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Hongfang L, Yangyang T, Lijuan Z, Na S. Evaluation of the impact of bundled nursing strategies on the prevention of venous thromboembolism in patients with cerebral hemorrhage. Medicine (Baltimore) 2024; 103:e38725. [PMID: 38996116 PMCID: PMC11245237 DOI: 10.1097/md.0000000000038725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 06/06/2024] [Indexed: 07/14/2024] Open
Abstract
To investigate the effectiveness and value of bundled nursing strategies for venous thromboembolism prevention in nonsurgical patients with cerebral hemorrhage, 200 patients who underwent treatment for cerebral hemorrhage in our hospital from January 2023 to July 2023 were chosen as the study subjects. Patients were divided into control group and experimental group according to different treatment methods. For patients in the control group, regular care was used, while for patients in the observation group, bundled care was used for intervention. This study used a venous thromboembolism risk factor assessment form to assess the probability of patients suffering from venous thromboembolism. It used the incidence of venous thromboembolism, disease cognition level, coagulation function, fibrinolysis, changes in blood routine, exercise ability, improvement in quality of life, and patient satisfaction with nursing mode as detection indicators to obtain the changes in various indicator values and patient satisfaction scores under different nursing interventions. Prior to nursing intervention, the significant statistical differences did not exist (P > .05) between the control group and the observation group in terms of general information, number of venous thromboembolism risk levels, degree of disease cognition, coagulation function, blood routine, exercise ability, quality of life, and fibrinolysis indicators. After a period of nursing intervention, the significant statistical difference existed (P < .05) between the control group and the observation group in terms of general information, number of venous thromboembolism risk levels, degree of disease cognition, coagulation function, blood routine, exercise ability, quality of life, and fibrinolysis indicators. In the patient satisfaction score, the satisfaction scores of male and female patients with general care were 0.865 and 0.878, respectively, and the satisfaction scores for bundled care were 0.942 and 0.965, respectively. In conclusion, bundled care can better improve the coagulation status and blood routine indexes of nonsurgical patients with cerebral hemorrhage compared with ordinary care, thus contributing to better prevention of venous thromboembolism in nonsurgical patients with cerebral hemorrhage.
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Affiliation(s)
- Lu Hongfang
- Neurosurgery, Baoji People’s Hospital, Baoji, Shaanxi, China
| | - Tian Yangyang
- Neurosurgery, Baoji People’s Hospital, Baoji, Shaanxi, China
| | - Zhao Lijuan
- Neurosurgery, Baoji People’s Hospital, Baoji, Shaanxi, China
| | - Sun Na
- Department of Nephrology, Baoji People’s Hospital, Baoji, Shaanxi, China
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Lampe D, Grosser J, Grothe D, Aufenberg B, Gensorowsky D, Witte J, Greiner W. How intervention studies measure the effectiveness of medication safety-related clinical decision support systems in primary and long-term care: a systematic review. BMC Med Inform Decis Mak 2024; 24:188. [PMID: 38965569 PMCID: PMC11225126 DOI: 10.1186/s12911-024-02596-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 07/01/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Medication errors and associated adverse drug events (ADE) are a major cause of morbidity and mortality worldwide. In recent years, the prevention of medication errors has become a high priority in healthcare systems. In order to improve medication safety, computerized Clinical Decision Support Systems (CDSS) are increasingly being integrated into the medication process. Accordingly, a growing number of studies have investigated the medication safety-related effectiveness of CDSS. However, the outcome measures used are heterogeneous, leading to unclear evidence. The primary aim of this study is to summarize and categorize the outcomes used in interventional studies evaluating the effects of CDSS on medication safety in primary and long-term care. METHODS We systematically searched PubMed, Embase, CINAHL, and Cochrane Library for interventional studies evaluating the effects of CDSS targeting medication safety and patient-related outcomes. We extracted methodological characteristics, outcomes and empirical findings from the included studies. Outcomes were assigned to three main categories: process-related, harm-related, and cost-related. Risk of bias was assessed using the Evidence Project risk of bias tool. RESULTS Thirty-two studies met the inclusion criteria. Almost all studies (n = 31) used process-related outcomes, followed by harm-related outcomes (n = 11). Only three studies used cost-related outcomes. Most studies used outcomes from only one category and no study used outcomes from all three categories. The definition and operationalization of outcomes varied widely between the included studies, even within outcome categories. Overall, evidence on CDSS effectiveness was mixed. A significant intervention effect was demonstrated by nine of fifteen studies with process-related primary outcomes (60%) but only one out of five studies with harm-related primary outcomes (20%). The included studies faced a number of methodological problems that limit the comparability and generalizability of their results. CONCLUSIONS Evidence on the effectiveness of CDSS is currently inconclusive due in part to inconsistent outcome definitions and methodological problems in the literature. Additional high-quality studies are therefore needed to provide a comprehensive account of CDSS effectiveness. These studies should follow established methodological guidelines and recommendations and use a comprehensive set of harm-, process- and cost-related outcomes with agreed-upon and consistent definitions. PROSPERO REGISTRATION CRD42023464746.
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Affiliation(s)
- David Lampe
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany.
| | - John Grosser
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany
| | - Dennis Grothe
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany
| | - Birthe Aufenberg
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany
| | | | | | - Wolfgang Greiner
- Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany
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Scott IA, De Guzman KR, Falconer N, Canaris S, Bonilla O, McPhail SM, Marxen S, Van Garderen A, Abdel-Hafez A, Barras M. Evaluating automated machine learning platforms for use in healthcare. JAMIA Open 2024; 7:ooae031. [PMID: 38863963 PMCID: PMC11165368 DOI: 10.1093/jamiaopen/ooae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/06/2024] [Accepted: 04/22/2024] [Indexed: 06/13/2024] Open
Abstract
Objective To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models. Materials and Methods Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes. Results The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case. Discussion A team of clinicians, data scientists, and key stakeholders developed a checklist which can be adapted to ML needs of healthcare organizations, the use case providing a relevant example. Conclusion An evaluative checklist was developed for selecting Auto ML platforms which requires validation in larger multi-site studies.
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Affiliation(s)
- Ian A Scott
- Centre for Health Services Research, University of Queensland, Brisbane, 4102, Australia
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, 4102, Australia
| | - Keshia R De Guzman
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, 4102, Australia
- School of Pharmacy, The University of Queensland, Brisbane, 4102, Australia
| | - Nazanin Falconer
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, 4102, Australia
- School of Pharmacy, The University of Queensland, Brisbane, 4102, Australia
| | - Stephen Canaris
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
| | - Oscar Bonilla
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
| | - Steven M McPhail
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia
| | - Sven Marxen
- Pharmacy Service, Logan and Beaudesert Hospitals, Logan, 4131, Australia
| | - Aaron Van Garderen
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
- Pharmacy Service, Logan and Beaudesert Hospitals, Logan, 4131, Australia
| | - Ahmad Abdel-Hafez
- Digital Health and Informatics, Metro South Health, Brisbane, 4102, Australia
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 4059, Australia
| | - Michael Barras
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, 4102, Australia
- School of Pharmacy, The University of Queensland, Brisbane, 4102, Australia
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Michel J, Manns A, Boudersa S, Jaubert C, Dupic L, Vivien B, Burgun A, Campeotto F, Tsopra R. Clinical decision support system in emergency telephone triage: A scoping review of technical design, implementation and evaluation. Int J Med Inform 2024; 184:105347. [PMID: 38290244 DOI: 10.1016/j.ijmedinf.2024.105347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/09/2024] [Accepted: 01/21/2024] [Indexed: 02/01/2024]
Abstract
OBJECTIVES Emergency department overcrowding could be improved by upstream telephone triage. Emergency telephone triage aims at managing and orientating adequately patients as early as possible and distributing limited supply of staff and materials. This complex task could be improved with the use of Clinical decision support systems (CDSS). The aim of this scoping review was to identify literature gaps for the future development and evaluation of CDSS for Emergency telephone triage. MATERIALS AND METHODS We present here a scoping review of CDSS designed for emergency telephone triage, and compared them in terms of functional characteristics, technical design, health care implementation and methodologies used for evaluation, following the PRISMA-ScR guidelines. RESULTS Regarding design, 19 CDSS were retrieved: 12 were knowledge based CDSS (decisional algorithms built according to guidelines or clinical expertise) and 7 were data driven (statistical, machine learning, or deep learning models). Most of them aimed at assisting nurses or non-medical staff by providing patient orientation and/or severity/priority assessment. Eleven were implemented in real life, and only three were connected to the Electronic Health Record. Regarding evaluation, CDSS were assessed through various aspects: intrinsic characteristics, impact on clinical practice or user apprehension. Only one pragmatic trial and one randomized controlled trial were conducted. CONCLUSION This review highlights the potential of a hybrid system, user tailored, flexible, connected to the electronic health record, which could work with oral, video and digital data; and the need to evaluate CDSS on intrinsic characteristics and impact on clinical practice, iteratively at each distinct stage of the IT lifecycle.
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Affiliation(s)
- Julie Michel
- SAMU 93-UF Recherche-Enseignement-Qualité, Université Paris 13, Sorbonne Paris Cité, Inserm U942, Hôpital Avicenne, 125, rue de Stalingrad, 93009 Bobigny, France
| | - Aurélia Manns
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France.
| | - Sofia Boudersa
- Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Côme Jaubert
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France
| | - Laurent Dupic
- Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Benoit Vivien
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Anita Burgun
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Florence Campeotto
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France; Faculté de Pharmacie, Université de Paris Cité, Inserm UMR S1139, Paris, France
| | - Rosy Tsopra
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
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Knox C, Wilson M, Klinger C, Franklin M, Oler E, Wilson A, Pon A, Cox J, Chin NE, Strawbridge S, Garcia-Patino M, Kruger R, Sivakumaran A, Sanford S, Doshi R, Khetarpal N, Fatokun O, Doucet D, Zubkowski A, Rayat D, Jackson H, Harford K, Anjum A, Zakir M, Wang F, Tian S, Lee B, Liigand J, Peters H, Wang RQ, Nguyen T, So D, Sharp M, da Silva R, Gabriel C, Scantlebury J, Jasinski M, Ackerman D, Jewison T, Sajed T, Gautam V, Wishart D. DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic Acids Res 2024; 52:D1265-D1275. [PMID: 37953279 PMCID: PMC10767804 DOI: 10.1093/nar/gkad976] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/09/2023] [Accepted: 10/16/2023] [Indexed: 11/14/2023] Open
Abstract
First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.
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Affiliation(s)
- Craig Knox
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Mike Wilson
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Christen M Klinger
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Mark Franklin
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Alex Wilson
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Allison Pon
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Jordan Cox
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Na Eun (Lucy) Chin
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Seth A Strawbridge
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Marysol Garcia-Patino
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Ray Kruger
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Aadhavya Sivakumaran
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Selena Sanford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Rahil Doshi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Nitya Khetarpal
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Omolola Fatokun
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Daphnee Doucet
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ashley Zubkowski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorsa Yahya Rayat
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Hayley Jackson
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Karxena Harford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Afia Anjum
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mahi Zakir
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Fei Wang
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Jaanus Liigand
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ruo Qi (Rachel) Wang
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Tue Nguyen
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Denise So
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Matthew Sharp
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Rodolfo da Silva
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Cyrella Gabriel
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Joshua Scantlebury
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Marissa Jasinski
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - David Ackerman
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Timothy Jewison
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H1, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 1C9, Canada
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