Insights gained as a result of this study include an understanding of the discrepancies between what the healthcare system expects of patients and their actual behavior when it comes to the creation of a care plan and the ways in which they take care of their health. Further research should examine the ability of various factors to enhance patient engagement. For example, it may be useful to focus on ways to improve the clinical summary to enhance engagement with the care plan and meet standards for a health literate document. Recommendations for the improvement of the clinical summary are provided. Finally, this study explored potential reasons for the infrequent use of online health information by older adults including the trusting relationship they enjoyed with their cardiologist.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
Professional nurse involvement in shaping the electronic health record continues to be minimal in spite of the presence of shared governance models. The redundancies and nurse dissatisfaction with the electronic health record requires a new approach. The advancement of a shared governance model to a professional governance model has resulted in an increase in professional role involvement in four areas:
1. Accountability.
2. Professional obligation.
3. Collateral relationships.
4. Decision-making.
Increased professional nurse involvement results in, nurses more actively engaged in problem solving to improve nurse satisfaction with the electronic health record. Evidence reflects a positive impact on nurse satisfaction when a professional shared governance structure is in place and guides the professional practice of nurses specific to autonomy and accountability. Additionally, evidence also revealed that nurses have a desire to be included in the quality of design, implementation and sustainability of electronic documentation.