Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders

Artificial intelligence to predict bed bath time in Intensive Care Units

OBJECTIVES

to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients.

METHODS

a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed.

RESULTS

among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%.

CONCLUSIONS

the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.

The effect of discharge care plans on statin prescription rates

BACKGROUND

Stroke discharge care bundles have been proposed to address inadequate provider statin prescription rates.

LOCAL PROBLEM

Discontinuation of statins has been associated with a 37% relative risk increase in mortality in patients with a stroke diagnosis. The project site had a statin prescription rate of 86.2%.

METHODS

The project was initiated at a 641-bed regional community teaching medical center. Statin prescription rates upon discharge on patients with the diagnosis of transient ischemic attack or stroke were evaluated and noted to be below the benchmark of 95%. Possible interventions to improve this benchmark were discussed with key stakeholders such as the information technology team, stroke care outcomes team, and neurology service providers. The proposed intervention was incorporated into the electronic health record. Provider prescription rates were tracked monthly along with the use of the proposed intervention. A one-sided z-test was used to analyze the data collected.

INTERVENTIONS

A stroke discharge power plan within an electronic health record was modified to increase the rate of statin prescriptions. The key modification included checking off the prescription of a statin on discharge. Reinforcement of its use was done through monthly reminders.

RESULTS

Use of discharge care plan yielded 100% compliance. Overall compliance was 9.7%. The null hypothesis of the one-sided z-test was 89%. The p-value for all tests was <0.05.

CONCLUSION

The use of a stroke discharge care plan within an electronic health record can positively affect secondary stroke prevention by increasing statin prescription rates.

Data transforming: A concept analysis

PURPOSE

The aims of this study are to clarify the concept of how data retrieved from electronic health records (EHR) are transformed into nurses' tacit knowledge for evidence-based practice from a cognitive perspective at a macro-organizational level, and to identify this concept's attributes, antecedents, and consequences in the nursing field.

SOURCE

A literature review was conducted by performing a search on scientific databases using the key terms "data," "transform," "EHR," "nursing," "tacit knowledge," "organization," "data," "interpretation," and "healthcare." Forty-nine articles and four books were selected for the analysis. The process was audited by two independent experts to ensure neutrality and credibility.

CONCLUSION

Data transforming is a complex cognitive process among different groups of data stakeholders at a macro-organizational level. The concept of data transforming has three attributes: analytical, respectful, and social. The antecedents of these attributes are skillful, immersive, and mission-driven. They have either positive or negative consequences for frontline nurses. These findings not only add to the body of knowledge but also serve as an important impetus for further theory development and research in nursing.

Data structuring may prevent ambiguity and improve personalized medical prognosis

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative

Aim

To develop a consensus paper on the central points of an international invitational think‐tank on nursing and artificial intelligence (AI).

Methods

We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3‐day invitational think tank in autumn 2019. Activities included a pre‐event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities.

Implications for nursing

Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice.

Conclusion

There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems.

Impact

We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

A Concept Analysis on the Use of Artificial Intelligence in Nursing

Artificial intelligence (AI) has a considerable present and future influence on healthcare. Nurses, representing the largest proportion of healthcare workers, are set to immensely benefit from this technology. However, the overall adoption of new technologies by nurses is quite slow, and the use of AI in nursing is considered to be in its infancy. The current literature on AI in nursing lacks conceptual clarity and consensus, which is affecting clinical practice, research activities, and theory development. Therefore, to set the foundations for nursing AI knowledge development, the purpose of this concept analysis is to clarify the conceptual components of AI in nursing and to determine its conceptual maturity. A concept analysis following Morse's approach was conducted, which examined definitions, characteristics, preconditions, outcomes, and boundaries on the state of AI in nursing. A total of 18 quantitative, qualitative, mixed-methods, and reviews related to AI in nursing were retrieved from the CINAHL and EMBASE databases using a Boolean search. Presently, the concept of AI in nursing is immature. The characteristics and preconditions of the use of AI in nursing are mixed between and within each other. The preconditions and outcomes on the use of AI in nursing are diverse and indiscriminately reported. As for boundaries, they can be more distinguished between robots, sensors, and clinical decision support systems, but these lines can become more blurred in the future. As of 2021, the use of AI in nursing holds much promise for the profession, but conceptual and theoretical issues remain.