A Technical Note on the PainChek™ System: A Web Portal and Mobile Medical Device for Assessing Pain in People With Dementia

Abstract

Background: Pain in dementia is predominant particularly in the advanced stages or in those who are unable to verbalize. Uncontrolled pain alters the course of behaviors in patients with dementia making them perturbed, unsettled, and devitalized. Current measures of assessing pain in this population group are inadequate and underutilized in clinical practice because they lack systematic evaluation and innovative design.Objective: To describe a novel method and system of pain assessment using a combination of technologies: automated facial recognition and analysis (AFRA), smart computing, affective computing, and cloud computing (Internet of Things) for people with advanced dementia.Methods and Results: Cognification and affective computing were used to conceptualize the system. A computerized clinical system was developed to address the challenging problem of identifying pain in non-verbal patients with dementia. The system is composed of a smart device enabled app (App) linked to a web admin portal (WAP). The App “PainChek™” uses AFRA to identify facial action units indicative of pain presence, and user-fed clinical information to calculate a pain intensity score. The App has various functionalities including: pain assessment, pain monitoring, patient profiling, and data synchronization (into the WAP). The WAP serves as a database that collects the data obtained through the App in the clinical setting. These technologies can assist in addressing the various characteristics of pain (e.g., subjectivity, multidimensionality, and dynamicity). With over 750 paired assessments conducted, the App has been validated in two clinical studies (n = 74, age: 60–98 y), which showed sound psychometric properties: excellent concurrent validity (r = 0.882–0.911), interrater reliability (Kw = 0.74–0.86), internal consistency (α = 0.925–0.950), and excellent test-retest reliability (ICC = 0.904), while it possesses good predictive validity and discriminant validity. Clinimetric data revealed high accuracy (95.0%), sensitivity (96.1%), and specificity (91.4%) as well as excellent clinical utility (0.95).Conclusions: PainChek™ is a comprehensive and evidence-based pain management system. This novel approach has the potential to transform pain assessment in people who are unable to verbalize because it can be used by clinicians and carers in everyday clinical practice.

A novel pain assessment tool incorporating automated facial analysis: interrater reliability in advanced dementia

“…The ePAT was designed by Curtin University researchers after reviewing the literature of pain, dementia, geriatric care, and pain facial expressions. 30 , 31 , 61 ePAT is a smart device application (App) that uses a combination of a selected set of facial AU codes and common pain behaviors reported in the literature (eg, items included in the American Geriatric Society [AGS] Indicators of Persistent Pain [2002]) to assess pain at the point of care. 29 , 32 A predefined set of facial AUs were included in the tool because they were associated with pain.…”

“…The tool uses digitization, real-time automated facial recognition and decoding using a deep learning (AI) approach, as a means of identifying and evaluating pain. 61 Digitization and smart device technology serve as a platform to facilitate documentation, while automated FACS decoding is integrated in the tool with the view to improve objectivity through reducing human observation errors. 61 Automated facial analysis identifies subtle facial muscle movements called AUs, which represent the smallest building blocks responsible for exerting microexpressions, each of which lasts for 100–500 milliseconds.…”

“… 61 Digitization and smart device technology serve as a platform to facilitate documentation, while automated FACS decoding is integrated in the tool with the view to improve objectivity through reducing human observation errors. 61 Automated facial analysis identifies subtle facial muscle movements called AUs, which represent the smallest building blocks responsible for exerting microexpressions, each of which lasts for 100–500 milliseconds. 36 The automated facial assessment consists of three steps ( Figure 1 ): Face detection and tracking ( Figure 2 ) Localization and extraction of facial features ( Figure 3 ) Detection of facial AUs ( Figure 4 ).…”

“…The ePAT is composed of six domains (Face, Voice, Movement, Behavior, Activity, and Body), which contains a total of 42 items. 31 , 61 Table 1 describes the ePAT domains and the corresponding items along with their operational definitions and primary conceptual basis. Each domain contains a number of items.…”

“…These intricacies have inspired us to develop the electronic Pain Assessment Tool (ePAT). 30 , 31 , 61 In this study, we aimed to examine interrater reliability of ePAT as a means of evaluating pain in aged care residents with moderate-to-severe dementia. Further, we examined the relationship between facial scores (which are determined using automated facial analysis) and total pain scores.…”