Use of Standardized Nursing Terminologies to Capture Social Determinants of Health Data: An Integrative Review

Addressing social determinants of health in nursing care is important for improving health outcomes and reducing health inequities. Using standardized nursing terminologies to capture this information generates sharable data that can be used to achieve these goals and create new knowledge. The purpose of this integrative review was to examine use of standardized nursing terminologies for collecting social determinants of health data in nursing research and practice. The CINAHL, MEDLINE, and Web of Science databases were searched using the terms "social determinants of health" [and] "nursing" [and] "standardized terminology" or names for each of the 12 American Nurses Association-approved terminologies. Limiters included peer-reviewed and English language. After removal of duplicates, 120 articles were found and screened for relevance and quality using a three-step process. This yielded a final sample of seven articles. Article data were extracted and analyzed for themes. In all articles, retrospective, observational, or secondary analysis research designs were used to analyze previously collected data from large, deidentified datasets or research studies. The Omaha System was the only standardized nursing terminology represented in the sample. All operational definitions of social determinants of health included behavioral items. In most studies, a social determinants of health index score was calculated, and data were analyzed using descriptive statistics and visualization methods. Results reported across the articles were diverse; some themes were identified. This review revealed published literature on this topic is limited. More quality improvement and multisite studies that examine the use of standardized nursing terminologies by nurses to collect and use social determinants of health data are needed.

Comparison of Weighting Methods to Understand Improved Outcomes Attributable to Public Health Nursing Interventions

BACKGROUND

The complex work of public health nurses (PHNs) specifically related to mental health assessment, intervention, and outcomes makes it difficult to quantify and evaluate the improvement in client outcomes attributable to their interventions.

OBJECTIVES

We examined heterogeneity across parents of infants served by PHNs receiving different interventions, compared the ability of traditional propensity scoring methods versus energy-balancing weight (EBW) techniques to adjust for the complex and stark differences in baseline characteristics among those receiving different interventions, and evaluated the causal effects of the quantity and variety of PHN interventions on client health and social outcomes.

METHODS

This retrospective study of 4,109 clients used existing Omaha System data generated during the routine documentation of PHN home visit data. We estimated the effects of intervention by computing and comparing weighted averages of the outcomes within the different treatment groups using two weighting methods: (a) inverse probability of treatment (propensity score) weighting and (b) EBWs.

RESULTS

Clients served by PHNs differed in baseline characteristics with clients with more signs/symptoms. Both weighting methods reduced heterogeneity in the sample. EBWs were more effective than inverse probability of treatment weighting in adjusting for multifaceted confounding and resulted in close balance of 105 baseline characteristics. Weighting the sample changed outcome patterns, especially when using EBWs. Clients who received more PHN interventions and a wider variety of them had improved knowledge, behavior, and status outcomes with no plateau over time, whereas the unweighted sample showed plateaus in outcomes over the course of home-visiting services.

DISCUSSION

Causal analysis of PHN-generated data demonstrated PHN intervention effectiveness for clients with mental health signs/symptoms. EBWs are a promising tool for evaluating the true causal effect of PHN home-visiting interventions.

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.

Future advancement of health care through standardized nursing terminologies: reflections from a Friends of the National Library of Medicine workshop honoring Virginia K. Saba

OBJECTIVE

To honor the legacy of nursing informatics pioneer and visionary, Dr. Virginia Saba, the Friends of the National Library of Medicine convened a group of international experts to reflect on Dr. Saba's contributions to nursing standardized nursing terminologies.

PROCESS

Experts led a day-and-a-half virtual update on nursing's sustained and rigorous efforts to develop and use valid, reliable, and computable standardized nursing terminologies over the past 5 decades. Over the course of the workshop, policymakers, industry leaders, and scholars discussed the successful use of standardized nursing terminologies, the potential for expanded use of these vetted tools to advance healthcare, and future needs and opportunities. In this article, we elaborate on this vision and key recommendations for continued and expanded adoption and use of standardized nursing terminologies across settings and systems with the goal of generating new knowledge that improves health.

CONCLUSION

Much of the promise that the original creators of standardized nursing terminologies envisioned has been achieved. Secondary analysis of clinical data using these terminologies has repeatedly demonstrated the value of nursing and nursing's data. With increased and widespread adoption, these achievements can be replicated across settings and systems.

Use of Social Determinants of Health Screening among Primary Health Care Nurses of Developed Countries: An Integrative Review

The aims of the study are to evaluate and synthesise research that has investigated social determinants of health screening by primary healthcare nurses; how and when primary health care nurses perform social determinants of health screening; and implications for advancing nursing practice. Systematic searches in electronic databases identified fifteen published studies which met the inclusion criteria. Studies were synthesised using reflexive thematic analysis. This review found little evidence of primary health care nurses using standardised social determinants of health screening tools. Eleven subthemes were identified and collapsed into three main themes: organisation and health system supports are required to enable primary health care nurses; primary health care nurses are often reluctant to perform social determinants of health screening; and the importance of interpersonal relationships for social determinants of health screening. The social determinants of health screening practices of primary health care nurses are poorly defined and understood. Evidence suggests that primary health care nurses are not routinely using standardised screening tools or other objective methods. Recommendations are made for valuing therapeutic relationships, social determinants of health education and the promotion of screening by health systems and professional bodies. Overall, further research examining the best social determinant of health screening method is required.

Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians

OBJECTIVE

To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors.

METHODS

We interviewed multidisciplinary HHC clinicians from January to March of 2021. Risk factors were mapped to standardized terminologies (eg, Omaha System). We used directed content analysis to identify risk factors for deterioration. We used inductive thematic analysis to understand HHC clinicians' response to risk factors and documentation of risk factors.

RESULTS

Fifteen HHC clinicians identified a total of 79 risk factors that were mapped to standardized terminologies. HHC clinicians most frequently responded to risk factors by communicating with the prescribing provider (86.7% of clinicians) or following up with patients and caregivers (86.7%). HHC clinicians stated that a majority of risk factors can be found in clinical notes (ie, care coordination (53.3%) or visit (46.7%)).

DISCUSSION

Clinicians acknowledged that social factors play a role in deterioration risk; but these factors are infrequently studied in HHC. While a majority of risk factors were represented in the Omaha System, additional terminologies are needed to comprehensively capture risk. Since most risk factors are documented in clinical notes, methods such as natural language processing are needed to extract them.

CONCLUSION

This study engaged clinicians to understand risk for deterioration during HHC. The results of our study support the development of an early warning system by providing a comprehensive list of risk factors grounded in clinician expertize and mapped to standardized terminologies.

Frailty and Social and Behavioral Determinants of Health: Algorithm Refinement and Pattern Validation

Existing frailty and social and behavioral determinants of health (SBDH) algorithms were refined and used to examine SBDH and frailty groups, revealing patterns in interventions and outcomes of older adults in a large community-based care data-set. The dataset was randomly split into training (n = 2,881) and testing (n = 1,441) sets. The training set was used to visually identify patterns in associations among SBDH, frailty, intervention doses, and outcomes, and the testing set was used to validate the patterns. Seven valid patterns were identified, showing increases in SBDH and frailty were associated with poorer health outcomes and more interventions (all p < 0.01). Findings suggest that the refined SBDH and frailty algorithms facilitate the identification of older adults with SBDH and frailty for intervention tailoring. [Journal of Gerontological Nursing, 48(4), 41-48.].

Identification of social determinants of health using multi-label classification of electronic health record clinical notes

OBJECTIVES

Social determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department.

METHODS AND MATERIALS

We labeled a gold-standard corpus of EHR clinical note sentences (N = 4063) with 6 identified SDH-related domains recommended by the Institute of Medicine for inclusion in the EHR. We then trained 5 classification models: linear-Support Vector Machine, K-Nearest Neighbors, Random Forest, XGBoost, and bidirectional Long Short-Term Memory (BI-LSTM). We adopted 5 common evaluation measures: accuracy, average precision-recall (AP), area under the curve receiver operating characteristic (AUC-ROC), Hamming loss, and log loss to compare the performance of different methods for MLL classification using the F1 score as the primary evaluation metric.

RESULTS

Our results suggested that, overall, BI-LSTM outperformed the other classification models in terms of AUC-ROC (93.9), AP (0.76), and Hamming loss (0.12). The AUC-ROC values of MLL models of SDH related domains varied between (0.59-1.0). We found that 44.6% of our study population (N = 1119) had at least one positive documentation of SDH.

DISCUSSION AND CONCLUSION

The proposed approach of training an MLL model on an SDH rich data source can produce a high performing classifier using only unstructured clinical notes. We also provide evidence that model performance is associated with lexical diversity by health professionals and the auto-generation of clinical note sentences to document SDH.

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

Given the complex health and social needs of older adults, the rapid growth of the aging population, and the increasing use of information technology in healthcare, there is a critical need for informatics solutions that advance gerontological nursing care and knowledge discovery. This article illustrates the value of standardized data for healthcare quality improvement throughout the life cycle of data capture and reuse. One such informatics solution is the MyStrengths+MyHealth app, which incorporates a whole-person perspective through the Simplified Omaha System Terms assessment, including the social and behavioral determinants of health, as well as resilience. The data describe whole-person health of older adults from MyStrengths+MyHealth for use in clinical encounters and as raw data for research. There is potential to use such standardized data to improve gerontological nursing care at the bedside and for population health management and research.