Patama Gomutbutra, MD: Home-Based Pain Monitoring and Decision Support
My life goal is to use digital technology to make pain and palliative care more accessible. I believe that technology can supplement, rather than replace, humanized comprehensive care in order to address the megatrend of an aging society. My current project, dubbed I-PAIN, aims to create a home-based pain monitoring and counseling system based on machine learning. This project still needs input from multidisciplinary experts as well support from the government public health agency. I believe attending the PAINWEEK conference will be an opportunity for me to share experiences, build a network, and, importantly, raising the interest of public health agency in Thailand and internationally for home-based pain management technology. This may change the way of caring for pain to a more comprehensive while less time-consuming method.
As a neurologist working at a medical referral center in Chiang Mai, Thailand, the majority of my patients are in advanced stages of disease such as metastatic cancer, late-stage organ failure, late-stage neurodegenerative disease. These patients are frequently neglected for their pain and spiritual suffering. This inspired me, and along with some of my family physician colleagues, we founded a palliative care service that included inpatient consultation, 6-bed comfort care suites, an out-patient clinic, and home visiting.
The majority of our patients are low to middle income elders living in rural areas alone or with a spouse while their children work in a different place. The current solution is to connect rural health agencies to help with follow-up; however, some patients need an expert level of adjusting medication (eg, pain with the comorbid neuropsychologic problem). Furthermore, the need for home-based pain reporting would become more prominent due to social distancing during COVID-19. This situation makes it difficult to follow up and caused a high rate of unplanned readmission due to uncontrolled pain or other distress symptoms. Therefore, we conducted an extensive review and were motivated Kunz et al1 and Dildine2 to utilize automated facial recognition as a personal identity and objective pain behavior assessment and decision-aid pain management to be a tool to enhance the capacity of rural health agencies.
It is fortunate that I have colleagues from computer science and application technologists as a committed team. This project was a grant by seeding fund from the Thailand neurological society of Thailand. I also submit the initial result on the abstract—“Classify Elderly Pain Severity from Open-Source Automated Facial Analysis. A Study from the Innovative Pain Artificial Intelligence Network (I-PAIN) Data Reciprocity”—for consideration for the PAINWEEK scientific poster section. Currently, the phase II project developing platform has been granted by the Chiang Mai university’s innovation support fund. However, the next step in using it in a nationwide health agency is challenging due to low interest from the government public health agency.
I believe that winning a scholarship from PAINWeek may provide feedback and approval that the idea regarding home-based pain management technology is valuable. Whether I win or not, I still plan to attend PAINWEEK in order to have a chance to meet and talk with respected pain experts. Their opinion would help me design a more feasible platform and better deal with public health strategies.
1. Kunz et al. PAIN. 2019;160(3):534-549.
2. Dildine. PAIN. 2019;160(8):1901-1902.