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Haberle T, Cleveland C, Snow GL, Barber C, Stookey N, Thornock C, Younger L, Mullahkhel B, Ize-Ludlow D. The impact of nuance DAX ambient listening AI documentation: a cohort study. J Am Med Inform Assoc 2024; 31:975-979. [PMID: 38345343 PMCID: PMC10990544 DOI: 10.1093/jamia/ocae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/29/2023] [Accepted: 01/23/2024] [Indexed: 04/05/2024] Open
Abstract
OBJECTIVE To assess the impact of the use of an ambient listening/digital scribing solution (Nuance Dragon Ambient eXperience (DAX)) on caregiver engagement, time spent on Electronic Health Record (EHR) including time after hours, productivity, attributed panel size for value-based care providers, documentation timeliness, and Current Procedural Terminology (CPT) submissions. MATERIALS AND METHODS We performed a peer-matched controlled cohort study from March to September 2022 to evaluate the impact of DAX in outpatient clinics in an integrated healthcare system. Primary outcome measurements included provider engagement survey results, reported patient safety events related to DAX use, patients' Likelihood to Recommend score, number of patients opting out of ambient listening, change in work relative values units, attributed value-based primary care panel size, documentation completion and CPT code submission deficiency rates, and note turnaround time. RESULTS A total of 99 providers representing 12 specialties enrolled in the study; 76 matched control group providers were included for analysis. Median utilization of DAX was 47% among active participants. We found positive trends in provider engagement, while non-participants saw worsening engagement and no practical change in productivity. There was a statistically significant worsening of after-hours EHR. There was no quantifiable effect on patient safety. DISCUSSION Nuance DAX use showed positive trends in provider engagement at no risk to patient safety, experience, or clinical documentation. There were no significant benefits to patient experience, documentation, or measures of provider productivity. CONCLUSION Our results highlight the potential of ambient dictation as a tool for improving the provider experience. Head-to-head comparisons of EHR documentation efficiency training are needed.
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Affiliation(s)
- Tyler Haberle
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Courtney Cleveland
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Greg L Snow
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Chris Barber
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Nikki Stookey
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Cari Thornock
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Laurie Younger
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Buzzy Mullahkhel
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
| | - Diego Ize-Ludlow
- Digital Technology Services, Intermountain Health, Salt Lake City, UT 84120, United States
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King AJ, Angus DC, Cooper GF, Mowery DL, Seaman JB, Potter KM, Bukowski LA, Al-Khafaji A, Gunn SR, Kahn JM. A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds. J Biomed Inform 2023; 146:104483. [PMID: 37657712 PMCID: PMC10591951 DOI: 10.1016/j.jbi.2023.104483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/21/2023] [Accepted: 08/29/2023] [Indexed: 09/03/2023]
Abstract
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.
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Affiliation(s)
- Andrew J King
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Derek C Angus
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Gregory F Cooper
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Offices at Baum 4th Floor, 5607 Baum Blvd, Pittsburgh, PA 15206, USA.
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania School of Medicine, Blockley Hall 8th Floor, 423 Guardian Drive, Philadelphia, PA 19104, USA.
| | - Jennifer B Seaman
- Department of Acute & Tertiary Care, University of Pittsburgh School of Nursing, 336 Victoria Building, 3500 Victoria Street, Pittsburgh, PA 15261, USA.
| | - Kelly M Potter
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Leigh A Bukowski
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Ali Al-Khafaji
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Scott R Gunn
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Jeremy M Kahn
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
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Chen MS, Wu KC, Tsai YL, Jiang BC. Data analysis of ambient intelligence in a healthcare simulation system: a pilot study in high-end health screening process improvement. BMC Health Serv Res 2021; 21:936. [PMID: 34496839 PMCID: PMC8424928 DOI: 10.1186/s12913-021-06949-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 08/26/2021] [Indexed: 11/24/2022] Open
Abstract
Background This study aimed to reduce the total waiting time for high-end health screening processes. Method The subjects of this study were recruited from a health screening center in a tertiary hospital in northern Taiwan from September 2016 to February 2017, where a total of 2342 high-end customers participated. Three policies were adopted for the simulation. Results The first policy presented a predetermined proportion of customer types, in which the total waiting time was increased from 72.29 to 83.04 mins. The second policy was based on increased bottleneck resources, which provided significant improvement, decreasing the total waiting time from 72.29 to 28.39 mins. However, this policy also dramatically increased the cost while lowering the utilization of this health screening center. The third policy was adjusting customer arrival times, which significantly reduced the waiting time—with the total waiting time reduced from 72.29 to 55.02 mins. Although the waiting time of this policy was slightly longer than that of the second policy, the additional cost was much lower. Conclusions Scheduled arrival intervals could help reduce customer waiting time in the health screening department based on the “first in, first out” rule. The simulation model of this study could be utilized, and the parameters could be modified to comply with different health screening centers to improve processes and service quality.
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Affiliation(s)
- Ming-Shu Chen
- Department of Healthcare Administration, College of Management and Healthcare, Asia Eastern University of Science and Technology, No.58, Sec. 2, Sihchuan Rd., Pan-Chiao Dist., New Taipei, 22061, Taiwan, Republic of China.
| | - Kun-Chih Wu
- Department of Management Center, National Chung-Shan Institute of Science & Technology, Taoyuan City, 32546, Taiwan, Republic of China
| | - Yu-Ling Tsai
- Department of Industrial Management, National Taiwan University of Science and Technology, Taipei City, 10607, Taiwan, Republic of China
| | - Bernard C Jiang
- Department of Industrial Management, National Taiwan University of Science and Technology, Taipei City, 10607, Taiwan, Republic of China
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Peralta A, Fernández-Caballero A, Latorre JM. Determining the ambient influences and configuration of optimised environments for emotional wellbeing of older adults. ERGONOMICS 2021; 64:1146-1159. [PMID: 33860739 DOI: 10.1080/00140139.2021.1909756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 03/24/2021] [Indexed: 06/12/2023]
Abstract
It is now widely recognised that aspects such as tiredness or mood state can have an impact on an individual's wellbeing. However, there also exist other less studied factors that might be influential, and whose analysis is important to maximise personal wellbeing. The aim of this study was to determine the influence of a set of 12 selected factors. Using the analysis of a 20-experiment case study by soft computing techniques the intention was to establish the most appropriate configuration for each factor to compose an optimal living environment to foster wellbeing. The analysis revealed that ambient lighting and stress level are the factors that most impact emotional wellbeing. To a lesser extent, being able to take a break, ambient temperature and ambient noise play a relatively determining role. The findings of this work can be used to establish a living environment for older persons that favours their emotional wellbeing. Practitioner summary: This study analyses the level of influence of a set of ambient factors on the emotional wellbeing of older people, conducting, to this end, a series of controlled experiments, and concluding that ambient lighting and stress level are the factors most relevant to promote a better living environment.KEY POINTSOlder adults' emotional interpretation of pictures depends on the environment and ambient factors.Ambient factors, such as lighting and stress, have a significant, positive effect on visual interpretation of stimuli and greater wellbeing.The use of soft computing techniques facilitates the quantification of the influence of factors affecting emotional wellbeing.
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Affiliation(s)
- Arturo Peralta
- Departamento de Tecnologías y Sistemas de Información, Escuela Superior de Informática, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Antonio Fernández-Caballero
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain
- CIBERSAM (Biomedical Research Networking Center in Mental Health), Madrid, Spain
| | - José Miguel Latorre
- Departamento de Psicología, Universidad de Castilla-La Mancha, Albacete, Spain
- Instituto de Investigación en Discapacidades Neurológicas (IDINE), Albacete, Spain
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Zhu H, Samtani S, Brown R, Chen H. A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal Patterns. MIS QUART 2021. [DOI: 10.25300/misq/2021/15574] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Ensuring the health and safety of senior citizens who live alone is a growing societal concern. The Activity of Daily Living (ADL) approach is a common means to monitor disease progression and the ability of these individuals to care for themselves. However, the prevailing sensor-based ADL monitoring systems primarily rely on wearable motion sensors, capture insufficient information for accurate ADL recognition, and do not provide a comprehensive understanding of ADLs at different granularities. Current healthcare IS and mobile analytics research focuses on studying the system, device, and provided services, and is in need of an end-to-end solution to comprehensively recognize ADLs based on mobile sensor data. This study adopts the design science paradigm and employs advanced deep learning algorithms to develop a novel hierarchical, multiphase ADL recognition framework to model ADLs at different granularities. We propose a novel 2D interaction kernel for convolutional neural networks to leverage interactions between human and object motion sensors. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks (e.g., support vector machines, DeepConvLSTM, hidden Markov models, and topic-modeling-based ADLR) on two real-life motion sensor datasets that consist of ADLs at varying granularities: Opportunity and INTER. Results and a case study demonstrate that our framework can recognize ADLs at different levels more accurately. We discuss how stakeholders can further benefit from our proposed framework. Beyond demonstrating practical utility, we discuss contributions to the IS knowledge base for future design science-based cybersecurity, healthcare, and mobile analytics applications.
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Gance-Cleveland B, McDonald CC, Walker RK. Use of theory to guide development and application of sensor technologies in Nursing. Nurs Outlook 2020; 68:698-710. [PMID: 32620271 DOI: 10.1016/j.outlook.2020.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 04/01/2020] [Accepted: 04/25/2020] [Indexed: 11/28/2022]
Abstract
Sensor technologies for health care, research, and consumers have expanded and evolved rapidly. Many technologies developed in commercial or engineering spaces, lack theoretical grounding and scientific evidence to support their need, safety, and efficacy. Theory is a mechanism for synthesizing and guiding knowledge generation for the discipline of nursing, including the design, implementation, and evaluation of sensors and related technologies such as artificial intelligence and machine learning. In this paper, three nurse scientists summarize their presentations at the Council for the Advancement of Nursing Science 2019 Advanced Methods Conference on Expanding Science of Sensor Technology in Research discussing the theoretical underpinnings of sensor technologies development and use in nursing research and practice. Multiple theories with diverse epistemological roots guide decision-making about whether or not to apply sensors to a given use; development of, components of, and mechanisms by which sensor technologies are expected to work; and possible outcomes.
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Affiliation(s)
| | - Catherine C McDonald
- University of Pennsylvania School of Nursing, University of Pennsylvania Injury Science Center, Center for Injury Research Prevention at the Children's Hospital of Philadelphia, Philadelphia, PA
| | - Rachel K Walker
- College of Nursing, IALS Center for Health & Human Performance, University of Massachusetts-Amherst, Amherst, MA
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Wang D, Liu S, Wu J, Lin Q. Purchase and use of home healthcare devices for the elderly: a pilot study in Shanghai, China. BMC Public Health 2020; 20:615. [PMID: 32366229 PMCID: PMC7197117 DOI: 10.1186/s12889-020-08757-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 04/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In China, home-based healthcare/rehabilitation has always been advocated by the government and is the most prevalent healthcare pattern. However, there is currently no data on how many each product has been purchased, and it is not clear what factors are associated with their use. The research aims to clarify the current practices and attitudes of the elderly on such matters, and further analyze their influence factors. METHODS This pilot study consisted of two-round regional survey, conducted from July 25 to August 3, 2015 and July 20 to August 10, 2018 respectively. Both surveys released on-site paper questionnaires and collected after filling out in different communities. RESULTS Two hundred forty-four valid questionnaires from 52 communities were collected. Compared with 2015 (30.8%), the number of people who did not purchase home healthcare devices in the same area decreased in 2018 (28.2%). Hemopiezometer (44.3%), glucometer (18.4%), massager (21.3%) and walking devices (19.3%) are the four main types of products that urbanites are most willing to buy. In addition, users' age group, education level, and income level were significantly correlated with the purchase of certain products. CONCLUSIONS The types of home healthcare devices purchased by respondents are consistent with the distribution of chronic diseases of urban residents in China. The analysis of product brands also revealed the existing problems and huge growth space of the industry market, which also requires the government to introduce relevant policies and measures to regulate the market and accelerate the development of the industry.
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Affiliation(s)
- Duojin Wang
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai, 200093, China.,Shanghai Engineering Research Center of Assistive Devices, Jungong Road 516, Shanghai, 200093, China
| | - Shiyu Liu
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai, 200093, China
| | - Jing Wu
- School of Economics & Management, Tongji University, Siping Road 1500, Shanghai, 200092, China
| | - Qinglian Lin
- School of Management, Xiamen University, Siming South Road, Xiamen, 361005, China.
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Zhu H, Chen H, Brown R. A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care. J Biomed Inform 2018; 84:148-158. [PMID: 30004019 DOI: 10.1016/j.jbi.2018.07.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Revised: 06/09/2018] [Accepted: 07/08/2018] [Indexed: 11/29/2022]
Abstract
Ensuring the health and safety of independent-living senior citizens is a growing societal concern. Researchers have developed sensor based systems to monitor senior citizens' Activity of Daily Living (ADL), a set of daily activities that can indicate their self-caring ability. However, most ADL monitoring systems are designed for one specific sensor modality, resulting in less generalizable models that is not flexible to account variations in real-life monitoring settings. Current classic machine learning and deep learning methods do not provide a generalizable solution to recognize complex ADLs for different sensor settings. This study proposes a novel Sequence-to-Sequence model based deep-learning framework to recognize complex ADLs leveraging an activity state representation. The proposed activity state representation integrated motion and environment sensor data without labor-intense feature engineering. We evaluated our proposed framework against several state-of-the-art machine learning and deep learning benchmarks. Overall, our approach outperformed baselines in most performance metrics, accurately recognized complex ADLs from different types of sensor input. This framework can generalize to different sensor settings and provide a viable approach to understand senior citizen's daily activity patterns with smart home health monitoring systems.
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Affiliation(s)
- Hongyi Zhu
- Department of Management Information Systems, University of Arizona, Tucson, AZ, United States.
| | - Hsinchun Chen
- Department of Management Information Systems, University of Arizona, Tucson, AZ, United States
| | - Randall Brown
- Department of Management Information Systems, University of Arizona, Tucson, AZ, United States
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Costa A, Rincon JA, Carrascosa C, Novais P, Julian V. Activities suggestion based on emotions in AAL environments. Artif Intell Med 2018; 86:9-19. [PMID: 29426681 DOI: 10.1016/j.artmed.2018.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2017] [Revised: 11/13/2017] [Accepted: 01/15/2018] [Indexed: 10/18/2022]
Abstract
The elderly population is increasing and the response of the society was to provide them with services directed to them to cope with their needs. One of the oldest solutions is the retirement home, providing housing and permanent assistance for the elderly. Furthermore, most of the retirement homes are inhabited by multiple elderly people, thus creating a community of people who are somewhat related in age and medical issues. The ambient assisted living (AAL) area tries to solve some of the elderly issues by producing technological products, some of them dedicated to elderly homes. One of the identified problem is that elderly people are sometimes discontent about the activities that consume most of their day promoted by the retirement home social workers. The work presented in this paper attempts to improve how these activities are scheduled taking into account the elderlies' emotional response to these activities. The aim is to maximize the group happiness by promoting the activities the group likes, minding if they are bored due to activities repetition. In this sense, this paper presents an extension of the Cognitive Life Assistant platform incorporating a social emotional model. The proposed system has been modelled as a free time activity manager which is in charge of suggesting activities to the social workers.
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Affiliation(s)
- Angelo Costa
- ISLab/ALGORITMI Center - University of Minho, Portugal.
| | - Jaime Andres Rincon
- Departamento de Sistemas Informáticos y Computación (DSIC), Universitat Politécnica de Valéncia, Spain.
| | - Carlos Carrascosa
- Departamento de Sistemas Informáticos y Computación (DSIC), Universitat Politécnica de Valéncia, Spain.
| | - Paulo Novais
- ISLab/ALGORITMI Center - University of Minho, Portugal.
| | - Vicente Julian
- Departamento de Sistemas Informáticos y Computación (DSIC), Universitat Politécnica de Valéncia, Spain.
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