Faculty Bibliography

Background/UNASSIGNED:In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report. Methods/UNASSIGNED:A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy. Results/UNASSIGNED:Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%. Conclusions/UNASSIGNED:A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.

Preventing adverse events among chronically ill older adults living in the community is a national health priority. The purpose of this study was to generate distinct risk profiles and compare these profiles in time to: hospitalization, emergency department (ED) visit or death in 371 community-dwelling older adults enrolled in a Medicare demonstration project. Guided by the Behavioral Model of Health Service Use, a secondary analysis was conducted using Latent Class Analysis to generate the risk profiles with Kaplan Meier methodology and log rank statistics to compare risk profiles. The Vuong-Lo-Mendell-Rubin Likelihood Ratio Test demonstrated optimal fit for three risk profiles (High, Medium, and Low Risk). The High Risk profile had significantly shorter time to hospitalization, ED visit, and death (p < 0.001 for each). These findings provide a road map for generating risk profiles that could enable more effective targeting of interventions and be instrumental in reducing health care costs for subgroups of chronically ill community-dwelling older adults.

Providing affordable, high-quality care for the 10 million persons who are dual-eligible beneficiaries of Medicare and Medicaid is an ongoing health-care policy challenge in the United States. However, the workforce and the care provided to dual-eligible beneficiaries are understudied. The purpose of this article is to provide a narrative of the challenges and lessons learned from an exploratory study in the use of clinical and administrative data to compare the workforce of two care models that deliver home- and community-based services to dual-eligible beneficiaries. The research challenges that the study team encountered were as follows: (a) comparing different care models, (b) standardizing data across care models, and (c) comparing patterns of health-care utilization. The methods used to meet these challenges included expert opinion to classify data and summative content analysis to compare and count data. Using descriptive statistics, a summary comparison of the two care models suggested that the coordinated care model workforce provided significantly greater hours of care per recipient than the integrated care model workforce. This likely represented the coordinated care model's focus on providing in-home services for one recipient, whereas the integrated care model focused on providing services in a day center with group activities. The lesson learned from this exploratory study is the need for standardized quality measures across home- and community-based services agencies to determine the workforce that best meets the needs of dual-eligible beneficiaries.

HOW TO OBTAIN CONTACT HOURS BY READING THIS ARTICLE INSTRUCTIONS 1.4 contact hours will be awarded by Villanova University College of Nursing upon successful completion of this activity. A contact hour is a unit of measurement that denotes 60 minutes of an organized learning activity. This is a learner-based activity. Villanova University College of Nursing does not require submission of your answers to the quiz. A contact hour certificate will be awarded once you register, pay the registration fee, and complete the evaluation form online at http://goo.gl/gMfXaf. To obtain contact hours you must: 1. Read the article, "Pharmacological Approaches for the Management of Persistent Pain in Older Adults: What Nurses Need to Know" found on pages 49-57, carefully noting any tables and other illustrative materials that are included to enhance your knowledge and understanding of the content. Be sure to keep track of the amount of time (number of minutes) you spend reading the article and completing the quiz. 2. Read and answer each question on the quiz. After completing all of the questions, compare your answers to those provided within this issue. If you have incorrect answers, return to the article for further study. 3. Go to the Villanova website listed above to register for contact hour credit. You will be asked to provide your name; contact information; and a VISA, MasterCard, or Discover card number for payment of the $20.00 fee. Once you complete the online evaluation, a certificate will be automatically generated. This activity is valid for continuing education credit until November 30, 2019. CONTACT HOURS This activity is co-provided by Villanova University College of Nursing and SLACK Incorporated. Villanova University College of Nursing is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center's Commission on Accreditation. ACTIVITY OBJECTIVES 1. Describe age-related barriers to pain assessment and key aspects of the assessment process. 2. Identify benefits and risks associated with commonly prescribed analgesic medications for the treatment of later life pain. DISCLOSURE STATEMENT Neither the planners nor the authors have any conflicts of interest to disclose. The current article addresses pharmacological treatment issues regarding the management of persistent pain in later life, which is a worldwide problem associated with substantial disability. Recommendations from guidelines were reviewed and data are presented regarding the benefits and risks of commonly prescribed analgesic medications. The evidence base supports a stepwise approach with acetaminophen as first-line therapy for mild-to-moderate pain. Oral nonsteroidal anti-inflammatory drugs are not recommended for long-term use. In properly selected older patients, opioid drugs should be considered if pain is not adequately controlled. Careful surveillance to monitor for benefits and harms of therapy is critical, given that advancing age increases risk for adverse effects. Key aspects of the pain care process that nurses routinely engage in are covered, including conducting pain assessments prior to initiating therapy, addressing barriers to effective pain care, educating patients and family members about the importance of reducing pain, discussing treatment-related risks and benefits, and formulating strategies to monitor for treatment outcomes. Finally, a case is presented to illustrate issues that arise in the care of affected patients. [Journal of Gerontological Nursing, 42(12), 49-57.].

BACKGROUND: Among older adults receiving long-term services and supports (LTSS), debilitating hospitalizations is a pervasive clinical and research problem. Multiple chronic conditions (MCCs) are prevalent in LTSS recipients. However, the combination of MCCs and diseases associated with hospitalizations of LTSS recipients is unclear. OBJECTIVE: The purpose of this analysis was to determine the association between classes of MCCs in newly enrolled LTSS recipients and the number of hospitalizations over a 1-year period following enrollment. METHODS: This report is based on secondary analysis of extant data from a longitudinal cohort study of 470 new recipients of LTSS, 60 years and older, receiving services in assisted living facilities, nursing homes, or through home- and community-based services. Using baseline chronic conditions reported in medical records, latent class analysis was used to identify classes of MCCs and posterior probabilities of membership in each class. Poisson regressions were used to estimate the relative ratio between posterior probabilities of class membership and number of hospitalizations during the 3-month period prior to the start of LTSS (baseline) and then every 3 months forward through 12 months. RESULTS: Three latent MCC-based classes named Cardiopulmonary, Cerebrovascular/Paralysis, and All Other Conditions were identified. The Cardiopulmonary class was associated with elevated numbers of hospitalizations compared to the All Other Conditions class (relative ratio [RR] = 1.88, 95% CI [1.33, 2.65], p < .001). CONCLUSION: Older LTSS recipients with a combination of MCCs that includes cardiopulmonary conditions have increased risk for hospitalization.

OBJECTIVES: To examine innovative models and other research-based interventions that hold potential to assure high-quality care for the growing older adult population living with cancer as one of multiple chronic conditions. Evidence from these care delivery approaches provides a roadmap for the development of future care models. DATA SOURCES: Published peer-reviewed literature, policy analyses, and web-based resources. CONCLUSION: Available evidence suggests the need for models that engage patients and their family caregivers, focus on patient's functional capacities, emphasize palliative care, and maximize the contributions of all team members. IMPLICATIONS FOR NURSING PRACTICE: Nurses are uniquely positioned to lead or play a major role in the evolution and implementation of care models targeting older adults with cancer, but must increase their knowledge and skills related to both oncology and geriatrics to maximize their contributions.

OBJECTIVES: The global challenge of accessible, affordable cancer care for all older adults requires a new research agenda for gero-oncology nursing to guide investigator skill development, identify priority areas for investigation, and direct resource distribution. DATA SOURCES: Published peer-reviewed literature and web-based resources. CONCLUSION: A cross-cutting theme of the research agenda is the need to determine the gero-oncology nursing care that will preserve economic resources, promote function, provide symptom management, and incorporate patient preferences. IMPLICATIONS FOR NURSING PRACTICE: In partnership with interprofessional colleagues, gero-oncology nurse scientists are poised to conduct global research that improves access to quality cancer care.