Knowledge discovery in clinical decision support systems for pain management: A systematic review |
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| Affiliation: | 1. Department of Informatics, University of Beira Interior, Rua Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal;2. Instituto de Telecomunicações and Department of Informatics, University of Beira Interior, Rua Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal;3. Faculty of Health Sciences, University of Beira Interior, Av. Infante D. Henrique, 6200-506 Covilhã, Portugal;1. Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan, Ann Arbor, MI;2. Center for Healthcare Outcomes & Policy, Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI;1. Department of Computer Engineering, University of Alcala, Madrid, 28805, Spain;2. Department of Computer Science, University of Alcala, Madrid, 28805, Spain;1. Department of Mathematics and Statistics, University of Tromsø, 9037 Tromsø, Norway;2. Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, 9038 Tromsø, Norway;3. Dermatology Office, Venloer Straße 107, 50259 Pulheim, Germany |
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| Abstract: | ObjectiveThe occurrence of pain accounts for billions of dollars in annual medical expenditures; loss of quality of life and decreased worker productivity contribute to indirect costs. As pain is highly subjective, clinical decision support systems (CDSSs) can be critical for improving the accuracy of pain assessment and offering better support for clinical decision-making. This review is focused on computer technologies for pain management that allow CDSSs to obtain knowledge from the clinical data produced by either patients or health care professionals.Methods and materialsA comprehensive literature search was conducted in several electronic databases to identify relevant articles focused on computerised systems that constituted CDSSs and include data or results related to pain symptoms from patients with acute or chronic pain, published between 1992 and 2011 in the English language. In total, thirty-nine studies were analysed; thirty-two were selected from 1245 citations, and seven were obtained from reference tracking.ResultsThe results highlighted the following clusters of computer technologies: rule-based algorithms, artificial neural networks, nonstandard set theory, and statistical learning algorithms. In addition, several methodologies were found for content processing such as terminologies, questionnaires, and scores. The median accuracy ranged from 53% to 87.5%.ConclusionsComputer technologies that have been applied in CDSSs are important but not determinant in improving the systems’ accuracy and the clinical practice, as evidenced by the moderate correlation among the studies. However, these systems play an important role in the design of computerised systems oriented to a patient's symptoms as is required for pain management. Several limitations related to CDSSs were observed: the lack of integration with mobile devices, the reduced use of web-based interfaces, and scarce capabilities for data to be inserted by patients. |
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| Keywords: | Clinical decision support system Pain measurement Machine learning Systematic review |
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