Digital health in pain assessment, diagnosis, and management: Overview and perspectives

Abstract

Managing pain is essential for social, psychological, physical, and economic reasons. It is also a human right with a growing incidence of untreated and under-treated pain globally. Barriers to diagnosing, assessing, treating, and managing pain are complicated, subjective, and driven by patient, healthcare provider, payer, policy, and regulatory challenges. In addition, conventional treatment methods pose their own challenges including the subjectivity of assessment, lack of therapeutic innovation over the last decade, opioid use disorder and financial access to treatment. Digital health innovations hold much promise in providing complementary solutions to traditional medical interventions and may reduce cost and speed up recovery or adaptation. There is a growing evidence base for the use of digital health in pain assessment, diagnosis, and management. The challenge is not only to develop new technologies and solutions, but to do this within a framework that supports health equity, scalability, socio-cultural consideration, and evidence-based science. The extensive limits to physical personal interaction during the Covid-19 pandemic 2020/21 has proven the possible role of digital health in the field of pain medicine. This paper provides an overview of the use of digital health in pain management and argues for the use of a systemic framework in evaluating the efficacy of digital health solutions.

Incorporation of “Artificial Intelligence” for Objective Pain Assessment: A Comprehensive Review

“…Several studies have found promising findings of AI-based pain detection through facial expressions [ 25 ]. ML and AI can use data, whether self-report measures, physiological markers, or medical records, to create algorithms to better understand pain, assess changes to pain, and potentially predict treatment outcomes [ 27 , 28 ]. These technologies can help improve the reliability of pain assessment tools.…”

“…AI and ML incorporate large datasets with complex compositions, combining multiple modalities from different sources and allow data sharing to extract information from the data that can be transformed into knowledge about the patient’s experience of pain [ 91 ]. These data can assist clinicians in identifying the underlying mechanisms of pain and offer appropriate treatment strategies [ 27 ]. They can classify pain into nociceptive, neuropathic, or inflammatory pain.…”

Analysis of pain research literature through keyword Co-occurrence networks

“…In current clinical settings, patients typically self-report their pain level using the Verbal Rating Scale (VRS), Visual Analogue Scale (VAS), and Numerical Rating Scale (NRS) [19][20][21]. These self-reported pain measurements are subjective and often impractical when patients are not in an alert state or unable to communicate their pain level [22,23]. Infants, toddlers, and adults with communication or cognitive deficits may be unable to convey their pain levels verbally [24].…”

Further Clarification of Pain Management Complexity in Radiotherapy: Insights from Modern Statistical Approaches

“…Research on complementary therapies such as acupuncture, massage, and mindfulness practices has shown potential benefits in managing pain and improving quality of life. These complementary approaches, when used in conjunction with standard pain management strategies, can contribute to more effective and holistic pain control [18].…”