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Prognostic COPD healthcare management system
Title
Prognostic COPD healthcare management system.
Creator
Jain, Piyush, Agarwal, Ankur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts....
Show moreHospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this project, we will focus on COPD (Chronic Obstructive Pulmonary Disease) which is one of the leading causes of disability and mortality worldwide. This project will design and develop a prognostic COPD healthcare management system which is a sustainable clinical decision-support system to reduce the number of readmissions by identifying those patients who need preventive interventions to reduce the probability of being readmitted. Based on patient’s clinical records and discharge summary, our system would be able to determine the readmission risk profile of patients treated for COPD. Suitable interventions could then be initiated with the objective of providing quality and timely care that helps prevent avoidable readmission.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004125, http://purl.flvc.org/fau/fd/FA00004125
Subject Headings
Integrated delivery of health care, Lungs -- Diseases, Obstructive -- Treatment, Medical care -- Quality control
Format
Document (PDF)
A Clinical Decision Support System for the Identification of Potential Hospital Readmission Patients
Title
A Clinical Decision Support System for the Identification of Potential Hospital Readmission Patients.
Creator
Baechle, Christopher, Agarwal, Ankur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Recent federal legislation has incentivized hospitals to focus on quality of patient care. A primary metric of care quality is patient readmissions. Many methods exist to statistically identify patients most likely to require hospital readmission. Correct identification of high-risk patients allows hospitals to intelligently utilize limited resources in mitigating hospital readmissions. However, these methods have seen little practical adoption in the clinical setting. This research attempts...
Show moreRecent federal legislation has incentivized hospitals to focus on quality of patient care. A primary metric of care quality is patient readmissions. Many methods exist to statistically identify patients most likely to require hospital readmission. Correct identification of high-risk patients allows hospitals to intelligently utilize limited resources in mitigating hospital readmissions. However, these methods have seen little practical adoption in the clinical setting. This research attempts to identify the many open research questions that have impeded widespread adoption of predictive hospital readmission systems. Current systems often rely on structured data extracted from health records systems. This data can be expensive and time consuming to extract. Unstructured clinical notes are agnostic to the underlying records system and would decouple the predictive analytics system from the underlying records system. However, additional concerns in clinical natural language processing must be addressed before such a system can be implemented. Current systems often perform poorly using standard statistical measures. Misclassification cost of patient readmissions has yet to be addressed and there currently exists a gap between current readmission system evaluation metrics and those most appropriate in the clinical setting. Additionally, data availability for localized model creation has yet to be addressed by the research community. Large research hospitals may have sufficient data to build models, but many others do not. Simply combining data from many hospitals often results in a model which performs worse than using data from a single hospital. Current systems often produce a binary readmission classification. However, patients are often readmitted for differing reasons than index admission. There exists little research into predicting primary cause of readmission. Furthermore, co-occurring evidence discovery of clinical terms with primary diagnosis has seen only simplistic methods applied. This research addresses these concerns to increase adoption of predictive hospital readmission systems.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00004880, http://purl.flvc.org/fau/fd/FA00004880
Subject Headings
Health services administration--Management., Medical care--Quality control--Statistical methods., Medical care--Quality control--Data processing., Medical care--Decision making., Evidence-based medicine., Outcome assessment (Medical care)
Format
Document (PDF)
A Systematic Review and Quantitative Meta-Analysis of the Accuracy of Visual Inspection for Cervical Cancer Screening: Does Provider Type or Training Matter?
Title
A Systematic Review and Quantitative Meta-Analysis of the Accuracy of Visual Inspection for Cervical Cancer Screening: Does Provider Type or Training Matter?.
Creator
Driscoll, Susan D., Tappen, Ruth M., Florida Atlantic University, Christine E. Lynn College of Nursing
Abstract/Description
Background: A global cervical cancer health disparity persists despite the demonstrated success of primary and secondary preventive strategies, such as cervical visual inspection (VI). Cervical cancer is the leading cause of cancer incidence and death for women in many low resource areas. The greatest risk is for those who are unable or unwilling to access screening. Barriers include healthcare personnel shortages, cost, transportation, and mistrust of healthcare providers and systems. Using...
Show moreBackground: A global cervical cancer health disparity persists despite the demonstrated success of primary and secondary preventive strategies, such as cervical visual inspection (VI). Cervical cancer is the leading cause of cancer incidence and death for women in many low resource areas. The greatest risk is for those who are unable or unwilling to access screening. Barriers include healthcare personnel shortages, cost, transportation, and mistrust of healthcare providers and systems. Using community health workers (CHWs) may overcome these barriers, increase facilitators, and improve participation in screening for women in remote areas with limited access to clinical resources. Aim: To determine whether the accuracy of VI performed by CHWs was comparable to VI by physicians or nurses and to consider the affect components of provider training had on VI accuracy. Methods: A systematic review and quantitative meta-analysis of published literature reporting on VI accuracy, provider type, and training was conducted. Strict inclusion/exclusion criteria, study quality, and publication bias assessments improved rigor and bivariate linear mixed modeling (BLMM) was used to determine the affect of predictors on accuracy. Unconditional and conditional BLMMs, controlling for VI technique, provider type, community, clinical setting, HIV status, and gynecological symptoms were considered. Results: Provider type was a significant predictor of sensitivity (p=.048) in the unconditional VI model. VI performed by CHWs was 15% more sensitive than physicians (p=.014). Provider type was not a significant predictor of accuracy in any other models. Didactic and mentored hours predicted sensitivity in both BLMMs. Quality assurance and use of a training manual predicted specificity in unconditional BLMMs, but was not significant in conditional models. Number of training days, with ≤5 being optimal, predicted sensitivity in both BLMMs and specificity in the unconditional model. Conclusion: Study results suggest that community based cervical cancer screening with VI conducted by CHWs can be as, if not more, accurate than VI performed by licensed providers. Locally based screening programs could increase access to screening for women in remote areas. Collaborative partnerships in “pragmatic solidarity” between healthcare systems, CHWs, and the community could promote participation in screening resulting in decreased cervical cancer incidence and mortality.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004755
Subject Headings
Women--Health and hygiene., Cervix uteri--Cancer--Diagnosis., Cervix uteri--Cancer--Prevention., Medical screening., Medical care--Quality control., Community health services.
Format
Document (PDF)
bureaucratic system: A positive or negative effect on nursing home quality of care?
Title
The bureaucratic system: A positive or negative effect on nursing home quality of care?.
Creator
Lipsman, Lisa A., Florida Atlantic University, Evans, Arthur S.
Abstract/Description
Over the last fifty years quality of care has been a consistent problem in nursing home facilities. The federal government implemented a bureaucratic system as an attempt to improve this standard. This thesis traces the emergence of this system in nursing homes and illustrates its failure to solve the problem. George Ritzer's four-point McDonaldization model of bureaucracy is applied to argue that the bureaucratic system for governing nursing homes has a negative effect on the quality of care...
Show moreOver the last fifty years quality of care has been a consistent problem in nursing home facilities. The federal government implemented a bureaucratic system as an attempt to improve this standard. This thesis traces the emergence of this system in nursing homes and illustrates its failure to solve the problem. George Ritzer's four-point McDonaldization model of bureaucracy is applied to argue that the bureaucratic system for governing nursing homes has a negative effect on the quality of care. Although this hypothesis has proven to be accurate, additional factors were consistently cited as having detrimental effects on resident care. These include issues such as insufficient pay and lack of training/education for CNAs. Moreover, human greed and societal views of the elderly appear to be the true root of the problem.
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Date Issued
2004
PURL
http://purl.flvc.org/fcla/dt/13148
Subject Headings
Long-term care of the sick--Quality control, Nursing home care--Quality control, Outcome assessment (Medical care), Long-term care facilities--Standards
Format
Document (PDF)
Text Mining and Topic Modeling for Social and Medical Decision Support
Title
Text Mining and Topic Modeling for Social and Medical Decision Support.
Creator
Hurtado, Jose Luis, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Effective decision support plays vital roles in people's daily life, as well as for professional practitioners such as health care providers. Without correct information and timely derived knowledge, a decision is often suboptimal and may result in signi cant nancial loss or compromises of the performance. In this dissertation, we study text mining and topic modeling and propose to use text mining methods, in combination with topic models, to discover knowledge from texts popularly available...
Show moreEffective decision support plays vital roles in people's daily life, as well as for professional practitioners such as health care providers. Without correct information and timely derived knowledge, a decision is often suboptimal and may result in signi cant nancial loss or compromises of the performance. In this dissertation, we study text mining and topic modeling and propose to use text mining methods, in combination with topic models, to discover knowledge from texts popularly available from a wide variety of sources, such as research publications, news, medical diagnose notes, and further employ discovered knowledge to assist social and medical decision support. Examples of such decisions include hospital patient readmission prediction, which is a national initiative for health care cost reduction, academic research topics discovery and trend modeling, and social preference modeling for friend recommendation in social networks etc. To carry out text mining, our research, in Chapter 3, first emphasizes on single document analyzing to investigate textual stylometric features for user pro ling and recognition. Our research confirms that by using properly designed features, it is possible to identify the authors who wrote the article, using a number of sample articles written by the author as the training data. This study serves as the base to assert that text mining is a powerful tool for capturing knowledge in texts for better decision making. In the Chapter 4, we advance our research from single documents to documents with interdependency relationships, and propose to model and predict citation relationship between documents. Given a collection of documents with known linkage relationships, our research will discover e ective features to train prediction models, and predict the likelihood of two documents involving a citation relationships. This study will help accurately model social network linkage relationships, and can be used to assist e ective decision making for friend recommendation in social networking, and reference recommendation in scienti c writing etc. In the Chapter 5, we advance a topic discovery and trend prediction principle to discover meaningful topics from a set of data collection, and further model the evolution trend of the topic. By proposing techniques to discover topics from text, and using temporal correlation between trend for prediction, our techniques can be used to summarize a large collection of documents as meaningful topics, and further forecast the popularity of the topic in a near future. This study can help design systems to discover popular topics in social media, and further assist resource planning and scheduling based on the discovered topics and the their evolution trend. In the Chapter 6, we employ both text mining and topic modeling to the medical domain for effective decision making. The goal is to discover knowledge from medical notes to predict the risk of a patient being re-admitted in a near future. Our research emphasizes on the challenge that re-admitted patients are only a small portion of the patient population, although they bring signficant financial loss. As a result, the datasets are highly imbalanced which often result in poor accuracy for decision making. Our research will propose to use latent topic modeling to carryout localized sampling, and combine models trained from multiple copies of sampled data for accurate prediction. This study can be directly used to assist hospital re-admission assessment for early warning and decision support. The text mining and topic modeling techniques investigated in the dissertation can be applied to many other domains, involving texts and social relationships, towards pattern and knowledge based e ective decision making.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004782, http://purl.flvc.org/fau/fd/FA00004782
Subject Headings
Social sciences--Research--Methodology., Data mining., Machine learning., Database searching., Discourse analysis--Data processing., Communication--Network analysis., Medical care--Quality control.
Format
Document (PDF)
Identifying descriptions of quality nursing care shared by nurse and patient in the acute care hospital environment
Title
Identifying descriptions of quality nursing care shared by nurse and patient in the acute care hospital environment.
Creator
Grimley, Karen A., Tappen, Ruth M., Florida Atlantic University, Christine E. Lynn College of Nursing
Abstract/Description
Nursing care is considered a primary predictor of patient assessment of the overall hospital experience. Yet, quality nursing care remains difficult to define. Limited research about nurse or patient perspectives on what constitutes quality nursing care in hospital settings prevents the identification of a shared description or insight into their possible interrelationship. Research about nurse and patient descriptions is needed to establish behaviors, attributes, and activities associated...
Show moreNursing care is considered a primary predictor of patient assessment of the overall hospital experience. Yet, quality nursing care remains difficult to define. Limited research about nurse or patient perspectives on what constitutes quality nursing care in hospital settings prevents the identification of a shared description or insight into their possible interrelationship. Research about nurse and patient descriptions is needed to establish behaviors, attributes, and activities associated with quality nursing care to improve the health and well-being of hospitalized patients.
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Date Issued
2015
PURL
http://purl.flvc.org/fau/fd/FA00004375
Subject Headings
Nursing--Philosophy, Nurse and patient, Medical care--Quality control, Intensive care nursing--Quality control, Outcome assessment--Medical care, Total quality management, Evidence-based nursing.
Format
Document (PDF)