Health Outcomes and Healthcare Efficiencies Associated with the Use of Electronic Health Records in Hospital Emergency Departments: a Systematic Review

Evaluating the use of clinical decision aids in an Australian emergency department: A cross-sectional survey

OBJECTIVE

To identify healthcare professionals' knowledge, self-reported use, and documentation of clinical decision aids (CDAs) in a large ED in Australia, to identify behavioural determinants influencing the use of CDAs, and healthcare professionals preferences for integrating CDAs into the electronic medical record (EMR) system.

METHODS

Healthcare professionals (doctors, nurses and physiotherapists) working in the ED at the Gold Coast Hospital, Queensland were invited to complete an online survey. Quantitative data were analysed using descriptive statistics, and where appropriate, mapped to the theoretical domains framework to identify potential barriers to the use of CDAs. Qualitative data were analysed using content analysis.

RESULTS

Seventy-four healthcare professionals (34 medical officers, 31 nurses and nine physiotherapists) completed the survey. Healthcare professionals' knowledge and self-reported use of 21 validated CDAs was low but differed considerably across CDAs. Only 4 out of 21 CDAs were reported to be used 'sometimes' or 'always' by the majority of respondents (Ottawa Ankle Rule for ankle injury, Wells' criteria for pulmonary embolism, Wells' criteria for deep vein thrombosis and PERC rule for pulmonary embolism). Most respondents wanted to increase their use of valid and reliable CDAs and supported the integration of CDAs into the EMR to facilitate their use and support documentation. Potential barriers impacting the use of CDAs represented three theoretical domains of knowledge, social/professional role and identity, and social influences.

CONCLUSIONS

CDAs are used variably by healthcare professionals and are inconsistently applied in the clinical encounter. Preferences of healthcare professionals need to be considered to allow the successful integration of CDAs into the EMR.

The impact of limited access to digital health records on doctors and their willingness to adopt electronic health record systems

Objective

Research over the past decade has extensively covered the benefits of electronic health records in developing countries. Yet, the specific impact of their limited access on doctors' workload and clinical decision-making, particularly in Bangladesh, remains underexplored. This study investigates current patients' medical history storage mechanisms and associated challenges. It explores how doctors in Bangladesh obtain and review patients' past medical histories, identifying the challenges they face. Additionally, it examines whether limited access to digital health records is an obstacle in clinical decision-making and explores factors influencing doctors' willingness to adopt electronic health record systems in such contexts.

Method

An online cross-sectional survey of 105 doctors with Bachelor of Medicine, Bachelor of Surgery/Bachelor of Dental Surgery (MBBS/BDS) degrees and at least 2 years of experience was conducted, covering (a) personal information, (b) workload, (c) patient history challenges, and (d) decision-making.

Results

Out of 105 participants, 51.4% of them use paper-based methods with 56% facing challenges, versus 20% using digital methods. Most (94.3%) interview patients directly, and 80.9% are interested in a web-based, comprehensive medical history system. An ordinal regression model identified that the physicians' disciplines, workload, and efficiency level of the current workplace in facilitating patient history-taking variables significantly affected willingness to adopt the described electronic health record in the survey.

Conclusion

Doctors in Bangladesh encounter significant challenges related to workload and clinical decision-making, largely attributed to restricted access to patients' past medical histories. Despite the prevalent use of paper-based records, there is a notable willingness among these medical professionals to embrace electronic health record systems, indicating a potential shift towards more efficient healthcare practices in the region.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

CLINICALTRIAL

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The effect of My Health Record use in the emergency department on clinician-assessed patient care: results from a survey

Background

The emergency department has been a major focus for the implementation of Australia’s national electronic health record, known as My Health Record. However, the association between use of My Health Record in the emergency department setting and patient care is largely unknown. The aim of this study was to explore the perspectives of emergency department clinicians regarding My Health Record use frequency, the benefits of My Health Record use (with a focus on patient care) and the barriers to use.

Methods

All 393 nursing, pharmacy, physician and allied health staff employed within the emergency department at a tertiary metropolitan public hospital in Melbourne were invited to participate in a web-based survey, between 1 May 2021 and 1 December 2021, during the height of the Delta and Omicron Covid-19 outbreaks in Victoria, Australia.

Results

Overall, the survey response rate was 18% (70/393). Approximately half of the sample indicated My Health Record use in the emergency department (n = 39, 56%, confidence interval [CI] 43–68%). The results showed that users typically only engaged with My Health Record less than once per shift (n = 15, 39%, CI 23–55%). Just over half (n = 19/39, 54%, CI 32–65%) of all participants who use My Health Record agreed they could remember a time when My Health Record had been critical to the care of a patient. Overall, clinicians indicated the biggest barrier preventing their use of My Health Record is that they forget to utilise the system.

Conclusion

The results suggest that My Health Record has not been adopted as routine practice in the emergency department, by the majority of participants. Close to half of self-identified users of My Health Record do not associate use as being critical to patient care. Instead, My Health Record may only be used in scenarios that clinicians perceive will yield the greatest benefit—which clinicians in this paper suggest is patients with chronic and complex conditions. Further research that explores the predictors to use and consumers most likely to benefit from use is recommended—and strategies to socialise this knowledge and educate clinicians is desperately required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01920-8.

Use of My Health Record by Clinicians in the Emergency Department: An Analysis of Log Data

Objectives: Leverage log data to explore access to My Health Record (MHR), the national electronic health record of Australia, by clinicians in the emergency department. Materials and Methods: A retrospective analysis was conducted using secondary routinely-collected data. Log data pertaining to all patients who presented to the emergency department between 2019 and 2021 of a not-for-profit hospital (that annually observes 23,000 emergency department presentations) were included in this research. Attendance data and human resources data were linked with MHR log data. The primary outcome was a dichotomous variable that indicated whether the MHR of a patient was accessed. Logistic regression facilitated the exploration of factors (user role, day of the week, and month) associated with access. Results: My Health Record was accessed by a pharmacist, doctor, or nurse in 19.60% (n = 9,262) of all emergency department presentations. Access was dominated by pharmacists (18.31%, n = 8,656). All users demonstrated a small, yet significant, increase in access every month (odds ratio = 1.07, 95% Confidence interval: 1.06-1.07, p ≤ 0.001). Discussion: Doctors, pharmacists, and nurses are increasingly accessing MHR. Based on this research, substantially more pharmacists appear to be accessing MHR, compared to other user groups. However, only one in every five patients who present to the emergency department have their MHR accessed, thereby indicating a need to accelerate and encourage the adoption and access of MHR by clinicians.

Unidentified patients in the emergency department: a historical cohort study

OBJECTIVES

To characterize unidentified patients presenting to a single, urban emergency department (ED) in Canada. We report their demographics, ED course, post-ED discharge outcomes, and mode of identification.

METHODS

We performed a retrospective chart review using descriptive analyses to assess unidentified patients admitted to Royal University Hospital and St. Paul's Hospital EDs between May 1, 2018, and April 30, 2019, in Saskatoon, Saskatchewan, Canada. We assessed demographic data, clinical presentation, mode of identification, discharge information, and major clinical outcomes.

RESULTS

Unidentified patients were disproportionately male (64.9%), and mostly presented as Canadian Triage and Acuity Scale (CTAS) 1 (41.6%) and CTAS 2 (44.2%). Most patients arrived via emergency medical services (80.7%). The most common presenting complaints were substance misuse (33.3%) and trauma (24.6%). The average ED length of stay was 8.7 h (SD 18.6). Many patients received an inpatient consult (58.8%), and 22.3% received support services (e.g., social work). The 30-day mortality of all patients was 13.2%. Of those patients who survived to ED discharge, common dispositions included: home (36.0%), police services (3.5%), or emergency shelters (3.5%). Four (3.5%) patients returned to the hospital unidentified within the study period, and 6.7% of patients discharged from the ED returned within 48 hours.

CONCLUSION

Unidentified patients are a high-needs demographic that present mostly with substance misuse or trauma. Repeat ED attendance, sometimes as unidentified patients again, calls for initiatives that facilitate prompt identification, better discharge planning, and linkage to social supports.

Modeling patient-related workload in the emergency department using electronic health record data

INTRODUCTION

Understanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The goal of the current study is to use electronic health record (EHR) data to predict the amount of work that individual patients contribute to clinician workload (patient-related workload).

METHODS

One month of EHR data was retrieved from an emergency department (ED). A list of workload indicators and five potential workload proxies were extracted from the data. Linear regression and four machine learning classification algorithms were utilized to model the relationship between the indicators and the proxies.

RESULTS

Linear regression proved that the indicators explained a substantial amount of variance of the proxies (four out of five proxies were modeled with R² > 0.80). Classification algorithms also showed success in classifying a patient as having high or low task demand based on data from early in the ED visit (e.g. 80 % accurate binary classification with data from the first hour).

CONCLUSION

The main contribution of this study is demonstrating the potential of using EHR data to predict patient-related workload automatically in the ED. The predicted workload can potentially help in managing clinician workload by supporting decisions around the assignment of new patients to providers. Future work should focus on identifying the relationship between workload proxies and actual workload, as well as improving prediction performance of regression and multi-class classification.

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records

The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and reporting diseases, injuries, disorders, and health conditions. Most previously-proposed disease predicting systems need clinical information collected by the medical staff from the patients in hospitals. In this paper, we propose a deep learning algorithm to classify disease types and identify diagnostic codes by using only the subjective component of progress notes in medical records. In this study, we have a dataset, consisting of about one hundred and sixty-eight thousand medical records, from a medical center, collected during 2003 and 2017. First, we apply standard text processing procedures to parse the sentences and word embedding techniques for vector representations. Next, we build a convolution neural network model on the medical records to predict the ICD-9 code by using a subjective component of the progress note. The prediction performance is evaluated by ten-fold cross-validation and yields an accuracy of 0.409, recall of 0.409 and precision of 0.436. If we only consider the “chapter match” of ICD-9 code, our model achieves an accuracy of 0.580, recall of 0.580, and precision of 0.582. Since our diagnostic code prediction model is solely based on subjective components (mainly, patients’ self-report descriptions), the proposed approach could serve as a remote and self-diagnosis assistance tool, prior to seeking medical advice or going to the hospital. In addition, our work may be used as a primary evaluation tool for discomfort in the rural area where medical resources are restricted.