Ways of knowing in precision health

Achieving Comprehensive, Patient-Centered Cancer Services: Optimizing the Role of Advanced Practice Nurses at the Core of Precision Health

OBJECTIVES

The field of oncology has been revolutionized by precision medicine, driven by advancements in molecular and genomic profiling. High-throughput genomic sequencing and non-invasive diagnostic methods have deepened our understanding of cancer biology, leading to personalized treatment approaches. Precision health expands on precision medicine, emphasizing holistic healthcare, integrating molecular profiling and genomics, physiology, behavioral, and social and environmental factors. Precision health encompasses traditional and emerging data, including electronic health records, patient-generated health data, and artificial intelligence-based health technologies. This article aims to explore the opportunities and challenges faced by advanced practice nurses (APNs) within the precision health paradigm.

METHODS

We searched for peer-reviewed and professional relevant studies and articles on advanced practice nursing, oncology, precision medicine and precision health, and symptom science.

RESULTS

APNs' roles and competencies align with the core principles of precision health, allowing for personalized interventions based on comprehensive patient characteristics. We identified educational needs and policy gaps as limitations faced by APNs in fully embracing precision health.

CONCLUSION

APNs, including nurse practitioners and clinical nurse specialists, are ideally positioned to advance precision health. Nevertheless, it is imperative to overcome a series of barriers to fully leverage APNs' potential in this context.

IMPLICATIONS FOR NURSING PRACTICE

APNs can significantly contribute to precision health through their competencies in predictive, preventive, and health promotion strategies, personalized and collaborative care plans, ethical considerations, and interdisciplinary collaboration. However, there is a need to foster education in genetics and genomics, encourage continuous professional development, and enhance understanding of artificial intelligence-related technologies and digital health. Furthermore, APNs' scope of practice needs to be reflected in policy making and legislation to enable effective contribution of APNs to precision health.

Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study

Background

Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months.

Methods

Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach.

Results

Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.

Conclusions

This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions.

Trial registration

The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.

An Evolutionary Concept Analysis of Precision Medicine, and Its Contribution to a Precision Health Model for Nursing Practice

Precision medicine is a new concept that has been routinely encountered in the literature for little more than a decade. With increasing use, it becomes crucial to understand the meaning of this concept as it is applied in various settings. An evolutionary concept analysis was conducted to develop an understanding of the essential features of precision medicine and its use. The analysis led to a comprehensive list of the antecedents, attributes, and consequences of precision medicine in multiple settings. With this understanding, precision medicine becomes part of the broader practice of precision health, an important process proposed by nursing scholars to provide complete, holistic care to our patients. A model for precision health is presented as a framework for care.

Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods

BACKGROUND

Patient cohorts generated by machine learning can be enhanced with clinical knowledge to increase translational value and provide a practical approach to patient segmentation based on a mix of medical, behavioral, and social factors.

OBJECTIVES

This study aimed to generate a pragmatic example of how machine learning could be used to quickly and meaningfully cohort patients using unsupervised classification methods. Additionally, to demonstrate increased translational value of machine learning models through the integration of nursing knowledge.

METHODS

A primary care practice dataset (N = 3,438) of high-need patients defined by practice criteria was parsed to a subset population of patients with diabetes (n = 1233). Three expert nurses selected variables for k-means cluster analysis using knowledge of critical factors for care coordination. Nursing knowledge was again applied to describe the psychosocial phenotypes in four prominent clusters, aligned with social and medical care plans.

RESULTS

Four distinct clusters interpreted and mapped to psychosocial need profiles, allowing for immediate translation to clinical practice through the creation of actionable social and medical care plans. (1) A large cluster of racially diverse female, non-English speakers with low medical complexity, and history of childhood illness; (2) a large cluster of English speakers with significant comorbidities (obesity and respiratory disease); (3) a small cluster of males with substance use disorder and significant comorbidities (mental health, liver and cardiovascular disease) who frequently visit the hospital; and (4) a moderate cluster of older, racially diverse patients with renal failure.

CONCLUSION

This manuscript provides a practical method for analysis of primary care practice data using machine learning in tandem with expert clinical knowledge.

Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach

Background and Objectives

Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads.

Research Design and Methods

Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015-2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR]).

Results

IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers' age, and the person living with dementia's income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703).

Discussion and Implications

Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study-that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care.

The omission of some patterns of knowing in clinical care: A qualitative study

BACKGROUND

Providing holistic and humanistic care to patients requires a variety of factors. A care solely based on objective knowledge might be unsafe and of low quality. Using the patterns of knowing in an integrated manner and relative to the context of caring is one of the necessities for proving a holistic and efficient nursing care. This study aimed to explore the role of patterns of knowing in the formation of uncaring behaviors.

MATERIALS AND METHODS

The researchers used a qualitative research design for this study. Participants included 19 clinical nurses who attended semi-structured and in-depth interviews. In addition, theoretical and purposeful sampling methods were used in this research. Observation of caring processes in different hospital wards was another method used for collecting data. The data analysis was carried out according to conventional content analysis technique.

RESULTS

The study findings revealed five categories for the theme of "omission of some patterns of knowing" including omission of scientific principles, omission of therapeutic relationship, omission of ethics, omission of social justice, and omission of flexibility.

CONCLUSIONS

The omission of some patterns of knowing creates an ugly image of nursing and a negative outcome of caring as well.

Precision health care strategies for older adults with diabetes in Indonesia: a Delphi consensus study

BACKGROUND Studies on precision health care for older adults with diabetes in Indonesia are still limited. This study was aimed to reach the experts consensus on the suitable precision health care strategies for older adults with diabetes. METHODS A total of 10 experts (4 physicians, 4 nurses, and 2 dietitians) agreed to participate in the 3-round interview using Delphi technique. The experts should have at least 5 years of experience in teaching or working as health professionals in a hospital. RESULTS Consensus was reached that precision health care consisted of eight elements: self-management, interdisciplinary collaborative practice, personalized genetic or lifestyle factors, glycemic target, patient preferences, glycemic control, patient priority-directed care, and biodata- or evidence-based practice. The strategies of precision health care for diabetes were divided into seven steps: conducting brief deducting teaching; assessing self-management level and risk of cardiovascular disease; organizing a brainstorming session among patients to exchange experiences on glycemic target and specific target behavior; making a list of patients’ needs and ranking the priorities; setting a goal and writing action; doing follow-up; and reporting the goal attempts. CONCLUSIONS The eight elements of precision health care provided the basis of precision health care strategies for diabetic older adults, which are the real and measurable strategies for precision health care implementation in clinical settings.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

In this paper, we propose and describe a robust and flexible modeling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modeling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions.

Precision Health Care Elements, Definitions, and Strategies for Patients with Diabetes: A Literature Review

Diabetes is a prevalent disease with a high risk of complications. The number of people with diabetes worldwide was reported to increase every year. However, new integrated individualized health care related to diabetes is insufficiently developed. Purpose: The objective of this study was to conduct a literature review and discover precision health care elements, definitions, and strategies. Methods: This study involved a 2-stage process. The first stage comprised a systematic literature search, evidence evaluation, and article extraction. The second stage involved discovering precision health care elements and defining and developing strategies for the management of patients with diabetes. Results: Of 1337 articles, we selected 35 relevant articles for identifying elements and definitions of precision health care for diabetes, including personalized genetic or lifestyle factors, biodata- or evidence-based practice, glycemic target, patient preferences, glycemic control, interdisciplinary collaboration practice, self-management, and patient priority direct care. Moreover, strategies were developed to apply precision health care for diabetes treatment based on eight elements. Conclusions: We discovered precision health care elements and defined and developed strategies of precision health care for patients with diabetes. precision health care is based on team foundation, personalized glycemic target, and control as well as patient preferences and priority, thus providing references for future research and clinical practice.

PhD programs and the advancement of nursing science

Nurses are well-positioned to be groundbreaking researchers, scientists, leaders, and innovators to improve the health and well-being of individuals, families, and communities. Nurse scientists are needed to contribute to scientific discoveries that inform effective strategies to improve patient care and outcomes and to inform future policies. Thoughtful consideration is required about the preparation of nurse scientists to ensure they are equipped with the knowledge and skill sets to meet the needs of society. Evolving health needs and priority areas of inquiry along with an ever-increasing array of sophisticated methodologies and centrality of interdisciplinary teams to solve complex problems should drive how we prepare PhD students. This paper reflects a panel and subsequent dialogue with nurse leaders at the PhD summit held at the University of Pennsylvania in October 2019. Three aspects of PhD education and the advancement of nursing science are discussed 1) examining important elements to support nurse scientist development; 2) identifying key gaps in science that the discipline needs to address in educating the next generation of nurse scientists; and 3) preparing nurse scientists for the competitive funding environment.

Evaluating Feasibility of Personal Diabetes Device Data Collection for Research

BACKGROUND

Diabetes devices, like insulin pumps and continuous glucose monitors (CGMs), capture and store patient adherence and utilization data that can be retrieved or downloaded providing objective information on self-management behaviors; yet, diabetes device data remain underutilized in research.

OBJECTIVE

The aim of the study was to examine the usability and feasibility of personal diabetes device data collected using a clinical download platform retooled for research purposes.

METHODS

Investigators evaluated the feasibility of raw diabetes device data collection. One hundred eight preteens and adolescents with Type 1 diabetes and their parents provided consent/assent.

RESULTS

Data were successfully collected from the diabetes devices (insulin pumps and CGM) of 97 youth using a clinical download software adapted for research, including data from all three commercially available CGM systems and insulin pumps brands, which contained all current and previous models of each insulin pump brand. The time required to download, mode of connection, and process varied significantly between brands. Despite the use of an agnostic download software, some outdated device brands and cloud-based CGM data were unsupported during data collection. Within the download software, dummy clinical accounts were created for each study participant, which were then linked back to a master study account for data retrieval. Raw device data were extracted into seven to eight Excel files per participant, which were then used to develop aggregate daily measures.

DISCUSSION

Our analysis is the first of its kind to examine the feasibility of raw diabetes device data using a clinical download software. The investigators highlight issues encountered throughout the research process, along with mitigating strategies to inform future inquiry.

CONCLUSION

This study demonstrates the feasibility of raw data collection, from a wide variety of insulin pump and CGM brands, through the retooling of a clinical download software. Data from these personal devices provide a unique opportunity to study self-management behavior and the glycemic response of individuals in their everyday environments.

Explanatory Power and Nursing Theory

A theme of this article is the theory-research link and its essential role in advancing nursing science and practice. Concern is expressed over the current status of nursing theory relative to the advances in research and practice. Soon-to-be and current theoreticians and scientists are encouraged to champion not just nursing theory proper but scientific nursing theories that have explanatory power. The role of the precision health movement in facilitating development of scientific theory is explored.

Sensor technology for nursing research

BACKGROUND

Electronic sensors measuring biological and behavioral aspects of health and the environment are becoming ubiquitous and, with advances in data science and ehealth technology, provide opportunities for new inquiry and innovative approaches to nursing research.

PURPOSE

To conceptualize the use of sensor technology from the perspective of nursing science.

METHODS

This review reports the keynote presentation from the Expanding Science of Sensor Technology in Nursing Research Conference presented by the Council for Advancement of Nursing Science in 2019 FINDINGS: Electronic sensors enable collection, recording, and transmission of data in real time in real life settings, remote monitoring, self-monitoring, and communication between health care professionals and patients. A deliberative approach to selecting and applying electronic sensors and analyzing and interpreting the data is needed for successful research.

DISCUSSION

Electronic sensors have high potential to advance nursing science.

Opioid use disorder research and the Council for the Advancement of Nursing Science priority areas

BACKGROUND

Chronic diseases, such as opioid use disorder (OUD) require a multifaceted scientific approach to address their evolving complexity. The Council for the Advancement of Nursing Science's (Council) four nursing science priority areas (precision health; global health, determinants of health, and big data/data analytics) were established to provide a framework to address current complex health problems.

PURPOSE

To examine OUD research through the nursing science priority areas and evaluate the appropriateness of the priority areas as a framework for research on complex health conditions.

METHOD

OUD was used as an exemplar to explore the relevance of the nursing science priorities for future research.

FINDINGS

Research in the four priority areas is advancing knowledge in OUD identification, prevention, and treatment. Intersection of OUD research population focus and methodological approach was identified among the priority areas.

DISCUSSION

The Council priorities provide a relevant framework for nurse scientists to address complex health problems like OUD.