Predicting Survivorship Appointment Nonattendance in a Community Cancer Center: A Machine-Learning Approach.

Understanding and predicting cancer survivors' health care utilization is critical to promote quality care. The consultative system of survivorship care uses a onetime consultative appointment to transition patients from active treatment into survivorship follow-up care. Knowledge of attributes associated with nonattendance at this essential appointment is needed. An ability to predict patients with a likelihood of nonattendance would be of value to practitioners. Unfortunately, traditional data modeling techniques may not be useful in working with large numbers of variables from electronic medical record platforms. A variety of machine-learning algorithms were used to develop a model for predicting 843 survivors' nonattendance at a comprehensive community cancer center in the southeastern United States. A parsimonious model resulted in a k-fold classification accuracy of 67.3% and included three variables. Practitioners may be able to increase utilization of follow-up care among survivors by knowing which patient groups are more likely to be survivorship appointment nonattenders.

Piloting a needs assessment to guide development of a survivorship program for a community cancer center.

PURPOSE/OBJECTIVES: To develop and pilot a survey to assess needs and distress of cancer survivors receiving care in a community cancer center. DESIGN: Descriptive, quantitative. SETTING: A community cancer center in the southeastern United States partnering with a local college of nursing faculty. SAMPLE: Convenience sample of 307 adult cancer survivors. METHODS: Voluntary completion of a modified survey of needs. MAIN RESEARCH VARIABLES: Cancer survivor needs and distress according to five subscales (physical effects, social issues, emotional aspects, spiritual issues, and other issues), age, and gender. FINDINGS: Patients on average reported experiencing more than 25 of 50 possible survivorship needs. Average distress scores associated with individual needs were low. The most frequently experienced needs were fatigue, fear of recurrence, and sleep disturbance. Middle-aged survivors experienced significantly greater need and distress across all subscales. CONCLUSIONS: Need and distress exist among adult cancer survivors receiving treatment and follow-up in community cancer care settings, with the middle-age phase of life creating unique barriers. Survey data may provide documentation of the multidimensional impact of cancer on quality of life and can help direct survivorship program development. IMPLICATIONS FOR NURSING: Nurses can address a barrier to survivorship care in community care settings by using the Pearlman-Mayo Survey of Needs to assess outcomes relevant to survivors. Partnership between community hospital RNs and college of nursing faculty may create local or regional solutions and serve as useful models for survivorship care.

Toward a risk-based assessment of the adult cancer survivor: late effects of chemotherapy.

Survivorship care of the cancer patient has become a growing concern among oncologists and primary care physicians in communities across the country. It is now recognized as a distinct phase in the continuum of cancer care. There are an estimated 12 million Americans with a history of cancer looking to their oncologists and primary care physicians for assistance in navigating the difficult waters of survivorship. Although a concerted effort is underway, there are few evidence-based guidelines or recommendations for managing long-term and late effects of adult cancer survivors. The Children 's Oncology Group has developed consensus guidelines to improve and standardize care of the survivors of childhood to young adult cancers. Their successful efforts could serve as a model for a similar undertaking to address the post-treatment care of adult survivors. In the absence of evidence-based or consensus guidelines, knowledge of late effects can inform and direct assessment of survivors presenting with late sequelae of chemotherapy agents.