Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic

"None of us are lying": an interpretive description of the search for legitimacy and the journey to access quality health services by individuals living with Long COVID

BACKGROUND

Understanding of Long COVID has advanced through patient-led initiatives. However, research about barriers to accessing Long COVID services is limited. This study aimed to better understand the need for, access to, and quality of, Long COVID services. We explored health needs and experiences of services, including ability of services to address needs.

METHODS

Our study was informed by the Levesque et al.'s (2013) "conceptual framework of access to health care." We used Interpretive Description, a qualitative approach partly aimed at informing clinical decisions. We recruited participants across five settings. Participants engaged in one-time, semi-structured, virtual interviews. Interviews were transcribed verbatim. We used reflexive thematic analysis. Best practice to ensure methodological rigour was employed.

RESULTS

Three key themes were generated from 56 interviews. The first theme illustrated the rollercoaster-like nature of participants' Long COVID symptoms and the resulting impact on function and health. The second theme highlighted participants' attempts to access Long COVID services. Guidance received from healthcare professionals and self-advocacy impacted initial access. When navigating Long COVID services within the broader system, participants encountered barriers to access around stigma; appointment logistics; testing and 'normal' results; and financial precarity and affordability of services. The third theme illuminated common factors participants liked and disliked about Long COVID services. We framed each sub-theme as the key lesson (stemming from all likes and dislikes) that, if acted upon, the health system can use to improve the quality of Long COVID services. This provides tangible ways to improve the system based directly on what we heard from participants.

CONCLUSION

With Long COVID services continuously evolving, our findings can inform decision makers within the health system to better understand the lived experiences of Long COVID and tailor services and policies appropriately.

Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis

BACKGROUND

Past research in the Western context found that people with dementia search for digital dementia information in peer-reviewed medical research articles, dementia advocacy and medical organizations, and blogs written by other people with dementia. This past work also demonstrated that people with dementia do not perceive English digital dementia information as emotionally or cognitively accessible.

OBJECTIVE

In this study, we sought to investigate the readability; linguistic, psychological, and emotional characteristics; and target audiences of digital dementia information. We conducted a textual analysis of 3 different types of text-based digital dementia information written in English: 300 medical articles, 35 websites, and 50 blogs.

METHODS

We assessed the text's readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level measurements, as well as tone, analytical thinking, clout, authenticity, and word frequencies using a natural language processing tool, Linguistic Inquiry and Word Count Generator. We also conducted a thematic analysis to categorize the target audiences for each information source and used these categorizations for further statistical analysis.

RESULTS

The median Flesch-Kincaid Grade Level readability score and Flesch Reading Ease score for all types of information (N=1139) were 12.1 and 38.6, respectively, revealing that the readability scores of all 3 information types were higher than the minimum requirement. We found that medical articles had significantly (P=.05) higher word count and analytical thinking scores as well as significantly lower clout, authenticity, and emotional tone scores than websites and blogs. Further, blogs had significantly (P=.48) higher word count and authenticity scores but lower analytical scores than websites. Using thematic analysis, we found that most of the blogs (156/227, 68.7%) and web pages (399/612, 65.2%) were targeted at people with dementia. Website information targeted at a general audience had significantly lower readability scores. In addition, website information targeted at people with dementia had higher word count and lower emotional tone ratings. The information on websites targeted at caregivers had significantly higher clout and lower authenticity scores.

CONCLUSIONS

Our findings indicate that there is an abundance of digital dementia information written in English that is targeted at people with dementia, but this information is not readable by a general audience. This is problematic considering that people with <12 years of education are at a higher risk of developing dementia. Further, our findings demonstrate that digital dementia information written in English has a negative tone, which may be a contributing factor to the mental health crisis many people with dementia face after receiving a diagnosis. Therefore, we call for content creators to lower readability scores to make the information more accessible to a general audience and to focus their efforts on providing information in a way that does not perpetuate overly negative narratives of dementia.

Evaluating Efficiency of a Provincial Telerehabilitation Service in Improving Access to Care During the COVID-19 Pandemic

Scope

Early in the COVID-19 pandemic, community rehabilitation stakeholders from a provincial health system designed a novel telerehabilitation service. The service provided wayfinding and self-management advice to individuals with musculoskeletal concerns, neurological conditions, or post-COVID-19 recovery needs. This study evaluated the efficiency of the service in improving access to care.

Methodology

We used multiple methods including secondary data analyses of call metrics, narrative analyses of clinical notes using artificial intelligence (AI) and machine learning (ML), and qualitative interviews.

Conclusions

Interviews revealed that the telerehabilitation service had the potential to positively impact access to rehabilitation during the COVID-19 pandemic, for individuals living rurally, and for individuals on wait lists. Call metric analyses revealed that efficiency may be enhanced if call handling time was reduced. AI/ML analyses found that pain was the most frequently-mentioned keyword in clinical notes, suggesting an area for additional telerehabilitation resources to ensure efficiency.

Effect of neuro-linguistic programming on COVID-19 fear in kidney transplant patients: A randomized controlled study

This study was conducted experimentally to evaluate the effect of neuro-linguistic programming (NLP) on fear of COVID-19 in kidney transplant patients. The study was carried out between June 2021 and October 2021. The Personal Information Form and COVID-19 Fear Scale (FCV-19S) were used to collect data. The obtained data obtained were evaluated using the SPSS 25 software. NLP was found to reduce the fear of COVID-19 in kidney transplant patients. Clinical nurses can use NLP techniques to support patients with fear in similar patient groups. Patients can be provided with access to training programs where they can learn NLP techniques. CLINICALTRIALS.GOV: NCT05115435.

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

Human-like tele-health robotics for older adults – A preliminary feasibility trial and vision

Introduction

The global increase of the aging population presents major challenges to healthcare service delivery. Further, the COVID-19 pandemic exposed older adults’ vulnerability to rapid deterioration of health when deprived of access to care due to the need for social distancing. Robotic technology advancements show promise to improve provision of quality care, support independence for patients and augment the capabilities of clinicians to perform tasks remotely.

Aim

This study explored the feasibility and end-user acceptance of using a novel human-like tele-robotic system with touch feedback to conduct a remote medical examination and deliver safe care.

Method

Testing of a remotely controlled robot was conducted with in-person clinician support to gather ECG readings of 11 healthy participants through a digital medical device. Post-study feedback about the system and the remote examinations conducted was obtained from study participants and study clinicians.

Results

The findings demonstrated the system’s capability to support remote examination of participants, and validated the system’s perceived acceptability by clinicians and end-users who all reported feeling safe interacting with the robot and 72% preferred remote robotic exam over in-person examination.

Conclusion

This paper discusses potential implications of robot-assisted telehealth for patients including older adults who are precluded from having in-person medical visits due to geographic distance or mobility, and proposes next steps for advancing robot-assisted telehealth delivery.

Predicting the Classification of Home Oxygen Therapy for Post-COVID-19 Rehabilitation Patients Using a Neural Network

OBJECTIVE

We evaluated the accuracy of a neural network to classify and predict the possibility of home oxygen therapy at the time of discharge from hospital based on patient information post-coronavirus disease (COVID-19) at admission.

METHODS

Patients who survived acute treatment with COVID-19 and were admitted to the Amagasaki Medical Co-operative Hospital during August 2020-December 2021 were included. However, only rehabilitation patients (n = 88) who were discharged after a rehabilitation period of at least 2 weeks and not via home or institution were included. The neural network model implemented in R for Windows (4.1.2) was trained using data on patient age, gender, and number of days between a positive polymerase chain reaction test and hospitalization, length of hospital stay, oxygen flow rate required at hospitalization, and ability to perform activities of daily living. The number of training trials was 100. We used the area under the curve (AUC), accuracy, sensitivity, and specificity as evaluation indicators for the classification model.

RESULTS

The model of states at rest had as AUC of 0.82, sensitivity of 75.0%, specificity of 88.9%, and model accuracy of 86.4%. The model of states on exertion had an ACU of 0.82, sensitivity of 83.3%, specificity of 81.3%, and model accuracy of 81.8%.

CONCLUSION

The accuracy of this study's neural network model is comparable to that of previous studies recommended by Japanese Guidelines for the Physical Therapy and is expected to be used in clinical practice. In future, it could be used as a more accurate clinical support tool by increasing the sample size and applying cross-validation.

Real-World Evidence of COVID-19 Patients' Data Quality in the Electronic Health Records

Despite the importance of electronic health records data, less attention has been given to data quality. This study aimed to evaluate the quality of COVID-19 patients' records and their readiness for secondary use. We conducted a retrospective chart review study of all COVID-19 inpatients in an academic healthcare hospital for the year 2020, which were identified using ICD-10 codes and case definition guidelines. COVID-19 signs and symptoms were higher in unstructured clinical notes than in structured coded data. COVID-19 cases were categorized as 218 (66.46%) "confirmed cases", 10 (3.05%) "probable cases", 9 (2.74%) "suspected cases", and 91 (27.74%) "no sufficient evidence". The identification of "probable cases" and "suspected cases" was more challenging than "confirmed cases" where laboratory confirmation was sufficient. The accuracy of the COVID-19 case identification was higher in laboratory tests than in ICD-10 codes. When validating using laboratory results, we found that ICD-10 codes were inaccurately assigned to 238 (72.56%) patients' records. "No sufficient evidence" records might indicate inaccurate and incomplete EHR data. Data quality evaluation should be incorporated to ensure patient safety and data readiness for secondary use research and predictive analytics. We encourage educational and training efforts to motivate healthcare providers regarding the importance of accurate documentation at the point-of-care.

Evaluating Community-Facing Virtual Modalities to Support Complex Neurological Populations During the COVID-19 Pandemic: Protocol for a Mixed Methods Study

Background

The COVID-19 pandemic and concomitant governmental responses have created the need for innovative and collaborative approaches to deliver services, especially for populations that have been inequitably affected. In Alberta, Canada, two novel approaches were created in Spring 2020 to remotely support patients with complex neurological conditions and rehabilitation needs. The first approach is a telehealth service that provides wayfinding and self-management advice to Albertans with physical concerns related to existing neurological or musculoskeletal conditions or post-COVID-19 recovery needs. The second approach is a webinar series aimed at supporting self-management and social connectedness of individuals living with spinal cord injury.

Objective

The study aims to evaluate the short- and long-term impacts and sustainability of two virtual modalities (telehealth initiative called Rehabilitation Advice Line [RAL] and webinar series called Alberta Spinal Cord Injury Community Interactive Learning Seminars [AB-SCILS]) aimed at advancing self-management, connectedness, and rehabilitation needs during the COVID-19 pandemic and beyond.

Methods

We will use a mixed-methods evaluation approach. Evaluation of the approaches will include one-on-one semistructured interviews and surveys. The evaluation of the telehealth initiative will include secondary data analyses and analysis of call data using artificial intelligence. The evaluation of the webinar series will include analysis of poll questions collected during the webinars and YouTube analytics data.

Results

The proposed study describes unique pandemic virtual modalities and our approaches to evaluating them to ensure effectiveness and sustainability. Implementing and evaluating these virtual modalities synchronously allows for the building of knowledge on the complementarity of these methods. At the time of submission, we have completed qualitative and quantitative data collection for the telehealth evaluation. For the webinar series, so far, we have distributed the evaluation survey following three webinars and have conducted five attendee interviews.

Conclusions

Understanding the impact and sustainability of the proposed telehealth modalities is important. The results of the evaluation will provide data that can be actioned and serve to improve other telehealth modalities in the future, since health systems need this information to make decisions on resource allocation, especially in an uncertain pandemic climate. Evaluating the RAL and AB-SCILS to ensure their effectiveness demonstrates that Alberta Health Services and the health system care about ensuring the best practice even after a shift to primarily virtual care.

International Registered Report Identifier (IRRID)

DERR1-10.2196/28267

Epidemiological and Clinical Characteristics of COVID-19: A Retrospective Multi-Center Study in Pakistan

The emergence of a pathogen responsible for a mysterious respiratory disease was identified in China and later called a novel coronavirus. This disease was named COVID-19. The present study seeks to determine the epidemiological and clinical characteristics of COVID-19 in Pakistan. This report will exhibit a linkage between epidemiology and clinical aspects which in turn can be helpful to prevent the transmission of the virus in Pakistan. A retrospective, multiple center study was performed by collecting the data from patients' with their demographics, epidemiological status, history of co-morbid conditions, and clinical manifestations of the disease. The data was collected from 31 public-sector and 2 private hospitals across Pakistan by on-field healthcare workers. A Chi-square test was applied to assess the relationship between categorical data entries. A total of 194 medical records were examined. The median age of these patients was found to be 34 years. A total of 53.6% active cases were present including 41.2% males and 12.4% females till the end of the study. Adults accounted for most of the cases (94.3%) of COVID-19. Fever (86.60%), cough (85.05%), fatigue (36.60%), dyspnea (24.74%), and gastrointestinal discomfort (10.31%) were among the most frequently reported signs and symptoms by the patients. However, 4.12% of the total patient population remained asymptomatic. The median duration of hospital stay was found to be 14 (0-19) days. The earliest source of the spread of the virus may be linked to the foreigners traveling to Pakistan. Spread among men was more as compared to women. A few cases were found to be positive, due to the direct contact with pets or livestock. Hypertension (7.73%), diabetes (4.64%), cardiovascular conditions (2.58%) were the most common co-morbidities. The percentage mortality was 2.50% with the highest mortality among elders.