Cost-effectiveness of Automated Medical Systems implementation in hospital setting: A systematic review and meta-analysis Article Swipe
Junting Chi
,
Xiaodan Niu
,
Jing Zhang
,
Haihui Ruan
,
Hongxia Tao
,
Yanhong Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/au.170664417.74035298/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/au.170664417.74035298/v1
Objectives: This systematic review summarized and synthesized the available evidence to examines the cost-effectiveness of the implementation of Automated Medical Systems. Method: PubMed, Embase, Web of Science, The Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Full-text Database (VIP), WanFang Database, China Biopharmaceutical Scientific Literature Database (CBM) were searched from inception to February 2020. The reference lists of eligible studies were hand searched. After two investigators independently screened the literature and extracted the data, the quality of the included articles was evaluated by using the Cochrane Intervention Risk of Bias Assessment Tool, the Newcastle-Ottawa Scale and the Agency for Healthcare Research and Quality Scale. Revman5.3 software was used for meta-analysis. Results: Sixteen articles (9 interventional studies, 6 cohort studies, 1 cross-sectional study) were finally included, 92,576 patients were included in analysis. Meta-analysis showed that: 1) compared with the traditional method, the incidence of adverse events (such as potential adverse drug reactions, deep vein thrombosis, etc.) was reduced after the implementation of the Automated Medical System (OR = 0.43, 95% CI = [0.20, 0.93]; P = 0.03); 2) the average medical costs incurred during the use of the Automated Medical System were lower than those of the traditional method (OR = 1.13, 95% CI = [1.02, 1.24]; P = 0.02), which was cost-effective (OR = 2.03, 95% CI = [1.34, 3.07]; P = 0.0008); 3) the quality-adjusted life years obtained by patients observed during the implementation of the Automated Medical System were significantly higher than those of the conventional medical system (OR = 1.13, 95% CI = [1.02, 1.24]; P = 0.02). Conclusion: A multicenter, large-sample randomized controlled trial is needed to comprehensively explore the cost-effectiveness of Automated Medical Systems using a unified economic evaluation model and considering all costs associated with Automated Medical Syst
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.22541/au.170664417.74035298/v1
- https://www.authorea.com/doi/pdf/10.22541/au.170664417.74035298
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391340393Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22541/au.170664417.74035298/v1Digital Object Identifier
- Title
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Cost-effectiveness of Automated Medical Systems implementation in hospital setting: A systematic review and meta-analysisWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-30Full publication date if available
- Authors
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Junting Chi, Xiaodan Niu, Jing Zhang, Haihui Ruan, Hongxia Tao, Yanhong WangList of authors in order
- Landing page
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https://doi.org/10.22541/au.170664417.74035298/v1Publisher landing page
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https://www.authorea.com/doi/pdf/10.22541/au.170664417.74035298Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.authorea.com/doi/pdf/10.22541/au.170664417.74035298Direct OA link when available
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Cochrane Library, Meta-analysis, Medicine, MEDLINE, Systematic review, Internal medicine, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Results: | 114 |
| abstract_inverted_index.Science, | 26 |
| abstract_inverted_index.Systems. | 20 |
| abstract_inverted_index.articles | 83, 116 |
| abstract_inverted_index.compared | 139 |
| abstract_inverted_index.economic | 286 |
| abstract_inverted_index.eligible | 62 |
| abstract_inverted_index.evidence | 9 |
| abstract_inverted_index.examines | 11 |
| abstract_inverted_index.included | 82, 132 |
| abstract_inverted_index.incurred | 185 |
| abstract_inverted_index.observed | 235 |
| abstract_inverted_index.obtained | 232 |
| abstract_inverted_index.patients | 130, 234 |
| abstract_inverted_index.screened | 71 |
| abstract_inverted_index.searched | 52 |
| abstract_inverted_index.software | 109 |
| abstract_inverted_index.studies, | 119, 122 |
| abstract_inverted_index.Automated | 18, 166, 191, 241, 280, 295 |
| abstract_inverted_index.Database, | 44 |
| abstract_inverted_index.Full-text | 40 |
| abstract_inverted_index.Knowledge | 32 |
| abstract_inverted_index.Revman5.3 | 108 |
| abstract_inverted_index.analysis. | 134 |
| abstract_inverted_index.available | 8 |
| abstract_inverted_index.evaluated | 85 |
| abstract_inverted_index.extracted | 75 |
| abstract_inverted_index.inception | 54 |
| abstract_inverted_index.incidence | 145 |
| abstract_inverted_index.included, | 128 |
| abstract_inverted_index.potential | 151 |
| abstract_inverted_index.reference | 59 |
| abstract_inverted_index.searched. | 66 |
| abstract_inverted_index.Assessment | 94 |
| abstract_inverted_index.Healthcare | 103 |
| abstract_inverted_index.Literature | 48 |
| abstract_inverted_index.Scientific | 47 |
| abstract_inverted_index.Technology | 38 |
| abstract_inverted_index.associated | 293 |
| abstract_inverted_index.controlled | 270 |
| abstract_inverted_index.evaluation | 287 |
| abstract_inverted_index.literature | 73 |
| abstract_inverted_index.randomized | 269 |
| abstract_inverted_index.reactions, | 154 |
| abstract_inverted_index.summarized | 4 |
| abstract_inverted_index.systematic | 2 |
| abstract_inverted_index.Conclusion: | 265 |
| abstract_inverted_index.Objectives: | 0 |
| abstract_inverted_index.considering | 290 |
| abstract_inverted_index.synthesized | 6 |
| abstract_inverted_index.thrombosis, | 157 |
| abstract_inverted_index.traditional | 142, 200 |
| abstract_inverted_index.Intervention | 90 |
| abstract_inverted_index.conventional | 251 |
| abstract_inverted_index.large-sample | 268 |
| abstract_inverted_index.multicenter, | 267 |
| abstract_inverted_index.Meta-analysis | 135 |
| abstract_inverted_index.independently | 70 |
| abstract_inverted_index.investigators | 69 |
| abstract_inverted_index.significantly | 245 |
| abstract_inverted_index.Infrastructure | 33 |
| abstract_inverted_index.cost-effective | 215 |
| abstract_inverted_index.implementation | 16, 163, 238 |
| abstract_inverted_index.interventional | 118 |
| abstract_inverted_index.meta-analysis. | 113 |
| abstract_inverted_index.comprehensively | 275 |
| abstract_inverted_index.cross-sectional | 124 |
| abstract_inverted_index.Newcastle-Ottawa | 97 |
| abstract_inverted_index.quality-adjusted | 229 |
| abstract_inverted_index.Biopharmaceutical | 46 |
| abstract_inverted_index.cost-effectiveness | 13, 278 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.00990922 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |