Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review
Objectives The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication err...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2023-03-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/3/e065301.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832570789515231232 |
|---|---|
| author | Gianfranco Damiani Antonio Oliva Gerardo Altamura Massimo Zedda Mario Cesare Nurchis Giovanni Aulino Aurora Heidar Alizadeh Francesca Cazzato Gabriele Della Morte Matteo Caputo Simone Grassi Maria Teresa Riccardi Martina Sapienza Giorgio Sessa |
| author_facet | Gianfranco Damiani Antonio Oliva Gerardo Altamura Massimo Zedda Mario Cesare Nurchis Giovanni Aulino Aurora Heidar Alizadeh Francesca Cazzato Gabriele Della Morte Matteo Caputo Simone Grassi Maria Teresa Riccardi Martina Sapienza Giorgio Sessa |
| collection | DOAJ |
| description | Objectives The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type.Methods A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials.Results Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety.Conclusion This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments. |
| format | Article |
| id | doaj-art-985d4566145e4c5985093a2e839f1795 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-985d4566145e4c5985093a2e839f17952025-02-02T14:10:08ZengBMJ Publishing GroupBMJ Open2044-60552023-03-0113310.1136/bmjopen-2022-065301Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review 0Gianfranco Damiani1Antonio Oliva2Gerardo Altamura3Massimo Zedda4Mario Cesare Nurchis5Giovanni Aulino6Aurora Heidar Alizadeh7Francesca Cazzato8Gabriele Della Morte9Matteo Caputo10Simone Grassi11Maria Teresa RiccardiMartina SapienzaGiorgio Sessa11 Kenya National Bureau of Statistics, Nairobi, Nairobi, KenyaDepartment of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, ItalyDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, ItalyDepartment of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, ItalyDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, ItalyDepartment of Woman and Child Health and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, ItalyDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, ItalyDepartment of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, ItalySection Legal Medicine, Institute of Public Health, Università Cattolica del Sacro Cuore - Campus di Roma, Roma, ItalyFaculty of Law, Università Cattolica del Sacro Cuore, Milano, ItalySection of Criminal Law, Department of Juridical Science, Università Cattolica del Sacro Cuore, Milano, ItalyDepartment of Health Sciences, Section of Forensic Medical Sciences, University of Florence, Firenze, ItalyObjectives The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type.Methods A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials.Results Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety.Conclusion This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.https://bmjopen.bmj.com/content/13/3/e065301.full |
| spellingShingle | Gianfranco Damiani Antonio Oliva Gerardo Altamura Massimo Zedda Mario Cesare Nurchis Giovanni Aulino Aurora Heidar Alizadeh Francesca Cazzato Gabriele Della Morte Matteo Caputo Simone Grassi Maria Teresa Riccardi Martina Sapienza Giorgio Sessa Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review BMJ Open |
| title | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
| title_full | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
| title_fullStr | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
| title_full_unstemmed | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
| title_short | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
| title_sort | potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care a systematic review |
| url | https://bmjopen.bmj.com/content/13/3/e065301.full |
| work_keys_str_mv | AT potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT gianfrancodamiani potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT antoniooliva potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT gerardoaltamura potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT massimozedda potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT mariocesarenurchis potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT giovanniaulino potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT auroraheidaralizadeh potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT francescacazzato potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT gabrieledellamorte potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT matteocaputo potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT simonegrassi potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT mariateresariccardi potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT martinasapienza potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview AT giorgiosessa potentialityofalgorithmsandartificialintelligenceadoptiontoimprovemedicationmanagementinprimarycareasystematicreview |