Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data

Palliative Care across Settings: Perspectives from Inpatient, Primary Care, and Home Health Care Providers and Staff

BACKGROUND

Early introduction of palliative care can improve patient-centered outcomes for older adults with complex medical conditions. However, identifying the need for and introducing palliative care with patients and caregivers is often difficult. We aim to identify how and why a multi-setting approach to palliative care discussions may improve the identification of palliative care needs and how to facilitate these conversations.

METHODS

Descriptive qualitative study to inform the development and future pilot testing of a model to improve recognition of, and support for, unmet palliative care needs in home health care (HHC). Thematic analysis of semi-structured interviews with providers across inpatient (n = 11), primary care (n = 17), and HHC settings (n = 10).

RESULTS

Four key themes emerged: 1) providers across settings can identify palliative care needs using their unique perspectives of the patient's care, 2) identifying palliative care needs is challenging due to infrequent communication and lack of shared information between providers, 3) importance of identifying a clinical lead of patient care who will direct palliative care discussions (primary care provider), and 4) importance of identifying a care coordination lead (HHC) to bridge communication among multi-setting providers. These themes highlight a multi-setting approach that would improve the frequency and quality of palliative care discussions.

CONCLUSIONS

A lack of structured communication across settings is a major barrier to introducing and providing palliative care. A novel model that improves communication and coordination of palliative care across HHC, inpatient and primary care providers may facilitate identifying and addressing palliative care needs in medically complex older adults.

The role of artificial intelligence in enhancing clinical nursing care: A scoping review

AIM

To present an overview of how artificial intelligence has been used to improve clinical nursing care.

BACKGROUND

Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care.

EVALUATION

A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched.

KEY ISSUES

Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence.

IMPLICATIONS FOR NURSING MANAGEMENT

Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.

A Description of Persons With Alzheimer Disease and Related Dementias Receiving Home Health Care: A National Analysis

The end-of-life period of individuals with Alzheimer disease and related dementias receiving home health care (HHC) is understudied. We sought to describe characteristics of HHC patients with Alzheimer disease and related dementias at risk of death within a year, based on clinician assessment. We conducted a secondary data analysis of a 5% random sample of the Outcome and Assessment Information Set data set for the year 2017. We used Outcome and Assessment Information Set-C item M1034 to identify HHC patients with overall status of progressive condition leading to death within a year. Multivariable logistic regression model was used to examine the association between sociodemographic, functional, clinical, and caregiving factors and likelihood of decline leading to death within a year, as identified by HHC clinicians. Clinician perception of decline leading to death within a year was higher for Whites (vs Blacks or Hispanics) (odds ratio [OR], 0.74 [95% confidence interval (CI), 0.69-0.80], and OR, 0.63 [95% CI, 0.57-0.69], respectively). Factors associated with increased odds of decline leading to death within a year included daily pain (OR, 1.11 [95% CI, 1.06-1.17]), anxiety daily or more often (OR, 1.58 [95% CI, 1.49-1.67]), shortness of breath (OR, 1.45 [95% CI, 1.34-1.57]), use of oxygen (OR, 1.60 [95% CI, 1.52-1.69]), disruptive behavior (OR, 1.26 [95% CI, 1.20-1.31]), and feeding difficulty (OR, 2.25 [95% CI, 2.09-2.43]). High symptom burden exists among HHC patients with Alzheimer disease and related dementias identified to have a status of decline leading to death within a year.

TILE-12 index: an interpretable instrument for identifying older adults at risk for transitions in living environment within the next 12-months

Few evidence-based tools exist to support identification of older community dwelling adults at risk for unwanted transitions in living environment leading to missed opportunities to modify care plans to support aging-in-place and/or establish end-of-life care goals. An interpretable and actionable tool for assessing a person's risk of experiencing a transition is introduced. Logistic regression analysis of 14,772 transition opportunities (i.e. 12-month periods) for 4,431 respondents to the National Health and Aging Trends Study (NHATS) rounds 1-7. Results were visualized in a nomogram. Unmarried males of increasing age with chronic disease, greater functional dependence, overnight hospitalizations, not living in a single-family home, and limited social network, have elevated risk of experiencing a transition in living environment in a 12-month period. Homecare nurses are uniquely qualified to identify social determinants of health and can use this evidence-based tool to identify individuals who may benefit from transitional care assistance.

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths

BACKGROUND

Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.

OBJECTIVES

To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.

METHODS

In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM).

RESULTS

Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.

CONCLUSION

In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

BACKGROUND

Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses-the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers.

OBJECTIVE

This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond.

METHODS

Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized.

RESULTS

A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI.

CONCLUSIONS

Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/17490.

Eliminating the Surprise Question Leaves Home Care Providers With Few Options for Identifying Mortality Risk

BACKGROUND

Precision health initiatives for end-of-life planning require robust methods for identifying patient risk for decline and mortality. The Outcome and Assessment Information Set (OASIS) surprise question (SQ; M1034 Overall Status) is the primary tool for evaluating risks in homebound older adults. However, the OASIS-D, Released in 2019, eliminates this question. This study examines the prognostic ability of 12- and 24-month mortality risk reflected in the OASIS-SQ and develops an alternative approach for classifying mortality risk to support decision-making in the absence of the OASIS-SQ.

DESIGN

Retrospective secondary data analysis.

SETTING/PARTICIPANTS

A nationally representative sample of 69 097 OASIS-C assessments (2012) linked to the Master Beneficiary Summary file (2012 and 2013).

MEASUREMENTS

Survival analysis, k-means clustering, and Cohen κ coefficient with Z test.

RESULTS

The OASIS-SQ predicts mortality (35% at 12 and 45% at 24 months; P < .001). Cluster analysis identified 2 risk groups: OASIS activity of daily living "ADL total scores" >15 = (lower risk) and ≤15 = (higher risk) for 24-month mortality. Model agreement is weak for both cluster 1 and cluster 2, the OASIS-SQ κ = 0.20, 95% confidence interval (CI) = .19 to .21, and "alive/not alive" κ = .17, 95% CI = .16 to .18.

CONCLUSION

The OASIS-SQ and "ADL total score" are almost equally likely to predict 24-month mortality; therefore, it was reasonable to use the "ADL total score" as a substitute for the OASIS-SQ. Removal of the OASIS-SQ leaves home care providers with few clear options for risk screening resulting in missed opportunities to refer to palliative or hospice services.

TakingAIM: A Precision Health Framework for Promoting Person-Centered Advance Care Planning

Advance care plans (ACP) align care with individual values and goals for the end of life, yet their rates are low. Communication barriers are a primary reason for these low rates. Precision health approaches that target individuals based on personal needs, behaviors, or choices may improve communication and positively influence rates of advance care plans. A framework to guide these efforts is needed. The purpose of this study is to develop a framework that guides clinicians in identifying individuals who will benefit from targeted advance care planning conversations. Walker and Avant's theory derivation strategy integrates concepts of social marketing theory, population segmentation, the marketing mix, and the transtheoretical model of behavior change into a novel framework. The Aligning Individuals with Meaningful End-of-Life Discussions to Promote ACP (TakingAIM) model promotes population segmentation by integrating social marketing theory and the marketing mix into conceptual definitions within the context of ACP: plan (product), perception (price), preference (promotion), and pathway (place). The transtheoretical model of behavior change further guides ACP conversations at the individual level. Identifying at-risk groups and targeting their specific needs may improve the rates of advance care plans. This framework is appropriate for use in any clinical setting and is ready for empirical testing.