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Song B, Li H, Zhang H, Jiao L, Wu S. Impact of electronic cigarette usage on the onset of respiratory symptoms and COPD among Chinese adults. Sci Rep 2024; 14:5598. [PMID: 38454045 PMCID: PMC10920732 DOI: 10.1038/s41598-024-56368-9] [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: 11/28/2023] [Accepted: 03/05/2024] [Indexed: 03/09/2024] Open
Abstract
The prevalence of dual usage and the relatively low cessation rate among e-cigarette (EC) users suggest that ECs have not demonstrated significant effectiveness as a smoking cessation tool. Furthermore, there has been a substantial increase in the prevalence of EC usage in recent years. Therefore, the objective of this study is to investigate the association between EC use and the incidence of respiratory symptoms and chronic obstructive pulmonary disease (COPD). A total of 10,326 participants aged between 20 and 55 years, without any respiratory diseases or COPD, were recruited for the study. These individuals attended employee physical examinations conducted at 16 public hospitals in Hebei province, China from 2015 to 2020. Logistic regression models were utilized to assess the association between EC use and the risk of respiratory symptoms and COPD using risk ratios along with their corresponding 95% confidence intervals. Restricted cubic spline functions were employed to investigate the dose-response non-linear relationship. The robustness of the logistic regression models was evaluated through subgroup analyses, and sensitivity analyses. During the 5-year follow-up period, a total of 1071 incident cases of respiratory symptoms and 146 incident cases of COPD were identified in this cohort study. After adjusting for relevant confounding factors, EC users demonstrated a respective increase in the risk of reporting respiratory symptoms and COPD by 28% and 8%. Furthermore, dual users who used both ECs and combustible cigarettes exhibited an elevated risk of incident respiratory symptoms and COPD by 41% and 18%, respectively, compared to those who had never used non-users of any cigarette products. The association between daily EC consumption and the development of respiratory symptoms, as well as COPD, demonstrated a significant J-shaped pattern. The potential adverse association between the consumption of ECs, particularly when used in combination with combustible cigarettes, and the development of respiratory symptoms and COPD necessitates careful consideration. Policymakers should approach ECs cautiously as a prospective smoking cessation tool.
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Affiliation(s)
- Beibei Song
- The First Department of Pulmonary and Critical Care Medicine, The Second Hospital of Hebei Medical University/Hebei Key Laboratory of Respiratory Critical Care/Hebei Institute of Respiratory Diseases, No. 215 Heping West Road, Shijiazhuang, 050000, China
| | - Honglin Li
- The First Department of Pulmonary and Critical Care Medicine, The Second Hospital of Hebei Medical University/Hebei Key Laboratory of Respiratory Critical Care/Hebei Institute of Respiratory Diseases, No. 215 Heping West Road, Shijiazhuang, 050000, China
| | - Huiran Zhang
- Department of Biological Pharmacy, Hebei Medical University, Shijiazhuang, 050000, China
| | - Libin Jiao
- Hebei Far East Communication System Engineering Company, Shijiazhuang, 050000, China
| | - Siyu Wu
- The Third Department of Respiratory and Critical Care Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
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Liu J, Zheng J, Cai X, Wu D, Yin C. A descriptive study based on the comparison of ChatGPT and evidence-based neurosurgeons. iScience 2023; 26:107590. [PMID: 37705958 PMCID: PMC10495632 DOI: 10.1016/j.isci.2023.107590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/21/2023] [Accepted: 08/04/2023] [Indexed: 09/15/2023] Open
Abstract
ChatGPT is an artificial intelligence product developed by OpenAI. This study aims to investigate whether ChatGPT can respond in accordance with evidence-based medicine in neurosurgery. We generated 50 neurosurgical questions covering neurosurgical diseases. Each question was posed three times to GPT-3.5 and GPT-4.0. We also recruited three neurosurgeons with high, middle, and low seniority to respond to questions. The results were analyzed regarding ChatGPT's overall performance score, mean scores by the items' specialty classification, and question type. In conclusion, GPT-3.5's ability to respond in accordance with evidence-based medicine was comparable to that of neurosurgeons with low seniority, and GPT-4.0's ability was comparable to that of neurosurgeons with high seniority. Although ChatGPT is yet to be comparable to a neurosurgeon with high seniority, future upgrades could enhance its performance and abilities.
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Affiliation(s)
- Jiayu Liu
- Department of Neurosurgery, the First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Jiqi Zheng
- School of Health Humanities, Peking University, Beijing 100191, China
| | - Xintian Cai
- Department of Graduate School, Xinjiang Medical University, Urumqi 830001, China
| | - Dongdong Wu
- Department of Information, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
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3
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Chandrashekar K, Setlur AS, Sabhapathi C A, Raiker SS, Singh S, Niranjan V. Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications. Cancer Inform 2023; 22:11769351221147244. [PMID: 36714384 PMCID: PMC9880585 DOI: 10.1177/11769351221147244] [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: 10/29/2022] [Accepted: 12/06/2022] [Indexed: 01/24/2023] Open
Abstract
Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.
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Affiliation(s)
| | | | | | | | | | - Vidya Niranjan
- Vidya Niranjan, Department of
Biotechnology, R V College of Engineering, Bengaluru, Karnataka 560059, India.
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4
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Association between waist circumference change and incident chronic obstructive pulmonary disease among Chinese adults: a 10-year cohort study. Sci Rep 2022; 12:18402. [PMID: 36319731 PMCID: PMC9626450 DOI: 10.1038/s41598-022-23248-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
The aim of our study was to investigate waist circumference (WC) change and the risk of incident chronic obstructive pulmonary disease (COPD) among Chinese adults. A total of 8164 participants aged > 18 years who attended health examinations with repeat measurements of WC and lung function [forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1)] from 2010 to 2019 were recruited. WC change was categorized as ≤ - 2.5%, - 2.5 to 2.5%, 2.5% to 5% and > 5% according to sex. Modified Poisson regression models were used to assess the association of WC gain and the risk of COPD. During the 10-year follow-up, a total of 917 COPD cases were identified. From baseline to follow-up, the mean FEV1 decreased from 3.20 to 2.79L among male participants and 2.28-1.95L among female participants. Compared with participants who did not have abdominal obesity, at either, baseline or follow-up, participants with abdominal obesity of both sexes after the follow-up were associated with a greater risk of COPD regardless of abdominal obesity at baseline. The risk of incident COPD increased 19% among male participants (RR = 1.19, 95%CI = 1.04-1.48) and 14% among female participants (RR = 1.14, 95%CI = 1.01-1.40) when WC gain increased > 5% during the 10-year follow-up. The COPD risk decreased 18% among male participants with a WC change ≤ - 2.5% (RR = 0.82, 95%CI = 0.67-0.99). The risk of incident COPD was positively associated with increasing WC among Chinese adults of both sexes.
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5
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McCoy AB, Russo EM, Johnson KB, Addison B, Patel N, Wanderer JP, Mize DE, Jackson JG, Reese TJ, Littlejohn S, Patterson L, French T, Preston D, Rosenbury A, Valdez C, Nelson SD, Aher CV, Alrifai MW, Andrews J, Cobb C, Horst SN, Johnson DP, Knake LA, Lewis AA, Parks L, Parr SK, Patel P, Patterson BL, Smith CM, Suszter KD, Turer RW, Wilcox LJ, Wright AP, Wright A. Clinician collaboration to improve clinical decision support: the Clickbusters initiative. J Am Med Inform Assoc 2022; 29:1050-1059. [PMID: 35244165 PMCID: PMC9093034 DOI: 10.1093/jamia/ocac027] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 01/19/2022] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts. MATERIALS AND METHODS We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program. RESULTS Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS. CONCLUSIONS At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.
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Affiliation(s)
- Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Elise M Russo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kevin B Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Bobby Addison
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Neal Patel
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan P Wanderer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Dara E Mize
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jon G Jackson
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - SyLinda Littlejohn
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lorraine Patterson
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tina French
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Debbie Preston
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Audra Rosenbury
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Charlie Valdez
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Scott D Nelson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Chetan V Aher
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of General Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mhd Wael Alrifai
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jennifer Andrews
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cheryl Cobb
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sara N Horst
- Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - David P Johnson
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lindsey A Knake
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam A Lewis
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Laura Parks
- Nursing Informatics Services, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sharidan K Parr
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Pratik Patel
- Union University College of Pharmacy, Memphis, Tennessee, USA
| | - Barron L Patterson
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Christine M Smith
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Krystle D Suszter
- Nursing Informatics Services, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert W Turer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lyndy J Wilcox
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Aileen P Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- HeathIT, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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6
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Heck CC, Tichy EM, Vonderheyde R, Jaszczur GR, McManus D, Topal JE, Rogers ME, Rouse GE. Optimizing pharmacist-driven protocols and documentation of interventions using clinical decision support systems. Am J Health Syst Pharm 2021; 77:830-834. [PMID: 32426844 DOI: 10.1093/ajhp/zxaa061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Cory C Heck
- Heart and Vascular Medicine Yale New Haven Hospital New Haven, CT
| | - Eric M Tichy
- Clinical Pharmacy Services Yale New Haven Health New Haven, CT
| | - Robyn Vonderheyde
- Information Technology Services Yale New Haven Health System New Haven, CT
| | - Gregory R Jaszczur
- Information Technology Services Yale New Haven Health System New Haven, CT
| | - Dayna McManus
- Infectious Diseases Yale New Haven Hospital New Haven, CT
| | - Jeffrey E Topal
- Department of Internal Medicine Section of Infectious Diseases Yale School of Medicine Yale New Haven Hospital New Haven, CT
| | - Mark E Rogers
- Lawrence and Memorial Hospital New London, CT Westerly Hospital Westerly, RI
| | - Ginger E Rouse
- Medical Intensive Care Yale New Haven Hospital New Haven, CT
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7
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Sholler D. Infrastructuring as an Occasion for Resistance: Organized Resistance to Policy-Driven Information Infrastructure Development in the U.S. Healthcare Industry. Comput Support Coop Work 2020. [DOI: 10.1007/s10606-020-09375-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Pawloski PA, Brooks GA, Nielsen ME, Olson-Bullis BA. A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice. J Natl Compr Canc Netw 2020; 17:331-338. [PMID: 30959468 DOI: 10.6004/jnccn.2018.7104] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 11/05/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND Electronic health records are central to cancer care delivery. Electronic clinical decision support (CDS) systems can potentially improve cancer care quality and safety. However, little is known regarding the use of CDS systems in clinical oncology and their impact on patient outcomes. METHODS A systematic review of peer-reviewed studies was performed to evaluate clinically relevant outcomes related to the use of CDS tools for the diagnosis, treatment, and supportive care of patients with cancer. Peer-reviewed studies published from 1995 through 2016 were included if they assessed clinical outcomes, patient-reported outcomes (PROs), costs, or care delivery process measures. RESULTS Electronic database searches yielded 2,439 potentially eligible papers, with 24 studies included after final review. Most studies used an uncontrolled, pre-post intervention design. A total of 23 studies reported improvement in key study outcomes with use of oncology CDS systems, and 12 studies assessing the systems for computerized chemotherapy order entry demonstrated reductions in prescribing error rates, medication-related safety events, and workflow interruptions. The remaining studies examined oncology clinical pathways, guideline adherence, systems for collection and communication of PROs, and prescriber alerts. CONCLUSIONS There is a paucity of data evaluating clinically relevant outcomes of CDS system implementation in oncology care. Currently available data suggest that these systems can have a positive impact on the quality of cancer care delivery. However, there is a critical need to rigorously evaluate CDS systems in oncology to better understand how they can be implemented to improve patient outcomes.
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Affiliation(s)
- Pamala A Pawloski
- aHealthPartners Institute, Minneapolis, Minnesota.,bThe Health Care Systems Research Network (HCSRN) Cancer Research Network (CRN)
| | - Gabriel A Brooks
- cDartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; and
| | - Matthew E Nielsen
- dUniversity of North Carolina School of Medicine, Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina
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9
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Van Dort BA, Zheng WY, Baysari MT. Prescriber perceptions of medication-related computerized decision support systems in hospitals: A synthesis of qualitative research. Int J Med Inform 2019; 129:285-295. [DOI: 10.1016/j.ijmedinf.2019.06.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/24/2019] [Accepted: 06/24/2019] [Indexed: 01/01/2023]
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10
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Farre A, Heath G, Shaw K, Bem D, Cummins C. How do stakeholders experience the adoption of electronic prescribing systems in hospitals? A systematic review and thematic synthesis of qualitative studies. BMJ Qual Saf 2019; 28:1021-1031. [PMID: 31358686 PMCID: PMC6934241 DOI: 10.1136/bmjqs-2018-009082] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 07/08/2019] [Accepted: 07/11/2019] [Indexed: 11/30/2022]
Abstract
Background Electronic prescribing (ePrescribing) or computerised provider/physician order entry (CPOE) systems can improve the quality and safety of health services, but the translation of this into reduced harm for patients remains unclear. This review aimed to synthesise primary qualitative research relating to how stakeholders experience the adoption of ePrescribing/CPOE systems in hospitals, to help better understand why and how healthcare organisations have not yet realised the full potential of such systems and to inform future implementations and research. Methods We systematically searched 10 bibliographic databases and additional sources for citation searching and grey literature, with no restriction on date or publication language. Qualitative studies exploring the perspectives/experiences of stakeholders with the implementation, management, use and/or optimisation of ePrescribing/CPOE systems in hospitals were included. Quality assessment combined criteria from the Critical Appraisal Skills Programme Qualitative Checklist and the Standards for Reporting Qualitative Research guidelines. Data were synthesised thematically. Results 79 articles were included. Stakeholders’ perspectives reflected a mixed set of positive and negative implications of engaging in ePrescribing/CPOE as part of their work. These were underpinned by further-reaching change processes. Impacts reported were largely practice related rather than at the organisational level. Factors affecting the implementation process and actions undertaken prior to implementation were perceived as important in understanding ePrescribing/CPOE adoption and impact. Conclusions Implementing organisations and teams should consider the breadth and depth of changes that ePrescribing/CPOE adoption can trigger rather than focus on discrete benefits/problems and favour implementation strategies that: consider the preimplementation context, are responsive to (and transparent about) organisational and stakeholder needs and agendas and which can be sustained effectively over time as implementations develop and gradually transition to routine use and system optimisation.
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Affiliation(s)
- Albert Farre
- School of Nursing and Health Sciences, University of Dundee, Dundee, UK
| | - Gemma Heath
- Life and Health Sciences, Aston University, Birmingham, UK
| | - Karen Shaw
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Danai Bem
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Carole Cummins
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
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11
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Lam JH, Ng O. Monitoring clinical decision support in the electronic health record. Am J Health Syst Pharm 2019; 74:1130-1133. [PMID: 28743777 DOI: 10.2146/ajhp160819] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- Jason H Lam
- UCSD Skaggs School of Pharmacy and Pharmaceutical SciencesLa Jolla, CASharp HealthcareSan Diego,
| | - Olivia Ng
- University of San DiegoSan Diego, CA
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12
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Bell D, Gachuhi N, Assefi N. Dynamic Clinical Algorithms: Digital Technology Can Transform Health Care Decision-Making. Am J Trop Med Hyg 2018; 98:9-14. [PMID: 29141738 PMCID: PMC5928722 DOI: 10.4269/ajtmh.17-0477] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Most health care in low-income countries is delivered at a primary care level by health workers who lack quality training and supervision, often distant from more experienced support. Lack of knowledge and poor communication result in a poor quality of care and inefficient delivery of health services. Although bringing great benefits in sectors such as finance and telecommunication in recent years, the Digital Revolution has lightly and inconsistently affected the health sector. These advances offer an opportunity to dramatically transform health care by increasing the availability and timeliness of information to augment clinical decision-making, based on improved access to patient histories, current information on disease epidemiology, and improved incorporation of data from point-of-care and centralized diagnostic testing. A comprehensive approach is needed to more effectively incorporate current digital technologies into health systems, bringing external and patient-derived data into the clinical decision-making process in real time, irrespective of health worker training or location. Such dynamic clinical algorithms could provide a more effective framework within which to design and integrate new digital health technologies and deliver improved patient care by primary care health workers.
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Affiliation(s)
- David Bell
- Intellectual Ventures Global Good Fund, Bellevue, Washington
| | - Noni Gachuhi
- Intellectual Ventures Global Good Fund, Bellevue, Washington
| | - Nassim Assefi
- Intellectual Ventures Global Good Fund, Bellevue, Washington
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13
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McCulloh RJ, Fouquet SD, Herigon J, Biondi EA, Kennedy B, Kerns E, DePorre A, Markham JL, Chan YR, Nelson K, Newland JG. Development and implementation of a mobile device-based pediatric electronic decision support tool as part of a national practice standardization project. J Am Med Inform Assoc 2018; 25:1175-1182. [PMID: 29889255 PMCID: PMC6118866 DOI: 10.1093/jamia/ocy069] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 05/02/2018] [Accepted: 05/15/2018] [Indexed: 11/12/2022] Open
Abstract
Objective Implementing evidence-based practices requires a multi-faceted approach. Electronic clinical decision support (ECDS) tools may encourage evidence-based practice adoption. However, data regarding the role of mobile ECDS tools in pediatrics is scant. Our objective is to describe the development, distribution, and usage patterns of a smartphone-based ECDS tool within a national practice standardization project. Materials and Methods We developed a smartphone-based ECDS tool for use in the American Academy of Pediatrics, Value in Inpatient Pediatrics Network project entitled "Reducing Excessive Variation in the Infant Sepsis Evaluation (REVISE)." The mobile application (app), PedsGuide, was developed using evidence-based recommendations created by an interdisciplinary panel. App workflow and content were aligned with clinical benchmarks; app interface was adjusted after usability heuristic review. Usage patterns were measured using Google Analytics. Results Overall, 3805 users across the United States downloaded PedsGuide from December 1, 2016, to July 31, 2017, leading to 14 256 use sessions (average 3.75 sessions per user). Users engaged in 60 442 screen views, including 37 424 (61.8%) screen views that displayed content related to the REVISE clinical practice benchmarks, including hospital admission appropriateness (26.8%), length of hospitalization (14.6%), and diagnostic testing recommendations (17.0%). Median user touch depth was 5 [IQR 5]. Discussion We observed rapid dissemination and in-depth engagement with PedsGuide, demonstrating feasibility for using smartphone-based ECDS tools within national practice improvement projects. Conclusions ECDS tools may prove valuable in future national practice standardization initiatives. Work should next focus on developing robust analytics to determine ECDS tools' impact on medical decision making, clinical practice, and health outcomes.
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Affiliation(s)
- Russell J McCulloh
- Department of Pediatrics, Children’s Hospital and Medical Center, Omaha, Nebraska, USA
- Department of Pediatrics, University of Nebraska Medical Center, Omaha, Nebraska, USA
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Sarah D Fouquet
- Department of Medical Informatics and Telemedicine, Children’s Mercy Kansas City, Kansas City, Missouri, USA
| | - Joshua Herigon
- Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, USA
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Eric A Biondi
- Department of Pediatrics, Johns Hopkins Children’s Center, Baltimore, Maryland, USA
| | - Brandan Kennedy
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri, USA
- Department of Pediatrics, University of Missouri—Kansas City School of Medicine, Kansas City, Missouri, USA
| | - Ellen Kerns
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri, USA
| | - Adrienne DePorre
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri, USA
- Department of Pediatrics, University of Missouri—Kansas City School of Medicine, Kansas City, Missouri, USA
| | - Jessica L Markham
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri, USA
- Department of Pediatrics, University of Missouri—Kansas City School of Medicine, Kansas City, Missouri, USA
| | - Y Raymond Chan
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri, USA
- Department of Pediatrics, University of Missouri—Kansas City School of Medicine, Kansas City, Missouri, USA
| | - Krista Nelson
- Center for Pediatric Innovation, Children’s Mercy Kansas City, Kansas City, Missouri, USA
| | - Jason G Newland
- Department of Pediatrics, Washington University, St. Louis, Missouri, USA and
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
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Al Mustafa SA, Khan M, Hussain M. Implementing Barcode Medication Administration Systems in Public Sector Medical Units. INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2018. [DOI: 10.4018/ijdsst.2018040102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Medication errors in healthcare have a high cost since it is one of the main causes of harming a patient; it leads to inefficient utilization of healthcare organization resources. The barcode medication administration system helps in improving the patients' safety. The purpose of this article is to determine preparatory needs for introducing a Barcoding Medication Administration System (BCMA) in the medical units in one of the largest tertiary hospital in Abu Dhabi City, United Arab Emirates. Analytical Hierarchical Process (AHP) has been employed to describe systematic decision-making by prioritizing different factors that affect the implementation of BCMA and how technology plays a role in helping to reduce or prevent human errors by promoting safety in the health care sectors. Five major domains are identified: leadership, technology, process, education, quality and safety. Leadership was found to be the most important factor oppositely of technology was the least important.
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Affiliation(s)
| | - Mehmood Khan
- College of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Matloub Hussain
- College of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates
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15
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Moja L, Passardi A, Capobussi M, Banzi R, Ruggiero F, Kwag K, Liberati EG, Mangia M, Kunnamo I, Cinquini M, Vespignani R, Colamartini A, Di Iorio V, Massa I, González-Lorenzo M, Bertizzolo L, Nyberg P, Grimshaw J, Bonovas S, Nanni O. Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients: the ONCO-CODES study protocol for a randomized controlled trial. Implement Sci 2016; 11:153. [PMID: 27884165 PMCID: PMC5123241 DOI: 10.1186/s13012-016-0514-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 10/24/2016] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Computerized decision support systems (CDSSs) are computer programs that provide doctors with person-specific, actionable recommendations, or management options that are intelligently filtered or presented at appropriate times to enhance health care. CDSSs might be integrated with patient electronic health records (EHRs) and evidence-based knowledge. METHODS/DESIGN The Computerized DEcision Support in ONCOlogy (ONCO-CODES) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio. The trial is designed to evaluate the effectiveness on clinical practice and quality of care of a multi-specialty collection of patient-specific reminders generated by a CDSS in the IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) hospital. We hypothesize that the intervention can increase clinician adherence to guidelines and, eventually, improve the quality of care offered to cancer patients. The primary outcome is the rate at which the issues reported by the reminders are resolved, aggregating specialty and primary care reminders. We will include all the patients admitted to hospital services. All analyses will follow the intention-to-treat principle. DISCUSSION The results of our study will contribute to the current understanding of the effectiveness of CDSSs in cancer hospitals, thereby informing healthcare policy about the potential role of CDSS use. Furthermore, the study will inform whether CDSS may facilitate the integration of primary care in cancer settings, known to be usually limited. The increasing use of and familiarity with advanced technology among new generations of physicians may support integrated approaches to be tested in pragmatic studies determining the optimal interface between primary and oncology care. TRIAL REGISTRATION ClinicalTrials.gov, NCT02645357.
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Affiliation(s)
- Lorenzo Moja
- Department of Biomedical Sciences for Health, University of Milan, Via Pascal 36, 20133 Milan, Italy
- Clinical Epidemiology Unit, IRCCS Orthopedic Institute Galeazzi, Via Galeazzi 4, 20161 Milan, Italy
| | - Alessandro Passardi
- Medical Oncology Unit, IRST Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori IRCCS, Meldola, Italy
| | - Matteo Capobussi
- School of Specialization in Hygiene and Preventive Medicine, University of Milan, Milan, Italy
| | - Rita Banzi
- IRCCS Mario Negri Institute for Pharmacological Research, Via La Masa 19, 20156 Milan, Italy
| | - Francesca Ruggiero
- Clinical Epidemiology Unit, IRCCS Orthopedic Institute Galeazzi, Via Galeazzi 4, 20161 Milan, Italy
| | - Koren Kwag
- Clinical Epidemiology Unit, IRCCS Orthopedic Institute Galeazzi, Via Galeazzi 4, 20161 Milan, Italy
| | - Elisa Giulia Liberati
- Cambridge Centre for Health Services Research (CCHSR), Department of Public Health and Primary Care, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge, CB2 0SR UK
| | | | - Ilkka Kunnamo
- Duodecim Medical Publications Ltd, Kaivokatu 10 A, 00101 Helsinki, Finland
| | - Michela Cinquini
- IRCCS Mario Negri Institute for Pharmacological Research, Via La Masa 19, 20156 Milan, Italy
| | - Roberto Vespignani
- Medical Oncology Unit, IRST Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori IRCCS, Meldola, Italy
| | - Americo Colamartini
- Medical Oncology Unit, IRST Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori IRCCS, Meldola, Italy
| | - Valentina Di Iorio
- Medical Oncology Unit, IRST Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori IRCCS, Meldola, Italy
| | - Ilaria Massa
- Medical Oncology Unit, IRST Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori IRCCS, Meldola, Italy
| | - Marien González-Lorenzo
- Department of Biomedical Sciences for Health, University of Milan, Via Pascal 36, 20133 Milan, Italy
- Clinical Epidemiology Unit, IRCCS Orthopedic Institute Galeazzi, Via Galeazzi 4, 20161 Milan, Italy
| | - Lorenzo Bertizzolo
- School of Specialization in Hygiene and Preventive Medicine, University of Milan, Milan, Italy
| | - Peter Nyberg
- Duodecim Medical Publications Ltd, Kaivokatu 10 A, 00101 Helsinki, Finland
| | - Jeremy Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute and Department of Medicine, University of Ottawa, 501 Smyth Road, Ottawa, ON K1H 8 L6 Canada
| | - Stefanos Bonovas
- Humanitas Clinical and Research Center, Via Manzoni 56, 20089 Rozzano, Milan Italy
| | - Oriana Nanni
- Medical Oncology Unit, IRST Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori IRCCS, Meldola, Italy
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Moja L, Polo Friz H, Capobussi M, Kwag K, Banzi R, Ruggiero F, González-Lorenzo M, Liberati EG, Mangia M, Nyberg P, Kunnamo I, Cimminiello C, Vighi G, Grimshaw J, Bonovas S. Implementing an evidence-based computerized decision support system to improve patient care in a general hospital: the CODES study protocol for a randomized controlled trial. Implement Sci 2016; 11:89. [PMID: 27389248 PMCID: PMC4936265 DOI: 10.1186/s13012-016-0455-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 06/18/2016] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Computerized decision support systems (CDSSs) are information technology-based software that provide health professionals with actionable, patient-specific recommendations or guidelines for disease diagnosis, treatment, and management at the point-of-care. These messages are intelligently filtered to enhance the health and clinical care of patients. CDSSs may be integrated with patient electronic health records (EHRs) and evidence-based knowledge. METHODS/DESIGN We designed a pragmatic randomized controlled trial to evaluate the effectiveness of patient-specific, evidence-based reminders generated at the point-of-care by a multi-specialty decision support system on clinical practice and the quality of care. We will include all the patients admitted to the internal medicine department of one large general hospital. The primary outcome is the rate at which medical problems, which are detected by the decision support software and reported through the reminders, are resolved (i.e., resolution rates). Secondary outcomes are resolution rates for reminders specific to venous thromboembolism (VTE) prevention, in-hospital all causes and VTE-related mortality, and the length of hospital stay during the study period. DISCUSSION The adoption of CDSSs is likely to increase across healthcare systems due to growing concerns about the quality of medical care and discrepancy between real and ideal practice, continuous demands for a meaningful use of health information technology, and the increasing use of and familiarity with advanced technology among new generations of physicians. The results of our study will contribute to the current understanding of the effectiveness of CDSSs in primary care and hospital settings, thereby informing future research and healthcare policy questions related to the feasibility and value of CDSS use in healthcare systems. This trial is seconded by a specialty trial randomizing patients in an oncology setting (ONCO-CODES). TRIAL REGISTRATION ClinicalTrials.gov, https://clinicaltrials.gov/ct2/show/NCT02577198?term=NCT02577198&rank=1.
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Affiliation(s)
- Lorenzo Moja
- Department of Biomedical Sciences for Health, University of Milan, Via Pascal 36, 20133 Milan, Italy
- Clinical Epidemiology Unit, IRCCS Orthopedic Institute Galeazzi, Via Galeazzi 4, 20161 Milan, Italy
| | - Hernan Polo Friz
- Internal Medicine Division, Medical Department, Vimercate Hospital, Via Santi Cosma e Damiano 10, 20871 Vimercate, Italy
| | - Matteo Capobussi
- School of Specialization in Hygiene and Preventive Medicine, University of Milan, Milan, Italy
| | - Koren Kwag
- Clinical Epidemiology Unit, IRCCS Orthopedic Institute Galeazzi, Via Galeazzi 4, 20161 Milan, Italy
| | - Rita Banzi
- IRCCS Mario Negri Institute for Pharmacological Research, Via La Masa 19, 20156 Milan, Italy
| | - Francesca Ruggiero
- Clinical Epidemiology Unit, IRCCS Orthopedic Institute Galeazzi, Via Galeazzi 4, 20161 Milan, Italy
| | - Marien González-Lorenzo
- Department of Biomedical Sciences for Health, University of Milan, Via Pascal 36, 20133 Milan, Italy
| | - Elisa Giulia Liberati
- Department of Health Science, Centre for Medicine, University of Leicester, University Road, Leicester, LE1 7RH UK
| | | | - Peter Nyberg
- Duodecim Medical Publications Ltd, Kaivokatu 10 A, 00101 Helsinki, Finland
| | - Ilkka Kunnamo
- Duodecim Medical Publications Ltd, Kaivokatu 10 A, 00101 Helsinki, Finland
| | - Claudio Cimminiello
- Internal Medicine Division, Medical Department, Vimercate Hospital, Via Santi Cosma e Damiano 10, 20871 Vimercate, Italy
| | - Giuseppe Vighi
- Internal Medicine Division, Medical Department, Vimercate Hospital, Via Santi Cosma e Damiano 10, 20871 Vimercate, Italy
| | - Jeremy Grimshaw
- Ottawa Hospital Research Institute & Department of Medicine, University of Ottawa, 501 Smyth Road, Ottawa, ON K1H 8L6 Canada
| | - Stefanos Bonovas
- Humanitas Clinical and Research Center, Via Manzoni 56, 20089 Rozzano Milan, Italy
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Gunn ML, Marin JR, Mills AM, Chong ST, Froemming AT, Johnson JO, Kumaravel M, Sodickson AD. A report on the Academic Emergency Medicine 2015 consensus conference “Diagnostic imaging in the emergency department: a research agenda to optimize utilization”. Emerg Radiol 2016; 23:383-96. [DOI: 10.1007/s10140-016-1398-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 04/12/2016] [Indexed: 11/29/2022]
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Beaudoin M, Kabanza F, Nault V, Valiquette L. Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs. Artif Intell Med 2016; 68:29-36. [PMID: 26947174 DOI: 10.1016/j.artmed.2016.02.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Revised: 01/29/2016] [Accepted: 02/15/2016] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Antimicrobial stewardship programs have been shown to limit the inappropriate use of antimicrobials. Hospitals are increasingly relying on clinical decision support systems to assist in the demanding prescription reviewing process. In previous work, we have reported on an emerging clinical decision support system for antimicrobial stewardship that can learn new rules supervised by user feedback. In this paper, we report on the evaluation of this system. METHODS The evaluated system uses a knowledge base coupled with a supervised learning module that extracts classification rules for inappropriate antimicrobial prescriptions using past recommendations for dose and dosing frequency adjustments, discontinuation of therapy, early switch from intravenous to oral therapy, and redundant antimicrobial spectrum. Over five weeks, the learning module was deployed alongside the baseline system to prospectively evaluate its ability to discover rules that complement the existing knowledge base for identifying inappropriate prescriptions of piperacillin-tazobactam, a frequently used antimicrobial. RESULTS The antimicrobial stewardship pharmacists reviewed 374 prescriptions, of which 209 (56% of 374) were identified as inappropriate leading to 43 recommendations to optimize prescriptions. The baseline system combined with the learning module triggered alerts in 270 prescriptions with a positive predictive value of identifying inappropriate prescriptions of 74%. Of these, 240 reviewed prescriptions were identified by the alerts of the baseline system with a positive predictive value of 82% and 105 reviewed prescriptions were identified by the alerts of the learning module with a positive predictive value of 62%. The combined system triggered alerts for all 43 recommendations, resulting in a rate of actionable alerts of 16% (43 recommendations of 270 reviewed alerts); the baseline system triggered alerts for 38 interventions, resulting in a rate of actionable alerts of 16% (38 of 240 reviewed alerts); and the learning module triggered alerts for 17 interventions, resulting in a rate of actionable alerts of 16% (17 of 105 reviewed alerts). The learning module triggered alerts for every inappropriate prescription missed by the knowledge base of the baseline system (n=5). CONCLUSIONS The learning module was able to extract clinically relevant rules for multiple types of antimicrobial alerts. The learned rules were shown to extend the knowledge base of the baseline system by identifying pharmacist interventions that were missed by the baseline system. The learned rules identified inappropriate prescribing practices that were not supported by local experts and were missing from its knowledge base. However, combining the baseline system and the learning module increased the number of false positives.
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Affiliation(s)
- Mathieu Beaudoin
- Department of Computer Science, Université de Sherbrooke, 2500 boul. de l'Université, Sherbrooke, Québec, Canada J1K 2R1.
| | - Froduald Kabanza
- Department of Computer Science, Université de Sherbrooke, 2500 boul. de l'Université, Sherbrooke, Québec, Canada J1K 2R1.
| | - Vincent Nault
- Department of Microbiology and Infectious Diseases, Université de Sherbrooke, 3001 12e Avenue Nord, Sherbrooke, Québec, Canada J1H 5N4.
| | - Louis Valiquette
- Department of Microbiology and Infectious Diseases, Université de Sherbrooke, 3001 12e Avenue Nord, Sherbrooke, Québec, Canada J1H 5N4.
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Probst MA, Dayan PS, Raja AS, Slovis BH, Yadav K, Lam SH, Shapiro JS, Farris C, Babcock CI, Griffey RT, Robey TE, Fortin EM, Johnson JO, Chong ST, Davenport M, Grigat DW, Lang EL. Knowledge Translation and Barriers to Imaging Optimization in the Emergency Department: A Research Agenda. Acad Emerg Med 2015; 22:1455-64. [PMID: 26568148 PMCID: PMC10548873 DOI: 10.1111/acem.12830] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 07/02/2015] [Indexed: 01/21/2023]
Abstract
Researchers have attempted to optimize imaging utilization by describing which clinical variables are more predictive of acute disease and, conversely, what combination of variables can obviate the need for imaging. These results are then used to develop evidence-based clinical pathways, clinical decision instruments, and clinical practice guidelines. Despite the validation of these results in subsequent studies, with some demonstrating improved outcomes, their actual use is often limited. This article outlines a research agenda to promote the dissemination and implementation (also known as knowledge translation) of evidence-based interventions for emergency department (ED) imaging, i.e., clinical pathways, clinical decision instruments, and clinical practice guidelines. We convened a multidisciplinary group of stakeholders and held online and telephone discussions over a 6-month period culminating in an in-person meeting at the 2015 Academic Emergency Medicine consensus conference. We identified the following four overarching research questions: 1) what determinants (barriers and facilitators) influence emergency physicians' use of evidence-based interventions when ordering imaging in the ED; 2) what implementation strategies at the institutional level can improve the use of evidence-based interventions for ED imaging; 3) what interventions at the health care policy level can facilitate the adoption of evidence-based interventions for ED imaging; and 4) how can health information technology, including electronic health records, clinical decision support, and health information exchanges, be used to increase awareness, use, and adherence to evidence-based interventions for ED imaging? Advancing research that addresses these questions will provide valuable information as to how we can use evidence-based interventions to optimize imaging utilization and ultimately improve patient care.
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Affiliation(s)
- Marc A Probst
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Peter S Dayan
- Department of Pediatrics, Division of Emergency Medicine, Columbia University College of Physicians & Surgeons, New York, NY
| | - Ali S Raja
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA
| | - Benjamin H Slovis
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kabir Yadav
- Department of Emergency Medicine, University of California, Los Angeles School of Medicine, Los Angeles, CA
| | - Samuel H Lam
- Department of Emergency Medicine, Advocate Christ Medical Center, Oak Lawn, IL
| | - Jason S Shapiro
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Coreen Farris
- RAND Corporation, Santa Monica, CA
- Central Michigan University College of Medicine, Mount Pleasant, MI
| | - Charlene I Babcock
- Department of Emergency Medicine, St. John Hospital and Medical Center, Detroit, MI
| | - Richard T Griffey
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO
| | - Thomas E Robey
- Department of Emergency Medicine, Waterbury Hospital, Yale University, New Haven, CT
| | - Emily M Fortin
- Central Michigan University College of Medicine, Mount Pleasant, MI
| | | | - Suzanne T Chong
- Department of Radiology, University of Michigan Health System, Ann Arbor, MI
| | - Moira Davenport
- Department of Emergency Medicine, Allegheny General Hospital, Pittsburgh, PA
| | - Daniel W Grigat
- Alberta Health Services, Emergency Strategic Clinical Network, Calgary, Alberta, Canada
| | - Eddy L Lang
- Department of Emergency Medicine, University of Calgary, Calgary, Alberta, Canada
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Merrill JA, Sheehan BM, Carley KM, Stetson PD. Transition Networks in a Cohort of Patients with Congestive Heart Failure: A Novel Application of Informatics Methods to Inform Care Coordination. Appl Clin Inform 2015; 6:548-64. [PMID: 26504499 DOI: 10.4338/aci-2015-02-ra-0021] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 07/10/2015] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Unnecessary hospital readmissions are one source of escalating costs that may be reduced through improved care coordination, but how best to design and evaluate coordination programs is poorly understood. Measuring patient flow between service visits could support decisions for coordinating care, particularly for conditions such as congestive heart failure (CHF) which have high morbidity, costs, and hospital readmission rates. OBJECTIVES To determine the feasibility of using network analysis to explore patterns of service delivery for patients with CHF in the context of readmissions. METHODS A retrospective cohort study used de-identified records for patients ≥18 years with an ICD-9 diagnosis code 428.0-428.9, and service visits between July 2011 and June 2012. Patients were stratified by admission outcome. Traditional and novel network analysis techniques were applied to characterize care patterns. RESULTS Patients transitioned between services in different order and frequency depending on admission status. Patient-to-service CoUsage networks were diffuse suggesting unstructured flow of patients with no obvious coordination hubs. In service-to-service Transition networks a specialty heart failure service was on the care path to the most other services for never admitted patients, evidence of how specialist care may prevent hospital admissions for some patients. For patients admitted once, transitions expanded for a clinic-based internal medicine service which clinical experts identified as a Patient Centered Medical Home implemented in the first month for which we obtained data. CONCLUSIONS We detected valid patterns consistent with a targeted care initiative, which experts could understand and explain, suggesting the method has utility for understanding coordination. The analysis revealed strong but complex patterns that could not be demonstrated using traditional linear methods alone. Network analysis supports measurement of real world health care service delivery, shows how transitions vary between services based on outcome, and with further development has potential to inform coordination strategies.
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Affiliation(s)
- J A Merrill
- Columbia University Medical Center , New York, NY, United States
| | - B M Sheehan
- Division of Health and Life Sciences, Intel Corporation, Santa Clara , CA, United States
| | - K M Carley
- Institute of Software Research, Carnegie Mellon University , Pittsburgh, PN, United States
| | - P D Stetson
- Memorial Sloan Kettering Cancer Center , New York, NY, United States
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Moja L, Kwag KH, Lytras T, Bertizzolo L, Brandt L, Pecoraro V, Rigon G, Vaona A, Ruggiero F, Mangia M, Iorio A, Kunnamo I, Bonovas S. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health 2014; 104:e12-22. [PMID: 25322302 PMCID: PMC4232126 DOI: 10.2105/ajph.2014.302164] [Citation(s) in RCA: 176] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2014] [Indexed: 01/18/2023]
Abstract
We systematically reviewed randomized controlled trials (RCTs) assessing the effectiveness of computerized decision support systems (CDSSs) featuring rule- or algorithm-based software integrated with electronic health records (EHRs) and evidence-based knowledge. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Abstracts of Reviews of Effects. Information on system design, capabilities, acquisition, implementation context, and effects on mortality, morbidity, and economic outcomes were extracted. Twenty-eight RCTs were included. CDSS use did not affect mortality (16 trials, 37395 patients; 2282 deaths; risk ratio [RR] = 0.96; 95% confidence interval [CI] = 0.85, 1.08; I(2) = 41%). A statistically significant effect was evident in the prevention of morbidity, any disease (9 RCTs; 13868 patients; RR = 0.82; 95% CI = 0.68, 0.99; I(2) = 64%), but selective outcome reporting or publication bias cannot be excluded. We observed differences for costs and health service utilization, although these were often small in magnitude. Across clinical settings, new generation CDSSs integrated with EHRs do not affect mortality and might moderately improve morbidity outcomes.
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Affiliation(s)
- Lorenzo Moja
- Lorenzo Moja is with the Department of Biomedical Sciences for Health, University of Milan, and the Unit of Clinical Epidemiology, IRCCS Orthopedic Institute Galeazzi, Milan, Italy. Koren H. Kwag is with the Unit of Clinical Epidemiology, IRCCS Orthopedic Institute Galeazzi, Milan. Theodore Lytras is with the Department of Epidemiological Surveillance and Intervention, Hellenic Centre for Disease Control and Prevention, Athens, Greece, the Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain, and the Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona. Lorenzo Bertizzolo and Francesca Ruggiero are with the Department of Biomedical Sciences for Health, University of Milan. Linn Brandt is with the Department of Internal Medicine, Inland Hospital Trust, Oslo, Norway, the Department of Internal Medicine, Diakonhjemmet Hospital, Oslo, and HELSAM, University of Oslo. Valentina Pecoraro is with the University of Milan. Giulio Rigon and Alberto Vaona are with Azienda ULSS 20, Verona, Italy. Massimo Mangia is with Medilogy SRL, Milan. Alfonso Iorio is with the Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario. Ilkka Kunnamo is with Duodecim Medical Publications Ltd, Helsinki, Finland. Stefanos Bonovas is with the Laboratory of Drug Regulatory Policies, IRCCS Mario Negri Institute for Pharmacological Research, Milan, and the Department of Pharmacology, School of Medicine, University of Athens, Athens
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Dixon BE, Simonaitis L, Perkins SM, Wright A, Middleton B. Measuring agreement between decision support reminders: the cloud vs. the local expert. BMC Med Inform Decis Mak 2014; 14:31. [PMID: 24720863 PMCID: PMC4004460 DOI: 10.1186/1472-6947-14-31] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Accepted: 03/19/2014] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND A cloud-based clinical decision support system (CDSS) was implemented to remotely provide evidence-based guideline reminders in support of preventative health. Following implementation, we measured the agreement between preventive care reminders generated by an existing, local CDSS and the new, cloud-based CDSS operating on the same patient visit data. METHODS Electronic health record data for the same set of patients seen in primary care were sent to both the cloud-based web service and local CDSS. The clinical reminders returned by both services were captured for analysis. Cohen's Kappa coefficient was calculated to compare the two sets of reminders. Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients. RESULTS The cloud-based CDSS generated 965 clinical reminders for 405 patient visits over 3 months. The local CDSS returned 889 reminders for the same patient visit data. When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 - 0.42) to 0.99 (95% CI 0.97 - 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders. CONCLUSIONS Preventive care reminders delivered by two disparate CDS systems show substantial agreement. Subtle differences in rule logic and terminology mapping appear to account for much of the discordance. Cloud-based CDSS therefore show promise, opening the door for future development and implementation in support of health care providers with limited resources for knowledge management of complex logic and rules.
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Affiliation(s)
- Brian Edward Dixon
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis, 410 W. St., Suite 2000, Indianapolis, IN 46202, USA
- Center for Biomedical Informatics, Regenstrief Institute, Inc, Indianapolis, IN USA
- Center for Health Information and Communication, Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service CIN 13–416, Indianapolis, IN USA
| | - Linas Simonaitis
- Center for Biomedical Informatics, Regenstrief Institute, Inc, Indianapolis, IN USA
- Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN USA
| | - Susan M Perkins
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, IN USA
- Indiana University Cancer Center, Indianapolis, IN USA
- Indiana Clinical Translational Science Institute, Indianapolis, IN USA
| | - Adam Wright
- Harvard Medical School, Boston, MA USA
- Division of General Medicine, Brigham and Women’s Hospital, Boston, MA USA
- Partners HealthCare, Boston, MA USA
| | - Blackford Middleton
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN USA
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Armada ER, Villamañán E, López-de-Sá E, Rosillo S, Rey-Blas JR, Testillano ML, Álvarez-Sala R, López-Sendón J. Computerized physician order entry in the cardiac intensive care unit: Effects on prescription errors and workflow conditions. J Crit Care 2014; 29:188-93. [DOI: 10.1016/j.jcrc.2013.10.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2013] [Revised: 10/15/2013] [Accepted: 10/20/2013] [Indexed: 10/26/2022]
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Craven CK, Sievert MC, Hicks LL, Alexander GL, Hearne LB, Holmes JH. CAH to CAH: EHR implementation advice to critical access hospitals from peer experts and other key informants. Appl Clin Inform 2014; 5:92-117. [PMID: 24734127 DOI: 10.4338/aci-2013-08-ra-0066] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Accepted: 12/12/2013] [Indexed: 11/23/2022] Open
Abstract
UNLABELLED The US government allocated $30 billion to implement electronic health records (EHRs) in hospitals and provider practices through policy addressing Meaningful Use (MU). Most small, rural hospitals, particularly those designated as Critical Access Hospitals (CAHs), comprising nearly a quarter of US hospitals, had not implemented EHRs before. Little is known about implementation in this setting. Socio-technical factors differ between larger hospitals and CAHs, which continue to lag behind other hospitals in EHR adoption. OBJECTIVE The main objective is to provide EHR implementation advice for CAHs from a spectrum of experts with an emphasis on recommendations from their peers at CAHs that have undertaken the process. The secondary objective is to begin to identify implementation process differences at CAHs v. larger hospitals. METHODS We interviewed 41 experts, including 16 CAH staff members from EHR teams at 10 CAHs that recently implemented EHRs. We qualitatively analyzed the interviews to ascertain themes and implementation recommendations. RESULTS Nineteen themes emerged. Under each theme, comments by experts provide in-depth advice on all implementation stages including ongoing optimization and use. We present comments for three top themes as ranked by number of CAH peer experts commenting - EHR System Selection, EHR Team, and Preparatory Work - and for two others, Outside Partners/Resources and Clinical Decision Support (CDS)/Knowledge Management (KM). Comments for remaining themes are included in tables. DISCUSSION CAH experts rank the themes differently from all experts, a likely indication of the differences between hospitals. Comments for each theme indicate the specific difficulties CAHs encountered. CAH staffs have little or no EHR experience before implementation. A factor across themes is insufficient system and process knowledge, compounded by compressed implementation schedules. Increased, proactive self-education, via available outside partners and information resources, will mitigate difficulties and aid CAHs in meeting increased CDS requirements in MU Stages 2 and 3.
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Affiliation(s)
- C K Craven
- MU Informatics Institute, University of Missouri , Columbia, Missouri
| | - M C Sievert
- MU Informatics Institute, University of Missouri , Columbia, Missouri
| | | | | | | | - J H Holmes
- Department of Biostatistics & Epidemiology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania
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Ethical, legal, and social implications of incorporating genomic information into electronic health records. Genet Med 2013; 15:810-6. [PMID: 24030434 DOI: 10.1038/gim.2013.117] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Accepted: 07/03/2013] [Indexed: 12/27/2022] Open
Abstract
The inclusion of genomic data in the electronic health record raises important ethical, legal, and social issues. In this article, we highlight these challenges and discuss potential solutions. We provide a brief background on the current state of electronic health records in the context of genomic medicine, discuss the importance of equitable access to genome-enabled electronic health records, and consider the potential use of electronic health records for improving genomic literacy in patients and providers. We highlight the importance of privacy, access, and security, and of determining which genomic information is included in the electronic health record. Finally, we discuss the challenges of reporting incidental findings, storing and reinterpreting genomic data, and nondocumentation and duty to warn family members at potential genetic risk.
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Monsen KA, Swanberg HL, Oancea SC, Westra BL. Exploring the value of clinical data standards to predict hospitalization of home care patients. Appl Clin Inform 2012; 3:419-36. [PMID: 23646088 DOI: 10.4338/aci-2012-05-ra-0016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2012] [Accepted: 11/09/2012] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND There is a critical need to reduce hospitalizations for Medicare patients and electronic health record (EHR) home care data provide new opportunities to evaluate risk of hospitalization for patients. OBJECTIVES The objectives of this study were to 1) develop a measure to predict risk of hospitalization among home care patients, the Hospitalization Risk Score (HRS), and 2) compare it with an existing severity of illness measure, the Charlson Index of Comorbidity (CIC). METHODS A convenience sample of clinical data from 14 home care agencies' EHRs, representing 1,643 home care patient episodes was used for the study. The development of the HRS was based on review of the literature, and expert panel evaluation to construct the HRS. Descriptive statistics and generalized linear models were used for comparative analysis; areas under curve (AUC) values were compared for receiver operating curves (ROC), and cut points predicting hospitalization were evaluated. RESULTS The HRS for this sample ranged from 0 to 5.6, with a median of 1.25. The CIC for this sample ranged from 0 to 9 and with a median of 0. Nearly three fourths of the sample was hospitalized at an HRS of 2, and a CIC of 1. AUC values for ROC were 0.63 for HRS and 0.59 for the CIC. The ROC curves were significantly different (t = -7.59, p <0.003). CONCLUSIONS This preliminary study demonstrates the potential value of the HRS using Omaha System EHR data. There was a statistically significant difference for predicting hospitalization of home care patients with the HRS versus the CIC; however the AUC values for both were low. Continued research is needed to further refine the HRS, determine whether it is more sensitive for particular subgroups of patients, and combine it with additional risk factors in understanding rehospitalization.
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