We have all experienced pain. But despite it being one of the most common symptoms people seek medical help for, it is also one of the most misunderstood and ineffectively treated.
Part of the reason is that one person’s experience of the same painful event can be significantly different from another’s. There is no one-size-fits-all approach.
Types of Pain
Pain is a general term that describes any kind of unpleasant or uncomfortable sensation in the body. There are many different types and causes of pain, and these can be grouped into eight different categories to help with pain management:
- Acute pain
- Chronic pain
- Breakthrough pain
- Bone pain
- Nerve pain
- Phantom pain
- Soft tissue pain
- Referred pain.
MeduPal with PEAK’s correlation of data derived from the personalized analytics between medication management, pain and emotion using P.E.A.K. based on psychology, empathy, anxiety and anxiety knowledge discovery and machine learning platform. Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence. It has been accepted that pain is a complex phenomenon.
Integrated pain management is a specialty that has grown in recent years. Patient-reported outcomes and AI in pain management can provide insights to improve treatment plans. During each visit to a clinician, patients complete assessments to inform their provider about their current conditions. Each of these assessments creates a profile for the patient about their current and ongoing symptoms. Each of these patient-reported outcomes is extremely useful in tracking the success of a treatment plan. Recently, artificial intelligence (AI) has become an integral factor in learning more about these outcomes. AI can be trained to learn how different factors in a patient’s treatment plan affect their outcomes.
With pain management, it is essential to assess each patient’s pain symptoms frequently. By digitally recording responses to patient assessments, these patient-reported outcomes can be tracked more efficiently. To provide more effective treatment plans, pain management providers are using patient-reported outcomes and AI more often.
Many different healthcare specialties can use AI with patient-reported outcomes. However, pain management tends to include a myriad of external factors for each patient. Pain management can include patients with medication dependencies, behavioral health diagnoses, and chronic diseases.
Contemporary methods of computational science can use complex clinical and experimental data to better understand the complexity of pain. Among data science techniques, machine learning that can automatically detect patterns in data and then use the uncovered patterns to predict or classify future data, to observe structures such as subgroups in the data, or to extract information from the data suitable to derive new knowledge. Together with (bio)statistics, artificial intelligence, machine learning aimed at learning from the PEAK Platform data and offering to our partners the following system:
•Provides applicable conversational therapy support
•Monitors 24/7 patient PEAK system emotion anxiety data correlation, triggers and response to medications – “Red Flag Alerts”
•Screening, Collaboration and Monitoring tool for psychology professionals •Identifies emotional changes that may foreshadow adverse events
•Enables screening & intervention for addiction, depression and suicide
•Provides Objective aids in psychoactive medication selection
•Can shorten hospital recovery times - Bed Turns
•Can raises satisfaction scores and maintain high reimbursement rates
•Provides automated self-serve questionnaire system saves Drs and Clinics time •Provides valuable historical and real-time reports to help make group decisions
*Provides several options including our automated medication delivery system
