Development and implementation of venous thromboembolism stewardship across a hospital network

Chong, Julianne; Curtain, Colin; Gad, Fady; Passam, Freda; Soo, Garry; Levy, Russell; Dunkley, Scott; Doo, Nicole Wong; Cunich, Michelle; Burke, Rosemary; Chen, Vivien

doi:10.1016/j.ijmedinf.2021.104575

Cited by 9 publications

(9 citation statements)

References 29 publications

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“…Most (33/45, 73%) of the articles were from North America and Europe [ 46 , 47 , 49 - 55 , 57 , 58 , 61 - 63 , 67 - 70 , 73 - 87 ], of which most (18/33, 55%) were from the United States [ 46 , 47 , 49 - 52 , 54 , 68 , 73 - 77 , 79 - 81 , 84 , 87 ]. The greatest number of AI systems were implemented either in hospital-wide settings (6/45, 13%) [ 50 , 55 , 56 , 65 , 74 , 80 ] or in radiology (6/45, 13%) [ 53 , 56 , 66 , 68 , 73 , 76 ]. Most (27/45, 60%) of the studies were authored by a multidisciplinary team [ 46 , 47 , 50 - 55 , 58 , 59 , 61 , 62 , 64 , 67 , 69 , 70 , 72 , 74 , 75 , 78 - 80 , 82 , 86 - 89 ], with clinical and IT or informatics backgrounds being the most common combination (9/27, 33%) [ 47 , 50 , 55 , 61 , 70 , 74 , 79 , …”

Section: Resultsmentioning

confidence: 99%

“…Most (27/45, 60%) of the studies were authored by a multidisciplinary team [ 46 , 47 , 50 - 55 , 58 , 59 , 61 , 62 , 64 , 67 , 69 , 70 , 72 , 74 , 75 , 78 - 80 , 82 , 86 - 89 ], with clinical and IT or informatics backgrounds being the most common combination (9/27, 33%) [ 47 , 50 , 55 , 61 , 70 , 74 , 79 , 87 , 89 ]. Among studies with authors from only 1 domain, the most common background was clinical (8/45, 18%) [ 63 , 65 , 66 , 68 , 71 , 73 , 76 , 84 ]. There was a wide range of study designs.…”

Section: Resultsmentioning

confidence: 99%

“…The most common type of AI application implemented was automation or optimization technology, reported in 71% (32/45) of the implemented systems [ 45 , 46 , 49 - 51 , 53 - 59 , 62 , 64 , 65 , 70 , 71 , 73 , 75 , 77 - 79 , 81 - 84 , 86 - 89 ]. Other technologies implemented included human language technologies, computer vision, and robotics technology ( Table 2 and Multimedia Appendices 2 and 3 ).…”

Section: Resultsmentioning

confidence: 99%

“…Most (33/45, 73%) of the articles were from North America and Europe[46,47,[49][50][51][52][53][54][55]57,58,[61][62][63][67][68][69][70][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87], of which most (18/33, 55%) were from the United States[46,47,49-52,54,68,73-77, 79-81,84,87]. The greatest number of AI systems were implemented either in hospital-wide settings (6/45, 13%)[50,55,56,65,74,80] or in radiology (6/45, 13%)[53,56,66,68,73,76]. Most (27/45, 60%) of the studies were authored by a multidisciplinary team[46,47,50-55,58,…”

mentioning

confidence: 99%

“…59,61,62,64,67,69,70,72,74,75,[78][79][80]82,[86][87][88][89], with clinical and IT or informatics backgrounds being the most common combination (9/27, 33%)[47,50,55,61,70,74,79,87,89]. Among studies with authors from only 1 domain, the most common background was clinical (8/45, 18%)[63,65,66,68,71,73,76,84]. There was a wide range of study designs.…”

mentioning

confidence: 99%

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Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

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Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

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A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement

Dezfouli

Delen²,

Zhao³

et al. 2022

J Healthc Inform Res

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The effect of pharmacist-led interventions on the appropriateness and clinical outcomes of anticoagulant therapy: a systematic review and meta-analysis

Kefale,

Peterson,

Mirkazemi

et al. 2024

European Heart Journal - Quality of Care and Clinical Outcomes

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Aim Although pharmacist-led interventions in anticoagulant (AC) therapy are widely accepted, there is a lack of evidence comparing their effectiveness to usual care in terms of AC therapy appropriateness and clinical outcomes. We aimed to estimate the comparative effectiveness of pharmacist-led interventions on the appropriateness and clinical outcomes of AC therapy. Methods and Results Adhering to the PRISMA guidelines, we searched PubMed, EMBASE, and Scopus databases to identify randomised controlled trials and quasi-experimental and cohort studies published between 2010 and 2023. A random-effects model was used to calculate pooled intervention effects. We assessed heterogeneity (using Higgins’ I2 and Cochran's Q) and publication bias (using Egger's test, the trim-and-fill method and visualisation of the funnel plot). In total, 35 studies involving 10 374 patients in the intervention groups and 11 840 in the control groups were included. The pharmacist-led interventions significantly improved the appropriateness of AC therapy (odds ratio [OR]: 3.43; 95% confidence interval [CI]: 2.33–5.06, p < 0.01). They significantly decreased total bleeding (relative risk [RR]: 0.75, 95% CI: 0.58–0.96, p = 0.03) and hospitalisation or readmission (RR: 0.64, 95% CI: 0.41–0.99, P = 0.04). However, the impact of the pharmacist-led interventions on thromboembolic events (RR: 0.69, 95% CI: 0.46–1.02, p = 0.07) and mortality (RR: 0.76, 95% CI: 0.51–1.13, p = 0.17) were not significant. Conclusion Pharmacist-led interventions demonstrated superior outcomes in optimising AC therapy compared to usual care. Further research is needed to evaluate pharmacist-led interventions’ cost-effectiveness and long-term sustainability. PROSPERO registration number CRD42023487362.

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Development and implementation of venous thromboembolism stewardship across a hospital network

Cited by 9 publications

References 29 publications

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Artificial Intelligence Applications in Health Care Practice: Scoping Review

A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement

The effect of pharmacist-led interventions on the appropriateness and clinical outcomes of anticoagulant therapy: a systematic review and meta-analysis

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