Information Model on Pain Management: An Analysis of Big Data

Information Model on pain management for elder adults aged 75 years or older

BACKGROUND

(1) The model attributes were classified as: pain assessment, interventions and reassessment.

BACKGROUND

(2) Greater importance is given to pain assessment and pharmacological interventions.

BACKGROUND

(3) There was greater variability in the pain care measures prescribed by nurses.

BACKGROUND

(4) Absence of multidimensional instruments for pain assessment was identified.

BACKGROUND

(5) Pain care interventions were prescribed recurrently in aged people at risk of falls.

to develop an information model on pain management in hospitalized aged people.

a Big Data retrospective and observational study guided by the Applied Healthcare Data Science Roadmap. The sample included all Electronic Health Records related to pain management in older adults aged at least 75 years old considered vulnerable in the institution and admitted to clinical and surgical units. Data science packages were used in Python ® for data analysis.

a total of 9,635 hospitalizations were found for 4,753 patients, with a mean age of 81 years old and 54% belonging to the female gender. The main reasons for hospitalization were diseases of the circulatory system (n=1,593; 28.6%), neoplasms (n=893; 16%) and diseases of the genitourinary system (n=508; 9.1%). A total of 60 attributes related to pain were identified and organized into groups: current pain, assessment instruments and characteristics, Nursing diagnosis, etiology of the Nursing diagnosis, interventions for relief, consultations to specialties and pain reassessment. The groups were classified into four large panels that constituted the information model.

the information model developed presented an overview of the healthcare reality of pain management in vulnerable aged people, supporting decision-making for pain management in this population segment.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.