A conceptual framework for clinicians working with artificial intelligence and health‐assistive Smart Homes

Dermody, Gordana; Fritz, Robert B.

doi:10.1111/nin.12267

Cited by 39 publications

(25 citation statements)

References 27 publications

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“…In case of an emergency, such as a fall or a heart problem, wearable devices can automatically report their health condition to doctors, caregivers, or families. Some sensors through a smart home design could be useful to assist older people to remain in their home and extend independence [13]. In addition, people can share the information recorded by the devices with their friends, or post them on social media through social networking applications [12].…”

Section: A the Proposed Frameworkmentioning

confidence: 99%

A Collaborative Framework With Artificial Intelligence for Long-Term Care

Chou

2020

IEEE Access

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The trend of aging population among working families has made health care services for sub-healthy people more important. In Taiwan, caregivers are often hired by human resource agencies to provide long-term care, and they are the main supervisors responsible for the care of the sub-healthy people. However, most agencies only consider the cost of their caregivers and have insufficient staff to take care of the sub-healthy people, leading to the failure of the long-term care system. The lack of an effective collaborative framework for long-term care leads to sub-healthy people being at high risks. Existing frameworks for longterm care are still in the early stages of capturing suitability information dynamically. This paper proposes a new framework that includes all possible features suitable to support the needs of all sub-healthy people and provides a solution for the issue of determining suitable features for collaboration. This study applies association rules to long-term care to handle the mapping process and uses artificial intelligence technology to solve the issues of adjusting human variability dynamically based on the mapping result of sub-healthy people. INDEX TERMS Artificial intelligence, collaborative framework, long-term care, sub-healthy people.

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Section: A the Proposed Frameworkmentioning

confidence: 99%

A Collaborative Framework With Artificial Intelligence for Long-Term Care

Chou

2020

IEEE Access

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“…We hypothesize that smart homes may assist with detecting behaviors that are influenced by pain (eg, sleep, socialization). However, data without contextual interpretation have little meaning in real-world situations and should be avoided in health care delivery [ 41 , 42 ]. Our smart home development methods illuminate one strategy for integrating clinical knowledge to support the development of a prototype pain-assessing smart home (PASH).…”

Section: Introductionmentioning

confidence: 99%

“…To train PASH, we used these existing pain-related clinical and sensor-based data. More information about the role of nurses in the longitudinal study is available in the literature [ 42 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

Fritz¹,

Wilson²,

Dermody³

et al. 2020

J Med Internet Res

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Background Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. Objective This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. Methods A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. Results We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. Conclusions Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

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“…Our approach is based on a participatory, mixed methods research design that places the clinician directly in the iterative design loop for developing AI (described below). See Dermody and Fritz (2018) for more information on the conceptual framework underpinning the Fritz Method. The use of this method can guide nurse researchers to obtain consistent clinical ground truth that is auditable and coherent for engineering and computer science colleagues.…”

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confidence: 99%

A nurse-driven method for developing artificial intelligence in “smart” homes for aging-in-place

Fritz

Dermody

2019

Nursing Outlook

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Objectives: To offer practical guidance to nurse investigators interested in multidisciplinary research that includes assisting in the development of artificial intelligence (AI) algorithms for "smart" health management and aging-in-place. Methods: Ten health-assistive Smart Homes were deployed to chronically ill older adults from 2015-2018. Data were collected using five sensor types (infrared motion, contact, light, temperature, and humidity). Nurses used telehealth and home visitation to collect health data and provide ground truth annotation for training intelligent algorithms using raw sensor data containing health events. Findings: Nurses assisting with the development of health-assistive AI may encounter unique challenges and opportunities. We recommend: (a) using a practical and consistent method for collecting field data, (b) using nurse-driven measures for data analytics, (c) multidisciplinary communication occur on an engineering-preferred platform. Conclusions: Practical frameworks to guide nurse investigators integrating clinical data with sensor data for training machine learning algorithms may build capacity for nurses to make significant contributions to developing AI for health-assistive Smart Homes.

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A conceptual framework for clinicians working with artificial intelligence and health‐assistive Smart Homes

Cited by 39 publications

References 27 publications

A Collaborative Framework With Artificial Intelligence for Long-Term Care

A Collaborative Framework With Artificial Intelligence for Long-Term Care

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

A nurse-driven method for developing artificial intelligence in “smart” homes for aging-in-place

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