Electronic Health Record Interactions through Voice: A Review

Technological barriers to providing pediatric mental and behavioral healthcare in emergency departments

In the United States, about 5% of pediatric Emergency Department (ED) visits involve mental and behavioral health (MBH) emergencies, and EDs are often ill-equipped to provide timely, appropriate care, leading to prolonged stays and increased risks of trauma and harm for these children and their families. This study investigated technological barriers affecting safe and effective pediatric mental and behavioral healthcare in emergency departments through observational studies and semi-structured interviews with 55 medical professionals across four ED settings: pediatric MBH unit, pediatric ED, and adult ED. A total of 12 barrier themes were identified through the thematic analysis of the interviews relating to technology use that impacts the care of pediatric MBH patients. The major themes include issues due to limited electronic medical record data management, ineffective communication in the ED, and usability issues with ED technologies. Other concerns included inadequate infrastructure, absence of streamlined processes, unsafe and inefficient integration of technology, and lack of training. Challenges stemming from patients' reluctance to accept telepsychiatry further complicate providing effective care in these settings. Future research needs to focus on designing systems and solutions to eliminate the barriers, thereby supporting the caregiving process of pediatric MBH in EDs.

Empowering nurses - a practical guide to artificial intelligence tools in healthcare settings: discussion paper

BACKGROUND

The rapid growth of artificial intelligence in healthcare is transforming how nurses deliver care and make clinical decisions. From supporting diagnostics to providing virtual health assistants, artificial intelligence offers new ways to enhance patient outcomes and streamline healthcare processes. However, these advancements also bring challenges, particularly around ethics, potential biases, and ensuring technology complements rather than replaces human expertise.

METHODS

A discussion paper designed to break down key artificial intelligence terms and demonstrate real-world applications to guide nurses to develop the skills needed to navigate this evolving technological landscape.

FINDINGS

This discussion emphasises the importance of maintaining the critical role of human clinical judgment, highlighting that artificial intelligence should support nurses' expertise rather than diminish it. The need for continuous education to keep nurses equipped with the knowledge to effectively integrate artificial intelligence into their practice is argued. With an inclusive approach, artificial intelligence has the potential to become a powerful tool that supports nurses in improving patient care while preserving the essential human touch in healthcare.

Deciphering Abbreviations in Malaysian Clinical Notes Using Machine Learning

OBJECTIVE

 This is the first Malaysian machine learning model to detect and disambiguate abbreviations in clinical notes. The model has been designed to be incorporated into MyHarmony, a natural language processing system, that extracts clinical information for health care management. The model utilizes word embedding to ensure feasibility of use, not in real-time but for secondary analysis, within the constraints of low-resource settings.

METHODS

 A Malaysian clinical embedding, based on Word2Vec model, was developed using 29,895 electronic discharge summaries. The embedding was compared against conventional rule-based and FastText embedding on two tasks: abbreviation detection and abbreviation disambiguation. Machine learning classifiers were applied to assess performance.

RESULTS

 The Malaysian clinical word embedding contained 7 million word tokens, 24,352 unique vocabularies, and 100 dimensions. For abbreviation detection, the Decision Tree classifier augmented with the Malaysian clinical embedding showed the best performance (F-score of 0.9519). For abbreviation disambiguation, the classifier with the Malaysian clinical embedding had the best performance for most of the abbreviations (F-score of 0.9903).

CONCLUSION

 Despite having a smaller vocabulary and dimension, our local clinical word embedding performed better than the larger nonclinical FastText embedding. Word embedding with simple machine learning algorithms can decipher abbreviations well. It also requires lower computational resources and is suitable for implementation in low-resource settings such as Malaysia. The integration of this model into MyHarmony will improve recognition of clinical terms, thus improving the information generated for monitoring Malaysian health care services and policymaking.

Identification of Use Cases, Target Groups, and Motivations Around Adopting Smart Speakers for Health Care and Social Care Settings: Scoping Review

BACKGROUND

Conversational agents (CAs) are finding increasing application in health and social care, not least due to their growing use in the home. Recent developments in artificial intelligence, machine learning, and natural language processing have enabled a variety of new uses for CAs. One type of CA that has received increasing attention recently is smart speakers.

OBJECTIVE

The aim of our study was to identify the use cases, user groups, and settings of smart speakers in health and social care. We also wanted to identify the key motivations for developers and designers to use this particular type of technology.

METHODS

We conducted a scoping review to provide an overview of the literature on smart speakers in health and social care. The literature search was conducted between February 2023 and March 2023 and included 3 databases (PubMed, Scopus, and Sociological Abstracts), supplemented by Google Scholar. Several keywords were used, including technology (eg, voice assistant), product name (eg, Amazon Alexa), and setting (health care or social care). Publications were included if they met the predefined inclusion criteria: (1) published after 2015 and (2) used a smart speaker in a health care or social care setting. Publications were excluded if they met one of the following criteria: (1) did not report on the specific devices used, (2) did not focus specifically on smart speakers, (3) were systematic reviews and other forms of literature-based publications, and (4) were not published in English. Two reviewers collected, reviewed, abstracted, and analyzed the data using qualitative content analysis.

RESULTS

A total of 27 articles were included in the final review. These articles covered a wide range of use cases in different settings, such as private homes, hospitals, long-term care facilities, and outpatient services. The main target group was patients, especially older users, followed by doctors and other medical staff members.

CONCLUSIONS

The results show that smart speakers have diverse applications in health and social care, addressing different contexts and audiences. Their affordability and easy-to-use interfaces make them attractive to various stakeholders. It seems likely that, due to technical advances in artificial intelligence and the market power of the companies behind the devices, there will be more use cases for smart speakers in the near future.

Perceptions about the use of virtual assistants for seeking health information among caregivers of young childhood cancer survivors

Objectives

This study examined the perceptions of caregivers of young childhood cancer survivors (YCCS) regarding the use of virtual assistant (VA) technology for health information seeking and care management. The study aim was to understand how VAs can support caregivers, especially those from underserved communities, in navigating health information related to cancer survivorship.

Methods

A qualitative study design was employed, involving semi-structured interviews and focus groups with 10 caregivers of YCCS from metropolitan, rural, and Appalachian regions, recruited from a large pediatric academic medical center in the Midwest. A web-based VA prototype was tested with caregivers, who provided feedback on its usability, utility, and feasibility. Data were analyzed using thematic analysis to identify key themes related to caregivers' interactions with and perceptions of the VA technology.

Results

We identified four major themes: Interface and Interaction, User Experience, Content Relevance, and Trust. Caregivers expressed preferences for multimodal interactions (voice and text), particularly valuing flexibility based on context. They emphasized the need for accurate, relevant, and easily retrievable health information tailored to their child's needs. Trust and confidentiality were critical, with caregivers favoring VAs integrated with trusted healthcare systems. While VAs were perceived as valuable tools for reducing search fatigue and cognitive burden, caregivers highlighted the need for improved conversational depth, personalization, and empathetic response.

Conclusions

VAs hold promise as support tools for caregivers of YCCS, particularly in underserved communities, by offering personalized, credible, and accessible health information. To maximize their potential, research and development efforts should focus on building trust-building, integrated, and personalized VAs. These enhancements can help VAs further ease caregiving tasks and support caregivers in managing complex health needs.

Effect of Ambient Voice Technology, Natural Language Processing, and Artificial Intelligence on the Patient-Physician Relationship

BACKGROUND

 The method of documentation during a clinical encounter may affect the patient-physician relationship.

OBJECTIVES

 Evaluate how the use of ambient voice recognition, coupled with natural language processing and artificial intelligence (DAX), affects the patient-physician relationship.

METHODS

 This was a prospective observational study with a primary aim of evaluating any difference in patient satisfaction on the Patient-Doctor Relationship Questionnaire-9 (PDRQ-9) scale between primary care encounters in which DAX was utilized for documentation as compared to another method. A single-arm open-label phase was also performed to query direct feedback from patients.

RESULTS

 A total of 288 patients were include in the open-label arm and 304 patients were included in the masked phase of the study comparing encounters with and without DAX use. In the open-label phase, patients strongly agreed that the provider was more focused on them, spent less time typing, and made the encounter feel more personable. In the masked phase of the study, no difference was seen in the total PDRQ-9 score between patients whose encounters used DAX (median: 45, interquartile range [IQR]: 8) and those who did not (median: 45 [IQR: 3.5]; p = 0.31). The adjusted odds ratio for DAX use was 0.8 (95% confidence interval: 0.48-1.34) for the patient reporting complete satisfaction on how well their clinician listened to them during their encounter.

CONCLUSION

 Patients strongly agreed with the use of ambient voice recognition, coupled with natural language processing and artificial intelligence (DAX) for documentation in primary care. However, no difference was detected in the patient-physician relationship on the PDRQ-9 scale.

Acceptance of voice assistant technology in dental practice: A cross sectional study with dentists and validation using structural equation modeling

Voice assistant technologies (VAT) has been part of our daily lives, as a virtual assistant to complete requested tasks. The integration of VAT in dental offices has the potential to augment productivity and hygiene practices. Prior to the adoption of such innovations in dental settings, it is crucial to evaluate their applicability. This study aims to assess dentists' perceptions and the factors influencing their intention to use VAT in a clinical setting. A survey and research model were designed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was sent to 7,544 Ohio-licensed dentists through email. The data was analyzed and reported using descriptive statistics, model reliability testing, and partial least squares regression (PLSR) to explain dentists' behavioral intention (BI) to use VAT. In total, 257 participants completed the survey. The model accounted for 74.2% of the variance in BI to use VAT. Performance expectancy and perceived enjoyment had significant positive influence on BI to use VAT. Perceived risk had significant negative influence on BI to use VAT. Self-efficacy had significantly influenced perceived enjoyment, accounting for 35.5% of the variance of perceived enjoyment. This investigation reveals that performance efficiency and user enjoyment are key determinants in dentists' decision to adopt VAT. Concerns regarding the privacy of VAT also play a crucial role in its acceptance. This study represents the first documented inquiry into dentists' reception of VAT, laying groundwork for future research and implementation strategies.

Investigating awareness of artificial intelligence in healthcare among medical students and professionals in Pakistan: a cross-sectional study

Objective

The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan's medical community so as to comment on its future in our healthcare system.

Methods

A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05).

Results

A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan's healthcare system.

Conclusion

This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals' perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.

Vanderbilt Electronic Health Record Voice Assistant Supports Clinicians

BACKGROUND

 Electronic health records (EHRs) present navigation challenges due to time-consuming searches across segmented data. Voice assistants can improve clinical workflows by allowing natural language queries and contextually aware navigation of the EHR.

OBJECTIVES

 To develop a voice-mediated EHR assistant and interview providers to inform its future refinement.

METHODS

 The Vanderbilt EHR Voice Assistant (VEVA) was developed as a responsive web application and designed to accept voice inputs and execute the appropriate EHR commands. Fourteen providers from Vanderbilt Medical Center were recruited to participate in interactions with VEVA and to share their experience with the technology. The purpose was to evaluate VEVA's overall usability, gather qualitative feedback, and detail suggestions for enhancing its performance.

RESULTS

 VEVA's mean system usability scale score was 81 based on the 14 providers' evaluations, which was above the standard 50th percentile score of 68. For all five summaries evaluated (overview summary, A1C results, blood pressure, weight, and health maintenance), most providers offered a positive review of VEVA. Several providers suggested modifications to make the technology more useful in their practice, ranging from summarizing current medications to changing VEVA's speech rate. Eight of the providers (64%) reported they would be willing to use VEVA in its current form.

CONCLUSION

 Our EHR voice assistant technology was deemed usable by most providers. With further improvements, voice assistant tools such as VEVA have the potential to improve workflows and serve as a useful adjunct tool in health care.

Co-design of a voice-based app to monitor long COVID symptoms with its end-users: A mixed-method study

Background

People living with Long COVID (PWLC), which is still a poorly understood disease, often face major issues accessing proper care and frequently feel abandoned by the healthcare system. PWLC frequently report impaired quality of life because of the medical burden, the variability and intensity of symptoms, and insecurity toward the future. These particular needs justify the development of innovative, minimally disruptive solutions to facilitate the monitoring of this complex and fluctuating disease. Voice-based interactions and vocal biomarkers are promising digital approaches for such health monitoring.

Methods

Based on a mixed-method approach, this study describes the entire co-design process of Long COVID Companion, a voice-based digital health app to monitor Long COVID symptoms. Potential end-users of the app, both PWLC and healthcare professionals (HCP) were involved to (1) understand the unmet needs and expectations related to Long COVID care and management, (2) to assess the barriers and facilitators regarding a health monitoring app, (3) to define the app characteristics, including future potential use of vocal biomarkers and (4) to develop a first version of the app.

Results

This study revealed high needs and expectations regarding a digital health app to monitor Long COVID symptoms and the readiness to use vocal biomarkers from end-users. The main expectations included improved care and daily life, and major concerns were linked to accessibility and data privacy. Long COVID Companion was developed as a web application and is composed of a health monitoring component that allows auto-evaluation of symptoms, global health, and scoring relevant symptoms and quality of life using standardized questionnaires.

Conclusions

The Long COVID Companion app will address a major gap and provide day-to-day support for PWLC. However, further studies will be needed following its release, to evaluate its acceptability, usability and effectiveness.

What does the literature say about the use of personal voice assistants in older adults? A scoping review

PURPOSE

With ageing, people may experience loss of function which may be compensated for by using technology. This review aims to examine the range and extent of personal voice assistants used for older adults living in the community, their technology readiness level, associated outcomes, and the strength of evidence.

METHODS

This study complies with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. The CINAHL, EMBASE, IEEE Xplore, MEDLINE, APA PsycInfo, Scopus, and Web of Science databases were used to identify studies that explored the use of personal voice assistant technology with older adults living in the community.

RESULTS

The search yielded 499 studies, 22 of which were included for final analysis. Consumer technologies (e.g., Amazon Alexa) were evaluated in 18 studies, while four studies evaluated novel technologies. Most of the studies exploring the use of personal voice assistants with older adults evaluated technology usability and acceptance. Personal voice assistants were most commonly used by older adults for setting up reminders, searching for information, and checking the weather. None of the included studies evaluated the effectiveness of the technologies' ability to improve the management of health conditions or to facilitate the functional capacity of older adults.

CONCLUSIONS

More research is needed to determine the possible impact of using personal voice assistants for older adults living in the community. IMPLICATIONS FOR REHABILITATIONThe TRL for personal voice assistants is high.Personal voice assistants are currently being used by older adults for a range of activities including setting up reminders, searching for information, and checking the weather.Whether personal voice assistants can support older adults' ability to age in place is still unknown.The use of personal voice assistants in older adults is ripe for future enquiry and intervention-based research.

A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds

OBJECTIVE

To evaluate the technical feasibility and potential value of a digital assistant that prompts intensive care unit (ICU) rounding teams to use evidence-based practices based on analysis of their real-time discussions.

METHODS

We evaluated a novel voice-based digital assistant which audio records and processes the ICU care team's rounding discussions to determine which evidence-based practices are applicable to the patient but have yet to be addressed by the team. The system would then prompt the team to consider indicated but not yet delivered practices, thereby reducing cognitive burden compared to traditional rigid rounding checklists. In a retrospective analysis, we applied automatic transcription, natural language processing, and a rule-based expert system to generate personalized prompts for each patient in 106 audio-recorded ICU rounding discussions. To assess technical feasibility, we compared the system's prompts to those created by experienced critical care nurses who directly observed rounds. To assess potential value, we also compared the system's prompts to a hypothetical paper checklist containing all evidence-based practices.

RESULTS

The positive predictive value, negative predictive value, true positive rate, and true negative rate of the system's prompts were 0.45 ± 0.06, 0.83 ± 0.04, 0.68 ± 0.07, and 0.66 ± 0.04, respectively. If implemented in lieu of a paper checklist, the system would generate 56% fewer prompts per patient, with 50%±17% greater precision.

CONCLUSION

A voice-based digital assistant can reduce prompts per patient compared to traditional approaches for improving evidence uptake on ICU rounds. Additional work is needed to evaluate field performance and team acceptance.

Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.

The Voice of the Patient and the Electronic Health Record

The patient's voice, which we define as the words the patient uses found in notes and messages and other sources, and their preferences for care and its outcomes, is too small a part of the electronic health record (EHR). To address this shortcoming will require innovation, research, funding, perhaps architectural changes to commercial EHRs, and that we address barriers that have resulted in this state, including clinician burden and financial drivers for care. Advantages to greater patient voice may accrue to many groups of EHR users and to patients themselves. For clinicians, the patient's voice, including symptoms, is invaluable in identifying new serious illness that cannot be detected by screening tests, and as an aid to accurate diagnosis. Informaticians benefit from greater patient voice in the EHR because it provides clues not found elsewhere that aid diagnostic decision support, predictive analytics, and machine learning. Patients benefit when their treatment priorities and care outcomes considered in treatment decisions. What patient voice there is in the EHR today can be found in locations not usually used by researchers. Increasing the patient voice needs be accomplished in equitable ways available to people with less access to technology and whose primary language is not well supported by EHR tools and portals. Use of direct quotations, while carrying potential for harm, permits the voice to be recorded unfiltered. If you are a researcher or innovator, collaborate with patient groups and clinicians to create new ways to capture the patient voice, and to leverage it for good.

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.

"Ask, 'When You Do This, How Much Pain Are You In?'": Content Preferences for a Conversational Pain Self-Management Software Application

The purpose of the current study was to examine older adults' preferences for conversational pain management content to incorporate in an interactive application (app) for pain self-management. Conversational statements and questions were written as a script to encourage evidence-based pain self-management behaviors. The content was converted from text to female chatbot speech and saved as four groups of MP3 files. A purposive sample of 22 older adults participated in a guided interaction through the MP3 files. One-on-one interviews were conducted to garner participants' conversational content preferences. Overall, participants want the conversational content to increase health care provider engagement in pain management communication. Older adults preferred the inclusion of conversational statements and questions for monitoring the multifaceted dimensions of pain, treatment accountability, guidance for alternative treatments, and undesirable effects from pain treatments. The design of mobile health apps must incorporate the needs and preferences of older adults. [Journal of Gerontological Nursing, 49(1), 11-17.].

Evaluating the adoption of voice recognition technology for real-time dictation in a rural healthcare system: A retrospective analysis of dragon medical one

BACKGROUND

In 2013, Marshfield Clinic Health System (MCHS) implemented the Dragon Medical One (DMO) system provided by Nuance Management Center (NMC) for Real-Time Dictation (RTD), embracing the idea of streamlined clinic workflow, reduced dictation hours, and improved documentation legibility. Since then, MCHS has observed a trend of reduced time in documentation, however, the target goal of 100% adoption of voice recognition (VR)-based RTD has not been met.

OBJECTIVE

To evaluate the uptake/adoption of VR technology for RTD in MCHS, between 2018-2020.

METHODS

DMO data for 1,373 MCHS providers from 2018-2020 were analyzed. The study outcome was VR uptake, defined as the median number of hours each provider used VR technology to dictate patient information, and classified as no/yes. Covariates included sex, age, US-trained/international medical graduates, trend, specialty, and facility. Descriptive statistics and unadjusted and adjusted logistic regression analyses were performed. Stata/SE.version.17 was used for analyses. P-values less than/equal to 0.05 were considered statistically significant.

RESULTS

Of the 1,373 MCHS providers, the mean (SD) age was 48.3 (12.4) years. VR uptake was higher than no uptake (72.0% vs. 28.0%). In both unadjusted and adjusted analyses, VR uptake was 4.3 times and 7.7 times higher in 2019-2020 compared to 2018, respectively (OR:4.30,95%CI:2.44-7.46 and AOR:7.74,95%CI:2.51-23.86). VR uptake was 0.5 and 0.6 times lower among US-trained physicians compared to internationally-trained physicians (OR:0.53,95%CI:0.37-0.76 and AOR:0.58,95%CI:0.35-0.97). Uptake was 0.2 times lower among physicians aged 60/above than physicians aged 29/less (OR:0.20,95%CI:0.10-0.59, and AOR:0.17,95%CI:0.27-1.06).

CONCLUSION

Since 2018, VR adoption has increased significantly across MCHS. However, it was lower among US-trained physicians than among internationally-trained physicians (although internationally physicians were in minority) and lower among more senior physicians than among younger physicians. These findings provide critical information about VR trends, physician factors, and which providers could benefit from additional training to increase VR adoption in healthcare systems.

Measuring patient acuity and nursing care needs in South Korea: application of a new patient classification system

BACKGROUND

An accurate and reliable patient classification system (PCS) can help inform decisions regarding adequate assignments for nurse staffing. This study aimed to evaluate the criterion validity of the Asan Patient Classification System (APCS), a new tertiary hospital-specific PCS, by comparing its rating and total scores with those of KPCS-1 and KPCS-GW for measuring patient activity and nursing needs.

METHODS

We performed a retrospective analysis of the medical records of 50,314 inpatients admitted to the general wards of a tertiary teaching hospital in Seoul, South Korea in March, June, September, and December 2019. Spearman's correlation and Kappa statistics according to quartiles were calculated to examine the criterion validity of the APCS compared with the KPCS-1 and KPCS-GW.

RESULTS

The average patient classification score was 28.3 points for APCS, 25.7 points for KPCS-1, and 21.6 points for KPCS-GW. The kappa value between APCS and KPCS-1 was 0.91 (95% CI:0.9072, 0.9119) and that between APCS and KPCS-GW was 0.88 (95% CI:0.8757, 0.8810). Additionally, Spearman's correlation coefficients among APCS, KPCS-1, and KPCS-GW showed a very strong correlation. However, 10.8% of the participants' results were inconsistent, and KPCS-1 tended to classify patients into groups with lower nursing needs compared to APCS.

CONCLUSION

This study showed that electronic health record-generated APCS can provide useful information on patients' severity and nursing activities to measure workload estimation. Additional research is needed to develop and implement a real-world EHR-based PCS system to accommodate for direct and indirect nursing care while considering diverse population and dynamic healthcare system.

[An Analysis of Tasks of Nurses Caring for Patients with COVID-19 in a Nationally-Designated Inpatient Treatment Unit]

PURPOSE

The purpose of this study was to provide foundational knowledge on nursing tasks performed on patients with COVID-19 in a nationally-designated inpatient treatment unit.

METHODS

This study employs both quantitative and qualitative approaches. The quantitative method investigated the content and frequency of nursing tasks for 460 patients (age ≥ 18 y, 57.4% men) from January 20, 2020, to September 30, 2021, by analyzing hospital information system records. Qualitative data were collected via focus group interviews. The study involved interviews with three focus groups comprising 18 nurses overall to assess their experiences and perspectives on nursing care during the pandemic from February 3, 2022, to February 15, 2022. The data were examined with thematic analysis.

RESULTS

Overall, 49 different areas of nursing tasks (n = 130,687) were identified based on the Korean Patient Classification System for nurses during the study period. Among the performed tasks, monitoring of oxygen saturation and measuring of vital signs were considered high-priority. From the focus group interview, three main themes and eleven sub-themes were generated. The three main themes are "Experiencing eventfulness in isolated settings," "All-around player," and "Reflections for solutions."

CONCLUSION

During the COVID-19 pandemic, it is imperative to ensure adequate staffing levels, compensation, and educational support for nurses. The study further propose improving guidelines for emerging infectious diseases and patient classification systems to improve the overall quality of patient care.

Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0

The Industry 4.0 initiative has been showing the way for industrial production to optimize operations based on collecting, processing, and sharing data. There are new requirements on the production floor: flexible but ultra-reliable, low latency wireless communications through interoperable systems can share data. Further challenges of data sharing and storage arise when diverse systems come into play at the Manufacturing Operations Management and Business Planning & Logistics levels. The emerging complex cyber-physical systems of systems need to be engineered with care. Regarding industrial requirements, the telecommunication industry has many similarities to production—including ultra-reliability, high complexity, and having humans “in-the-loop”. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. These toolsets range from model-driven engineering through system interoperability frameworks, 5G- and 6G-supported manufacturing, and the telco-cloud to speech recognition in noisy environments.

Within Clinic Reliability and Usability of a Voice-Based Amazon Alexa Administration of the Patient Health Questionnaire 9 (PHQ 9)

Over the last two decades, metric-based instruments have garnered popularity in mental health. Self-administered surveys, such as the Patient Health Questionnaire 9 (PHQ 9), have been leveraged to inform treatment practice of Major Depressive Disorder (MDD). The aim of this study was to measure the reliability and usability of a novel voice-based delivery system of the PHQ 9 using Amazon Alexa within a patient population. Forty-one newly admitted patients to a behavioral medicine clinic completed the PHQ 9 at two separate time points (first appointment and one-month follow up). Patients were randomly assigned to a version (voice vs paper) completing the alternate format at the next appointment. Patients additionally completed a 26-item User Experience Questionnaire (UEQ) and open-ended questionnaire at each session. Assessments between PHQ 9 total scores for the Alexa and paper version showed a high degree of reliability (α = .86). Quantitative UEQ results showed significantly higher overall positive attitudes towards the Alexa format with higher subscale scores on attractiveness, stimulation, and novelty. Further qualitative responses supported these findings with 85.7% of participants indicating a willingness to use the device at home. With the benefit of user instruction in a clinical environment, the novel Alexa delivery system was shown to be consistent with the paper version giving evidence of reliability between the two formats. User experience assessments further showed a preference for the novel version over the traditional format. It is our hope that future studies may examine the efficacy of the Alexa format in improving the at-home clinical treatment of depression.

Caregiver Expectations for Interfacing with Voice Assistants to Support Complex Home Care: Mixed-Methods Study (Preprint)

Background

Providing care in home environments is complex, and often the pressure is on caregivers to document information and ensure care continuity. Digital information management and communication technologies may support care coordination among caregivers. However, they have yet to be adopted in this context, partly because of issues with supporting long-term disease progression and caregiver anxiety. Voice assistant (VA) technology is a promising method for interfacing with digital health information that may aid in multiple aspects of being a caregiver, thereby influencing adoption. Understanding the expectations for VAs to support caregivers is fundamental to inform the practical development of this technology.

Objective

This study explored caregivers’ perspectives on using VA technology to support caregiving and inform the design of future digital technologies in complex home care.

Methods

This study was part of a larger study of caregivers across North America on the design of digital health technologies to support health communication and information management in complex home care. Caregivers included parents, guardians, and hired caregivers such as personal support workers and home care nurses. Video interviews were conducted with caregivers to capture their mental models on the potential application of VAs in complex home care and were theoretically analyzed using the technology acceptance model. Interviews were followed up with Likert-scale questions exploring perspectives on other VA applications beyond participants’ initial perceptions.

Results

Data were collected from 22 caregivers, and 3 themes were identified: caregivers’ perceived usefulness of VAs in supporting documentation, care coordination, and person-centered care; caregivers’ perceived ease of use in navigating information efficiently (they also had usability concerns with this interaction method); and caregivers’ concerns, excitement, expected costs, and previous experience with VAs that influenced their attitudes toward use. From the Likert-scale questions, most participants (21/22, 95%) agreed that VAs should support prompted information recording and retrieval, and all participants (22/22, 100%) agreed that they should provide reminders. They also agreed that VAs should support them in an emergency (18/22, 82%)—but only for calling emergency services—and guide caregivers through tasks (21/22, 95%). However, participants were less agreeable on VAs expressing a personality (14/22, 64%)—concerned they would manipulate caregivers’ perceptions—and listening ambiently to remind caregivers about their documentation (16/22, 73%). They were much less agreeable about VAs providing unprompted assistance on caregiving tasks (9/22, 41%).

Conclusions

The interviews and Likert-scale results point toward the potential for VAs to support family caregivers and hired caregivers by easing their information management and health communication at home. However, beyond information interaction, the potential impact of VA personality traits on caregivers’ perceptions of the care situation and the passive collection of audio data to improve user experience through context-specific interactions are critical design considerations that should be further examined.

Comparing Older and Younger Adults Perceptions of Voice and Text-based Search for Consumer Health Information Tasks

The increased prevalence of voice search presents opportunities to address consumer challenges accessing online health information. However, it is essential to understand how users' perceptions of voice affect their search processes for health information, concerns, and different scenarios for using voice for health information tasks. We conducted semi-structured interviews with 16 younger (18-25) and older (60-64) adult participants to understand and compare their perceptions of using voice and text-based search for non-health-related and health-related tasks. While most participants preferred traditional text search, younger adults were not inclined to use voice search for health information due to concerns about privacy, credibility, and perceived efficiency in filtering results. Older adults found voice search potentially beneficial for reducing manual query generation burdens; however, some were unsure of how to use the technology effectively. We provide a set of considerations to address concerns about voice search for health information tasks in the future.

Application of the i-PARIHS framework in the implementation of speech recognition technology as a way of addressing documentation burden within a mental health context

Documentation burden continues to be a critical issue in the adoption of comprehensive electronic health record systems. This case study demonstrates how the i-PARIHS framework can be applied to support the implementation of interventions in reducing documentation and EHR-related burden in a mental health context. As part of pre-adoption implementation activities for Speech Recognition Technology (SRT), a cross-sectional survey was conducted with physicians, residents, and fellows at an academic mental health hospital to explore their perceptions on SRT. Open-ended responses and follow-up interviews explored challenges and concerns on using SRT in practice. Through an analysis using the i-PARIHS framework, key considerations were mapped across the four components of the framework. This study demonstrates the value of applying well-established implementation frameworks, such as the i-PARIHS framework, in mitigating challenges related to documentation burden. Future studies should explore how implementation frameworks can be systematically embedded in addressing EHR-related burden.

Black Swan Events and Intelligent Automation for Routine Safety Surveillance

Effective identification of previously implausible safety signals is a core component of successful pharmacovigilance. Timely, reliable, and efficient data ingestion and related processing are critical to this. The term 'black swan events' was coined by Taleb to describe events with three attributes: unpredictability, severe and widespread consequences, and retrospective bias. These rare events are not well understood at their emergence but are often rationalized in retrospect as predictable. Pharmacovigilance strives to rapidly respond to potential black swan events associated with medicine or vaccine use. Machine learning (ML) is increasingly being explored in data ingestion tasks. In contrast to rule-based automation approaches, ML can use historical data (i.e., 'training data') to effectively predict emerging data patterns and support effective data intake, processing, and organisation. At first sight, this reliance on previous data might be considered a limitation when building ML models for effective data ingestion in systems that look to focus on the identification of potential black swan events. We argue that, first, some apparent black swan events-although unexpected medically-will exhibit data attributes similar to those of other safety data and not prove algorithmically unpredictable, and, second, standard and emerging ML approaches can still be robust to such data outliers with proper awareness and consideration in ML system design and with the incorporation of specific mitigatory and support strategies. We argue that effective approaches to managing data on potential black swan events are essential for trust and outline several strategies to address data on potential black swan events during data ingestion.

Alternative Input for Perfusion Management Devices: Voice Recognition for Data Input and the Effects on Charting and Perioperative Calculation Use

Technology in healthcare has become increasingly prevalent and user friendly. In the last decade, advances in hands-free methods of data input have become more viable in a variety of medical professions. The aim of this study was to assess the advantages or disadvantages of hands-free charting through a voice-to-text app designed for perfusionists. Twelve clinical perfusion students using two different simulated bypass cases were recorded and assessed for the number of events noticed and charted, as well as the speed at which they accomplished these steps. Paper charts were compared with a custom app with voice-to-text charting capability. Data was analyzed using linear mixed models to detect differences in length of time until a chartable event was noticed, and how long after noticing an event it took to record the event. Timeliness of recording an event was made by assessing log-transformed time data. There was significantly more information recorded when charting on paper, while charting with voice-to-text resulted in significantly faster mean time from noticing an event to the recording of it. There was no significant difference between how many events were noticed and recorded. When using paper charting, a higher percentage of events that were missed were drug administration events, while voice charting had a higher percentage of missed events that were associated with cardioplegia delivery or bypass timing. With a decreased time interval between noticing an event and charting the event, speech-to-text for perfusion could be of benefit in situations where many events occur at once, such as emergency situations or highly active portions of bypass such as initiation and termination. While efforts were made to make the app as intuitive as possible, there is room for improvement.

An Environmental Scan of Anesthesia Information Management Systems in the American and Canadian Marketplace

Anesthesia Information Management Systems are specialized forms of electronic medical records used by anesthesiologists to automatically and reliably collect, store, and present perioperative patient data. There are no recent academic publications that outline the names and features of AIMS in the current American and Canadian marketplace. An environmental scan was performed to first identify existing AIMS in this marketplace, and then describe and compare these AIMS. We found 13 commercially available AIMS but were able to describe in detail the features and functionalities of only 10 of these systems, as three vendors did not participate in the study. While all AIMS have certain key features, other features and functionalities are only offered by some of the AIMS. Features less commonly offered included patient portals for pre-operative questionnaires, clinical decision support systems, and voice-to-text capability for documentation. The findings of this study can inform AIMS procurement efforts by enabling anesthesia departments to compare features across AIMS and find an AIMS whose features best fit their needs and priorities. Future studies are needed to describe the features and functionalities of these AIMS at a more granular level, and also assess the usability and costs of these systems.

Understanding Barriers and Design Opportunities to Improve Healthcare and QOL for Older Adults through Voice Assistants

Voice-based Intelligent Virtual Assistants (IVAs) promise to improve healthcare management and Quality of Life (QOL) by introducing the paradigm of hands-free and eye-free interactions. However, there has been little understanding regarding the challenges for designing such systems for older adults, especially when it comes to healthcare related tasks. To tackle this, we consider the processes of care delivery and QOL enhancements for older adults as a collaborative task between patients and providers. By interviewing 16 older adults living independently or semi-independently and 5 providers, we identified 12 barriers that older adults might encounter during daily routine and while managing health. We ultimately highlighted key design challenges and opportunities that might be introduced when integrating voice-based IVAs into the life of older adults. Our work will benefit practitioners who study and attempt to create full-fledged IVA-powered smart devices to deliver better care and support an increased QOL for aging populations.

Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions

PURPOSE OF REVIEW

Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment.

RECENT FINDINGS

The integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology.

SUMMARY

Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.

Interaction Time with Electronic Health Records: A Systematic Review

BACKGROUND

The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood.

OBJECTIVE

This study aimed to evaluate the time that health care providers spend interacting with electronic health records (EHR).

METHODS

Data are retrieved from Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, (Ovid) Embase, CINAHL, and SCOPUS.

STUDY ELIGIBILITY CRITERIA

Peer-reviewed studies that describe the use of EHR and include measurement of time either in hours, minutes, or in the percentage of a clinician's workday. Papers were written in English and published between 1990 and 2021.

PARTICIPANTS

All physicians and nurses involved in inpatient and outpatient settings.

STUDY APPRAISAL AND SYNTHESIS METHODS

A narrative synthesis of the results, providing summaries of interaction time with EHR. The studies were rated according to Quality Assessment Tool for Studies with Diverse Designs.

RESULTS

Out of 5,133 de-duplicated references identified through database searching, 18 met inclusion criteria. Most were time-motion studies (50%) that followed by logged-based analysis (44%). Most were conducted in the United States (94%) and examined a clinician workflow in the inpatient settings (83%). The average time was nearly 37% of time of their workday by physicians in both inpatient and outpatient settings and 22% of the workday by nurses in inpatient settings. The studies showed methodological heterogeneity.

CONCLUSION

This systematic review evaluates the time that health care providers spend interacting with EHR. Interaction time with EHR varies depending on clinicians' roles and clinical settings, computer systems, and users' experience. The average time spent by physicians on EHR exceeded one-third of their workday. The finding is a possible indicator that the EHR has room for usability, functionality improvement, and workflow optimization.

Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews

BACKGROUND

The recent, rapid development of digital technologies offers new possibilities for more efficient implementation of electronic health record (EHR) and personal health record (PHR) systems. A growing volume of healthcare data has been the hallmark of this digital transformation. The large healthcare datasets' complexity and their dynamic nature pose various challenges related to processing, analysis, storage, security, privacy, data exchange, and usability.

MATERIALS AND METHODS

We performed a systematic review of systematic reviews to assess technological progress in EHR and PHR systems. We searched MEDLINE, Cochrane, Web of Science, and Scopus for systematic literature reviews on technological advancements that support EHR and PHR systems published between January 1, 2010, and October 06, 2020.

RESULTS

The searches resulted in a total of 2,448 hits. Of these, we finally selected 23 systematic reviews. Most of the included papers dealt with information extraction tools and natural language processing technology (n = 10), followed by studies that assessed the use of blockchain technology in healthcare (n = 8). Other areas of digital technology research included EHR and PHR systems in austere settings (n = 1), de-identification methods (n = 1), visualization techniques (n = 1), communication tools within EHR and PHR systems (n = 1), and methodologies for defining Clinical Information Models that promoted EHRs and PHRs interoperability (n = 1).

CONCLUSIONS

Technological advancements can improve the efficiency in the implementation of EHR and PHR systems in numerous ways. Natural language processing techniques, either rule-based, machine-learning, or deep learning-based, can extract information from clinical narratives and other unstructured data locked in EHRs and PHRs, allowing secondary research (i.e., phenotyping). Moreover, EHRs and PHRs are expected to be the primary beneficiaries of the blockchain technology implementation on Health Information Systems. Governance regulations, lack of trust, poor scalability, security, privacy, low performance, and high cost remain the most critical challenges for implementing these technologies.

Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology

Artificial intelligence–driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care–leveraging innovative telehealth solutions during the COVID-19 pandemic. They allow for automatic acute care triaging and chronic disease management, including remote monitoring, preventive care, patient intake, and referral assistance. This paper focuses on the current clinical needs and applications of artificial intelligence–driven voice chatbots to drive operational effectiveness and improve patient experience and outcomes.

HITECH to 21st century cures: clinician burden and evolving health IT policy

Adoption and use of health information technology (IT) was identified as 1 solution to quality and safety issues that permeate the United States health care system. Implementation of health IT has accelerated across the US over the past decade, in part, as a result of legislative and regulatory requirements and incentives. However, adoption of these systems has burdened clinician users due to design, configuration, and implementation issues, resulting in poor usability, challenges to workflow integration, and cumbersome documentation requirements. The path to alleviating these clinician burdens requires a clear understanding of the intent and evolution of pertinent regulations and the context in which they exist. This article reviews the Office of the National Coordinator of Health Information Technology's efforts, documents current regulatory actions, and discusses additional policy opportunities that can further improve clinician satisfaction and effectiveness in providing health care with health IT that is an asset, not an obstacle.

Delivering Perinatal Health Information via a Voice Interactive App (SMILE): Mixed Methods Feasibility Study

BACKGROUND

Perinatal health care is critically important for maternal health outcomes in infants. The United States fares considerably worse than comparable countries for maternal and infant mortality rates. As such, alternative models of care or engagement are warranted. Ubiquitous digital devices and increased use of digital health tools have the potential to extend the reach to women and infants in their everyday lives and make a positive impact on their health outcomes. As voice technology becomes more mainstream, research is prudent to establish evidence-based practice on how to best leverage voice technology to promote maternal-infant health.

OBJECTIVE

The aim of this study is to assess the feasibility of using voice technology to support perinatal health and infant care practices.

METHODS

Perinatal women were recruited from a large Midwest Children's Hospital via hospital email announcements and word of mouth. Owing to the technical aspects of the intervention, participants were required to speak English and use an iPhone. Demographics, patterns of technology use, and technology use specific to perinatal health or self-care practices were assessed at baseline. Next, participants were onboarded and asked to use the intervention, Self-Management Intervention-Life Essentials (SMILE), over the course of 2 weeks. SMILE provided users with perinatal health content delivered through mini podcasts (ranging from 3 to 8 minutes in duration). After each podcast, SMILE prompted users to provide immediate verbal feedback to the content. An exit interview was conducted with participants to gather feedback on the intervention and further explore participants' perceptions of voice technology as a means to support perinatal health in the future.

RESULTS

In total, 19 pregnant women (17 to 36 weeks pregnant) were consented. Themes identified as important for perinatal health information include establishing routines, expected norms, and realistic expectations and providing key takeaways. Themes identified as important for voice interaction include customization and user preferences, privacy, family and friends, and context and convenience. Qualitative analysis suggested that perinatal health promotion content delivered by voice should be accurate and succinctly delivered and highlight key takeaways. Perinatal health interventions that use voice should provide users with the ability to customize the intervention but also provide opportunities to engage family members, particularly spouses. As a number of women multitasked while the intervention was being deployed, future interventions should leverage the convenience of voice technology while also balancing the influence of user context (eg, timing or ability to listen or talk versus nonvoice interaction with the system).

CONCLUSIONS

Our findings demonstrate the short-term feasibility of disseminating evidence-based perinatal support via podcasts and curate voice-captured data from perinatal women. However, key areas of improvement have been identified specifically for perinatal interventions leveraging voice technology. Findings contribute to future content, design, and delivery considerations of perinatal digital health interventions.

Putting the "why" in "EHR": capturing and coding clinical cognition

Complaints about electronic health records, including information overload, note bloat, and alert fatigue, are frequent topics of discussion. Despite substantial effort by researchers and industry, complaints continue noting serious adverse effects on patient safety and clinician quality of life. I believe solutions are possible if we can add information to the record that explains the “why” of a patient’s care, such as relationships between symptoms, physical findings, diagnostic results, differential diagnoses, therapeutic plans, and goals. While this information may be present in clinical notes, I propose that we modify electronic health records to support explicit representation of this information using formal structure and controlled vocabularies. Such information could foster development of more situation-aware tools for data retrieval and synthesis. Informatics research is needed to understand what should be represented, how to capture it, and how to benefit those providing the information so that their workload is reduced.

Quantified electronic health record (EHR) use by academic surgeons

BACKGROUND

The electronic health record has improved medical billing, research, and sharing of patient data, but its clinical use by physicians has been linked to rising physician burnout leading to numerous subjective editorials about the electronic health record inefficiencies and detriment to frontline caregivers. This study aimed to quantify electronic health record use by surgeons.

METHODS

The study is a retrospective review and descriptive analysis of deidentified electronic health record data from September 2016 to June 2017. A binary time series was created for each attending to calculate electronic health record system login times. The primary outcome was the total amount of time a surgeon logged into the electronic health record system during the study period.

RESULTS

Fifty-one general surgery attendings (31 males, 20 females), spanning 9 specialties spent a mean of 2.0 hours per day and 13.8 hours per week logged into the electronic health record. The top 15% of users were logged in for an average of 4.6 hours per weekday. Sixty-five percent of overall electronic health record use occurred on-site, and 35% was remote. A greater proportion of remote use occurred during nighttime hours and Sundays. Clinic days required the largest amount of electronic health record use time compared with operating room and administrative days.

CONCLUSION

General surgery attendings spend a considerable amount of time using the electronic health record. Ultimately, the goal of these quantitative electronic health record results is to correlate with burnout and job satisfaction data to facilitate the implementation of programs to improve efficiency and decrease the burden of charting. Further investigation needs to focus on subgroups who are high electronic health record users to better identify the barriers to efficient electronic health record use.

Design Space for Voice-Based Professional Reporting

Speech technology has matured so that voice-based reporting utilizing speech-to-text can be applied in various domains. Speech has two major benefits: it enables efficient reporting and speech input improves the quality of the reports since reporting can be done as a part of the workflow without delays between work and reporting. However, designing reporting voice user interfaces (VUIs) for professional use is challenging, as there are numerous aspects from technology to organization and language that need to be considered. Based on our experience in developing professional reporting VUIs with different stakeholders representing both commercial and public sector, we define a design space for voice-based reporting systems. The design space consists of 28 dimensions grouped into five categories: Language Processing, Structure of Reporting, Technical Limitations in the Work Domain, Interaction Related Aspects in the Work Domain, and Organization. We illustrate the design space by discussing four voice-based reporting systems, designed and implemented by us, and describing a design process that utilizes it. The design space enables designers to identify critical aspects of professional reporting VUIs and optimize those for their target domain. The design space can be used as a practical tool especially by designers with limited experience on speech technologies.

Physician perceptions of documentation methods in electronic health records

This study sought to determine physician, specialty and practice factors influencing choice of method for electronic health record (EHR) documentation: direct typing (DT), electronic transcription (ET), human transcription (HT), and scribes. A survey assessing physician documentation practices was developed and distributed online. The primary outcome was the proportion of physicians using each method. Secondary outcomes were provider-rated accuracy, efficiency, and ease of navigation on a 1-5 Likert scale. Means were compared using linear mixed models with Bonferroni adjustment. The 818 respondents were mostly outpatient (46%) adult (79%) physicians, practiced for a mean 15.8 years, and used DT for EHR documentation (72%). Emergency physicians were more likely to use scribes (p < 0.0001). DT was rated less efficient than all other methods (p < 0.0001). ET was rated less accurate than DT (p < 0.001) and HT (p < 0.001). HT was rated less easy to navigate than DT (p = 0.002) and scribe (p < 0.001), and ET less than scribe (p = 0.002). Two hundred and forty-three respondents provided free-text comments that further described opinions. DT was the most commonly used EHR method but rated least efficient. Scribes were rated easy to navigate and efficient but infrequently used outside of emergency settings. Further innovation is needed to design systems responsive to all physician EHR needs.

Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice

Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual's voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers for diagnosis, risk prediction, and remote monitoring of various clinical outcomes and symptoms, we offer in this review an overview of the various applications of voice for health-related purposes. We discuss the potential of this rapidly evolving environment from a research, patient, and clinical perspective. We also discuss the key challenges to overcome in the near future for a substantial and efficient use of voice in healthcare.

Pediatrician Electronic Health Record Time Use for Outpatient Encounters

BACKGROUND

The time providers spend using their electronic health records (EHRs) delivering care and its potential impact on patient care are of concern for the health care system. In studies to date, researchers have focused on providers who primarily care for adults. Scant information exists for pediatricians. Given this gap, it is important to quantify EHR activity for this group.

METHODS

We studied pediatricians practicing in US-based ambulatory practices using the Cerner Millennium EHR by extracting data from software log files in the Lights On Network for the calendar year 2018 and summarizing the time spent on each of 13 clinically-focused EHR functions according to clinical specialty.

RESULTS

Our data included >20 million encounters by almost 30 thousand physicians from 417 health systems. Pediatric physicians spent an average of 16 minutes per encounter using their EHR. Chart review (31%), documentation (31%), and ordering (13%) functions accounted for most of the time. The distribution of time spent by providers using their EHR is highly variable within subspecialty but is similar across specialties. Because of data limitations, we were unable to examine geographic or health system-specific variation.

CONCLUSIONS

Pediatricians, like physicians who care for adults, spend a large portion of their day using their EHR. Additionally, although chart review and documentation accounted for 62% of the activity, as in previously published studies, in our study, we found that chart review accounted for half of that time. Wide variation suggests opportunities to optimize both the processes of entering information and searching for patient data within the EHR.

Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic

To prevent the spread of COVID-19 and to continue responding to healthcare needs, hospitals are rapidly adopting telehealth and other digital health tools to deliver care remotely. Intelligent conversational agents and virtual assistants, such as chatbots and voice assistants, have been utilized to augment health service capacity to screen symptoms, deliver healthcare information, and reduce exposure. In this commentary, we examined the state of voice assistants (e.g., Google Assistant, Apple Siri, Amazon Alexa) as an emerging tool for remote healthcare delivery service and discussed the readiness of the health system and technology providers to adapt voice assistants as an alternative healthcare delivery modality during a health crisis and pandemic.

A scoping review of patient-facing, behavioral health interventions with voice assistant technology targeting self-management and healthy lifestyle behaviors

Engaging in positive healthy lifestyle behaviors continues to be a public health challenge, requiring innovative solutions. As the market for voice assistants (Amazon Alexa, Google Assistant, and Apple Siri) grows and people increasingly use them to assist their daily tasks, there is a pressing need to explore how voice assistant (VA) technology may be used in behavioral health interventions. A scoping review of literature was conducted to address a PICO (Population, Intervention, Comparison, and Outcome) question: across populations, how does the use of voice assistants in behavioral health research/interventions influence healthy lifestyle behaviors versus control or comparison interventions? To inform the science, a secondary aim of this review was to explore characteristics of VAs used in behavioral health research. The review was conducted following Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines with scoping review extension (PRISMA-ScR). Ten studies satisfied the inclusion criteria, representing research published through February 2019. Studies spanned pediatric to elderly populations, covering a vast array of self-management and healthy lifestyle behaviors. The majority of interventions were multicomponent, involving more than one of the following behavior change techniques grouped by cluster: shaping knowledge, self-belief, repetition and substitution, feedback and monitoring, goals and planning, antecedents, natural consequences, comparison of behavior, and identification. However, most studies were in early stages of development, with limited efficacy trials. VA technology continues to evolve and support behavioral interventions using various platforms (e.g., Interactive Voice Response [IVR] systems, smartphones, and smart speakers) which are used alone or in conjunction with other platforms. Feasibility, usability, preliminary efficacy, along with high user satisfaction of research adapted VAs, in contrast to standalone commercially available VAs, suggest a role for VAs in behavioral health intervention research.

Effect of Speech Recognition on Problem Solving and Recall in Consumer Digital Health Tasks: Controlled Laboratory Experiment

Background

Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored.

Objective

The aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall.

Methods

Fifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured.

Results

Compared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (Z=–4.08, P<.001) and simple tasks (Z=–2.24, P=.03). Complex tasks took significantly longer to complete (Z=–2.52, P=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (Z=–3.30, P=.001). However, there was no effect on errors.

Conclusions

Use of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.

Physician use of speech recognition versus typing in clinical documentation: A controlled observational study

IMPORTANCE

Speech recognition (SR) is increasingly used directly by clinicians for electronic health record (EHR) documentation. Its usability and effect on quality and efficiency versus other documentation methods remain unclear.

OBJECTIVE

To study usability and quality of documentation with SR versus typing.

DESIGN

In this controlled observational study, each subject participated in two of five simulated outpatient scenarios. Sessions were recorded with Morae® usability software. Two notes were documented into the EHR per encounter (one dictated, one typed) in randomized order. Participants were interviewed about each method's perceived advantages and disadvantages. Demographics and documentation habits were collected via survey. Data collection occurred between January 8 and February 8, 2019, and data analysis was conducted from February through September of 2019.

SETTING

Brigham and Women's Hospital, Boston, Massachusetts, USA.

PARTICIPANTS

Ten physicians who had used SR for at least six months.

MAIN OUTCOMES AND MEASURES

Documentation time, word count, vocabulary size, number of errors, number of corrections and quality (clarity, completeness, concision, information sufficiency and prioritization).

RESULTS

Dictated notes were longer than typed notes (320.6 vs. 180.8 words; p = 0.004) with more unique words (170.9 vs. 120.4; p = 0.01). Documentation time was similar between methods, with dictated notes taking slightly less time to complete than typed notes. Typed notes had more uncorrected errors per note than dictated notes (2.9 vs. 1.5), although most were minor misspellings. Dictated notes had a higher mean quality score (7.7 vs. 6.6; p = 0.04), were more complete and included more sufficient information.

CONCLUSIONS AND RELEVANCE

Participants felt that SR saves them time, increases their efficiency and allows them to quickly document more relevant details. Quality analysis supports the perception that SR allows for more detailed notes, but whether dictation is objectively faster than typing remains unclear, and participants described some scenarios where typing is still preferred. Dictation can be effective for creating comprehensive documentation, especially when physicians like and feel comfortable using SR. Research is needed to further improve integration of SR with EHR systems and assess its impact on clinical practice, workflows, provider and patient experience, and costs.

Nephrology Fellows' and Program Directors' Perceptions of Hospital Rounds in the United States

BACKGROUND AND OBJECTIVES

Hospital rounds are a traditional vehicle for patient-care delivery and experiential learning for trainees. We aimed to characterize practices and perceptions of rounds in United States nephrology training programs.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We conducted a national survey of United States nephrology fellows and program directors. Fellows received the survey after completing the 2019 National Board of Medical Examiners Nephrology In-Training Exam. Program directors received the survey at the American Society of Nephrology's 2019 Nephrology Training Program Directors' Retreat. Surveys assessed the structure and perceptions of rounds, focusing on workload, workflow, value for patient care, and fellows' clinical skill-building. Directors were queried about their expectations for fellow prerounds and efficiency of rounds. Responses were quantified by proportions.

RESULTS

Fellow and program director response rates were 73% (n=621) and 70% (n=55). Most fellows (74%) report a patient census of >15, arrive at the hospital before 7:00 am (59%), and complete progress notes after 5:00 pm (46%). Among several rounding activities, fellows most valued bedside discussions for building their clinical skills (34%), but only 30% examine all patients with the attending at the bedside. Most directors (71%) expect fellows to both examine patients and collect data before attending-rounds. A majority (78%) of directors commonly complete their documentation after 5:00 pm, and for 36%, after 8:00 pm. Like fellows, directors most value bedside discussion for development of fellows' clinical skills (44%). Lack of preparedness for the rigors of nephrology fellowship was the most-cited barrier to efficient rounds (31%).

CONCLUSIONS

Hospital rounds in United States nephrology training programs are characterized by high patient volumes, early-morning starts, and late-evening clinical documentation. Fellows use a variety of prerounding styles and examine patients at the beside with their attendings at different frequencies.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2020_03_17_CJN.10190819.mp3.

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.

Pediatrician Attitudes toward Digital Voice Assistant Technology Use in Clinical Practice

OBJECTIVE

Digital voice assistant technology provides unique opportunities to enhance clinical practice. We aimed to understand factors influencing pediatric providers' current and potential use of this technology in clinical practice.

METHODS

We surveyed pediatric providers regarding current use and interest in voice technology in the workplace. Regression analyses evaluated provider characteristics associated with voice technology use. Among respondents not interested in voice technology, we elicited individual concerns.

RESULTS

Among 114 respondents, 19 (16.7%) indicated current use of voice technology in clinical practice, and 51 (44.7%) indicated use of voice technology for nonclinical purposes. Fifty-four (47.4%) reported willingness to try digital voice assistant technology in the clinical setting. Providers who had longer clinic visits (odds ratio [OR], 3.11, 95% confidence interval [CI], 1.04, 9.33, p = 0.04), fewer patient encounters per day (p = 0.02), and worked in hospital-based practices (OR, 2.95, 95% CI, 1.08, 8.07, p = 0.03) were more likely to currently use voice technology in the office. Younger providers (p = 0.02) and those confident in the accuracy of voice technology (OR, 3.05, 95% CI, 1.38, 6.74, p = 0.005) were more willing to trial digital voice assistants in the clinical setting. Among respondents unwilling or unsure about trying voice assistant technology, the most common reasons elicited were concerns related to its accuracy (35%), efficiency (33%), and privacy (28%).

CONCLUSION

This national survey evaluating use and attitudes toward digital voice assistant technology by pediatric providers found that while only one-eighth of pediatric providers currently use digital voice assistant technology in the clinical setting, almost half are interested in trying it in the future. Younger provider age and confidence in the accuracy of voice technology are associated with provider interest in using voice technology in the clinical setting. Future development of voice technology for clinical use will need to consider accuracy of information, efficiency of use, and patient privacy for successful integration into the workplace.