Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models.

Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four online health information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and Reddit.com. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time, the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.

Integrating Family Caregiver Support Into a Gynecologic Oncology Practice: An ASCO Quality Training Program Project.

PURPOSE: A needs assessment of family caregivers (CGs) in our gynecologic oncology clinic found that 50% of CGs report nine or more distressing unmet needs, but only 19% of patients had a documented CG. We conducted an ASCO Quality Training Program project with the following aims: (1) to identify and document primary CGs for 85% of patients within two clinic visits of a gynecologic cancer diagnosis, and (2) assess the needs of and provide interventions to 75% of identified family CGs. METHODS: Plan-Do-Study-Act (PDSA) methodology and tools endorsed by the ASCO Quality Training Program were used. An interprofessional team reviewed baseline data (ie, any mention of a family CG in the electronic health record visit note; CG distress survey), defined the problem and project aims, created process maps, and identified root causes of poor CG identification and documentation. Eight successive PDSA cycles were implemented between October 2018 and March 2019 to address identified root causes. RESULTS: For aim 1, CG identification increased from 19% at baseline to 57% postimplementation, whereas for aim 2, assessment improved from 28% at baseline to 60% postimplementation. Results fell somewhat short of initial goals, but they represent an important initial improvement in care. The core team has begun additional PDSA cycles to improve CG identification rates and extend the momentum of the project. CONCLUSION: This project demonstrated that a CG assessment protocol can be implemented in a large, academic, gynecologic oncology clinic. Additional efforts to integrate CG identification, assessment, and intervention more fully within the clinic and electronic health record are under way.

Caring for Survivors of Gynecologic Cancer: Assessment and Management of Long-term and Late Effects.

OBJECTIVE: To define important aspects of survivorship care for the more than 1.2million survivors of gynecologic cancer currently living in the US. DATA SOURCES: Research articles, reviews, position statements and white papers, and evidence-based guidelines. CONCLUSION: Survivorship care includes a coordinated plan of care, ongoing surveillance, health promotion support, and management of long-term and late effects of treatment. IMPLICATIONS FOR NURSING PRACTICE: Nurses need to be aware of the current guidelines for post-treatment surveillance and health promotion recommendations for survivors of gynecologic cancers. Early identification of long-term and late effects of treatment followed by coordinated medical intervention and self-management education are essential to improve quality of life.

Defining Essential Elements of Caregiver Support in Gynecologic Cancers Using the Modified Delphi Method.

PURPOSE: Family caregivers are instrumental to patients with gynecologic cancer and can be deeply affected by the demands of caregiving. Our aims were as follows: (1) increase awareness of unmet needs of caregivers and (2) identify and prioritize the unmet needs of caregivers and essential support services to be provided in gynecologic cancer centers. METHODS: From July 2017 to June 2018, a 16-person steering committee and 40 stakeholders, including caregivers, patients, and clinicians, participated in a modified Delphi approach to generate, review, and prioritize a set of essential elements for caregiver support. Stakeholders prioritized caregiver needs and brainstormed, discussed, and prioritized essential support services to meet those needs, using three rounds of a consensus-building protocol. Basic descriptive statistics were performed to feed means and rankings back to stakeholders before each round. RESULTS: The top three caregiver needs were as follows: (1) to obtain information about their loved one's cancer, (2) how to provide support and comfort to their loved one, and (3) how to maintain their own emotional health and well-being. Fifteen essential elements of support for caregivers of patients with gynecologic cancer were identified and ranged from elements currently attainable (eg, information on managing symptoms, skilled-care training, a point person to help navigate the system) to more aspirational elements (eg, integrative services to promote caregiver well-being). CONCLUSION: To ensure comprehensive quality care, clinicians and health care providers should strive to provide caregivers with the identified essential elements of support. Health care settings should work to incorporate caregiver needs into cancer care delivery.