Decision Support for Patient Discharge in Hospitals – Analyzing the Relationship Between Length of Stay and Readmission Risk, Cost, and Profit

Eigner, Isabella; Bodendorf, Freimut

doi:10.1007/978-3-030-59595-1_6

Cited by 1 publication

(7 citation statements)

References 15 publications

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“…The remaining 20 studies were read full text. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] The relevant data were extracted and the 2 reviewers independently further assessed the quality of the articles per the CHEERS and Philips checklists.…”

Section: Search Resultsmentioning

confidence: 99%

“…A general overview of the included studies, including the details of the reported AI applications, is provided in Table 2. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] The majority of studies was published in 2019 or later. The studies were conducted in a range of medical specialties, yet ophthalmology was evidently the dominant field.…”

Section: General Overview Of the Included Studiesmentioning

confidence: 99%

“…[19][20][21][22][27][28][29][30]32 A total of 6 studies (30%) evaluated pattern recognition in clinical data for predictive modeling. [24][25][26][36][37][38] Nevertheless, all phases of the care pathway from prevention to follow-up were supported by AI in at least 1 included study; screening and treatment monitoring (intervention) were the most prevalent phases of the care pathway in which AI was applied. Notably, 7 of 20 studies (35%) reported complete government funding, and 6 (30%) reported industry funding.…”

Section: General Overview Of the Included Studiesmentioning

confidence: 99%

“…23 A complete overview of the health economic methodological details of the included studies can be found in Table 3. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]…”

Section: Health Economic Analysismentioning

confidence: 99%

“…A score was calculated as the percentage of criteria fulfilled for the analysis of the CHEERS results. As shown in Table 2, [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] the scores ranged from 25% to 96%, and the average score was 66%. The study with the highest score was an elaborate HTA study commissioned by the National Health Service in the United Kingdom.…”

Section: Quality Assessmentmentioning

confidence: 99%

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Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah

et al. 2022

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This study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs.Methods: A systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies.Results: A total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon .1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items.Conclusions: HEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, modelbased long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.

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