Karlene M. Kerfoot, PhD, RN, CNAA, FAAN

A Hospital's Roadmap for Improving Nursing Excellence Using AACN's Healthy Work Environment Standards.

You have accessThe ASHA LeaderFeature1 Jun 2011Productivity in Audiology and Speech-Language Pathology Kyle Dennis andPhD, CCC-A Stephen A. GonzenbachEdD, CCC-A/SLP Kyle Dennis Google Scholar More articles by this author , PhD, CCC-A and Stephen A. Gonzenbach Google Scholar More articles by this author , EdD, CCC-A/SLP https://doi.org/10.1044/leader.FTR2.16062011.16 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In http://www.asha.org/Publications/leader/2011/110517/Productivity-in-Audiology-and-Speech-Language-Pathology.htm As health care dollars grow increasingly tight, health care organizations must focus on being more concerned about accountability and efficiency. Accordingly, in member surveys ASHA has tracked the requirements for productivity—defined as the number of hours in direct patient care divided by the number of hours worked. In the ASHA 2009 SLP Health Care Survey, for example, 59% of respondents (n=1915) reported their facility had a productivity requirement. The ASHA Care Survey Report: Workforce and Work Conditions Trends, 2005–2007, showed that in 2007, as in 2005, most respondents indicated that their facility had a productivity requirement (60% and 61%, respectively). Productivity models are an effective way for audiology and speech-language pathology programs to examine how efficiently they provide services (not merely counting the number of services they perform). Some basic principles of productivity are particularly applicable to hospital settings. Productivity Factors Several factors influence productivity, including: Complexity. Complexity of services affects productivity because of the amount of time it takes to perform services. In addition, the type of service performed (e.g., diagnostic services, treatment services, or aftercare services), age of patients, and type and severity of communicative disorders also affect the complexity of clinical services, and consequently the number of services that can be performed and the time required to perform them. Staffing. The availability of professional and support staff greatly affects productivity. Although large clinical staffs tend to generate more patient visits, they are not necessarily more efficient in service delivery. For example, a clinic with clerks and assistants may be more productive than a clinic without such support because clerical and technical activities have to be performed by the professional staff. Assistants free the clinician to concentrate on more complex procedures, which typically require more time. Clinic Space and Equipment. A clinic can perform procedures only when equipment or space is available. The availability of space (e.g., exam rooms, sound suites, and special procedure rooms) and equipment limits the patient flow through the clinic. On-duty Hours, Leave, and Non-clinical Duties. On-duty hours and leave (e.g., vacation, sick days, personal days) affect time available for direct patient care. For example, a clinician could be assigned duties other than direct patient care (e.g., administration, mentoring, or research). This time is productive from the standpoint of clinic operations, but it does not generate direct patient care hours. Coding Systems. Coding systems affect productivity if they do not capture all of the services the clinic provides. Ideally, there should be a procedure code for every service performed. In reality, code systems such as CPT (Common Procedural Terminology, © American Medical Association) do not capture all audiology and speech-language pathology services. For example, audiologists and speech-language pathologists may not be able to capture professional services such as decision making, care planning, coordination of care, counseling, team management, and device handling. Coding systems designed for reimbursement (billing) also may not capture non-covered services. As a result, these systems may actually undercount productivity because some professional services will not be included. Labor Mapping. Because the work day typically involves time dedicated to direct patient care and other professional activities that are important to the function of the clinic but do not generate direct patient care hours, labor mapping is a useful way to allocate time during the work day. Labor can be allocated to the following areas: Direct patient care—Time devoted to prepare for, provide for, and follow-up on patients’ clinical care needs. It includes time spent rendering care to patients (pre-, intra-, and post-service), care coordination, documentation, continuing education, and staff meetings focused on patient care. It may include time spent supervising or mentoring trainees participating in direct patient care. Administration—Time devoted to program management, staff supervision, or managerial functions. Administration also may include time working on department and hospital committees or serving on state or national committees, advisory boards, or professional societies. Education—Time devoted to formal didactic education and teaching at a university. It may include managing a training program, but does not include time spent receiving continuing education or training or supervision or mentoring of students. Research—Time devoted to performing formal, approved health care research, or in activities in direct support of approved research. Research can be laboratory, clinical, or health services research. Examples include working in a research lab, serving on hospital or university research committees, supervising research, writing for publications or grants, attending research meetings, presenting at meetings, and preparing presentations or publications. Time spent in clinical research that produces clinical workload may be allocated to direct patient care. Labor mapping assigns labor cost and hours to the work unit in which the work occurred. If a clinician works in more than one unit, labor time is allocated to each unit. If an SLP works 40 hours in Clinic A and 40 hours in Clinic B, that clinician's labor time would be distributed as 50% in each unit. If the employee were mapped 100% to Clinic A, even though he or she worked there only half of the time, Clinic A would appear to be less productive because of the additional labor that would be mapped to that unit without any direct patient care production. Conversely, the productivity of Clinic B would be overstated because the employee's labor was not mapped to that unit although the provider contributed patient care hours to that unit. Productivity Methods Productivity can be measured in many ways, such as counting the number of patients seen, the number of visits or encounters per clinician, or the number of billable hours, or by calculating the percentage of on-duty hours spent in direct patient care (Table 1 [PDF]). Three common productivity methods are based on workload, capacity, and relative value units (RVUs). Workload-Based Method Perhaps the simplest productivity statistics to compile are workload-based systems. They typically include the number of visits, procedures, encounters, or patients. They provide an easily understood indication of the volume of work. The major disadvantage of workload-based methods, however, is that they usually do not take into account the complexity of services—all services are counted equally. Capacity-Based Method Capacity measures also are easy to construct. They are based on typical appointment length and are usually adjusted for clinician availability (e.g., the model adjusts to scheduled hours in the clinic, vacation time, and projected no-shows or other planned down time). These models are usually prospective (i.e., they show how many encounters a clinician should generate). When compared with workload models, the capacity model can demonstrate how well clinicians meet productivity goals (e.g., number of patients seen). Like workload-based methods, capacity methods value each encounter equally, regardless of complexity. Capacity models also do not provide information on how efficiently services are provided. The capacity models shown in Table 2 [PDF] illustrate how adjustment of time allocated to each appointment slot will affect the expected productivity. The models were constructed with the following assumptions: 260 possible work days (52 weeks/year, five days/week) reduced by scheduled holidays, leave, mandatory continuing education, and related professional assignments, leaving 202 possible work days per year. Assuming a typical work day of eight hours, minus one hour for lunch and breaks, there are seven hours of direct patient care per day or 1,414 DPC hours per year. The key element that determines productivity is the duration of the appointment slot. In Model C, for example, the typical appointment takes 45 minutes; a full-time employee should generate 1,885 patient visits per year. Patient no-shows—scheduled time that does not generate direct patient care time (wasted capacity)—also must be factored in the capacity model. This model, also called panel size, indicates the number of patients for which an audiologist or SLP is responsible, given scheduled availability. This model can be particularly useful in projecting how many clinicians a facility will need to hire for an expected demand. The danger in using this kind of model is the temptation to increase capacity by reducing appointment length. This scenario creates an ethical dilemma by reducing patient care time and possibly affecting the quality of patient care. It may be unsustainable because other factors—such as time needed to complete documentation, analyze results, coordinate care, attend team meetings, process orders, or teach students—are not included in the model. Driving capacity upward by reducing the time allocated to each patient may have undesirable consequences such as poor quality, errors, low morale, and low patient satisfaction. In this model, it is better to overestimate the appointment time to account for time that is not face-to-face direct patient care but is nevertheless essential to delivering quality patient care. RVU–Based Method Workload- or capacity-based models count each episode of care equally—that is, they do not account for the complexity of services. The relative value unit (RVU) productivity method has the advantage of weighting procedures by their complexity. There are three methods for establishing an RVU: Time Studies. A clinic can conduct a time study to determine how much time each procedure takes. This time becomes the RVU. An alternative method is to appoint an expert panel to arrive at a consensus on procedure times, a method that reflects actual local practice. Conversely, these RVUs cannot be standardized across health care facilities. Resource-Based Relative Scale. Another option is the Resource-Based Relative Value System (RBRVS) of the Centers for Medicare and Medicaid Services, which receives substantial input from the American Medical Association (AMA). RVUs in this methodology are dimensionless. As the name implies, they are based on a relative value scale that weights all CPT procedure codes. The AMA, with input from specialty societies, assigns a relative value to each CPT code. The RVU has three components: professional work (time, technical skill, physical effort, stress, and professional judgment); practice expense (overhead costs and non-physician labor); and professional liability (malpractice costs). RVUs are available in the Physician Fee Schedule published by the Centers for Medicare and Medicare Services (CMS). These RVUs have two major advantages: They account for complexity on a relative scale and they can be benchmarked to the productivity of other facilities. The major disadvantage of using RVUs as a productivity measure is that not all audiology and speech-language pathology services are captured by CPT codes or covered by Medicare. This method also may not capture professional time involved for case histories, decision making, floor time, counseling, coordination of care, documentation, data analysis, or chart review (evaluation and management services). Labor Time. Fortunately, the RVUs include time-based clinical labor components. CMS maintains a file of direct labor times for each procedure. These time values can be applied to time-based productivity calculations. Table 3 [PDF] shows examples of direct labor values for two audiology procedures (92553 and 92633) and two speech-language pathology codes (92506 and 92507) extracted from the CMS practice expense labor file. Some audiology and speech-language pathology procedures have professional work values. This component also contains a direct labor time value. Table A [PDF] shows examples of procedures from the physician labor file. Some audiology and speech-language pathology services have both professional work and practice expense (technical) components. Table B [PDF] shows examples of procedures that have both professional (modifier 26) components and technical (modifier TC) components. If the clinician administers and interprets the test, the combined value (known as the global) is the RVU time. Using RVU-Based Productivity Simply defined, time-based productivity is the ratio of labor output (time needed to generate clinical procedures) to labor input (worked hours). For example, a clinical procedure takes 10 minutes to perform (the RVU). The clinician generates 1,000 of these procedures (the clinical volume). Therefore, it takes 10,000 minutes (or 166.66 hours)—the labor output—to perform these procedures. This labor output (also called specified hours) becomes the numerator of the productivity ratio. Computing productivity requires knowing how much time the clinician worked. Payroll shows 180 worked hours. This time becomes the denominator of the productivity ratio. The productivity is calculated as: 166.66/180 = 92.58%. That is, 92.58% (154.3 hours) of the clinician's possible on-duty hours were spent in direct patient care. Conversely, 7.42% (13.34 hours) were not specified (i.e., not associated with production). This level is a very strong degree of individual productivity. It has been long established in social science research that productivity ratios at or above 75%–78% are good levels of productivity. Table 4 [PDF], which shows application to the clinic level, depicts a productivity report for a clinic with 7.8 employees. The report shows work as hours and full-time employee equivalents (FTEs). FTE is calculated by dividing the total hours by the number of possible work hours in a year: 2,080 hours (52 weeks at 40 hours per week). There were 16,224 total paid hours (7.8 FTE x 2080 hours). Clinicians used 2,438 hours of vacation, sick leave, and holiday time, or 1.17 FTE, resulting in 13,786 possible on-duty work hours or 6.66 FTE. The clinic generated 11,063 direct patient care hours or 5.32 FTE. Direct patient care (specified) hours are the accumulated RVUs associated with procedures multiplied by the clinical volume of the procedures performed. The productivity ratio is calculated by dividing the total direct patient care hours by the worked hours. In this example, the productivity (specified percent worked) was 80.24%. Managers need to pay particular attention to unspecified hours (difference between the possible worked hours and direct patient care hours). In this example, there were 2,723 unspecified hours (1.31 FTE). Unspecified hours do not necessarily mean non-productive hours, and may include direct patient care activities such as analyzing data, making decisions, planning and coordinating care, documentation, ordering and handling devices, and inter-disciplinary team meetings that are not associated with specific procedure codes. Comparing Productivity Methods Workload-based statistics (e.g., number of visits, encounters, or caseload) give information about the volume of work, but not how efficiently the work is being delivered. For example, Clinic A produced 10,000 visits and Clinic B produced 5,000 visits. Based on workload-based statistics, Clinic A is more productive than Clinic B. If both clinics have the same staff size (3.0 FTE), Clinic A generated 3,333 visits per FTE and Clinic B generated 1,667 visits per FTE. Using a per-FTE workload-based metric, we would again say that Clinic A is more productive because it generates more visits. Looking at the complexity of the procedure performed by the two clinics reveals a different picture. Clinic A generated 10,000 procedures with RVU=10 minutes. Clinic B produced 5,000 procedures with RVU=60 minutes. Under an RVU-based analysis, Clinic A generated 100,000 RVU minutes (1,667 RVU hours) and Clinic B produced 300,000 RVU minutes (5,000 RVU hours). Both clinics had 5,520 possible work hours (3.0 FTE x 1,840 on-duty work hours). Clinic A has a productivity of 30.2% and Clinic B has a productivity of 90.6%. A clinic can look very productive in terms of visits but actually be quite inefficient when the complexity of procedures is considered. A Bigger Picture RVU-based productivity models provide a simple and informative alternative to traditional workload-based or capacity-based methods. These RVU methods are powerful and flexible. For example, Medicare RVU data provide a standardized way to weight procedures by complexity and can be used to calculate billable productivity (percent of on-duty hours that generate billable hours). Time-based productivity methods allow easy calculations of the percentage of on-duty hours associated with direct patient care. Finally, these models can be used to create models to predict how much staff will be needed to meet expected demand. Simply measuring productivity, however, is not an effective solution to cost management. Clinicians in hospital settings also need to consider other factors including long-term sustainability, staff morale, outcomes, quality, and constraints that limit patient flow through the clinic. The opinions expressed herein are those of the authors and do not necessarily reflect the opinions or official positions of the Department of Veterans Affairs or the U.S. Government. References American Speech-Language-Hearing Association (2009). ASHA SLP Health Care Survey Report: Workforce and Work Conditions Trends, 2005–2009. Google Scholar American Speech-Language-Hearing Association (2009). ASHA SLP Health Care Survey 2009: Workforce and Work Conditions. Google Scholar American Speech-Language-Hearing Association (2009). Productivity. Available from www.asha.org/slp/productivity.htm. (Members only). Google Scholar Centers for Medicare and Medicare Services. Physician Fee Schedule. www.cms.gov/PhysicianFeeSched/PFSFRN/itemdetail.asp?filterType=none&filterByDID=99&sortByDID=4&sortOrder=descending&itemID=CMS1223902&intNumPerPage=10 Google Scholar Author Notes is an audiologist at the National Audiology and Speech Pathology National Program Office for the Department of Veterans Affairs. Contact him at [email protected]. is chief of the Audiology and Speech Pathology Service at the VA New York Harbor Health Care System. Contact him at [email protected]. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetailsCited ByPerspectives of the ASHA Special Interest Groups7:4 (1120-1136)15 Aug 2022Impact of Clinical Education of Student Clinicians on Speech-Language Pathologists' Productivity in Medical SettingsJennifer St. Clair, Karen J. Mainess, Paige Shaughnessy and Benjamin BecerraAmerican Journal of Audiology28:3 (628-659)13 Sep 2019Pediatric Audiology Productivity: Results From a Multicenter SurveyWendy Steuerwald, Lisa L. Hunter and Roanne Karzon Volume 16Issue 6June 2011 Get Permissions Add to your Mendeley library History Published in print: Jun 1, 2011 Metrics Downloaded 2,841 times Topicsasha-topicsleader_do_tagleader-topicsasha-article-typesCopyright & Permissions© 2011 American Speech-Language-Hearing AssociationLoading ...

Leader to Honor: Amy E. Brown, MS, RN, NE-BC

Few studies in Taiwan have looked into the burden of caregivers for the mentally ill and the influence of the burden on the quality of life among caregivers. The aim of this study is to explore the risk factors that may aggravate care burden and to assess the relationship between the caregivers' burden and their quality of life. Ninety caregivers of patients with mental illness, who were attending outpatient clinic services in Taipei City Psychiatric Centre, were assessed using a burden questionnaire and the brief questionnaire of the World Health Organization Quality of Life instrument (WHOQOL-BREF). Burden scores were significantly correlated with the number of care hours the caregivers spent daily with the patient, irrespective of their age, gender, kinship and educational level. Caregivers of patients with different psychiatric illnesses had similar levels of burden. Higher burden scores were correlated with a lower quality of life and retained unique predictive variance in multiple regressions in all four domains of the WHOQOL-BREF. These findings indicate that care burden has a significant impact on caregivers' quality of life. Daily care hours with the patient are the unique determinant of caregivers' burden in Taiwan. Measures to reduce daily care hours should be considered.

End-stage kidney disease (ESKD) patients require specific and continuous care, which affects caregivers' quality of life (QOL). It is necessary to define the basic problems and restrictions upon family caregivers of renal patients affecting their physical and psychological status. The main objectives of this narrative review were to examine the literature over the past 10 years, to describe factors associated with QOL of caregivers of patients with ESKD, and to identify the level of subjective burden reported by caregivers. A literature search was carried out using the following electronic databases: PubMed, Medscape, Science Direct, Scopus, PsychINFO and other scientific sources. Keywords included 'quality of life', 'caregivers', 'end stage kidney or renal disease patients', 'burden' and a combination of these terms. Only studies from January 2010 to December 2020 were included in this study. The results found that there was significant burden and distress experienced by caregivers that affected their QOL. Patients' QOL is associated with caregivers' QOL. The hours of caring per day and the long-term replacement therapy are associated with great burden. More awareness to caregivers' QOL is required to meet their needs, reduce anxiety and to improve patients' QOL. Caregiver support could empower and prepare them for initiation of replacement therapy. This can potentially enhance their diseased family members' QOL and could also restrict the use of health care system resources. Given how difficult it is to conceptualize QOL, a holistic approach to patients and caregivers require QOL assessment in each stage of the kidney disease.

To examine the relationship between depression among medically ill, frail elders and family caregivers' hours of care, health status, and quality of life. A cross-sectional study of 193 family caregivers of seniors treated in the emergency department (ED) was conducted. Measures included patient depression (Geriatric Depression Scale-15), and caregivers' hours of care, mental health and physical functioning (SF-36), and quality of life (EQ-5D). Mean caregiver age was 60.0 +/- 16.1 years and 70.5% were female. More caregivers of depressed seniors provided more care in the previous month (37.3% vs 22.4%, p = 0.03), had poor mental health (63.5% vs 47.0%, p = 0.03), and poor perceived quality of life (63.5% vs 50.4%, p = 0.04) compared to caregivers of non-depressed seniors. Multiple logistic regression analyses indicated that patient depression was associated with poor caregiver quality of life (OR = 3.15, 95% CI 1.48, 6.73), and poor mental health in spousal and adult child caregivers (OR = 2.72, 95% CI = 0.88, 8.39, and OR = 3.29, 95% CI = 1.10, 9.86, respectively). Psychosocial support may be needed for caregivers of depressed seniors.

"This therapy aims to address this complex comorbidity so that physically impaired elders can reverse the depression-disability spiral and build a satisfactory life for themselves."

This study assessed overall quality of life of caregivers, using a path model in which burden was conceptualized as distinct from well-being. Data were drawn from a representative sample of caregivers to dementia and nondementia care receivers in British Columbia, Canada (N = 243). The model used was based on a previously proposed stress/appraisal path model and used multiple regression path estimates. Primary stressors were care receiver cognitive status, physical function, and behavioral problems; the primary appraisal variable was hours of caregiving during the previous week. Mediators were perceived social support, frequency of getting a break, and hours of formal service use; secondary appraisal was subjective burden. The outcome measure was generalized well-being. Well-being was directly affected by four variables: perceived social support, burden, self-esteem, and hours of informal care. Burden was affected directly by behavioral problems, frequency of getting a break, self-esteem, and informal hours of care and was not affected by perceived social support. The finding that perceived social support is strongly related to well-being but unrelated to burden reinforces the conceptual distinctiveness of the latter two concepts. This suggests that quality of life of caregivers could be improved even with burden in their lives and that the overwhelming focus in caregiving research on burden should be supplemented with an emphasis on quality of life.

BackgroundInformal care of older adults impacts the lives of millions worldwide. Critically, low‐ and middle‐income countries have the highest proportion of dementia costs related to informal care. Research suggests that older adults’ cognitive decline is associated with burden among caregivers, which corroborates the worsening in caregivers’ quality of life and mental health. Understanding factors related to this relationship may contribute to creating strategies to reduce the caregiver’s burden. This study analyzes whether hours and days dedicated to care mediate the relationship between older adults’ cognitive performance and caregiver burden.MethodParticipants were informal caregivers (n = 92) of healthy older adults (n = 74) and persons living with Alzheimer’s Disease (PLwAD, n = 18) from a middle‐income country (Brazil). The assessment comprised the following instruments: a sociodemographic questionnaire, Zarit burden scale, Memory Complaint Scale‐caregiver’s version (MCS), Pfeffer Functional Activities Questionnaire (FAQ), and IQCODE. ACE‐R was used to assess older adults’ cognitive performance.ResultCaregivers were mostly female (72.8%), daughters (42.4%), or wives (43.5%), who were dedicated to caring, on average, 7.6 (±9.1) hours per day and 5 (±2) days per week. PLwAD caregivers had higher scores on the Zarit Scale (t = ‐5.35; p<0.001), MCS (t = ‐11.54; p<0.001), FAQ (t = ‐14.31; p<0.001), IQCODE (t = ‐6.36; p<0.00). They also provided more hours of care (t = ‐3.97; p<0.001; 95%CI [‐15.89 to ‐4.93]), for more days per week (t = ‐2.30; p = 0.028; 95%CI [‐2.65 to ‐0.16]). Mediation analysis indicated that cognitive performance had a total effect of ‐0.374 (p<0.001; 95%CI [‐0.52 to ‐0.23]) on caregivers’ burden. When controlling for days of care, the direct effect of cognitive performance on the burden was ‐0.3251 (p<0.001; 95%CI [‐47 to ‐0.18]), and there was an indirect effect of ‐0.0698 (95%BCaCI [‐0.14 to ‐0.01]) through the mediation of days of care. Considering hours of care per day, the indirect effect of cognitive performance on the burden was not significant (95%BCaCI [‐0.21 to 0.005]), which suggests it does not mediate this relationship.ConclusionOur findings suggest that days dedicated to care mediate the effect of older adults’ cognitive performance on caregivers' burden. These findings underscore the importance of considering the temporal aspects of caregiving in developing strategies to alleviate caregiver burden.

Consequences of Hidden Tinnitus and Hearing Loss

17th International Conference on Malignant Lymphoma, Palazzo dei Congressi, Lugano, Switzerland, 13 - 17 June, 2023.

ABSTRACTBackground:Caring role, especially in chronic diseases, has a negative impact on the health of family caregivers and can affect their quality of life. Therefore, this study aimed to investigate the care burden and quality of life in family caregivers of hemodialysis patients and their relationship with some characteristics of caregivers and patients. Methods:This study was conducted as a descriptive-analytic study in Isfahan from January to February 2017. Sampling was done using census. The number of participants was 254. The data gathering tools consisted of a three-part questionnaire including demographic characteristics, the Zarit questionnaire for caring burden, and SF-36 quality of life questionnaire. Data were analyzed using descriptive statistics, Pearson correlation coefficient test, Spearman’s coefficient, ANOVA, and univariate general linear regression. A significant level of 5% was considered. Results:The mean scores of the quality of life and caring burden were 30.54±9.89 and 44.98±6.82, respectively in caregivers. The age of the patient under care (P<0.001), cost of medications (P=0.008), and hours of care in 24 hours (P<0.001) had a significant relationship with care givers’ quality of life. Also, univariate general linear regression revealed that care burden had a significant relationship with the quality of life (P=0.003). Conclusion:Family caregivers who experienced more caring burden had a low quality of life. The researchers suggest that supportive and educational programs should be designed and implemented for this group of patients and their caregivers.

Quality of life (QOL) among elderly individuals in long-term care (LTC) facilities encompasses several dimensions, ranging from social interaction, health status, and support systems. With the aging of populations across the globe, the identification of factors that influence QOL among elderly residents in LTC facilities provides a key factor to enhance care delivery and better well-being. The study examines the QOL of elderly people in LTC facilities in Chennai, India, and identifies factors that affect their well-being in physical, psychological, as well as social arenas. Assessment could be beneficial in tailoring care. A 2-year cross-sectional study among 302 elderly people with an age group of >60 years residing in selected LTC facilities in the city of Chennai was evaluated using a structured questionnaire eliciting sociodemographic characteristics and health status, for which QOL was determined on the World Health Organization QOL-BREF scale. The data were collected, and results of the study were analyzed through SPSS with the aid of descriptive and inferential statistics to establish significant influences on QOL. Physical and psychological domains of QOL were notably low among residents experiencing health problems, social isolation, and lack of personalized care. Those with high QOL scores have received consistent social support and retained autonomy. The findings are consistent with earlier research stating that social engagement and enough hours of care are important for improvement in the QOL in LTC. The research also strengthens support systems, formal programs of organized social activities, and adequate personnel in LTC facilities, among other things, to enhance the QOL in the elderly. All these should be enhanced to provide a more supportive and fulfilling environment.

Caregivers of older adults with cancer assist both with cancer care and other health issues, which may make them vulnerable to consequences of caregiving. Hospitalization may represent a time when a caregiver's ability to provide care at home is exceeded. We sought to characterize caregivers of hospitalized older adults with cancer, determine their quality of life (QOL), and identify factors associated with caregiver QOL. Patients (n = 100), aged 65 years and older, with an unplanned hospitalization and their caregivers were included. Caregivers completed a questionnaire about their health, social support, caregiving relationship, QOL (Caregiver Quality of Life Index-Cancer [CQOLC] tool), and patient function. Patient medical history was obtained via chart review. The association between patient, caregiving, and caregiver factors and CQOLC was determined using multivariate linear regression. Most patients (73%) had metastatic/advanced disease, and 71% received treatment for their cancer within 30 days of hospitalization. Median Karnofsky Performance Status (KPS) was 60%, and 89% required help with instrumental activities of daily living, as reported by caregivers. Median caregiver age was 65 years (range = 29-84 years). The majority (60%) had no major comorbidities and rated their health as excellent/good (79%), though 22% reported worsening health due to caregiving. Caregivers had a median Mental Health Inventory-18 score of 70 (range = 0-97), a median Medical Outcomes Study (MOS)-social activity score of 56 (range = 0-87.5), and a median MOS-Social Support Survey score of 68 (range = 0-100). Caregivers provided a median of 35 hours of care per week (range = 0-168 hours of care per week). Mean CQOLC was 84.6 ± 23.5. Lower caregiver QOL was associated with poorer caregiver mental health, less social support, and poorer patient KPS (P < .05). Caregivers of hospitalized older adults with cancer are older but generally in good health. Those with poorer mental health, less social support, and caring for patients with poorer performance status are more likely to experience lower QOL. J Am Geriatr Soc 67:978-986, 2019.

Medication Errors in the Intensive Care Unit