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Health Information Management

Management of health data and information systems, including medical coding, health records administration, data privacy, healthcare analytics, and clinical documentation

5 papers in this specialization

Paperzilla Papers in Health Information Management

Using patient-reported measures to drive change in healthcare: the experience of the digital, continuous and systematic PREMs observatory in Italy

A digital, continuous, and systematic PREMs observatory implemented in Italian hospitals found increased participation and response rates over time. The real-time reporting of both quantitative and qualitative data, including positive patient narratives, is viewed as a valuable tool for service improvement and staff recognition at all hospital levels, including highlighting the contributions of often-overlooked care assistants.

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s12913-020-05099-4.pdf

Jul 14, 11:19 AM

Characteristics of successful changes in health care organizations: an interview study with physicians, registered nurses and assistant nurses

Healthcare professionals consider organizational changes successful when they have the opportunity to influence the change, are prepared for it, and perceive its value, especially if it benefits patients. These three characteristics appear to be interdependent, suggesting that successful change is more likely when multiple factors are addressed.

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s12913-020-4999-8.pdf

Jul 14, 11:19 AM

Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery

GPT-3 holds potential for various healthcare applications, such as automating routine tasks and improving patient experiences. However, it's not a replacement for human interaction in critical clinical settings due to limitations like semantic repetition, coherence issues, potential biases, and lack of dynamic adaptation to conversation tone or body language.

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s41746-021-00464-x.pdf

Jul 14, 11:19 AM

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

This study found that machine learning models, trained on only serum creatinine and ejection fraction, can accurately predict the survival of heart failure patients. These two factors alone outperformed models using all available clinical features, highlighting their potential importance in clinical practice.

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s12911-020-1023-5.pdf

Jul 14, 11:19 AM

Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

This paper proposes a new heart disease identification system using a fast conditional mutual information (FCMIM) feature selection algorithm and a support vector machine (SVM) classifier. The FCMIM-SVM system achieved an accuracy of 92.37% on the Cleveland Heart Disease dataset, outperforming several existing methods. However, further validation and addressing limitations related to the small dataset and evaluation metrics are needed.