Watson's Authentic Presence: Philosophical and Theoretical Approaches

Presence remains a fundamental aspect of nursing practice and theory and yet remains poorly defined. The author describes Watson's phenomenon of authentic presence in both the nursing and interdisciplinary literature. The emerging themes are applied to Watson's theoretical and philosophical perspectives of human caring science.

What Total Body Water Measurement Should Be Used for Prescribing the Dialysis Dose in Low-Flow Home Daily Dialysis?

INTRODUCTION

In low-flow home daily dialysis (HDD), the dialysis dose is evaluated from the total body water (TBW). TBW can be estimated by anthropometric methods or bioimpedance spectroscopy.

METHODS

A multicentric cross-sectional study of patients in HDD for >3 months was conducted to assess the correlation and the difference between the anthropometric estimate of TBW (Watson-TBW) and the bioimpedance estimate (BIS-TBW) and to analyse the impact on the dialysate volume prescribed.

RESULTS

Forty patients from 10 centres were included. The median BIS-TBW and Watson-TBW were 35.1 (29.1-41.4 L) and 36.9 (32-42.4 L), respectively. The 2 methods had a good correlation (r = 0.87, p < 0.05). However, Bland-Altman analysis showed an overestimation of TBW with Watson's formula, with a bias of 2.77 L. For 4, 5, or 6 sessions per week, the use of Watson-TBW increases the dialysate prescription per week by 100 L, 45 L, or 10 L, respectively, over our entire cohort. There is no increase in the volume of dialysate prescribed with the 7 sessions per week schedule.

CONCLUSION

BIS-TBW and Watson-TBW estimation have a good correlation; however, Watson's equation overestimates TBW. This overestimation is negligible for a prescription frequency of >5 sessions per week.

Critical Care Equipment Management Reimagined in an Emergency

SUMMARY

The COVID19 pandemic created a surge in demand for critical care equipment against a backdrop of fast-moving geographic virus hotspots. A team from IBM Europe was put together to prove that a devolved healthcare system can be rapidly bridged by a mix of advanced and legacy technologies to provide a federated view of critical care equipment deployment and use during an emergency. This was achieved with the deployment of predictive analytics and blockchain, integrated with conventional hospital management system. The corollary investigation determined the manner in which this system can be harnessed in a postemergency recovery to provide a national supply chain efficiency backbone.

METHOD

During a period of 2 weeks, a team of IBM consultants set up a technology sandbox environment to represent a network of an equipment manufacturer, a central national emergency monitoring center, and several hospitals managed by their respective trust organization. Within this environment, a hospital asset management system, Maximo, was configured to manage and track critical care equipment within a hospital; a blockchain traceability platform, IBM's Blockchain Transparency System, was configured to ingest multiple hospital data reports; and a predictive analytic dashboard, Watson Analytics, would retrieve data from the blockchain platform to supplement other data sources to provide national views and support decision-making for the supply and movement of equipment. Three key principles in the design of this environment are speed, reuse, and minimal intrusion.

RESULTS

The hypothesis was to test whether the chosen technologies can overcome the challenges of misaligned demand and supply of critical care equipment during a national emergency. The execution of the tests led to successful simulation of three scenarios: (1) the tracking of the location and usage history of any single equipment that has been placed into the network; (2) the movement of equipment between independent hospitals is recorded and reported; (3) a real-time interrogation of the current location and status of all registered equipment.

CONCLUSIONS

The successful completion of this proof of concept has demonstrated that emerging technology can be used to overcome poor macro level coordination and planning, which are the drawbacks of a devolved healthcare system. The corollary was that this proof also demonstrated that blockchain technology can be used to prolong the useful life of conventional technology.

Becoming a Channel of God: How Faith Community Nurses Develop Their Spiritual Practice

PURPOSE

This study explored the underlying process faith community nurses (FCNs) experience in developing their spiritual nursing practice.

DESIGN

A qualitative, exploratory design was used.

METHOD

Data from interviews with six FCNs were generated and analyzed using Glaserian grounded theory.

FINDINGS

The basic social psychological process, cultivating the soul to become a channel of God, explains the steps these nurses take to achieve stages of presence. Going through these stages of presence, FCNs develop a foundation of God-related beliefs and values, presence with self, presence with God, presence with others, presence with God and others, and become a channel of God.

CONCLUSIONS

Developing spiritual care competence in assessing and meeting clients' spiritual needs is necessary to enhance person-centered practice, a vital aspect of holistic care. The model of presence can inform the development of spiritual care competencies and link to other nursing theories including Watson's theory of caring and Benner's novice to expert theory. Workplace support is needed for nurses to refine spiritual nursing care practices and integrate spiritual care into practice. Further research regarding the stages of presence could foster deeper understanding of how foundations of God-related values develop.

Self-care as an ethical obligation for nurses

As members of the largest and most trusted healthcare profession, nurses are role models and critical partners in the ongoing quest for the health of their patients. Findings from the American Nurses Association Health Risk Appraisal suggested that nurses give the best patient care when they are operating at the peak of their own wellness. They also revealed that 68% of the surveyed nurses place their patients' health, safety, and wellness before their own. Globally, several nursing codes of ethics include the requirement of self-care. Often, these codes embed the responsibility to protect and promote one's own health within the clearly described obligation to provide safe patient care. The American Nurses Association Code of Ethics for Nurses is unique in that it states explicitly that nurses must adopt self-care as a duty to self in addition to their duty to provide care to patients. One of the basic assumptions of Watson's Philosophy and Science of Caring is that caring science is the essence of nursing and the foundational disciplinary core of the profession. Watson's theory of human caring provides support for the engagement in self-care. Two important value assumptions of Watson's Caritas are that "we have to learn how to offer caring, love, forgiveness, compassion, and mercy to ourselves before we can offer authentic caring and love to others" and we also must "treat ourselves with loving-kindness and equanimity, gentleness, and dignity before we can accept, respect, and care for others within a professional caring-healing model." Embedded within several caritas processes is an outline for a holistic approach to caring for self and others that can guide nurses to improve their mental, physical, emotional, and spiritual health.

Jin Shin Jyutsu® Self-Help Reduces Nurse Stress: A Randomized Controlled Study

Purpose: The purpose of this research was to explore the impact of Jin Shin Jyutsu (JSJ) Self-Help on personal stress and the caring efficacy of nurses. Design: A randomized, controlled comparison study, with crossover design was conducted. Method: Stress and caring efficacy were measured via surveys at baseline, posteducation, and again 30 to 40 days after completion of the JSJ educational intervention. Self-reported stress was the primary endpoint as measured with the validated Personal and Organizational Quality Assessment-Revised 4 Scale (POQA-R4) survey. Caring Efficacy was measured using the Coates Caring Efficacy Scale. Findings: A total of 41 nurses consented and completed the study; 18 were in the education group and 23 were in the control group. Changes in stress were sustained in the education group for the POQA-R4. Changes observed in the control group were not sustained. Statistical differences were observed when comparing education and control from baseline to final surveys for measures of emotional vitality and buoyancy. Increases in nursing caring efficacy were observed in both groups. Scores were consistently higher in the education group. Statistically significant differences were observed from baseline to final measure for the education group. Conclusions: Results show JSJ as a viable option for stress reduction in nurses.

Altruism and the Difficult Patient

BACKGROUND

For some nurses, providing altruistic care to difficult patients is a challenge, leading to the use of negative coping strategies such as anger or avoidance, leaving the nurse frustrated and patients feeling rejected. Yet other nurses can deal positively with difficult patients.

PURPOSE

To study how the stress of caring for difficult patients affects the level of altruism and use of negative coping strategies in their care and to find out what positive coping strategies and interventions could be used.

DESIGN/METHOD

A mixed-methods design was used. A survey tested the level of altruism, the use of negative coping strategies, and several demographics. Thematic analysis examined narratives generated by participants to three questions regarding how to provide altruistic care to difficult patients.

FINDINGS

A purposive sample of 67 registered nurses (RNs) participated. The average level of altruism used by RNs with difficult patients was 99.7/120 points. An inverse relationship was found between the level of altruism and use of negative coping strategies (r = -0.577, p < .001). Qualitative analysis of narratives identified three themes-Developing Psychological Hardiness, Bearing Witness, and Fending for Oneself.

CONCLUSIONS

This study provides insight into the experiences of RNs caring for difficult patients and how to ensure altruistic caring.

What volume to choose to assess online Kt/V?

INTRODUCTION

Urea distribution volume (V) can be assessed in different ways, among them the anthropometric Watson Volume (V_W). However, many studies have shown that V_W does not coincide with V and that the latter can be more accurately estimated with other methods. The present multicentre study was designed to answer the question: what V to choose to assess online Kt/V?

MATERIALS AND METHODS

Pre- and postdialysis blood urea nitrogen concentrations and the usual input data set for urea kinetic modelling were obtained for a single dialysis session in 201 Caucasian patients treated in 9 Italian dialysis units. Only dialysis machines measuring ionic dialysance (ID) were utilized. ID reflects very accurately the mean effective dialyser urea clearance (Kd). Six different V values were obtained: the first one was V_W; the second one was computed from the equation established by the HEMO Study to predict the single pool-adjusted modelled V from V_W (V_H) (Daugirdas JT et al. KI 64: 1108, 2003); the others were estimated kinetically as: 1. V_ID, in which ID is direct input in the in the double pool variable volume (dpVV) calculation by means of the Solute-solver software; 2. V_Kd, in which the estimated Kd is direct input in the dpVV calculation by means of the Solute-solver software; 3. V_KTV, in which V is calculated by means of the second generation Daugirdas equation; 4. V_SPEEDY, in which ID is direct input in the dpVV calculation by means of the SPEEDY software able to provide results quite similar to those provided by Solute-solver.

RESULTS

Mean± SD of the main data are reported: measured ID was 190.6 ± 29.6 mL/min, estimated Kd was 211.6 ± 29.0 mL/min. The relationship between paired data was poor (R² = 0.34) and their difference at the Bland-Altman plot was large (21 ± 27 mL/min). V_W was 35.3 ± 6.3 L, V_H 29.5 ± 5.5, V_{_}ID 28.99 ± 7.6 L, V_{_}SPEEDY 29.4 ± 7.6 L, V_KTV 29.7 ± 7.0 L. The mean ratio V_W/V_ID was 1.22, (i.e. V_W overestimated V_ID by about 22%). The mean ratio V_H/V_ID was 1.02 (i.e. V_H overestimated V_ID by only 2%). The relationship between paired data of V_ID and V_W was poor (R² = 0.48) and their mean difference at the Bland-Altman plot was very large (-  6.39 ± 5.59 L). The relationship between paired data of V_ID and V_H was poor (R² = 47) and their mean difference was small but with a large SD (- 0.59 ± 5.53 L). The relationship between paired data of V_ID and V_SPEEDY was excellent (R² = 0.993) and their mean difference at the Bland-Altman plot was very small (- 0.54 ± 0.64 L). The relationship between paired data of V_ID and V_KTV was excellent (R² = 0.985) and their mean difference at the Bland-Altman plot was small (- 0.85 ± 1.06 L).

CONCLUSIONS

V_ID can be considered the reference method to estimate the modelled V and then the first choice to assess Kt/V. V_SPEEDY is a valuable alternative to V_ID. V_KTV can be utilized in the daily practice, taking also into account its simple way of calculation. V_W is not advisable because it leads to underestimation of Kt/V by about 20%.

MicroRNAs and Cancer: A Long Story for Short RNAs

More than six decades ago Watson and Crick published the chemical structure of DNA. This discovery revolutionized our approach to medical science and opened new perspectives for the diagnosis and treatment of many diseases including cancer. Since then, progress in molecular biology, together with the rapid advance of technologies, allowed to clone hundreds of protein-coding genes that were found mutated in all types of cancer. Normal and aberrant gene functions, interactions, and mechanisms of mutations were studied to identify the intricate network of pathways leading to cancer. With the acknowledgment of the genetic nature of cancer, new diagnostic, prognostic, and therapeutic strategies have been attempted and developed, but very few have found their way in the clinical field. In an effort to identify new translational targets, another great discovery has changed our way to look at genes and their functions. MicroRNAs have been the first noncoding genes involved in cancer. This review is a brief chronological history of microRNAs and cancer. Through the work of few of the greatest scientists of our times, this chapter describes the discovery of microRNAs from C. elegans to their debut in cancer and in the medical field, the concurrent development of technologies, and their future translational applications. The purpose was to share the exciting path that lead to one of the most important discoveries in cancer genetics in the past 20 years.

A peek into the future of radiology using big data applications

Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs – Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs – Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR), volumetric rendering (VR), and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, “big data should not become “dump data” due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the era of personalized and individualized healthcare.

Implementation of a web based universal exchange and inference language for medicine: Sparse data, probabilities and inference in data mining of clinical data repositories

We extend Q-UEL, our universal exchange language for interoperability and inference in healthcare and biomedicine, to the more traditional fields of public health surveys. These are the type associated with screening, epidemiological and cross-sectional studies, and cohort studies in some cases similar to clinical trials. There is the challenge that there is some degree of split between frequentist notions of probability as (a) classical measures based only on the idea of counting and proportion and on classical biostatistics as used in the above conservative disciplines, and (b) more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems. Samples in the above kind of public health survey are typically small compared with our earlier "Big Data" mining efforts. An issue addressed here is how much impact on decisions should sparse data have. We describe a new Q-UEL compatible toolkit including a data analytics application DiracMiner that also delivers more standard biostatistical results, DiracBuilder that uses its output to build Hyperbolic Dirac Nets (HDN) for decision support, and HDNcoherer that ensures that probabilities are mutually consistent. Use is exemplified by participating in a real word health-screening project, and also by deployment in a industrial platform called the BioIngine, a cognitive computing platform for health management.

Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine

As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

Strand-seq: a unifying tool for studies of chromosome segregation

Non random segregation of sister chromatids has been implicated to help specify daughter cell fate (the Silent Sister Hypothesis [1]) or to protect the genome of long-lived stem cells (the Immortal Strand Hypothesis [2]). The idea that sister chromatids are non-randomly segregated into specific daughter cells is only marginally supported by data in sporadic and often contradictory studies. As a result, the field has moved forward rather slowly. The advent of being able to directly label and differentiate sister chromatids in vivo using fluorescence in situ hybridization [3] was a significant advance for such studies. However, this approach is limited by the need for large tracks of unidirectional repeats on chromosomes and the reliance on quantitative imaging of fluorescent probes and rigorous statistical analysis to discern between the two competing hypotheses. A novel method called Strand-seq which uses next-generation sequencing to assay sister chromatid inheritance patterns independently for each chromosome [4] offers a comprehensive approach to test for non-random segregation. In addition Strand-seq enables studies on the deposition of chromatin marks in relation to DNA replication. This method is expected to help unify the field by testing previous claims of non-random segregation in an unbiased way in many model systems in vitro and in vivo.

Making IBM's Computer, Watson, Human

This essay uses the recent victory of an IBM computer (Watson) in the TV game, Jeopardy, to speculate on the abilities Watson would need, in addition to those it has, to be human. The essay's basic premise is that to be human is to behave as humans behave and to function in society as humans function. Alternatives to this premise are considered and rejected. The viewpoint of the essay is that of teleological behaviorism. Mental states are defined as temporally extended patterns of overt behavior. From this viewpoint (although Watson does not currently have them), essential human attributes such as consciousness, the ability to love, to feel pain, to sense, to perceive, and to imagine may all be possessed by a computer. Most crucially, a computer may possess self-control and may act altruistically. However, the computer's appearance, its ability to make specific movements, its possession of particular internal structures (e.g., whether those structures are organic or inorganic), and the presence of any nonmaterial "self," are all incidental to its humanity.