Incorporating a Whole-Person Perspective in Consumer-Generated Data: Social Determinants, Resilience, and Hidden Patterns

Machine learning methods to discover hidden patterns in well-being and resilience for healthy aging

BACKGROUND

A whole person approach to healthy aging can provide insight into social factors that may be critical. Digital technologies, such as mobile health (mHealth) applications, hold promise to provide novel insights for healthy aging and the ability to collect data between clinical care visits. Machine learning/artificial intelligence methods have the potential to uncover insights into healthy aging. Nurses and nurse informaticians have a unique lens to shape the future use of this technology.

METHODS

The purpose of this research was to apply machine learning methods to MyStrengths+MyHealth de-identified data (N = 988) for adults 45 years of age and older. An exploratory data analysis process guided this work.

RESULTS

Overall (n = 988), the average Strength was 66.1% (SD = 5.1), average Challenges 66.5% (SD = 7.5), and average Needs 60.06% (SD = 3.1). There was a significant difference between Strengths and Needs (p < 0.001), between Challenges and Needs (p < 0.001), and no significant differences between average Strengths and Challenges. Four concept groups were identified from the data (Thinking, Moving, Emotions, and Sleeping). The Thinking group had the most statistically significant challenges (11) associated with having at least one Thinking Challenge and the highest average Strengths (66.5%) and Needs (83.6%) compared to the other groups.

CONCLUSION

This retrospective analysis applied machine learning methods to de-identified whole person health resilience data from the MSMH application. Adults 45 and older had many Strengths despite numerous Challenges and Needs. The Thinking group had the highest Strengths, Challenges, and Needs, which aligns with the literature and highlights the co-occurring health challenges experienced by this group. Machine learning methods applied to consumer health data identify unique insights applicable to specific conditions (e.g., cognitive) and healthy aging. The next steps involve testing personalized interventions with nurses leading artificial intelligence integration into clinical care.

Removing the roadblocks to promoting health equity: finding the social determinants of health addressed in standardized nursing classifications

Providing 80% of healthcare worldwide, nurses focus on physiologic and psychosocial aspects of health, which incorporate social determinants of health (SDOH). Recognizing their important role in SDOH, nurse informatics scholars included standardized measurable terms that identify and treat issues with SDOH in their classification systems, which have been readily available for over 5 decades. In this Perspective, we assert these currently underutilized nursing classifications would add value to health outcomes and healthcare, and to the goal of decreasing disparities. To illustrate this, we mapped 3 rigorously developed and linked classifications: NANDA International (NANDA-I), Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC) called NNN (NANDA-I, NIC, NOC), to 5 Healthy People 2030 SDOH domains/objectives, revealing the comprehensiveness, usefulness, and value of these classifications. We found that all domains/objectives were addressed and NNN terms often mapped to multiple domains/objectives. Since SDOH, corresponding interventions and measurable outcomes are easily found in standardized nursing classifications (SNCs), more incorporation of SNCs into electronic health records should be occurring, and projects addressing SDOHs should integrate SNCs like NNN into their ongoing work.

Comparison of SIREN social needs screening tools and Simplified Omaha System Terms: informing an informatics approach to social determinants of health assessments

OBJECTIVE

Numerous studies indicate that the social determinants of health (SDOH), conditions in which people work, play, and learn, account for 30%-55% of health outcomes. Many healthcare and social service organizations seek ways to collect, integrate, and address the SDOH. Informatics solutions such as standardized nursing terminologies may facilitate such goals. In this study, we compared one standardized nursing terminology, the Omaha System, in its consumer-facing form, Simplified Omaha System Terms (SOST), to social needs screening tools identified by the Social Interventions Research and Evaluation Network (SIREN).

MATERIALS AND METHODS

Using standard mapping techniques, we mapped 286 items from 15 SDOH screening tools to 335 SOST challenges. The SOST assessment includes 42 concepts across 4 domains. We analyzed the mapping using descriptive statistics and data visualization techniques.

RESULTS

Of the 286 social needs screening tools items, 282 (98.7%) mapped 429 times to 102 (30.7%) of the 335 SOST challenges from 26 concepts in all domains, most frequently from Income, Home, and Abuse. No single SIREN tool assessed all SDOH items. The 4 items not mapped were related to financial abuse and perceived quality of life.

DISCUSSION

SOST taxonomically and comprehensively collects SDOH data compared to SIREN tools. This demonstrates the importance of implementing standardized terminologies to reduce ambiguity and ensure the shared meaning of data.

CONCLUSIONS

SOST could be used in clinical informatics solutions for interoperability and health information exchange, including SDOH. Further research is needed to examine consumer perspectives regarding SOST assessment compared to other social needs screening tools.

Using data visualization to characterize whole-person health of public health nurses

OBJECTIVE

To characterize patterns in whole-person health of public health nurses (PHNs).

DESIGN AND SAMPLE

Survey of a convenience sample of PHNs (n = 132) in 2022. PHNs self-identified as female (96.2%), white (86.4%), between the ages 25-44 (54.5%) and 45-64 (40.2%), had bachelor's degrees (65.9%) and incomes of $50-75,000 (30.3%) and $75-100,000/year (29.5%).

MEASUREMENTS

Simplified Omaha System Terms (SOST) within the MyStrengths+MyHealth assessment of whole-person health (strengths, challenges, and needs) across Environmental, Psychosocial, Physiological, and Health-related Behaviors domains.

RESULTS

PHNs had more strengths than challenges; and more challenges than needs. Four patterns were discovered: (1) inverse relationship between strengths and challenges/needs; (2) Many strengths; (3) High needs in Income; (4) Fewest strengths in Sleeping, Emotions, Nutrition, and Exercise. PHNs with Income as a strength (n = 79) had more strengths (t = 5.570, p < .001); fewer challenges (t = -5.270, p < .001) and needs (t = -3.659, p < .001) compared to others (n = 53).

CONCLUSIONS

PHNs had many strengths compared to previous research with other samples, despite concerning patterns of challenges and needs. Most PHN whole-person health patterns aligned with previous literature. Further research is needed to validate and extend these findings toward improving PHN health.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

OBJECTIVES

The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

METHODS

We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework.

RESULTS

Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care.

CONCLUSION

In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Creating a quadruple aim model for nursing education

Baccalaureate nursing education is moving to adopt the new American Association of Colleges of Nursing Essentials for Professional Nursing Education. As identified in two of the six domains of the essentials, graduates need to be prepared to address population health and utilize informatics and healthcare technologies. Community/public health nursing also has eight domains for generalist nurses linked to population health which will help prepare a skilled nursing workforce for the 21st century. The Institute for Healthcare Improvement's Triple Aim which evolved into the Quadruple Aim is focused on improving health outcomes within healthcare delivery. Through a literature review, a need for a Quadruple Aim model for nursing education was identified. Mirroring the Institute for Healthcare Improvement's Triple Aim for healthcare delivery, a Quadruple Aim for Nursing Education Model was developed. The model dimensions include (1) Population-focused Care, (2) Maximize Student Learning Experience, (3) Cost-effective Pedagogy, and (4) Nurse Educator Well-being. The Quadruple Aim for Nursing Education Model supports nursing education to prepare future nurses effectively and efficiently bridging population health concepts and issues with nursing informatics. Nurse educators are encouraged to utilize the model to transform nursing education.

Understanding Whole-Person Health and Resilience During the COVID-19 Pandemic and Beyond: A Cross-sectional and Descriptive Correlation Study

BACKGROUND

The COVID-19 pandemic has prompted an interest in whole-person health and emotional well-being. Informatics solutions through user-friendly tools such as mobile health apps offer immense value. Prior research developed a consumer-facing app MyStrengths + MyHealth using Simplified Omaha System Terms (SOST) to assess whole-person health. The MyStrengths + MyHealth app assesses strengths, challenges, and needs (SCN) for 42 concepts across four domains (My Living, My Mind and Networks, My Body, My Self-care; eg, Income, Emotions, Pain, and Nutrition, respectively). Given that emotional well-being was a predominant concern during the COVID-19 pandemic, we sought to understand whole-person health for participants with/without Emotions challenges.

OBJECTIVE

This study aims to use visualization techniques and data from attendees at a Midwest state fair to examine SCN overall and by groups with/without Emotions challenges, and to explore the resilience of participants.

METHODS

This cross-sectional and descriptive correlational study surveyed adult attendees at a 2021 Midwest state fair. Data were visualized using Excel and analyzed using descriptive and inferential statistics using SPSS.

RESULTS

The study participants (N=182) were primarily female (n=123, 67.6%), aged ≥45 years (n=112, 61.5%), White (n=154, 84.6%), and non-Hispanic (n=177, 97.3%). Compared to those without Emotions challenges, those with Emotions challenges were aged 18-44 (P<.001) years, more often female (P=.02), and not married (P=.01). Overall, participants had more strengths (mean 28.6, SD 10.5) than challenges (mean 12, SD 7.5) and needs (mean 4.2, SD 7.5). The most frequent needs were in Emotions, Nutrition, Income, Sleeping, and Exercising. Compared to those without Emotions challenges, those with Emotions challenges had fewer strengths (P<.001), more challenges (P<.001), and more needs (P<.001), along with fewer strengths for Emotions (P<.001) and for the cluster of health-related behaviors domain concepts, Sleeping (P=.002), Nutrition (P<.001), and Exercising (P<.001). Resilience was operationalized as correlations among strengths for SOST concepts and visualized for participants with/without an Emotions challenge. Those without Emotions challenges had more positive strengths correlations across multiple concepts/domains.

CONCLUSIONS

This survey study explored a large community-generated data set to understand whole-person health and showed between-group differences in SCN and resilience for participants with/without Emotions challenges. It contributes to the literature regarding an app-aided and data-driven approach to whole-person health and resilience. This research demonstrates the power of health informatics and provides researchers with a data-driven methodology for additional studies to build evidence on whole-person health and resilience.

Understanding Women's Cardiovascular Health Using MyStrengths+MyHealth: A Patient-Generated Data Visualization Study of Strengths, Challenges, and Needs Differences

PURPOSE

The purpose of this data visualization study was to identify patterns in patient-generated health data (PGHD) of women with and without Circulation signs or symptoms. Specific aims were to (a) visualize and interpret relationships among strengths, challenges, and needs of women with and without Circulation signs or symptoms; (b) generate hypotheses based on these patterns; and (c) test hypotheses generated in Aim 2.

DESIGN

The design of this visualization study was retrospective, observational, case controlled, and exploratory.

METHODS

We used existing de-identified PGHD from a mobile health application, MyStrengths+MyHealth (N = 383). From the data, women identified with Circulation signs or symptoms (n = 80) were matched to an equal number of women without Circulation signs or symptoms. Data were analyzed using data visualization techniques and descriptive and inferential statistics.

FINDINGS

Based on the patterns, we generated nine hypotheses, of which four were supported. Visualization and interpretation of relationships revealed that women without Circulation signs or symptoms compared to women with Circulation signs or symptoms had more strengths, challenges, and needs-specifically, strengths in connecting; challenges in emotions, vision, and health care; and needs related to info and guidance.

CONCLUSIONS

This study suggests that visualization of whole-person health including strengths, challenges, and needs enabled detection and testing of new health patterns. Some findings were unexpected, and perspectives of the patient would not have been detected without PGHD, which should be valued and sought. Such data may support improved clinical interactions as well as policies for standardization of PGHD as sharable and comparable data across clinical and community settings.

CLINICAL RELEVANCE

Standardization of patient-generated whole-person health data enabled clinically relevant research that included the patients' perspective.