collaborating with the Technological Leadership Institute and Minnesota medical providers on a use case that describes the challenge and desired security characteristics. The use case will be finalized and used to develop a practice guide with example solutions to securely deploy wireless infusion pumps.
Diabetes Data Science
Philip Bourne discusses the opportunities for data science in addressing diabetes. Data science involves using diverse digital data to ask and answer relevant questions, arriving at statistically significant conclusions not otherwise possible. It also involves sharing findings in a way that can improve lives. Diabetes is well-suited for data science approaches due to increasing data from genomics, wearables, electronic health records, and predictive modeling successes. However, data science must be done carefully with input from experts to account for confounders and ensure accurate outcomes for complex health issues like diabetes.
resume41716
Debra Sanford has over 10 years of experience in healthcare IT, most recently as an IT project manager and systems administrator. She enjoys using her technical and clinical skills to implement and improve healthcare systems and processes. She has a background in medical technology and extensive experience managing IT projects, applications, servers, and clinical systems.
Future of Connected Health Survey from Ipsos Healthcare
1) Doctors are open to using patient-generated health data from fitness trackers and connected devices to inform diagnostic decisions, and many want this data integrated into electronic medical records.
2) Data handling and security is a major concern for doctors, as is establishing defined and certified technical standards for connected health devices.
3) Doctors see potential for patient data to be harnessed in multiple ways, such as reducing unnecessary emergency room visits through remote monitoring of chronic conditions.
Big Data to Artificial Intelligence in Healthcare
Big data in healthcare is studied because electronic health data sets are large, complex and growing. They contain 90% unstructured data that will increase 25 times over the next decade. Examples of artificial intelligence in healthcare include IBM Watson which provides evidence-based treatment options to oncologists, Medical Sieve which assists with clinical decision making in radiology and cardiology, and an app from AiCure supported by NIH that uses a smartphone's camera to confirm patients are adhering to their prescriptions. Deep Genomics also aims to identify patterns in genetic data to inform doctors about the effects of genetic variations at a cellular level. Overall, big data and AI can help make the right healthcare decisions for patients.
Using Big Data to Personalize the Healthcare Experience in Cancer, Genomics a...
1. The document discusses how big data is being used to personalize healthcare experiences through genomics, cancer/clinical trials research, and mobile health applications.
2. It provides examples of companies in each area using big data to analyze genomes, personalize cancer treatments, and develop health-focused mobile apps.
3. The main bottlenecks slowing progress are identified as issues of data interoperability, sharing, and privacy concerns across genomics, clinical research, and mobile health.
Paul Coplan, VP, Johnson & Johnson_mHealth Israel
Pesentation, October 19th, 2021: What’s Next in RWE for Medical Devices: The Art of the Possible. Presented by Paul Coplan, ScD, MBA, FISPE, Vice President, Med Device Epidemiology and RWD Sciences, Johnson & Johnson; Adjunct Professor, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine; Fellow of the International Society of Pharmacoepidemiology
- Why RWE is Important for Medical Devices: Challenges with Clinical Trials of Medical Devices (Blinding, Surgeon skill/technique, Hospital process, Product modifications, Long term Follow up, Enrolment challenges)
- Types of Real-World Data Sources (Complaints like MAUDE, Eudramed and Company Databases, Hospital Databases, Electronic Health Records, Claims, Registries, Patient surveys, Surgeon surveys, PROs, Patient Preferences, wearables, sensors, social media, Surgical videos, device generated data, radiographic images)
- FDA CDRH Report on RWE Examples for Regulatory Decisions
- J&J Med Device Epidemiology & Real-World Data Sciences
- US National Evaluation System for Health Technology (NEST)
- RWE for Safety Assessments: Cobalt in Implants and at Work and Risk of Cancer
- Summary of Cobalt Exposure and All-Site Cancer Risk, by Study Type
- Comparative Effectiveness Studies Using RWE
- Summary
a. Use of RWE is important to benefit patients globally and enhance the safety and innovation of medical devices
b. Regulators are interested in using RWE for regulatory decisions but data quality and evidence needs to be regulatory grade
c. NEST has been a useful forum to advance the use of RWE for regulatory decisions in the US
d. RWE can be used for safety assessments, regulatory decisions, comparative effectiveness research, and R&D of products
Electronic Health Records
Electronic Health Records: : An electronic health record (EHR) system is now a standard method of using information technology within the healthcare industry. Smaller clinics and practices that continue to use paper systems need to seriously consider investing in this technology
Care Intervention Assistant - Omaha Clinical Data Information System
See all on-demand Graph + AI Sessions: https://www.tigergraph.com/graph-ai-world-sessions/
Get TigerGraph: https://www.tigergraph.com/get-tigergraph/
Research Poster
This research poster summarizes technologies that improve efficiency in hospitals. It finds that radio frequency identification (RFID) technology, iPad use in radiology, vascular pattern identification, ultrasonic sensors for tracking, and live endoscopic video all help to enhance efficiency. RFID technology improves processes, reduces costs and errors. iPads streamline radiologist work flows. Vascular patterns and ultrasonic sensors aid patient identification and tracking. Live endoscopic video benefits education, diagnostics and documentation. Overall, the technologies highlighted save time, lower expenses and improve workflows to increase both provider and patient satisfaction.
Medical Computer Vision: Current Limitations of Vision Datasets | CVPR 2021
This document discusses current limitations of medical computer vision datasets. It notes that existing datasets have limited data sizes and coverage across modalities, lack multiple images per patient, and have few examples of specific abnormalities. It also calls for more diverse datasets from different medical centers to preserve privacy, as well as more inclusive datasets representing all patient demographics to reduce model bias. The goal is datasets that can support clinical deployment and evaluation.
Was ist angesagt? (20)
Submit20your20 powerpoint20file20here barota10_attempt_2012-12-04-22-03-37_pa...
Submit20your20 powerpoint20file20here barota10_attempt_2012-12-04-22-03-37_pa...
Digital Pathology streamlines tissue diagnostics and helps protect patient sa...
Digital Pathology streamlines tissue diagnostics and helps protect patient sa...
I. Glenn Cohen, "Big Data, AI, and Health Care, Legal and Ethical Issues"
I. Glenn Cohen, "Big Data, AI, and Health Care, Legal and Ethical Issues"
Predictive Analytics and Machine Learning for Healthcare - Diabetes
Predictive Analytics and Machine Learning for Healthcare - Diabetes
Future of Connected Health Survey from Ipsos Healthcare
Future of Connected Health Survey from Ipsos Healthcare
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Ähnlich wie KAMAS Health 2.0 Presentation
Kinect Abnormal Movement Assessment System Presentation at Health 2.0 Boston
Presentation given at the end of the Boston Health 2.0 hackday on 2/19/11 demonstrating and explaining the system we built that day for assessing involuntary movement as part of psychiatric patient tracking.
Evidence Base for Using Technology Solutions in Behavioral Health Care
Evidence Base for Using Technology Solutions in Behavioral Health Care National Center for Telehealth & Technology
Recent advances in the evidence base for technology-based behavioral health applications have provided clinicians a better understanding and guidance on the integration of these tools into clinical care. Participants will learn about research findings on current technologies in use in clinical practice, such as audio conferencing, video conferencing, and virtual reality, in addition to tools available for use between patients, such as the use of websites and mobile applications and wearable sensors.E-Symptom Analysis System to Improve Medical Diagnosis and Treatment Recommen...
: A wealth of data in public health care systems has been collected and meanwhile there are plenty
of new technological improvements which have considerable influence on current data pool. Nevertheless,
important obstacles are challenging to utilize existing clinical data. Enhanced technological improvements lead
patients to search their symptoms and corresponding diagnosis on online resources. In this study, it is aimed to
develop a machine learning model to suit in different availability of users. Most of the current systems allow
people to choose related symptom in web interfaces or Q&A forums. In addition to these applications it is aimed
to implement a new technique which extracts the text-based symptoms and its related parameters such as, severity,
duration, location, cause, accompanied by any other indicators. This study is applicable for patient`s everyday
language statements besides medical expression of symptoms for corresponding symptoms. Extracted terms are
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accomplished.
E-Symptom Analysis System to Improve Medical Diagnosis and Treatment Recommen...
A wealth of data in public health care systems has been collected and meanwhile there are plenty
of new technological improvements which have considerable influence on current data pool. Nevertheless,
important obstacles are challenging to utilize existing clinical data. Enhanced technological improvements lead
patients to search their symptoms and corresponding diagnosis on online resources. In this study, it is aimed to
develop a machine learning model to suit in different availability of users. Most of the current systems allow
people to choose related symptom in web interfaces or Q&A forums. In addition to these applications it is aimed
to implement a new technique which extracts the text-based symptoms and its related parameters such as, severity,
duration, location, cause, accompanied by any other indicators. This study is applicable for patient`s everyday
language statements besides medical expression of symptoms for corresponding symptoms. Extracted terms are
used as an input of the model and analyzed for matching diagnosis where an accuracy of 72.5% has been
accomplished.
Measuring the Effectiveness of eHealth Initiatives in Hospitals
Prof Johanna Westbrook
Health Informatics Research & Evaluation Unit, The University of Sydney
www.fhs.usyd.edu.au/hireu/
(2/10/09, Forum, 10.50)
Meaningful engagement of decision makers in priority-setting
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An emergency department quality improvement project
The document discusses improving vital sign documentation during triage in emergency departments. It aims to investigate factors affecting vital sign data quality during measurement and documentation, and provide recommendations for improvement. A literature review found that timely and accurate vital sign documentation is important for identifying deteriorating patients. However, studies on nursing workflows and documentation of vital signs are limited. The objective is to study nurses' vital sign documentation process through a questionnaire of nurses and analysis of the data. Results showed teamwork and quality improvement efforts like education and training can enhance compliance with vital sign documentation standards during triage. Recommendations include departments addressing challenges in measurement time and reviewing results to improve performance.
2012 02 11 EHRs - healthcare system chicken soup or rotten egg
This document summarizes a presentation on electronic health records (EHRs) given to the CSM 2012 HPA Tech SIG. The presentation covered why EHRs are important, how to select an EHR system, considerations for implementation, and a case study. The presentation discussed how EHRs can help accelerate a vision of coordinated, consumer-centered care by enabling data reuse, clinical decision support, and interoperability between systems through standards. Barriers to EHR adoption include workflow changes and training needs, while success factors include staff participation and data standardization.
Running Head EVALUATION PLAN FOCUSEVALUATION PLAN FOCUS 1.docx
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 ...
Develop a project plan including project management knowledge areas in.docx
The document discusses criteria for evaluating an electronic health record system across three stages:
1) Stage 1 criteria include the ability to electronically transmit lab test orders and results, pharmacy orders, and diagnoses to clinicians.
2) Stage 2 criteria include having a single clinical data repository where clinicians can access and view all patient details such as orders, results, and assessments.
3) Stage 3 criteria include supporting online nursing documentation of admissions, assessments, tasks, notifications, and reminders. The documentation should be systematically retrievable without external software.
Develop a project plan including project management knowledge areas in.docx
The document discusses criteria for evaluating an electronic health record system across three stages:
1) Stage 1 criteria include the ability to electronically transmit lab test orders and results, pharmacy orders, and diagnoses to clinicians.
2) Stage 2 criteria include having a single clinical data repository where clinicians can access and view all patient details such as orders, results, and assessments.
3) Stage 3 criteria include supporting all online nursing documentation, including admissions, tasks, assessments, notifications, and reminders. The system should be able to reconcile and systematically retrieve documentation without external software.
Next generation electronic medical records and search a test implementation i...
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.
Health IT at Kaiser Permanente
Andrew M. Wiesenthal, MD, SM
Associate Executive Director, The Permanente Federation
HINZ Seminar Series
8 July 2008
Leveraging Clinical IT for Dengue: Opportunities for Tomorrow
1) Dengue is a rapidly spreading mosquito-borne viral disease that poses a significant burden.
2) Clinical management of dengue involves various diagnostic, treatment, and monitoring decisions.
3) Health information technology can help support clinical decision making for dengue and reduce errors by inexperienced providers through alerts, reminders, and clinical guidelines.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION
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.
RESULTS SUMMARY
This study surveyed family practice clinics in Alberta, Canada to assess meaningful electronic medical record (EMR) use levels and identify factors contributing to higher use. The study found clinics in Edmonton reported the highest EMR use. Factors correlated with increased use included participation in programs like POSP, better innovation-values fit, stronger implementation climate with clear goals and support, and intrinsic rather than extrinsic motivation. The study also identified limitations like a small sample size and need for further validation of the meaningful EMR use scale.
J0956064
This document discusses health information technology (HIT) problems at Universiti Teknologi Malaysia's clinic. It begins with an introduction to HIT and its benefits, including cost reduction, quality improvement, and better patient experience. However, HIT implementation can be difficult and introduce new issues. The document then examines specific problems, including new errors from HIT systems, such as incorrect drug selections, and information overload for clinicians. It also notes challenges from the variety and complexity of clinic workflows. Interviews with clinic staff and doctors identified current HIT system problems at the Universiti Teknologi Malaysia clinic.
Application Evaluation Project Part 1 Evaluation Plan FocusTec.docx
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 .
Key Topics in Health Care Technology EvaluationThe amount of new i.docx
Key Topics in Health Care Technology Evaluation
The amount of new information and data, and the number of available technologies are growing at an ever-accelerating rate. Did you know that during any given 24 hours, humanity generates enough new information to fill the Library of Congress 70 times (Smolan & Erwitt, 2012)? As a nurse informaticist, it is important to keep current on new developments in the field, but with the rapid pace of change, that effort can be overwhelming. It is easier to keep current with key trends if nurse informaticists focus on selected issues.
In this Discussion, you consider key topics in the field of health care technology. You then consider the different approaches you could take when designing an evaluation in these areas. For example, if you are interested in usability, your goal could be to determine if a system is user friendly from the viewpoint of a nurse. A different goal might be to determine if the location of the system facilitates ease of use from the viewpoint of physicians.
Note:
This Discussion serves as practice for the first part of your Evaluation Project. What you derive from your Discussion with colleagues will likely inform the work that you do in Part 1 of the Evaluation Project.
The Discussion focuses on the following major topics in the health care information field:
Implementing HIT Systems
Consumer health information
Computerized Physician Order Entry (CPOE)
Decision support systems
Electronic health records (EHR)
Tele-medicine and eHealth
Nursing documentation
Other Issues Related to the Use of HIT Systems
Interoperability
Unforeseen consequences
Usability
To prepare:
Select at least
two
topics from the
lists above
that are relevant to your current organization or that are of particular interest to you. Read the articles in this week’s Learning Resources that relate to these topics. Consider why these topics are of interest to you, what relevance they have to health care organizations, and how they impact your professional responsibilities. Choose one topic to be the focus of your Evaluation Project, and consider potential evaluation goals.
Determine the viewpoint from which you would approach the evaluation, and why.
By tomorrow, post a minimum of 550 words essay in APA format with a minimum of 3 references from the list of required resources below, that addresses the level one headings as numbered below:
1)
Post
the two topics you identified as most relevant to your organization or to you personally, and explain why you selected those topics.
2)
Identify the topic you selected for your Evaluation Project, and propose three potential evaluation goals for this topic.
3)
Identify the viewpoint you would use with each goal, and explain why.
Required Readings
Friedman, C. P., & Wyatt, J. C. (2010). Evaluation methods in biomedical informatics (2nd ed.). New York, NY: Springer Science+Business Media, Inc
.
Chapter 2, “Evaluation as a Field” (pp. 21–47)
This chapter defines.
The Electronic Health Record ( Ehr ) System
The document discusses the electronic health record (EHR) system. It explains that an EHR is a digital version of a patient's medical history that contains their personal details, health concerns, medical histories, test results, medications, and more. The EHR allows quick access to patient information for medical practitioners and facilitates improved care. It also assists clinicians in scheduling and supports evidence-based decision making. However, successful implementation of an EHR system depends on technical, behavioral, and management factors.
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Leveraging Clinical IT for Dengue: Opportunities for Tomorrow
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- 1. March 21, 2011 Copyright 2011: Team Triangle Kinect Abnormal Movement Assessment System (KAMAS) Team TriangleClaus C Becker, MSc, PhD, MBA Greg Borenstein, MPS (candidate) Johnny Hujol, MSCS Daniel Karlin, MD, MA Greg Kust, MPH, MBA
- 2. March 21, 2011 Copyright 2011: Team Triangle Executive Summary Conceived and developed in 8 hours at Boston Code-a-Thon Symptomatic diseases with excess of movement Microsoft Kinect-based application to automate assessment Home-grown and integrated open-source code Multiple stakeholder perspectives (clinicians, payers, patients) Framework for future development
- 3. March 21, 2011 Copyright 2011: Team Triangle Overview of Movement Disorders Focused on hyperkinetic movement disorders Dyskinesias, Choreas, Akathesias Neurological conditions affecting speed, fluency, and ease of movement; and the ability to stop movement KAMAS matches & complements existing clinical practices for the diagnosis & assessment of movement disorders
- 4. March 21, 2011 Chorea van Sydenham http://www.youtube.com/watch?v=RsIQFeYOkAg
- 5. March 21, 2011 Copyright 2011: Team Triangle Prevalence and Severity
- 6. March 21, 2011 Copyright 2011: Team Triangle Problem in Current Practice Inadequate assessment of movement disorders (AIMS test) Standardization Frequency Inter-rater reliability poor, masking progression Disease progression, on a more granular & temporal basis Current assessment every 3-6 months, at best Gaps between assessments allow development of severe disease Response to therapy (e.g. efficacy, dosing, adverse events) Adherence to therapy Remote monitoring
- 7. March 21, 2011 Copyright 2011: Team Triangle Our Approach Leverage MS Kinect Processing application using PrimeSense’s OpenNI middleware and OSCeleton library Tracks spatial hand & knee movement Calculates score based upon degree of involuntary motion Clinical scorecard: Severity score compared to baseline MPR* + Patient self-report + KAMAS** = Health Score *MPR: Medicine possession ratio
- 8. March 21, 2011 Copyright 2011: Team Triangle KAMAS Demonstration
- 9. March 21, 2011 Copyright 2011: Team Triangle Clinical Benefits Patient Less disruptive / more convenient Engaged participation and self care Improved care Closed feedback loops Earlier detection of non-response and disease progression Provider Clinical effectiveness & dose modification Adherence measures (prescription status) Time efficient Reliable, quantifiable scoring EMR integration (video & data) At home or in clinic
- 10. March 21, 2011 Copyright 2011: Team Triangle Payer Benefits Less $ waste (quick detection of ineffective therapy) Avoid a lifetime of advanced disease costs Reduce risk of expensive side effects and irreversible disease Reduce hospitalizations and all cause total costs (1) Clinical assessment without an office visit Telemedicine compatible Low cost, transparent, reproducible, auditable Start-up costs not capital intensive “Efforts to promote medication adherence…may lead to cost savings for managed care systems”3 1) Delea et al. CNS Drugs. 2011 Jan 1;25(1):53-66, 2) Wei et al. Amer J Ger Pharm. 2010 Aug;8(4):384-394, 3) Davis KL et al. Prevalence and cost of medication nonadherence in Parkinson's disease: evidence from administrative claims data. Mov Disord. 2010 Mar 15;25(4):474-80.
- 11. March 21, 2011 Copyright 2011: Team Triangle Issues we wrestled with Short timeline (ad hoc project) Kinect development environment rapidly changing Investment needed to further validate and develop for clinical use
- 12. March 21, 2011 Copyright 2011: Team Triangle Next Steps With interest from investors, refine software to include directions, progression measures, and increased range of movements Data archiving and security compliance Improve statistical treatment of data Clinical validation possible at Tufts Medical Center
- 13. March 21, 2011 Copyright 2011: Team Triangle References http://www.neurologychannel.com/movementdisorders/overview-of-movement-disorders.shtml http://www.nlm.nih.gov/medlineplus/movementdisorders.html http://www.atlantapsychiatry.com/forms/AIMS.pdf http://www.webmd.com Delea et al. CNS Drugs. 2011 Jan 1;25(1):53-66, Wei et al. Amer J Ger Pharm. 2010 Aug;8(4):384-394 Davis KL et al. Prevalence and cost of medication nonadherence in Parkinson's disease: evidence from administrative claims data. Mov Disord. 2010 Mar 15;25(4):474-80. Rosenheck, RA. Evaluating the cost-effectiveness of reduced tardive dyskinesia with secong-generation antipsychotics. B J Psychiatry. 2007 (191): 238-245.