Building a dementia-capable nursing workforce

Vasquez, Lyndsy; Schultz, Melinda; Ishado, Emily; Borson, Soo; Sadak, Tatiana

doi:10.1016/j.profnurs.2023.02.007

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Another key consideration to reflect upon is the low inclination for nursing students to work with older adults and people living with dementia [2]. Evidence highlights nursing students experience less favorable attitudes toward dementia care and see this route as a less favorable career path following graduation [52].…”

Section: Preparedness For the Care Of People Living With Dementiamentioning

confidence: 99%

“…Vasquez et al [2] report that 92% of nursing students surveyed (N=76) indicated that they had no plans to work with people living with dementia on graduation, and 38% reported they would actively avoid a person living with dementia who was agitated. The conclusion of this study recommends that new ways must be developed to inspire and prepare the latest generation of nursing undergraduates to meet the needs of this vulnerable population and to challenge the stigma of caring for older people and people living with dementia.…”

Section: Preparedness For the Care Of People Living With Dementiamentioning

confidence: 99%

“…Undergraduate student nurses can experience a wide range of clinical experience; however, dementia care is often overlooked as an essential training and education component of preregistration nursing education [1], leaving student nurses' ill-prepared and lacking core nursing skills [2].…”

Section: Introductionmentioning

confidence: 99%

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Student Nurse Simulation: Preparedness for the Care of People Living with Dementia – A Review

Price

2023

IJANER

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Pre-registration nursing simulation promotes learning in an environment replicating clinical practice and allows nursing students to gain experience before professional registration. Simulation is widely used in Higher Education Institutions (HEIs) to develop student nurses' skills, knowledge, and attitudes in an environment that allows for error, where patients are not exposed to risk. With the challenges being faced by HEIs in finding clinical placements for pre-registration nursing students, simulation is an effective method of addressing the theory-practice gap while enabling student nurses to gain valuable experience in preparation for registered nursing practice. One area underutilized in simulation activity for pre-registrant nurses in dementia care, with many HEIs not delivering specific dementia simulation learning and having little dementia care included in curriculums. With the increasing numbers of people living with dementia accessing health and social care services, this article explores nursing simulation and its role in preparing student nurses for the care of people living with dementia.

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Section: Preparedness For the Care Of People Living With Dementiamentioning

confidence: 99%

Section: Preparedness For the Care Of People Living With Dementiamentioning

confidence: 99%

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Student Nurse Simulation: Preparedness for the Care of People Living with Dementia – A Review

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2023

IJANER

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Additional Value From Free-Text Diagnoses in Electronic Health Records: Hybrid Dictionary and Machine Learning Classification Study

Mehra,

Wekhof,

Keller

2024

JMIR Med Inform

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Background Physicians are hesitant to forgo the opportunity of entering unstructured clinical notes for structured data entry in electronic health records. Does free text increase informational value in comparison with structured data? Objective This study aims to compare information from unstructured text-based chief complaints harvested and processed by a natural language processing (NLP) algorithm with clinician-entered structured diagnoses in terms of their potential utility for automated improvement of patient workflows. Methods Electronic health records of 293,298 patient visits at the emergency department of a Swiss university hospital from January 2014 to October 2021 were analyzed. Using emergency department overcrowding as a case in point, we compared supervised NLP-based keyword dictionaries of symptom clusters from unstructured clinical notes and clinician-entered chief complaints from a structured drop-down menu with the following 2 outcomes: hospitalization and high Emergency Severity Index (ESI) score. Results Of 12 symptom clusters, the NLP cluster was substantial in predicting hospitalization in 11 (92%) clusters; 8 (67%) clusters remained significant even after controlling for the cluster of clinician-determined chief complaints in the model. All 12 NLP symptom clusters were significant in predicting a low ESI score, of which 9 (75%) remained significant when controlling for clinician-determined chief complaints. The correlation between NLP clusters and chief complaints was low (r=−0.04 to 0.6), indicating complementarity of information. Conclusions The NLP-derived features and clinicians’ knowledge were complementary in explaining patient outcome heterogeneity. They can provide an efficient approach to patient flow management, for example, in an emergency medicine setting. We further demonstrated the feasibility of creating extensive and precise keyword dictionaries with NLP by medical experts without requiring programming knowledge. Using the dictionary, we could classify short and unstructured clinical texts into diagnostic categories defined by the clinician.

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Additional Value From Free-Text Diagnoses in Electronic Health Records: Hybrid Dictionary and Machine Learning Classification Study (Preprint)

Mehra,

Wekhof,

Keller

2023

Preprint

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BACKGROUND Physicians are hesitant to forgo the opportunity of entering unstructured clinical notes for structured data entry in electronic health records. Does the flexibility of free text also increase informational value? OBJECTIVE To compare information from unstructured clinical notes harvested and processed by a natural language processing algorithm, to clinician-entered structured data as to their potential utility for automated improvement of patient workflows. METHODS EHR of 293’298 patient visists at a Swiss university hospital from 01/2014 to 10/2021 were analyzed. Using emergency department overcrowding as a case in point, we compared supervised natural language processing (NLP)-based symptom clusters from unstructured clinical notes and clinician-entered chief complaints from a structured drop-down menu with the two outcomes hospitalization and high ESI score. RESULTS In 11 of the 12 symptom clusters, the NLP cluster was significant in predicting hospitalization. Eight clusters remained significant even when controlling for the cluster of clinician-determined chief complaints in the model. All 12 NLP symptom clusters were significant in predicting a low ESI score, of which 9 remained significant when controlling for clinician-determined chief complaints. The correlation of NLP clusters with chief complaints was low (-0.04-0.6), indicating a complementarity of information. CONCLUSIONS The NLP-derived features and clinicians’ knowledge were complementary in explaining patient outcome heterogeneity. They can provide an efficient approach to patient flow management, for example, in an emergency medicine setting. We further demonstrate the feasibility of creating extensive and precise keyword dictionaries with NLP by medical experts without requiring programming knowledge. Using the dictionary, we could classify short and unstructured clinical texts into diagnostic categories defined by the clinician. CLINICALTRIAL None

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Building a dementia-capable nursing workforce

Cited by 3 publications

References 25 publications

Student Nurse Simulation: Preparedness for the Care of People Living with Dementia – A Review

Student Nurse Simulation: Preparedness for the Care of People Living with Dementia – A Review

Additional Value From Free-Text Diagnoses in Electronic Health Records: Hybrid Dictionary and Machine Learning Classification Study

Additional Value From Free-Text Diagnoses in Electronic Health Records: Hybrid Dictionary and Machine Learning Classification Study (Preprint)

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