The document summarizes a cost-benefit analysis of implementing electronic medical records in primary care settings. It found that the estimated net benefit per primary care physician over 5 years was $86,400. Benefits came from reduced drug and radiology costs, improved charge capture, and fewer billing errors. The analysis was sensitive to factors like the proportion of capitated patients. Even under pessimistic assumptions, the study found electronic medical records could break even or provide a net benefit financially to healthcare organizations.
Long Term Care - Improving Patient care and decreasing costs through EHRsshawtho2
The document discusses how the use of electronic health records (EHRs) can help improve patient care and reduce costs in long-term care facilities. It summarizes research showing that EHRs were associated with reductions in common adverse events like falls, polypharmacy, pressure ulcers, and inappropriate anti-psychotic drug use in nursing homes. The document concludes that wider adoption of EHRs in long-term care has the potential to improve quality of care while lowering healthcare expenditures.
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.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
IRJET- Heart Failure Risk Prediction using Trained Electronic Health RecordIRJET Journal
The document describes a study that uses an electronic health record and the K-dimensional tree classifier to predict the risk of heart failure. The study aims to use more risk factors from a patient's electronic health record to more accurately predict heart failure risk compared to previous methods. The proposed method involves preprocessing the electronic health record data, using an admin module to input patient details, and applying the K-dimensional tree classifier to partition the data and determine the risk level. The results show that the K-dimensional tree approach can reliably predict heart failure risk. Future work could analyze each heart failure risk factor and predict the level of risk as high or low.
The cis clinical_information_ppt--for turn inmhowardsbu
This document discusses clinical information systems (CIS) and their implementation and use in healthcare. It provides an overview of key CIS components and benefits, including improved access to patient data, clinical decision support, and increased safety and quality of care. The document also addresses CIS education and training for users, costs and funding, legal and regulatory requirements like HIPAA, and the importance of clinician and staff involvement to realize the full benefits of CIS. Regular system updates and customization are needed to address errors or changes in workflow.
IRJET - Blockchain for Medical Data Access and Permission ManagementIRJET Journal
This document discusses using blockchain technology for medical data access and permission management. It begins with an abstract that outlines how medical data is currently created, distributed, stored, and accessed, and how blockchain could help improve healthcare by enhancing patient care and reducing costs. It then describes the objectives, benefits, and challenges of the system. The rest of the document provides details on the proposed system, including module descriptions, architectural design, literature review on related work, and a conclusion discussing challenges of using blockchain for healthcare data like immutability and data size.
IRJET- Genetic Algorithm for Feature Selection to Improve Heart Disease Predi...IRJET Journal
This document discusses using genetic algorithms for feature selection and support vector machines for classification to improve prediction of heart disease. It first reviews literature on using various machine learning techniques for heart disease prediction, including support vector machines, neural networks, random forests, naive Bayes classifiers and decision trees. The methodology section then outlines the steps taken, which include collecting a dataset on heart disease from a public repository, preprocessing the data using genetic algorithms to select important features, and classifying the reduced data using naive Bayes, random forest and support vector vector machine classifiers to predict heart disease. The goal is to select key features and improve prediction accuracy.
Long Term Care - Improving Patient care and decreasing costs through EHRsshawtho2
The document discusses how the use of electronic health records (EHRs) can help improve patient care and reduce costs in long-term care facilities. It summarizes research showing that EHRs were associated with reductions in common adverse events like falls, polypharmacy, pressure ulcers, and inappropriate anti-psychotic drug use in nursing homes. The document concludes that wider adoption of EHRs in long-term care has the potential to improve quality of care while lowering healthcare expenditures.
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.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
IRJET- Heart Failure Risk Prediction using Trained Electronic Health RecordIRJET Journal
The document describes a study that uses an electronic health record and the K-dimensional tree classifier to predict the risk of heart failure. The study aims to use more risk factors from a patient's electronic health record to more accurately predict heart failure risk compared to previous methods. The proposed method involves preprocessing the electronic health record data, using an admin module to input patient details, and applying the K-dimensional tree classifier to partition the data and determine the risk level. The results show that the K-dimensional tree approach can reliably predict heart failure risk. Future work could analyze each heart failure risk factor and predict the level of risk as high or low.
The cis clinical_information_ppt--for turn inmhowardsbu
This document discusses clinical information systems (CIS) and their implementation and use in healthcare. It provides an overview of key CIS components and benefits, including improved access to patient data, clinical decision support, and increased safety and quality of care. The document also addresses CIS education and training for users, costs and funding, legal and regulatory requirements like HIPAA, and the importance of clinician and staff involvement to realize the full benefits of CIS. Regular system updates and customization are needed to address errors or changes in workflow.
IRJET - Blockchain for Medical Data Access and Permission ManagementIRJET Journal
This document discusses using blockchain technology for medical data access and permission management. It begins with an abstract that outlines how medical data is currently created, distributed, stored, and accessed, and how blockchain could help improve healthcare by enhancing patient care and reducing costs. It then describes the objectives, benefits, and challenges of the system. The rest of the document provides details on the proposed system, including module descriptions, architectural design, literature review on related work, and a conclusion discussing challenges of using blockchain for healthcare data like immutability and data size.
IRJET- Genetic Algorithm for Feature Selection to Improve Heart Disease Predi...IRJET Journal
This document discusses using genetic algorithms for feature selection and support vector machines for classification to improve prediction of heart disease. It first reviews literature on using various machine learning techniques for heart disease prediction, including support vector machines, neural networks, random forests, naive Bayes classifiers and decision trees. The methodology section then outlines the steps taken, which include collecting a dataset on heart disease from a public repository, preprocessing the data using genetic algorithms to select important features, and classifying the reduced data using naive Bayes, random forest and support vector vector machine classifiers to predict heart disease. The goal is to select key features and improve prediction accuracy.
A clinical information system (CIS) is a technology-based system used at the point of care to support the acquisition, processing, storage, and sharing of patient information across locations. Key components of a CIS include the type of application, number of users, where data is stored, and backup procedures. Implementation requires input from medical staff, IT, and management to ensure accuracy, privacy, and system reliability. Larger healthcare facilities can expect to pay $10 million to $1 billion to establish a CIS, with annual maintenance fees of $1 million or more.
Leaf Disease Detection and Selection of Fertilizers using Artificial Neural N...IRJET Journal
This document proposes a system for detecting leaf diseases and selecting appropriate fertilizers using artificial neural networks. The system involves image acquisition, preprocessing, feature extraction using gray level co-occurrence matrix (GLCM) and statistical moments, training an artificial neural network, classifying diseases, and identifying the disease name and recommended fertilizer. It is intended to provide farmers with preventative treatment recommendations. The system is tested on mango and lemon leaves with two diseases each. If implemented, it could help farmers identify diseases early and apply the correct fertilizers to improve crop quality and yields.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
Evaluation of a clinical information system (cis)nikita024
This power point presentation provides an overview of a clinical information system (CIS). It discusses what a CIS is, how CIS have evolved, and the key players involved in designing CIS. It also examines the electronic health record component of a CIS and discusses the eight basic components that make up an EHR. Additional topics covered include clinical decision making systems, safety, costs, and education regarding CIS. The presentation was created by four students with each student covering specific slides and aspects of the topic.
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...IRJET Journal
This document discusses using data mining techniques to predict heart disease outcomes. It analyzes clinical data on cardiovascular diseases using predictive algorithms like naive Bayes, k-means clustering, and decision trees. The study aims to build models that can help identify relationships in medical data and predict future health system details for heart conditions. It compares the performance of different predictive data mining methods on a cardiovascular disease database. The top-performing technique was found to be naive Bayes classification. The models seek to help doctors better understand heart disease risk factors and trends to improve diagnosis.
Cms 5 star webinar health care may 6 2015Polsinelli PC
Changes to CMS' Five-Star Ratings System Turn Up the Heat on Nursing Homes and Increase Risks for Psychotropic Use. On Feb. 20, 2015, the Centers for Medicare & Medicaid (CMS) unveiled Version 3.0 of its Nursing Home Compare, which updates the current 5-Star Quality Rating System to reflect higher performance standards. Because of these changes, not only will it will be increasingly difficult for nursing homes to earn the much-desired four-star and five-star ratings, but also the ratings for many nursing homes may immediately fall by one or more stars.
Additional topics of discussion:
What will change
What providers should do
What providers should know
Presenters:
Matthew J Murer, Shareholder and Healthcare Practice Group Chair, Polsinelli
Kathryn M. Stalmack, Shareholder, Polsinelli
- The study examines how meaningful use requirements are impacting hospitals and EHR vendors by analyzing if vendors and CIOs view the requirements as a floor or ceiling for EHR development and implementation.
- Interviews with 17 CIOs and 8 EHR vendor executives are analyzed along with adoption data for two EHR functions - barcode medication administration (BCMA) and computerized physician order entry (CPOE).
- Key findings are that meaningful use can serve as either a floor or ceiling depending on abilities, an excessive focus on meeting requirements risks overlooking broader healthcare system changes, and meaningful use has accelerated some functions but slowed development of others.
Recognition of Tomato Late Blight by using DWT and Component Analysis Yayah Zakaria
Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e. late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.
IRJET- Chronic Kidney Disease Prediction based on Naive Bayes TechniqueIRJET Journal
This document discusses using a Naive Bayes technique to predict chronic kidney disease (CKD) based on patient data. It begins by introducing data mining and its applications in healthcare to extract useful information from large datasets. It then reviews literature on using classification algorithms like Naive Bayes for disease detection. Next, it describes the limitations of existing manual CKD prediction systems. The proposed system would automate CKD prediction using a Naive Bayes classifier to help doctors diagnose the disease which affects many worldwide. The methodology involves collecting clinical data, pre-processing it, then applying the Naive Bayes technique to extract patterns and predict CKD.
IRJET - Chronic or Acute Disease with Doctor Specialist using Data MiningIRJET Journal
This document discusses using data mining techniques to identify chronic or acute diseases and recommend appropriate medications and doctors. It describes collecting patient data on symptoms, disease duration, predicted diseases, and suggesting specialist doctors and locations. The document proposes using Naive Bayes data mining to analyze the datasets, predict diseases as chronic or acute, and display relevant doctors and locations to aid treatment. The goal is to help users efficiently find specialist doctors based on their symptoms and medical history.
The document describes the development and implementation of a Hospital Information System (HIS) at Christian Medical College (CMC) in Vellore, India. The HIS integrated various hospital departments like labs, medical records, pharmacy, dietary, and inpatient and outpatient areas. It allowed for real-time sharing of patient information between departments. This reduced costs and errors, improved efficiency of healthcare delivery, and enabled better decision-making at CMC.
WealthTrust-Arizona - Five Fallacies for Improving Healthcare WealthTrust-Arizona
Educational workshop presented by WealthTrust-Arizona and world-renowned guest Robert K. Smoldt, Chief Administrative Officer Emeritus at Mayo Clinic and Associate Director of Healthcare Delivery & Policy Programs at Arizona State University. Mr. Smoldt has been involved in health care administration for more than 30 years and is currently pursuing U.S. health reform in close partnership with Mayo Clinic’s Emeritus President and CEO.
At this workshop Robert examines a number of general statements that are, in his view, fallacious.
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
This document summarizes a research paper that analyzed different machine learning algorithms for predicting heart disease. It discusses using the Naive Bayes and Decision Tree classifiers on a Cleveland Heart Disease dataset containing 303 records and 19 attributes. The Naive Bayes and Decision Tree algorithms were applied to the preprocessed data and their accuracies were compared. The results showed that the Decision Tree algorithm had better performance and accuracy than the Naive Bayes classifier for predicting heart disease. Future work will focus on using a Selective Naive Bayes classifier to potentially improve prediction accuracy by removing irrelevant attributes.
An Integrated Framework for Telediagnosis and Prescriptions in Herbal MedicineEswar Publications
Herbal medicine has been an age long tradition for the treatment and curing of diseases globally. Previous studies
on telediagnostic and prescription of orthodox medicine have been examined using the application of modern technology device to improve health care services. In spite of this, there is yet an exhaustive study on the integration of technological framework for telediagnosis and prescription in herbal medicine. Therefore, this research focused on development of collaborative teleconsultation and telediagnosis in sharing of information on herbal medication for patients in remote areas to improve healthcare delivery. WAVA based collaborative
framework was designed for telediagnosis and prescription in herbal medicine, it has multimedia features for video conferencing, and ability to record, capture and replay consultations with the capacity for edit, data compression and short messages (sms) between the teleconsultants. The framework study the propagation time, link media delay, packet loss, processing delay between all (tele-herbal consultant 1, 2, 3…n) connected to the system. Each herbal tele-consultant was aloted with peer IP address in order to join the telediagnosis video conference from their remote areas. The framework displays paradigms for data acquisition on herbal
medications, video-recording, and imagery of patients. The integration of this collaborative framework enhanced
telediagnosis of patients with better prescriptions on effective herbal drugs for speedy recovery.
IRJET - Classification and Prediction for Hospital Admissions through Emergen...IRJET Journal
This document discusses using machine learning techniques to predict hospital admissions from emergency departments in order to improve patient flow and reduce overcrowding. It compares the performance of logistic regression and random forest algorithms on a dataset. Logistic regression identified several factors related to admissions including age, arrival mode, previous admissions. Random forests had the lowest accuracy. Predictive models could allow advance planning of resources to prevent bottlenecks. Future work involves exploring additional machine learning methods.
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.
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.
The document discusses the costs associated with Electronic Medical Records (EMR) software. EMR costs can range from $1,000 to over $40,000 depending on the size of the practice and features of the software. In addition to the upfront software costs, practices need to consider costs of training, implementation, hardware, support/maintenance, hiring IT staff, and ongoing updates. However, the document notes that despite the high initial costs, EMR systems save money in the long run through increased efficiency and reduced filing/storage costs compared to paper records. It recommends analyzing needs and seeking government assistance to help finance EMR costs.
EMR implementation: Money Maker or Bust?
Purpose:
To identify whether EHR implementation will end up costing financially more than it benefits
To identify the recipients of any costs or savings
This document discusses planning for successful implementation of electronic health records (EHRs). It notes that EHR implementation projects often fail, with failure rates as high as 70%. To achieve success, the document recommends: 1) establishing an effective implementation team, 2) finalizing goals and priorities, 3) developing an implementation strategy and scope, 4) creating a detailed implementation plan and timeline, 5) emphasizing communication, and 6) establishing benchmarks to measure success. By following these steps, practices can keep EHR implementations on track to realize the benefits of digitization.
This document discusses Community Health Connections' implementation of an electronic health record system. It provides an overview of the organization and outlines their plan to implement OpenVista EHR software across three clinics by February 2011. It describes the anticipated benefits of EHR including reduced errors, improved workflows and access to patient information. The implementation plan includes teams for project management, hardware, software and stakeholders. It also covers training, data migration, technical infrastructure including servers and network upgrades, meeting meaningful use requirements and realizing financial benefits and savings.
This document discusses the implementation of electronic medical records (EMR). It outlines reasons to implement EMR, such as reducing medical errors from illegible handwriting and inaccurate abbreviations. The implementation process involves choosing software and a vendor, testing, and training. There are costs for equipment, lawsuits, and unnecessary medical procedures that EMR can reduce. EMR also allows for faster treatment decisions and easier transfer of patient information. While costly initially, EMR provides long-term financial benefits and improves patient healthcare overall.
A clinical information system (CIS) is a technology-based system used at the point of care to support the acquisition, processing, storage, and sharing of patient information across locations. Key components of a CIS include the type of application, number of users, where data is stored, and backup procedures. Implementation requires input from medical staff, IT, and management to ensure accuracy, privacy, and system reliability. Larger healthcare facilities can expect to pay $10 million to $1 billion to establish a CIS, with annual maintenance fees of $1 million or more.
Leaf Disease Detection and Selection of Fertilizers using Artificial Neural N...IRJET Journal
This document proposes a system for detecting leaf diseases and selecting appropriate fertilizers using artificial neural networks. The system involves image acquisition, preprocessing, feature extraction using gray level co-occurrence matrix (GLCM) and statistical moments, training an artificial neural network, classifying diseases, and identifying the disease name and recommended fertilizer. It is intended to provide farmers with preventative treatment recommendations. The system is tested on mango and lemon leaves with two diseases each. If implemented, it could help farmers identify diseases early and apply the correct fertilizers to improve crop quality and yields.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
Evaluation of a clinical information system (cis)nikita024
This power point presentation provides an overview of a clinical information system (CIS). It discusses what a CIS is, how CIS have evolved, and the key players involved in designing CIS. It also examines the electronic health record component of a CIS and discusses the eight basic components that make up an EHR. Additional topics covered include clinical decision making systems, safety, costs, and education regarding CIS. The presentation was created by four students with each student covering specific slides and aspects of the topic.
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...IRJET Journal
This document discusses using data mining techniques to predict heart disease outcomes. It analyzes clinical data on cardiovascular diseases using predictive algorithms like naive Bayes, k-means clustering, and decision trees. The study aims to build models that can help identify relationships in medical data and predict future health system details for heart conditions. It compares the performance of different predictive data mining methods on a cardiovascular disease database. The top-performing technique was found to be naive Bayes classification. The models seek to help doctors better understand heart disease risk factors and trends to improve diagnosis.
Cms 5 star webinar health care may 6 2015Polsinelli PC
Changes to CMS' Five-Star Ratings System Turn Up the Heat on Nursing Homes and Increase Risks for Psychotropic Use. On Feb. 20, 2015, the Centers for Medicare & Medicaid (CMS) unveiled Version 3.0 of its Nursing Home Compare, which updates the current 5-Star Quality Rating System to reflect higher performance standards. Because of these changes, not only will it will be increasingly difficult for nursing homes to earn the much-desired four-star and five-star ratings, but also the ratings for many nursing homes may immediately fall by one or more stars.
Additional topics of discussion:
What will change
What providers should do
What providers should know
Presenters:
Matthew J Murer, Shareholder and Healthcare Practice Group Chair, Polsinelli
Kathryn M. Stalmack, Shareholder, Polsinelli
- The study examines how meaningful use requirements are impacting hospitals and EHR vendors by analyzing if vendors and CIOs view the requirements as a floor or ceiling for EHR development and implementation.
- Interviews with 17 CIOs and 8 EHR vendor executives are analyzed along with adoption data for two EHR functions - barcode medication administration (BCMA) and computerized physician order entry (CPOE).
- Key findings are that meaningful use can serve as either a floor or ceiling depending on abilities, an excessive focus on meeting requirements risks overlooking broader healthcare system changes, and meaningful use has accelerated some functions but slowed development of others.
Recognition of Tomato Late Blight by using DWT and Component Analysis Yayah Zakaria
Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e. late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.
IRJET- Chronic Kidney Disease Prediction based on Naive Bayes TechniqueIRJET Journal
This document discusses using a Naive Bayes technique to predict chronic kidney disease (CKD) based on patient data. It begins by introducing data mining and its applications in healthcare to extract useful information from large datasets. It then reviews literature on using classification algorithms like Naive Bayes for disease detection. Next, it describes the limitations of existing manual CKD prediction systems. The proposed system would automate CKD prediction using a Naive Bayes classifier to help doctors diagnose the disease which affects many worldwide. The methodology involves collecting clinical data, pre-processing it, then applying the Naive Bayes technique to extract patterns and predict CKD.
IRJET - Chronic or Acute Disease with Doctor Specialist using Data MiningIRJET Journal
This document discusses using data mining techniques to identify chronic or acute diseases and recommend appropriate medications and doctors. It describes collecting patient data on symptoms, disease duration, predicted diseases, and suggesting specialist doctors and locations. The document proposes using Naive Bayes data mining to analyze the datasets, predict diseases as chronic or acute, and display relevant doctors and locations to aid treatment. The goal is to help users efficiently find specialist doctors based on their symptoms and medical history.
The document describes the development and implementation of a Hospital Information System (HIS) at Christian Medical College (CMC) in Vellore, India. The HIS integrated various hospital departments like labs, medical records, pharmacy, dietary, and inpatient and outpatient areas. It allowed for real-time sharing of patient information between departments. This reduced costs and errors, improved efficiency of healthcare delivery, and enabled better decision-making at CMC.
WealthTrust-Arizona - Five Fallacies for Improving Healthcare WealthTrust-Arizona
Educational workshop presented by WealthTrust-Arizona and world-renowned guest Robert K. Smoldt, Chief Administrative Officer Emeritus at Mayo Clinic and Associate Director of Healthcare Delivery & Policy Programs at Arizona State University. Mr. Smoldt has been involved in health care administration for more than 30 years and is currently pursuing U.S. health reform in close partnership with Mayo Clinic’s Emeritus President and CEO.
At this workshop Robert examines a number of general statements that are, in his view, fallacious.
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
This document summarizes a research paper that analyzed different machine learning algorithms for predicting heart disease. It discusses using the Naive Bayes and Decision Tree classifiers on a Cleveland Heart Disease dataset containing 303 records and 19 attributes. The Naive Bayes and Decision Tree algorithms were applied to the preprocessed data and their accuracies were compared. The results showed that the Decision Tree algorithm had better performance and accuracy than the Naive Bayes classifier for predicting heart disease. Future work will focus on using a Selective Naive Bayes classifier to potentially improve prediction accuracy by removing irrelevant attributes.
An Integrated Framework for Telediagnosis and Prescriptions in Herbal MedicineEswar Publications
Herbal medicine has been an age long tradition for the treatment and curing of diseases globally. Previous studies
on telediagnostic and prescription of orthodox medicine have been examined using the application of modern technology device to improve health care services. In spite of this, there is yet an exhaustive study on the integration of technological framework for telediagnosis and prescription in herbal medicine. Therefore, this research focused on development of collaborative teleconsultation and telediagnosis in sharing of information on herbal medication for patients in remote areas to improve healthcare delivery. WAVA based collaborative
framework was designed for telediagnosis and prescription in herbal medicine, it has multimedia features for video conferencing, and ability to record, capture and replay consultations with the capacity for edit, data compression and short messages (sms) between the teleconsultants. The framework study the propagation time, link media delay, packet loss, processing delay between all (tele-herbal consultant 1, 2, 3…n) connected to the system. Each herbal tele-consultant was aloted with peer IP address in order to join the telediagnosis video conference from their remote areas. The framework displays paradigms for data acquisition on herbal
medications, video-recording, and imagery of patients. The integration of this collaborative framework enhanced
telediagnosis of patients with better prescriptions on effective herbal drugs for speedy recovery.
IRJET - Classification and Prediction for Hospital Admissions through Emergen...IRJET Journal
This document discusses using machine learning techniques to predict hospital admissions from emergency departments in order to improve patient flow and reduce overcrowding. It compares the performance of logistic regression and random forest algorithms on a dataset. Logistic regression identified several factors related to admissions including age, arrival mode, previous admissions. Random forests had the lowest accuracy. Predictive models could allow advance planning of resources to prevent bottlenecks. Future work involves exploring additional machine learning methods.
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.
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.
The document discusses the costs associated with Electronic Medical Records (EMR) software. EMR costs can range from $1,000 to over $40,000 depending on the size of the practice and features of the software. In addition to the upfront software costs, practices need to consider costs of training, implementation, hardware, support/maintenance, hiring IT staff, and ongoing updates. However, the document notes that despite the high initial costs, EMR systems save money in the long run through increased efficiency and reduced filing/storage costs compared to paper records. It recommends analyzing needs and seeking government assistance to help finance EMR costs.
EMR implementation: Money Maker or Bust?
Purpose:
To identify whether EHR implementation will end up costing financially more than it benefits
To identify the recipients of any costs or savings
This document discusses planning for successful implementation of electronic health records (EHRs). It notes that EHR implementation projects often fail, with failure rates as high as 70%. To achieve success, the document recommends: 1) establishing an effective implementation team, 2) finalizing goals and priorities, 3) developing an implementation strategy and scope, 4) creating a detailed implementation plan and timeline, 5) emphasizing communication, and 6) establishing benchmarks to measure success. By following these steps, practices can keep EHR implementations on track to realize the benefits of digitization.
This document discusses Community Health Connections' implementation of an electronic health record system. It provides an overview of the organization and outlines their plan to implement OpenVista EHR software across three clinics by February 2011. It describes the anticipated benefits of EHR including reduced errors, improved workflows and access to patient information. The implementation plan includes teams for project management, hardware, software and stakeholders. It also covers training, data migration, technical infrastructure including servers and network upgrades, meeting meaningful use requirements and realizing financial benefits and savings.
This document discusses the implementation of electronic medical records (EMR). It outlines reasons to implement EMR, such as reducing medical errors from illegible handwriting and inaccurate abbreviations. The implementation process involves choosing software and a vendor, testing, and training. There are costs for equipment, lawsuits, and unnecessary medical procedures that EMR can reduce. EMR also allows for faster treatment decisions and easier transfer of patient information. While costly initially, EMR provides long-term financial benefits and improves patient healthcare overall.
The document discusses the concept of cost-benefit analysis (CBA) for evaluating information systems projects. CBA measures and compares the costs and benefits of a project to determine if its benefits outweigh its costs. The CBA process involves identifying the tangible and intangible costs and benefits of a project, evaluating them, and choosing the system with the lowest costs but highest benefits. CBA is useful for decision making by individuals, companies, and governments.
This document provides an overview of cost benefit analysis (CBA). It discusses the history and principles of CBA, including key indicators like net present value. Challenges of CBA like inaccurate cost and benefit estimation are outlined. The document also presents a case study of implementing new computer equipment in an organization and calculating the costs, benefits, and payback period. It concludes that performing a thorough CBA is important for evaluating projects and avoiding erroneous conclusions.
The Sumerians established advanced city-states in Mesopotamia around 3000 BC, where rulers claimed divine approval and religion played a central role. Sargon later conquered the region to form the first empire, though it did not last after his death as rival city-states regained power. The Sumerians developed new technologies, built walled cities like Ur for protection, and practiced polytheism with gods influencing all aspects of life.
This document provides information and tips for using LinkedIn effectively. It discusses setting up a professional LinkedIn profile with a complete background and experience section. It emphasizes using keywords, a professional photo, and an active status. The document also covers using LinkedIn groups to expand your network and generate leads. It provides instructions for creating a professional LinkedIn company page and strategies for engagement. Resources with additional LinkedIn training and best practices are also included.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against developing mental illness and improve symptoms for those who already have a condition.
Todd Dearborn "New Car Internet Pricing"Sean Bradley
The document discusses strategies for setting optimal online car prices. It covers how customers don't buy for various reasons like pricing, and the importance of knowing a lead's source. Specific pricing factors are examined, such as how incentives, fees, and the local dealer are handled. The presentation recommends giving prices upfront, fitting the local market, using customer/lead notes, and selling additional products to improve conversions. It stresses training employees on the various online "4 Ps" of products, people, process, and promotions.
This document discusses the role of clearing corporations/houses in facilitating the settlement of trades done on stock exchanges for dematerialized shares. It lists the major stock exchanges in India and their linkages to NSDL. It then outlines the requirements for a clearing corporation/house to be admitted as a user on the depository, including ensuring payment against delivery, having the operational capability to provide clearing and settlement services, and cooperating to resolve client grievances. Finally, it provides details on the services provided by NSCCL, such as clearing and settlement, inter-region clearing, risk management, and securities lending/borrowing.
This document discusses and analyzes different types of digipaks. It begins by defining a digipak as an alternative CD or DVD packaging, usually in a gatefold or book-style format made of paperboard or card. Positive aspects of digipaks include being eye-catching to promote sales, being more durable than jewel cases, and allowing creative expression from artists. Negatives include declining popularity with online music and potential lack of success turning buyers away from an album. Examples of digipaks from different genres are shown and analyzed for their use of color, style, focus on the artist, and effectiveness in promotion.
The document discusses three upcoming films - Marvel's Avengers coming out on May 4th, Men in Black on May 25th, and Step Up Revolution on July 27th. It also notes that while the films are not yet rated, the author would give one of them, presumably Men in Black 3, a four star rating based on his opinion.
De juegos, sueños y esperanza. cuento de felipeada48salamanca
El documento habla sobre juegos, sueños y esperanzas a lo largo de 14 páginas, terminando con la frase "Y colorín colorado este cuento de Eduardito se ha terminado".
Nobre Norte Clube Residencial, RJZ Cyrella, Apartamentos ao lado do Norte Sho...Suely Maia
Ao lado da Nova Expansão do Norte Shopping
Condomínio residencial fechado composto por 3 edifícios ( ed. Duque, Ed, Barão e Ed. Conde), com 470 apartamentos de 2 e 3 quartos com suíte, vaga de garagem e uma área de lazer para todas as idades. As unidades tipo de 2 quartos medem 55,03m² até 59,35m², as unidades tipo de 3 quartos medem 65m² e 66m² e as unidades Garden de 2 quartos 90,22m² e 93,80m². A unidades Garden de 3 quartos medem 75,53m², 89,78m² e 105,43m².
Com um hall social belíssimo com um pé direito duplo dando todo requinte para o Nobre Norte Clube Residencial.
O Ed. Duque contém 10 unidades por pavimentos e nos Ed. Barão e Conde 12 unidades por pavimentos. Cada edifício com 14 pavimentos, sendo que o 1º pavimento fica localizado no mesmo nível do PUC.
Temos 472 vagas de garagens sendo que 324 cobertas e 148 descobertas.
A área de lazer completa para todas as idades.
Área do Terreno: 9.217,38 m²
Vendas e Informações: (21) 2556-5838
Principles of Healthcare Reimbursement Student Workb.docxharrisonhoward80223
Principles of Healthcare
Reimbursement
Student Workbook
Chapter 6
Medicare-Medicaid Prospective
Payment Systems for Inpatients
2
Activities
Theory into Practice and Real-World Case
The Medicare Provider Analysis and Review (MedPAR) file is a database that the
Centers for Medicare and Medicaid Services maintains. For each year, it includes the
records from all the claims for hospital discharges of Medicare beneficiaries. The
MedPAR file contains several gigabytes of data per year. Rather than being an inert
archive, these data can be used to improve the quality of care for Medicare beneficiaries
(Ash et al. 2003; Stringham and Young 2005).
The MedPAR file is an administrative database. The data include many
administrative fields, such as diagnosis and procedure codes, claim costs and charges,
the diagnosis-related group (DRG) and—as of fiscal year 2008—MS-DRGs, and the
length of stay. However, as an administrative database, it has limitations to its
usefulness as a means of assessing the quality of patient care. The database does not
include some clinical risk factors, such as the results of diagnostic tests. The number of
other diagnoses used to record complications and comorbidities is restricted to eight.
The benefits of using the database, though, far outweigh the limitations. Cost is minimal.
The database already exists. No forms or procedures need to be created. No data
collectors need to be hired nor trained. Data collection occurs in the usual course of
business. Finally, though, research has found that the MedPAR file can be used to assess
the quality of patient care for both Medicare patients and other-payer patients
(Needleman et al. 2003).
Ash and colleagues used MedPAR claims data to predict mortality in patients
who had suffered acute myocardial infarction (AMI). They studied the years 1995
through 1999 with more than 300,000 cases per year (305,468; 308,997; 306,224; 304,882;
306,175; totaling 1,531,746). The validation data showed up to 80 percent mortality one
year post-AMI for cases in the highest risk group. Moreover, the authors found that,
prior to the AMI in the study, the patients had had a previous AMI, diabetes, or
congestive heart failure. This information about health status at admission is important
for the care of patients and for the improvement of care outcomes (Ash et al. 2003).
Stringham and Young used the MedPAR file to examine rates of urinary tract
infections (UTI) at acute inpatient hospitals (Stringham and Young 2005). The authors
noted that Medicare makes additional payments for complications, even complications
that are possibly preventable. Frequently, the Medicare payment system has paired
DRGs: one DRG for the condition and one DRG for the condition with a complication or
comorbidity (CC). The relative weight of the DRG with the CC is higher than the relative
weight for the DRG without the CC.
Nosocomial UTIs are an example of a potentially pr.
The document provides an overview of electronic medical records (EMRs) and their use and benefits. It discusses that currently only around 24% of practices nationwide use EMRs in a meaningful way according to studies. Barriers to adoption include costs, lost productivity during implementation, and software limitations. The document outlines the functions of EMRs and their potential to improve health outcomes and reduce costs through improved care coordination and reduced medical errors. Federal incentives through the HITECH Act and meaningful use criteria aim to accelerate EMR adoption nationally and in West Virginia.
This document discusses the cost-effectiveness of electronic medical record (EMR) systems. It first provides background on rising healthcare costs in the US. Then, it defines what an EMR system is and how it allows fast and secure exchange of patient information. The document summarizes several studies that found EMR systems can improve quality of care while decreasing costs through increased efficiency and reduced errors. It concludes that EMR systems are a step toward reducing healthcare spending while maintaining high quality care.
IRJET- Predicting Diabetes Disease using Effective Classification TechniquesIRJET Journal
This document discusses predicting diabetes disease using machine learning techniques. It begins with an abstract introducing diabetes mellitus and the importance of early detection. It then discusses the Pima Indian diabetes dataset that is commonly used for research. The document outlines the existing research which focuses mainly on one or two techniques, while the proposed research will take a more comprehensive approach, comparing multiple techniques. It describes evaluating classifiers like deep neural networks and support vector machines on the Pima Indian dataset. The best technique identified achieved 77.86% accuracy. Feature relevance is also analyzed to modify the dataset for future studies. The goal is to automate diabetes identification and help physicians detect the disease earlier.
This study assessed the costs and effects of different degrees of task shifting for anti-retroviral therapy (ART) from physicians to other health professionals in Ethiopia. The study found that (1) facilities with maximal task shifting, where non-physicians performed most ART tasks, had similar patient outcomes and costs as facilities with minimal/moderate task shifting; (2) over 88% of patients remained active on ART after two years across all facility types; and (3) maximal task shifting cost $36 more per patient over two years but resulted in 0.4% fewer patients remaining active, though this difference was not statistically significant.
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.
1. The document discusses the advantages and disadvantages of implementing an electronic health record (EHR) system to replace a paper-based system.
2. A key disadvantage is the high cost of implementation, with the cost of Alberta's new clinical information system estimated at $1.6 billion over 10 years.
3. Another disadvantage is a lack of interoperability between existing EHR systems, which prevents patient information from being shared and understood across health settings.
The document discusses implementing an electronic medication reconciliation system at Taranaki District Health Board (TDHB) in New Zealand. The goals are to record and verify patient medications within 24 hours of admission, clearly communicate medication changes during and after admission, and provide medication information to patients. The system will integrate with other systems to automatically update medication records and generate discharge summaries and patient instructions. Expected benefits include reducing adverse drug events, lowering hospital costs from shorter stays, and improving communication between hospitals and primary care providers.
IT trends in the US healthcare sector are driven by incentives to cut costs while improving care integration. Spending on healthcare IT is projected to grow from $54 billion in 2010 to $80 billion in 2017. Emerging technologies like mobile health, bring your own device (BYOD), big data analytics, and interoperable electronic health records aim to enhance care delivery and lower costs. Adoption of standards like ICD-10, HL7, and meaningful use incentives also promote IT-enabled transformation across providers, payers, and life sciences organizations.
A clinical information system (CIS) is a technology-based system used at the point of care to support processing and storing patient information. It includes electronic health records, clinical data repositories, decision support, and communication tools. Implementing a CIS requires representation from all areas of healthcare to ensure success. Effective CIS can reduce errors, improve guideline-based care, and decrease healthcare utilization through components like clinical decision support systems. However, ensuring data security, accuracy, and privacy is important when using and networking CIS.
This document discusses clinical information systems and their role in healthcare. It begins with background on healthcare and how information technology has helped address issues with declining resources and rapid knowledge growth. It then defines and discusses hospital information systems, clinical information systems, clinical decision support systems, and electronic medical records. It explains how these systems help with tasks like data management, decision making, and improving quality of care. The document also covers healthcare strategy making and how clinical information systems are developed and integrated.
The document discusses the value of healthcare information technology (HIT) and electronic health records (EHRs). It notes that HIT can help address issues like medical errors, patient safety, and healthcare costs. Studies show HIT systems can save thousands of dollars per provider annually and billions nationally through reduced errors and unnecessary care. Widespread use of EHRs and health information exchange could save hundreds of billions over 10 years by improving care coordination and reducing redundant tests. Successful HIT programs like Partners HealthCare demonstrate these benefits through improved quality, efficiency and clinical outcomes.
The document discusses the meaningful use requirements of the HITECH Act which provides incentives for hospitals and providers to adopt electronic health record (EHR) systems. It evaluates three elements of meaningful use - electronic prescribing, exchange of health information, and privacy/security of patient data - and identifies both potential benefits and risks to patient safety from implementation of EHRs. While EHRs can improve care coordination and reduce errors, proper policies, workflows and software design are needed to fully realize benefits and ensure patient safety.
IRJET- Data Mining Techniques to Predict DiabetesIRJET Journal
This document discusses using data mining techniques to predict diabetes. It begins with an introduction to diabetes and what causes high blood sugar. It then discusses how data mining of patient purchase histories can show connections to medication adherence. Various data mining techniques are explored, including decision trees and the Apriori algorithm, to analyze medical data and extract patterns to improve diagnosis and treatment recommendations for patients. The goal is to help doctors and patients choose the most effective and lowest cost treatment options based on analyses of large diabetes datasets.
1) Description of how technology has affected or could affect deli.docxdorishigh
1) Description of how technology has affected or could affect delivery, if applicable
a) Interoperability and widespread health information exchange;
i) Continuity of care
ii) Less medical error,
(1) Reduction in Malpractice claims and costs
b) Automated, real-time
i) Instant access to a medical record for billing patient and physician access
c) Quality and cost measurement;
i) Meaningful use
ii) Ability to report and measure outcomes, presentations and other quality information pertaining to the care of patients
(1) Physician performance and quality
d) smarter analytic capacities
i) The delivery of the actual costs of health care.
Hillestad, R., Bigelow, J., Bower, A., Girosi F., Meili R., Scoville R., and Taylor R. (2013) Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, and costs.Health Aff September 2005 24:51103-1117; doi:10.1377/hlthaff.24.5.1103
Retrieved from http://content.healthaffairs.org/content/24/5/1103.full
_______________________________________________________________
_______________________________________________________________
Report Information from ProQuest
April 30 2013 22:45
_______________________________________________________________
30 April 2013 ProQuest
Table of contents
1. Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, And
Costs................................................................................................................................................................ 1
Bibliography...................................................................................................................................................... 11
30 April 2013 ii ProQuest
Document 1 of 1
Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings,
And Costs
Author: Hillestad, Richard; Bigelow, James; Bower, Anthony; Girosi, Federico; et al
Publication info: Health Affairs 24. 5 (Sep/Oct 2005): 1103-17.
ProQuest document link
Abstract: To broadly examine the potential health and financial benefits of health information technology (HIT),
this paper compares health care with the use of IT in other industries. It estimates potential savings and costs of
widespread adoption of electronic medical record (EMR) systems, models important health and safety benefits,
and concludes that effective EMR implementation and networking could eventually save more than $81 billion
annually - by improving health care efficiency and safety - and that HIT-enabled prevention and management of
chronic disease could eventually double those savings while increasing health and other social benefits.
However, this is unlikely to be realized without related changes to the health care system. [PUBLICATION
ABSTRACT]
Links: Linking Service
Full text: Headnote The adoption of interoperable EMR systems could produce efficiency and safety savings of
$142-$371 billion. Headnote ...
Heart Disease Prediction Using Data MiningIRJET Journal
This document discusses using data mining techniques like Naive Bayes and Weighted Associative Classifier (WAC) to predict heart disease. It analyzes a dataset containing factors like age, sex, medical history, and test results. Naive Bayes and WAC are used to generate rules for predicting whether a patient has heart disease risk. The system was able to indicate heart disease risk levels based on the patient's data. The document concludes the approach was effective for heart disease prediction and automation could further improve clinical decision making.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Presentation for UP MSHI HI201 Health Informatics class under Dr. Iris Tan and Dr. Mike Muin. Check out my blog - http://jdonsoriano.wordpress.com/2014/10/09/fitting-the-pi…making-it-work/
System for Recommending Drugs Based on Machine Learning Sentiment Analysis of...IRJET Journal
This document presents a system for recommending drugs based on machine learning sentiment analysis of drug reviews. The system aims to minimize the workload on experts by providing drug recommendations based on patient feedback. It uses various machine learning and deep learning techniques like naive bayes, logistic regression, LSTM, and GRU to analyze drug reviews and predict sentiment. Based on the sentiment analysis of features extracted from reviews, the system conditionally recommends medications for specific patients and conditions. It achieved up to 93% accuracy in recommending the top drugs for common conditions like acne, high blood pressure, anxiety, and depression. The system aims to enhance healthcare access and reduce medical errors by supplementing expert recommendations with an AI-powered recommendation platform.
Electronic Health Records And The Healthcare FieldDiane Allen
Electronic health records and the transition from paper records to digital systems has significantly impacted the healthcare field over the last couple decades. While EHR technology has been available, many hospitals were slow to adopt it and still used paper records. The main problem is the lack of utilization of available IT resources in healthcare organizations. Proper implementation of EHRs can help organizations improve quality of care through increased medical efficiency, reduced costs, improved research, and earlier disease detection. Fully adopting EHRs remains a challenge as only a small percentage of physicians and hospitals were reported to have fully functional systems in 2008.
NAVIGATING THE HORIZONS OF TIME LAPSE EMBRYO MONITORING.pdfRahul Sen
Time-lapse embryo monitoring is an advanced imaging technique used in IVF to continuously observe embryo development. It captures high-resolution images at regular intervals, allowing embryologists to select the most viable embryos for transfer based on detailed growth patterns. This technology enhances embryo selection, potentially increasing pregnancy success rates.
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7shruti jagirdar
Unit 4: MRA 103T Regulatory affairs
This guideline is directed principally toward new Molecular Entities that are
likely to have significant use in the elderly, either because the disease intended
to be treated is characteristically a disease of aging ( e.g., Alzheimer's disease) or
because the population to be treated is known to include substantial numbers of
geriatric patients (e.g., hypertension).
Nano-gold for Cancer Therapy chemistry investigatory projectSIVAVINAYAKPK
chemistry investigatory project
The development of nanogold-based cancer therapy could revolutionize oncology by providing a more targeted, less invasive treatment option. This project contributes to the growing body of research aimed at harnessing nanotechnology for medical applications, paving the way for future clinical trials and potential commercial applications.
Cancer remains one of the leading causes of death worldwide, prompting the need for innovative treatment methods. Nanotechnology offers promising new approaches, including the use of gold nanoparticles (nanogold) for targeted cancer therapy. Nanogold particles possess unique physical and chemical properties that make them suitable for drug delivery, imaging, and photothermal therapy.
“Psychiatry and the Humanities”: An Innovative Course at the University of Mo...Université de Montréal
“Psychiatry and the Humanities”: An Innovative Course at the University of Montreal Expanding the medical model to embrace the humanities. Link: https://www.psychiatrictimes.com/view/-psychiatry-and-the-humanities-an-innovative-course-at-the-university-of-montreal
Giloy in Ayurveda - Classical Categorization and SynonymsPlanet Ayurveda
Giloy, also known as Guduchi or Amrita in classical Ayurvedic texts, is a revered herb renowned for its myriad health benefits. It is categorized as a Rasayana, meaning it has rejuvenating properties that enhance vitality and longevity. Giloy is celebrated for its ability to boost the immune system, detoxify the body, and promote overall wellness. Its anti-inflammatory, antipyretic, and antioxidant properties make it a staple in managing conditions like fever, diabetes, and stress. The versatility and efficacy of Giloy in supporting health naturally highlight its importance in Ayurveda. At Planet Ayurveda, we provide a comprehensive range of health services and 100% herbal supplements that harness the power of natural ingredients like Giloy. Our products are globally available and affordable, ensuring that everyone can benefit from the ancient wisdom of Ayurveda. If you or your loved ones are dealing with health issues, contact Planet Ayurveda at 01725214040 to book an online video consultation with our professional doctors. Let us help you achieve optimal health and wellness naturally.
The skin is the largest organ and its health plays a vital role among the other sense organs. The skin concerns like acne breakout, psoriasis, or anything similar along the lines, finding a qualified and experienced dermatologist becomes paramount.
Osvaldo Bernardo Muchanga-GASTROINTESTINAL INFECTIONS AND GASTRITIS-2024.pdfOsvaldo Bernardo Muchanga
GASTROINTESTINAL INFECTIONS AND GASTRITIS
Osvaldo Bernardo Muchanga
Gastrointestinal Infections
GASTROINTESTINAL INFECTIONS result from the ingestion of pathogens that cause infections at the level of this tract, generally being transmitted by food, water and hands contaminated by microorganisms such as E. coli, Salmonella, Shigella, Vibrio cholerae, Campylobacter, Staphylococcus, Rotavirus among others that are generally contained in feces, thus configuring a FECAL-ORAL type of transmission.
Among the factors that lead to the occurrence of gastrointestinal infections are the hygienic and sanitary deficiencies that characterize our markets and other places where raw or cooked food is sold, poor environmental sanitation in communities, deficiencies in water treatment (or in the process of its plumbing), risky hygienic-sanitary habits (not washing hands after major and/or minor needs), among others.
These are generally consequences (signs and symptoms) resulting from gastrointestinal infections: diarrhea, vomiting, fever and malaise, among others.
The treatment consists of replacing lost liquids and electrolytes (drinking drinking water and other recommended liquids, including consumption of juicy fruits such as papayas, apples, pears, among others that contain water in their composition).
To prevent this, it is necessary to promote health education, improve the hygienic-sanitary conditions of markets and communities in general as a way of promoting, preserving and prolonging PUBLIC HEALTH.
Gastritis and Gastric Health
Gastric Health is one of the most relevant concerns in human health, with gastrointestinal infections being among the main illnesses that affect humans.
Among gastric problems, we have GASTRITIS AND GASTRIC ULCERS as the main public health problems. Gastritis and gastric ulcers normally result from inflammation and corrosion of the walls of the stomach (gastric mucosa) and are generally associated (caused) by the bacterium Helicobacter pylor, which, according to the literature, this bacterium settles on these walls (of the stomach) and starts to release urease that ends up altering the normal pH of the stomach (acid), which leads to inflammation and corrosion of the mucous membranes and consequent gastritis or ulcers, respectively.
In addition to bacterial infections, gastritis and gastric ulcers are associated with several factors, with emphasis on prolonged fasting, chemical substances including drugs, alcohol, foods with strong seasonings including chilli, which ends up causing inflammation of the stomach walls and/or corrosion. of the same, resulting in the appearance of wounds and consequent gastritis or ulcers, respectively.
Among patients with gastritis and/or ulcers, one of the dilemmas is associated with the foods to consume in order to minimize the sensation of pain and discomfort.
Are you looking for a long-lasting solution to your missing tooth?
Dental implants are the most common type of method for replacing the missing tooth. Unlike dentures or bridges, implants are surgically placed in the jawbone. In layman’s terms, a dental implant is similar to the natural root of the tooth. It offers a stable foundation for the artificial tooth giving it the look, feel, and function similar to the natural tooth.
Travel Clinic Cardiff: Health Advice for International TravelersNX Healthcare
Travel Clinic Cardiff offers comprehensive travel health services, including vaccinations, travel advice, and preventive care for international travelers. Our expert team ensures you are well-prepared and protected for your journey, providing personalized consultations tailored to your destination. Conveniently located in Cardiff, we help you travel with confidence and peace of mind. Visit us: www.nxhealthcare.co.uk
2. A Cost-Benefit Analysis of Electronic Medical Records/Wang et al
Table 1. Costs of Electronic Medical Record System Used in the Model (Per Provider in 2002 U.S.
Dollars)
Sensitivity Analysis
Base Case (Range) Reference
System costs
Software (annual license) $1600 $ 800–$3200 *
†
Implementation $3400
Support and maintenance $1500 $ 750–$3000 *
Hardware (3 computers ϩ network) $6600 $3300–$9900 *
Induced costs
Temporary productivity loss $11,200 $5500–$16,500 *
* Data from Partners HealthCare System, Boston, Massachusetts.
†
B. Middleton, MD, MPH, MSc, MedicaLogic, written communication, 1998.
system (29) at Partners HealthCare System, an integrated tivity analyses, software costs were varied from 50% to
delivery network formed in 1994 by the Brigham and 200% of the base value.
Women’s Hospital and the Massachusetts General Hos- Implementation costs, estimated at $3400 per provider
pital. in the first year, included workflow process redesign,
We constructed a hypothetical primary care provider training, and historical paper chart abstracting. Ongoing
patient panel using average statistics from our institution. annual maintenance and support costs were estimated to
This panel included 2500 patients, 75% of whom were be $1500 per provider per year and included the costs of
under 65 years of age; 17% of patients under 65 years old additional technical support staff and system/network
belonged to capitated plans. In sensitivity analyses, panel administration.
size was varied from 2000 to 3000 patients, and the pro- Hardware costs were calculated to be $6600 per pro-
portion of patients under the age of 65 years whose cases vider for three desktop computers, a printer, and network
were capitated was varied from 0% to 28.7%. According installation. We assumed that hardware would be re-
to industry estimates, health maintenance organization placed every 3 years.
enrollment was 28.7% of the U.S. population in 2000 Based on our experience, we modeled the induced
(30,31). costs of temporary loss of productivity using a decreasing
Costs stepwise approach, assuming an initial productivity loss
There are two categories of costs associated with elec- of 20% in the first month, 10% in the second month,
tronic medical record implementation: system costs and and 5% in the third month, with a subsequent return
induced costs (Table 1). System costs include the cost of to baseline productivity levels. Using the average annual
the software and hardware, training, implementation, provider revenues for our model patient panel, this
and ongoing maintenance and support. Induced costs are amounted to a revenue loss of $11,200 in the first year.
those involved in the transition from a paper to electronic Benefits
system, such as the temporary decrease in provider pro- Financial benefits included averted costs and increased
ductivity after implementation. revenues. We obtained figures for average annual expen-
The software costs of $1600 per provider per year were ditures for a primary care provider at our institution be-
based on the costs for our electronic medical record sys- fore the implementation of an electronic medical record,
tem at Partners HealthCare on an annual per-provider and applied to this the estimated percentage cost savings
basis (as an “application service provider” model); this after implementation (Table 2). For each item, the esti-
figure includes the costs of the design and development of mated savings was varied across the indicated range of
the system, interfaces to other systems (e.g., registration, values in the sensitivity analysis. Benefits were divided
scheduling, laboratory), periodic upgrades, and costs of into three categories: payer-independent benefits, bene-
user accounts for support staff. Although these software fits under capitated reimbursement, and benefits under
costs were based on our internally developed system, they fee-for-service reimbursement (32– 40).
are consistent with license fees for commercially available Payer-independent benefits, which apply to both capi-
systems, which have been estimated at between $2500 tated and fee-for-service patients, come from reductions
and $3500 per provider for the initial software purchase, in paper chart pulls and transcription. The average cost of
plus annual maintenance and support fees of 12% to 18% a chart pull at our institution is approximately $5, ac-
(K. MacDonald, First Consulting Group, Lexington, counting for the time and cost of medical records person-
Massachusetts, written communication, 1999). In sensi- nel to retrieve and then re-file a paper chart. After con-
398 April 1, 2003 THE AMERICAN JOURNAL OF MEDICINE Volume 114
3. A Cost-Benefit Analysis of Electronic Medical Records/Wang et al
Table 2. Annual Expenditures Per Provider (in 2002 U.S. Dollars) before Electronic Medical Record System Implementation and
Expected Savings after Implementation
Annual Expenditures before
Implementation Expected Savings after Implementation
Base Case Sensitivity Analysis
Amount Reference Estimated Savings (Range) Reference
Payer independent
Chart pulls $5 (per chart) * 600 charts 300–1200 *
Transcription $9600 * 28% 20%–100% *,32
Capitated patients
‡
Adverse drug events $6500 33–36 34% 10%–70%
† ‡
Drug utilization $109,000 15% 5%–25%
†
Laboratory utilization $27,600 8.8% 0–13% 37–39
† ‡
Radiology utilization $59,100 14% 5%–20%
Fee-for-service patients
†
Charge capture $383,100 2% (increase) 1.5%–5% 25,40
† ‡
Billing errors $9700 78% 35%–95%
* Primary data from the Partners HealthCare Electronic Medical Record System, Boston, Massachusetts.
†
From the Department of Finance, Brigham and Women’s Hospital, Partners HealthCare System.
‡
Expert panel consensus.
version to the electronic medical record system, chart izing the encounter form process can improve the cap-
pulls can be reduced by 600 charts (range, 300 to 1200) ture of in-office procedures that were performed but not
per year, based on the experience at one Partners Health- documented. Based on other studies (25,40), we pro-
Care clinic. Transcription costs were reduced by 28% jected a 2% improvement in billing capture (range, 1.5%
from partial elimination of dictation. In the sensitivity to 5%). By using an electronic medical record system that
analysis, we varied the savings from 20% to 100% based either supplies or prompts for certain required fields, bill-
on the experiences from other implementations (32). ing error losses can be reduced. The expert panel esti-
Benefits under capitated reimbursement accrue to the mated that computerizing the encounter form could de-
practice and health care organization primarily from crease these errors by 78% (range, 35% to 95%).
averted costs as a result of decreased utilization. Clinical
decision support alerts and reminders can decrease utili- Statistical Analysis
zation by reducing adverse drug events, offering alterna- We assumed that initial costs would be paid at the begin-
tives to expensive medications, and reducing the use of ning of year 1 and that benefits would accrue at the end of
laboratory and radiology tests (37–39,41– 44). The expert each year (Table 3). We assumed a phased implementa-
panel consensus was that adverse drug events would be tion, in which only basic electronic medical record fea-
reduced by approximately 34% (range, 10% to 70%) as a
tures were available in the first years (e.g., medication-
result of basic medication decision support. We used
related decision support), and more advanced features
standard financial benchmarks (33–35) to assign baseline
were added in subsequent years (e.g., laboratory, radiol-
costs for adverse drug events, which took into account
ogy, and billing benefits). The primary outcome measure
additional outpatient visits, prescriptions, and admis-
was net benefit or cost per primary care provider. A dis-
sions due to adverse drug events (36).
The expert panel estimated that alternative drug sug- count rate of 5% was used in the base case and varied
gestion reminders would save 15% (range, 5% to 25%) of from 0% to 10% in the sensitivity analysis.
total drug costs per year, and this was applied to the base- One-way and two-way sensitivity analyses were per-
line annual drug expenditures for the capitated patients formed using the ranges shown in Tables 1 and 2. Two-
in the panel. We estimated that laboratory charges could way sensitivity analyses were performed using all combi-
be reduced by 8.8% (range, 0 to 13%) using decision sup- nations of the five most important variables identified in
port (37–39). Based on information from other studies, the one-way sensitivity analysis, and with pairwise com-
the expert panel estimated that decision support for radi- binations of one benefit variable with each of the three
ology ordering would achieve average savings of 14% primary cost variables (software, hardware, and support).
(range, 5% to 20%). A five-way sensitivity analysis was performed using the
Benefits under fee-for-service reimbursement in- most and least favorable conditions for the five variables.
cluded increased revenue and reduced losses. Computer- The time horizon was also varied from 2 to 10 years.
April 1, 2003 THE AMERICAN JOURNAL OF MEDICINE Volume 114 399
4. A Cost-Benefit Analysis of Electronic Medical Records/Wang et al
Table 3. 5-Year Return on Investment Per Provider for Electronic Medical Record Implementation
Initial Cost Year 1 Year 2 Year 3 Year 4 Year 5 Total
Costs
Software license (annual) $1600 $1600 $1600 $1600 $1600 $1600
Implementation $3400
Support $1500 $1500 $1500 $1500 $1500 $1500
Hardware (refresh every 3 years) $6600 $6600
Productivity loss $11,200
Annual costs $13,100 $14,300 $3100 $9700 $3100 $3100 $46,400
Present value of annual costs* $13,100 $13,619 $2812 $8379 $2550 $2429 $42,900
Benefits
Chart pull savings $3000 $3000 $3000 $3000 $3000
Transcription savings $2700 $2700 $2700 $2700 $2700
Prevention of adverse drug events $2200 $2200 $2200 $2200
Drug savings $16,400 $16,400 $16,400 $16,400
Laboratory savings $2400 $2400
Radiology savings $8300 $8300
Charge capture improvement $7700 $7700
Billing error decrease $7600 $7600
Annual benefits $5700 $24,300 $24,300 $50,300 $50,300 $154,900
Present value of annual benefits* $5429 $22,041 $20,991 $41,382 $39,411 $129,300
Net benefit (cost) $(13,100) $(8600) $21,200 $14,600 $47,200 $47,200 $108,500
Present value of net benefit (cost)* $(13,100) $(8190) $19,229 $12,612 $38,832 $36,982 $86,400
* Assumes a 5% discount rate.
To account for variations in functionality among dif- expenditures made up the largest proportion of the ben-
ferent systems, we constructed two additional models in efits (33% of the total). Of the remaining categories, al-
which only subsets of the full functionality were included most half of the total savings came from decreased radi-
(Table 4). The “light” electronic medical record system ology utilization (17%), decreased billing errors (15%),
included savings from chart pulls and transcriptions and improvements in charge capture (15%).
only, whereas the “medium” system also included bene-
fits from electronic prescribing (adverse drug event pre- Sensitivity Analyses
The model was most sensitive to variations in the propor-
vention and drug expenditure savings).
tion of patients in capitated health plans; the net benefit
varied from $8400 to $140,100 (Figure). The model was
least sensitive to variations in laboratory savings, in which
RESULTS
the net benefit ranged from $82,500 to $88,300.
In the 5-year cost-benefit model (Table 3), the net benefit In two-way sensitivity analyses, the pair of input vari-
of implementing a full electronic medical record system ables that yielded the least favorable outcome was a low
was $86,400 per provider. Of this amount, savings in drug proportion of capitated patients and a high discount rate;
Table 4. Effect of Electronic Medical Record Feature Set Variations on Net Benefits
Feature Benefit Light EMR Medium EMR Full EMR
Online patient charts Chart pull savings ϩ ϩ ϩ
Transcription savings ϩ ϩ ϩ
Electronic prescribing Adverse drug event prevention ϩ ϩ
Alternative drug suggestions ϩ ϩ
Laboratory order entry Appropriate testing guidance ϩ
Radiology order entry Appropriate testing guidance ϩ
Electronic charge capture Increased billing capture ϩ
Decreased billing errors ϩ
Net benefits (costs): ($18,200) $44,600 $86,400
EMR ϭ Electronic Medical Record.
400 April 1, 2003 THE AMERICAN JOURNAL OF MEDICINE Volume 114
5. A Cost-Benefit Analysis of Electronic Medical Records/Wang et al
Figure. Tornado diagram showing the one-way sensitivity analysis of net 5-year benefits per provider. Each bar depicts the overall
effect on net benefits as that input is varied across the indicated range of values, while other input variables are held constant. The
vertical line indicates the base case.
the net benefit range was as low as $3000 per provider. range of assumptions. The primary areas of benefit are
The pair that had the most favorable outcome was a high from reductions in drug expenditures, improved utiliza-
proportion of capitated patients and greater savings from tion of radiology tests, improvements in charge capture,
drug suggestions; the net benefit was as high as $202,200 and decreased billing errors. Benefits increase as more
per provider. For the two-way sensitivity analyses per- features are used and as the time horizon is lengthened. In
formed with the three primary cost variables, the pair of sensitivity analyses, the net return was positive except
variables that yielded the least favorable outcome was a when the most pessimistic assumptions were used.
low proportion of capitated patients and a high annual Savings to the health care organization are obtained
software license (net cost of $200 per provider), and the under both capitated and fee-for-service reimbursement,
pair with the most favorable outcome was a high propor- but these savings depend on the reimbursement mix: the
tion of capitated patients and a low hardware cost (net greater the proportion of capitated patients, the greater
benefit of $146,200 per provider). the total return. Among fee-for-service patients, a large
In the five-way sensitivity analyses, when the most pes- portion of the savings from improved utilization may ac-
simistic assumptions were made, the model showed a net crue to the payer instead of the provider organization. As
cost of $2300 per provider. When the most optimistic a result, payers may be motivated to offer incentives to
assumptions were used, this analysis yielded a net benefit providers to use an electronic medical record to help con-
of $330,900 per provider. trol costs. In addition, although full capitation appears to
When the time horizon was reduced to 2 years instead be less prevalent now than several years ago, with the
of 5 years, the net cost was $2100 per provider, and when continued rise in health care expenditures, other types of
the time horizon was lengthened to 10 years, the net ben- risk-sharing arrangements are likely to become more
efit was $237,300 per provider. common in the future (45), such as partial capitation, risk
For the “light” electronic medical record, in which the pools, and pharmacy withholds.
system is used only to reduce paper chart pulls and tran- We used conservative estimates of cost savings from an
scription costs, the net cost was $18,200 per provider (Ta- electronic medical record. For example, one clinic was
ble 4). For the “medium” electronic medical record, in able to reduce chart pulls by 60% to 70% and its medical
which benefits from electronic prescribing are added, the records staff by 50%, for an annual savings of about $4000
net benefit was $44,600 per provider. per provider (15). Others have identified even larger sav-
ings from the use of drug suggestions for certain classes of
medications (46). In one outpatient clinic, display of for-
DISCUSSION
mulary information at the time of ordering lowered drug
Our analysis indicates that the net financial return to a costs by up to 26% (M. Overhage, MD, Regenstrief Insti-
health care organization from using an ambulatory elec- tute, Indianapolis, Indiana, written communication,
tronic medical record system is positive across a wide 2001). Savings due to prevention of adverse drug events
April 1, 2003 THE AMERICAN JOURNAL OF MEDICINE Volume 114 401
6. A Cost-Benefit Analysis of Electronic Medical Records/Wang et al
in the model did not include costs of malpractice settle- implementation of an electronic medical record. It may
ments, injury to patients, or decreased quality of life for also be worthwhile to take the societal perspective, which
patients, so the actual savings may be higher. We may would include benefits to payers and patients. For exam-
have also underestimated future cost savings because the ple, despite the trend away from global risk capitation,
model did not account for the annual growth rate of ex- payers are moving toward patient cost-sharing ap-
penditures, which may outpace inflation in some catego- proaches, such as differential co-payments, high deduct-
ries, such as in drug and radiology costs. ible options, and health savings accounts. With these
Other potential areas of savings were not included in types of arrangements, patients may prefer to seek care
the model because adequate data were not available. with providers who use electronic medical records to
These include savings in malpractice premium costs (40), control costs and improve quality of care.
storage and supply costs (47), generic drug substitutions Not all benefits of an electronic medical record are
(48), increased provider productivity (19,23,24), de- measurable in financial terms; other benefits include im-
creased staffing requirements (23,24,49), increased reim- proved quality of care, reduced medical errors, and better
bursement from more accurate evaluation and manage- access to information (2,3,50 –54). A cost-benefit analysis
ment coding, and decreased claims denials from inade- is only one part of a complete analysis of the effects of
quate medical necessity documentation. implementing an electronic medical record system. At
Although we accounted for a temporary (3-month) our institution, the electronic medical record is a key
loss of productivity in our model, some providers may component of a strategic goal of clinical system integra-
have a longer period of reduced productivity. To measure tion to allow providers to move between sites in the net-
this effect, we performed a sensitivity analysis that in- work to deliver seamless care at the most appropriate pri-
cluded a prolonged 10% productivity loss for 12 months mary, secondary, or tertiary care location.
and found that there was still a 5-year net benefit of Based on a combination of savings data from our in-
$57,500 per provider. stitution and projections from other published studies,
This study has several limitations. The cost-benefit we conclude that implementing an ambulatory electronic
model was based on primary data from our institution, medical record system can yield a positive return on in-
estimates from published literature, and expert opinion. vestment to health care organizations. The magnitude
The effectiveness of some of these interventions has been and timing of this financial return varies, but is positive in
demonstrated in the inpatient setting, but outpatient ef- the long run across a wide range of assumptions. Because
fectiveness is less certain. There may be other costs asso- of their quality and cost benefits, electronic medical
ciated with implementation of an electronic medical records should be used in primary care, and incentives to
record. For example, system integration costs may be accelerate their adoption should be considered at the na-
greater at other institutions, depending on the number tional level.
and complexity of system interfaces that are required.
However, the majority of benefits in this model can be
obtained even with a minimal number of interfaces (i.e., ACKNOWLEDGMENT
registration, scheduling, and transcription). In addition, We would like to thank Marc Overhage, MD, Homer Chin,
there may be other unforeseen expenses associated with MD, Barry Blumenfeld, MD, and Tejal Gandhi, MD, who
clinic workflow process redesign, reassignment of clinic joined three of the coinvestigators to serve on our expert panel.
staff, or productivity loss during unscheduled computer
system or network outages.
In most cases, clinical decision support features will REFERENCES
decrease utilization by suggesting more appropriate test-
1. Elson RB, Connelly DP. Computerized patient records in primary
ing. This leads to cost savings among capitated patients, care. Their role in mediating guideline-driven physician behavior
but it could also have an adverse effect on revenues from change. Arch Fam Med. 1995;4:698 –705.
fee-for-service patients that may offset billing improve- 2. Shea S, DuMouchel W, Bahamonde L. A meta-analysis of 16 ran-
ments. The overall net effect would depend on the mix of domized controlled trials to evaluate computer-based clinical re-
capitated versus fee-for-service patients. minder systems for preventive care in the ambulatory setting. J Am
Med Inform Assoc. 1996;3:399 –409.
Our cost-benefit model was geared toward primary 3. Hunt DL, Haynes RB, Hanna SE, et al. Effects of computer-based
care providers. Diagnostic test utilization may be higher clinical decision support systems on physician performance and
for specialists, so there may be more opportunities for patient outcomes: a systematic review. JAMA. 1998;280:1339 –
cost-saving interventions. On the other hand, specialists 1346.
may be less likely to comply with computer reminders 4. Bates DW. Medication errors. How common are they and what can
be done to prevent them? Drug Saf. 1996;15:303–310.
recommending alternative medications or tests. 5. Institute of Medicine Committee on Quality of Health Care in
This study was framed from the perspective of the America. Crossing the Quality Chasm: A New Health System for the
health care organization to aid in making decisions about 21st Century. Washington, DC: National Academy Press; 2001.
402 April 1, 2003 THE AMERICAN JOURNAL OF MEDICINE Volume 114
7. A Cost-Benefit Analysis of Electronic Medical Records/Wang et al
6. The Leapfrog Group for Patient Safety; Rewarding Higher Stan- 31. United States Census 2000. Ranking Tables for States. Population in
dards. Available at: http://www.leapfroggroup.org. Accessed April 2000 and Population Change from 1990 to 2000 (PHC-T-2). Wash-
23, 2002. ington, DC: U.S. Census Bureau; 2000.
7. Heard S, Grivel T, Schloeffel P, Doust J. The benefits and difficulties 32. Ambulatory EMR. Establishing a business case. Available at: http://
of introducing a national approach to electronic health records www.medicalogic.com/emr/business_benefits/index.html. Accessed
in Australia. Available at: http://www.health.gov.au/healthonline/ April 20, 2002.
ehr_apxb.pdf. Accessed May 6, 2002. 33. Medicare RBRVS Physician Fee Schedule 2002. Washington, DC:
8. Jones N. Telematics systems in primary care. Available at: http:// Centers for Medicare and Medicaid Services; 2001.
www.eihms.surrey.ac.uk/abbott/IT-EDUCTRA/html/p415_h.htm. 34. Prescription Drug Expenditures in 2001. Washington, DC: National
Accessed April 23, 2002. Institute for Health Care Management; 2002.
9. The Royal College of General Practitioners—information sheet, 35. Jha AK, Kuperman GJ, Rittenberg E, et al. Identifying hospital ad-
general practice computerisation. Available at: http://www.rcgp. missions due to adverse drug events using a computer-based mon-
org.uk/rcgp/information/publications/information/rcf0007/Rcf0007. itor. Pharmacoepidemiol Drug Saf. 2001;10:113–119.
36. Honigman B, Lee J, Rothschild J, et al. Using computerized data to
asp. Accessed April 23, 2002.
identify adverse drug events in outpatients. J Am Med Inform Assoc.
10. Taking the Pulse: Physicians and the Internet. New York: Deloitte &
2001;8:254 –266.
Touche; 2000.
37. Tierney WM, Miller ME, McDonald CJ. The effect on test ordering
11. Hammond KW, Prather RJ, Date VV, et al. A provider-interactive
of informing physicians of the charges for outpatient diagnostic
medical record system can favorably influence costs and quality of
tests. N Engl J Med. 1990;322:1499 –1504.
medical care. Comput Biol Med. 1990;20:267–279.
38. Tierney WM, McDonald CJ, Martin DK, et al. Computerized dis-
12. Rawitz JG, Cowan WY, Paige BM. Justifying costs of computer play of past test results. Effect on outpatient testing. Ann Intern
software purchases. Healthc Financ Manage. 1990;44:46 –51. Med. 1987;107:569 –574.
13. Davis MW. Reaping the benefits of electronic medical record sys- 39. Tierney WM, McDonald CJ, Hui SL, et al. Computer predictions of
tems. Healthc Financ Manage. 1993;47:60 –62. abnormal test results. Effects on outpatient testing. JAMA. 1988;
14. Renner K. Electronic medical records in the outpatient setting (part 259:1194 –1198.
1). Med Group Manage J. 1996;43:52–5754,56. 40. Renner K. Cost-justifying electronic medical records. Healthc Fi-
15. Renner K. Electronic medical records in the outpatient setting (part nanc Manage. 1996;50:63–64.
2). Med Group Manage J. 1996;43:60 –65. 41. Bates DW, Boyle DL, Rittenberg E, et al. What proportion of com-
16. Pliskin N, Glezerman M, Modai I, et al. Spreadsheet evaluation of mon diagnostic tests appear redundant? Am J Med. 1998;104:361–
computerized medical records: the impact on quality, time, and 368.
money. J Med Syst. 1996;20:85–100. 42. Bates DW, Kuperman GJ, Rittenberg E, et al. A randomized trial of
17. Khoury A. Finding value in EMRs (electronic medical records). a computer-based intervention to reduce utilization of redundant
Health Manag Technol. 1997;18:34 –36. laboratory tests. Am J Med. 1999;106:144 –150.
18. Mohr DN. Benefits of an electronic clinical information system. 43. Harpole LH, Khorasani R, Fiskio J, et al. Automated evidence-based
Healthc Inf Manage. 1997;11:49 –57. critiquing of orders for abdominal radiographs: impact on utiliza-
19. Bingham A. Computerized patient records benefit physician of- tion and appropriateness. J Am Med Inform Assoc. 1997;4:511–521.
fices. Healthc Financ Manage. 1997;51:68 –70. 44. Rothschild JM, Khorasani R, Bates DW. Guidelines and decision
20. Nelson R. Computerized patient records improve practice effi- support help improve image utilization. Diagn Imaging. 2000;22:
ciency and patient care. Healthc Financ Manage. 1998;52:86 –88. 95–9799,101.
21. Sandrick K. Calculating ROI for CPRs. Health Manag Technol. 45. Reinhardt UE. Reforming American healthcare: an interim report.
1998;19:16 –20. J Rheumatol. 1999;26(suppl):6 –10.
22. Didear K, Kalata M. CPR success stories. J AHIMA. 1998;69:54 –57. 46. Jones DL, Kroenke K, Landry FJ, et al. Cost savings using a stepped-
23. Zdon L, Middleton B. Ambulatory electronic medical records im- care prescribing protocol for nonsteroidal anti-inflammatory
plementation cost benefit: An enterprise case study. Healthc Inform drugs. JAMA. 1996;275:926 –930.
Manage and Syst Soc. 1999;4:97–117. 47. Evans JC, Hayashi AM. Implementing on-line medical records. Doc
Manage. 1994;Sep/Oct:12–17.
24. Ury A. The business case for an electronic medical record system.
48. The quantum experience: a case study from Ernst & Young.
Group Pract J. 2000;49:1–6.
Available at: http://www.allscripts.com/ahcs/news_2.asp?Sϭ
25. Pifer EA, Smith S, Keever GW. EMR to the rescue. An ambulatory
2000&IDϭ2. Accessed April 10, 2002.
care pilot project shows that data sharing equals cost shaving.
49. Kian LA, Stewart MW, Bagby C, et al. Justifying the cost of a com-
Healthc Inform. 2001;18:111–114.
puter-based patient record. Healthc Financ Manage. 1995;49:58 –
26. Mildon J, Cohen T. Drivers in the electronic medical records mar-
60.
ket. Health Manag Technol. 2001;22:14 –1618. 50. Essex D. Skip the song & dance. Healthc Inform. 1999;16:49 –52
27. Gross domestic product deflator inflation calculator. Available at: 54,56.
http://www.jsc.nasa.gov/bu2/inflateGDP.html. Accessed October 51. Balas EA, Weingarten S, Garb CT, et al. Improving preventive care
5, 2002. by prompting physicians. Arch Intern Med. 2000;160:301–308.
28. Snyder-Halpern R, Thompson CB, Schaffer J. Comparison of 52. Bates DW, Teich JM, Lee J, et al. The impact of computerized phy-
mailed vs. Internet applications of the Delphi technique in clinical sician order entry on medication error prevention. J Am Med In-
informatics research. Proc AMIA Symp. 2000:809 –813. form Assoc. 1999;6:313–321.
29. Spurr CD, Wang SJ, Kuperman GJ, et al. Confirming and delivering 53. McDonald CJ, Hui SL, Smith DM, et al. Reminders to physicians
the benefits of an ambulatory electronic medical record for an from an introspective computer medical record. A two-year ran-
integrated delivery system. TEPR 2001 Conference Proceedings. domized trial. Ann Intern Med. 1984;100:130 –138.
Newton, Massachusetts: Medical Records Institute; 2001. 54. McDonald CJ, Overhage JM, Tierney WM, et al. The Regenstrief
30. The Competitive Edge: HMO Industry Report 10.2. St. Paul, MN: Medical Record System: a quarter century experience. Int J Med Inf.
Interstudy Publications; 2000. 1999;54:225–253.
April 1, 2003 THE AMERICAN JOURNAL OF MEDICINE Volume 114 403