Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances

Use of artificial intelligence to support prehospital traumatic injury care: A scoping review

Background

Artificial intelligence (AI) has transformative potential to support prehospital clinicians, emergency physicians, and trauma surgeons in acute traumatic injury care. This scoping review examines the literature evaluating AI models using prehospital features to support early traumatic injury care.

Methods

We conducted a systematic search in August 2023 of PubMed, Embase, and Web of Science. Two independent reviewers screened titles/abstracts, with a third reviewer for adjudication, followed by a full-text analysis. We included original research and conference presentations evaluating AI models-machine learning (ML), deep learning (DL), and natural language processing (NLP)-that used prehospital features or features available immediately upon emergency department arrival. Review articles were excluded. The same investigators extracted data and systematically categorized outcomes to ensure consistency and transparency. We calculated kappa for interrater reliability and descriptive statistics.

Results

We identified 1050 unique publications, with 49 meeting inclusion criteria after title and abstract review (kappa 0.58) and full-text review. Publications increased annually from 2 in 2007 to 10 in 2022. Geographic analysis revealed a 61% focus on data from the United States. Studies were predominantly retrospective (88%), used local (45%) or national level (41%) data, focused on adults only (59%) or did not specify adults or pediatrics (27%), and 57% encompassed both blunt and penetrating injury mechanisms. The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. The most common study objectives were to predict the need for critical care and life-saving interventions (29%), assist in triage (22%), and predict survival (20%).

Conclusions

A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.

The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review

BACKGROUND

Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear.

METHODS

We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines.

RESULTS

From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%-100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%-100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms.

CONCLUSIONS

Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported.

Emergency Medical Service Attendances for Adults with Repeat Falls in Western Australia: A State-Wide Retrospective Cohort Study

OBJECTIVES

The risk of falls increases with age and often requires an emergency medical service (EMS) response. We compared the characteristics of patients attended by EMS in response to repeat falls within 30 days and 12 months of their first EMS-attended fall; and explored the number of days between the index fall and the subsequent fall(s).

METHODS

This retrospective cohort study included all adults (> =18 years of age) who experienced their first EMS-attended fall between 1 January 2016 and 31 December 2020, followed up until 31 December 2021. Patients who experienced > =1 subsequent fall, following their first recorded fall, were defined as experiencing repeat falls. Multivariable logistic regression was used to identify the factors associated with repeat falls; and Kaplan-Meier analysis was used to estimate the time (in days) between consecutive EMS-attended falls.

RESULTS

A total of 128,588 EMS-attended fall-related incidents occurred involving 77,087 individual patients. Most patients, 54,554 (71%) were attended only once for a fall-related incident (30,280 females; median age 73 years, inter-quartile range (IQR): 55-84). A total of 22,533 (29%) patients experienced repeat EMS-attended falls (13,248 females; median age 83 years, IQR: 74-89, at first call). These 22,533 patients accounted for 58% (74,034 attendances) of all EMS-attendances to fall-related incidents. Time between EMS-attended falls decreased significantly the more falls a patient sustained. Among the 22,533 patients who experienced repeat falls, 13,363 (59%) of repeat falls occurred within 12 months: 3,103 (14%) of patients sustained their second fall within 30 days of their index fall, and 10,260 (46%) between 31 days to 12 months. Patients who were transported to the hospital, via any urgency, at their first EMS-attended fall, had a reduced odds of sustaining a second EMS-attended fall within both 30 days and 31 days to 12 months, compared to non-transported patients.

CONCLUSION

Nearly 30% of all patients attended by EMS for a fall, sustained repeat falls, which collectively accounted for nearly 60% of all EMS-attendances to fall-related incidents. Further exploration of the role EMS clinicians play in identifying and referring patients who sustain repeat falls into alternative pathways is needed.

The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review

Falls are a common problem associated with significant morbidity, mortality, and economic costs. Current fall prevention policies in local healthcare settings are often guided by information provided by fall risk assessment tools, incident reporting, and coding data. This review was conducted with the aim of identifying studies which utilized natural language processing (NLP) for the automated detection and prediction of falls in the healthcare setting. The databases Ovid Medline, Ovid Embase, Ovid Emcare, PubMed, CINAHL, IEEE Xplore, and Ei Compendex were searched from 2012 until April 2023. Retrospective derivation, validation, and implementation studies wherein patients experienced falls within a healthcare setting were identified for inclusion. The initial search yielded 2611 publications for title and abstract screening. Full-text screening was conducted on 105 publications, resulting in 26 unique studies that underwent qualitative analyses. Studies applied NLP towards falls risk factor identification, known falls detection, future falls prediction, and falls severity stratification with reasonable success. The NLP pipeline was reviewed in detail between studies and models utilizing rule-based, machine learning (ML), deep learning (DL), and hybrid approaches were examined. With a growing literature surrounding falls prediction in both inpatient and outpatient environments, the absence of studies examining the impact of these models on patient and system outcomes highlights the need for further implementation studies. Through an exploration of the application of NLP techniques, it may be possible to develop models with higher performance in automated falls prediction and detection.

Identifying nursing sensitive indicators from electronic health records in acute cardiac care-Towards intelligent automated assessment of care quality

AIM

The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing-sensitive indicators in acute cardiac care.

BACKGROUND

Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data-based solutions that automatically extract and help interpret data from electronic health records.

METHODS

This is a deductive descriptive study that followed the theory of value-added analysis. A random sample from electronic health records of 230 patients was analysed for selected indicators. The data included documentation in structured and free-text format.

RESULTS

One thousand six hundred seventy-six expressions were extracted and divided into (1) established and (2) unestablished expressions, providing positive, neutral and negative descriptions related to care quality.

CONCLUSIONS

Electronic health records provide a potential source of information for information systems to support assessment of care quality. More research is warranted to develop, test and evaluate the effectiveness of such tools in practice.

IMPLICATIONS FOR NURSING MANAGEMENT

Knowledge-based health care management would benefit from the development and implementation of advanced information systems, which use continuously generated already available real-time big data for improved data access and interpretation to better support nursing management in quality assessment.