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Zhang H, Huo X, Ren L, Lu J, Li J, Zheng X, Liu J, Ma W, Yuan J, Diao X, Wu C, Zhang X, Wang J, Zhao W, Hu S. Design and rationale of the Comprehensive intelligent Hypertension managEment SyStem (CHESS) evaluation study: A cluster randomized controlled trial for hypertension management in primary care. Am Heart J 2024; 273:90-101. [PMID: 38575049 DOI: 10.1016/j.ahj.2024.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/30/2024] [Accepted: 03/31/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Hypertension management in China is suboptimal with high prevalence and low control rate due to various barriers, including lack of self-management awareness of patients and inadequate capacity of physicians. Digital therapeutic interventions including mobile health and computational device algorithms such as clinical decision support systems (CDSS) are scalable with the potential to improve blood pressure (BP) management and strengthen the healthcare system in resource-constrained areas, yet their effectiveness remains to be tested. The aim of this report is to describe the protocol of the Comprehensive intelligent Hypertension managEment SyStem (CHESS) evaluation study assessing the effect of a multifaceted hypertension management system for supporting patients and physicians on BP lowering in primary care settings. MATERIALS AND METHODS The CHESS evaluation study is a parallel-group, cluster-randomized controlled trial conducted in primary care settings in China. Forty-one primary care sites from 3 counties of China are randomly assigned to either the usual care or the intervention group with the implementation of the CHESS system, more than 1,600 patients aged 35 to 80 years with uncontrolled hypertension and access to a smartphone by themselves or relatives are recruited into the study and followed up for 12 months. In the intervention group, participants receive patient-tailored reminders and alerts via messages or intelligent voice calls triggered by uploaded home blood pressure monitoring data and participants' characteristics, while physicians receive guideline-based prescription instructions according to updated individual data from each visit, and administrators receive auto-renewed feedback of hypertension management performance from the data analysis platform. The multiple components of the CHESS system can work synergistically and have undergone rigorous development and pilot evaluation using a theory-informed approach. The primary outcome is the mean change in 24-hour ambulatory systolic BP from baseline to 12 months. DISCUSSION The CHESS trial will provide evidence and novel insight into the effectiveness and feasibility of an implementation strategy using a comprehensive digital BP management system for reducing hypertension burden in primary care settings. TRIAL REGISTRATION https://www. CLINICALTRIALS gov, NCT05605418.
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Affiliation(s)
- Haibo Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiqian Huo
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lixin Ren
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiapeng Lu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Zheng
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiamin Liu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenjun Ma
- Hypertension Center of Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Jing Yuan
- Information Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaolin Diao
- Information Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chaoqun Wu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jin Wang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Zhao
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shengshou Hu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Vaseur RME, Te Braake E, Beinema T, d'Hollosy WON, Tabak M. Technology-supported shared decision-making in chronic conditions: A systematic review of randomized controlled trials. PATIENT EDUCATION AND COUNSELING 2024; 124:108267. [PMID: 38547638 DOI: 10.1016/j.pec.2024.108267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVES To describe the role of patients with a chronic disease, healthcare professionals (HCPs) and technology in shared decision making (SDM) and the use of clinical decision support systems (CDSSs), and to evaluate the effectiveness of SDM and CDSSs interventions. METHODS Randomized controlled studies published between 2011 and 2021 were identified and screened independently by two reviewers, followed by data extraction and analysis. SDM elements and interactive styles were identified to shape the roles of patients, HCPs and technology. RESULTS Forty-three articles were identified and reported on 21 SDM-studies, 15 CDSS-studies, 2 studies containing both an SDM-tool and a CDSS, and 5 studies with other decision support components. SDM elements were mostly identified in SDM-tools and interactions styles were least common in the other decision support components. CONCLUSIONS Patients within the included RCTs mainly received information from SDM-tools and occasionally CDSSs when it concerns treatment strategies. HCPs provide and clarify information using SDM-tools and CDSSs. Technology provides interactions, which can support more active SDM. SDM-tools mostly showed evidence for positive effects on SDM outcomes, while CDSSs mostly demonstrated positive effects on clinical outcomes. PRACTICE IMPLICATIONS Technology-supported SDM has potential to optimize SDM when patients, HCPs and technology collaborate well together.
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Affiliation(s)
- Roswita M E Vaseur
- Department of Biomedical Signals and Systems; University of Twente, Enschede, The Netherlands.
| | - Eline Te Braake
- Department of Biomedical Signals and Systems; University of Twente, Enschede, The Netherlands; Roessingh Research and Development, Enschede, The Netherlands
| | - Tessa Beinema
- Department of Human-Media Interaction; University of Twente, Enschede, The Netherlands
| | | | - Monique Tabak
- Department of Biomedical Signals and Systems; University of Twente, Enschede, The Netherlands
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Reich C, Frey N, Giannitsis E. [Digitalization and clinical decision tools]. Herz 2024; 49:190-197. [PMID: 38453708 DOI: 10.1007/s00059-024-05242-5] [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] [Accepted: 02/13/2024] [Indexed: 03/09/2024]
Abstract
Digitalization in cardiovascular emergencies is rapidly evolving, analogous to the development in medicine, driven by the increasingly broader availability of digital structures and improved networks, electronic health records and the interconnectivity of systems. The potential use of digital health in patients with acute chest pain starts even in the prehospital phase with the transmission of a digital electrocardiogram (ECG) as well as telemedical support and digital emergency management, which facilitate optimization of the rescue pathways and reduce critical time intervals. The increasing dissemination and acceptance of guideline apps and clinical decision support tools as well as integrated calculators and electronic scores are anticipated to improve guideline adherence, translating into a better quality of treatment and improved outcomes. Implementation of artificial intelligence to support image analysis and also the prediction of coronary artery stenosis requiring interventional treatment or impending cardiovascular events, such as heart attacks or death, have an enormous potential especially as conventional instruments frequently yield suboptimal results; however, there are barriers to the rapid dissemination of corresponding decision aids, such as the regulatory rules related to approval as a medical product, data protection issues and other legal liability aspects, which must be considered.
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Affiliation(s)
| | | | - E Giannitsis
- Medizinische Klinik III, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Deutschland.
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Elias S, Chen Y, Liu X, Slone S, Turkson-Ocran RA, Ogungbe B, Thomas S, Byiringiro S, Koirala B, Asano R, Baptiste DL, Mollenkopf NL, Nmezi N, Commodore-Mensah Y, Himmelfarb CRD. Shared Decision-Making in Cardiovascular Risk Factor Management: A Systematic Review and Meta-Analysis. JAMA Netw Open 2024; 7:e243779. [PMID: 38530311 PMCID: PMC10966415 DOI: 10.1001/jamanetworkopen.2024.3779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/30/2024] [Indexed: 03/27/2024] Open
Abstract
Importance The effect of shared decision-making (SDM) and the extent of its use in interventions to improve cardiovascular risk remain unclear. Objective To assess the extent to which SDM is used in interventions aimed to enhance the management of cardiovascular risk factors and to explore the association of SDM with decisional outcomes, cardiovascular risk factors, and health behaviors. Data Sources For this systematic review and meta-analysis, a literature search was conducted in the Medline, CINAHL, Embase, Cochrane, Web of Science, Scopus, and ClinicalTrials.gov databases for articles published from inception to June 24, 2022, without language restrictions. Study Selection Randomized clinical trials (RCTs) comparing SDM-based interventions with standard of care for cardiovascular risk factor management were included. Data Extraction and Synthesis The systematic search resulted in 9365 references. Duplicates were removed, and 2 independent reviewers screened the trials (title, abstract, and full text) and extracted data. Data were pooled using a random-effects model. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Main Outcomes and Measures Decisional outcomes, cardiovascular risk factor outcomes, and health behavioral outcomes. Results This review included 57 RCTs with 88 578 patients and 1341 clinicians. A total of 59 articles were included, as 2 RCTs were reported twice. Nearly half of the studies (29 [49.2%]) tested interventions that targeted both patients and clinicians, and an equal number (29 [49.2%]) exclusively focused on patients. More than half (32 [54.2%]) focused on diabetes management, and one-quarter focused on multiple cardiovascular risk factors (14 [23.7%]). Most studies (35 [59.3%]) assessed cardiovascular risk factors and health behaviors as well as decisional outcomes. The quality of studies reviewed was low to fair. The SDM intervention was associated with a decrease of 4.21 points (95% CI, -8.21 to -0.21) in Decisional Conflict Scale scores (9 trials; I2 = 85.6%) and a decrease of 0.20% (95% CI, -0.39% to -0.01%) in hemoglobin A1c (HbA1c) levels (18 trials; I2 = 84.2%). Conclusions and Relevance In this systematic review and meta-analysis of the current state of research on SDM interventions for cardiovascular risk management, there was a slight reduction in decisional conflict and an improvement in HbA1c levels with substantial heterogeneity. High-quality studies are needed to inform the use of SDM to improve cardiovascular risk management.
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Affiliation(s)
- Sabrina Elias
- Johns Hopkins School of Nursing, Baltimore, Maryland
| | - Yuling Chen
- Johns Hopkins School of Nursing, Baltimore, Maryland
| | - Xiaoyue Liu
- New York University Rory Meyers College of Nursing, New York, New York
| | - Sarah Slone
- Johns Hopkins School of Nursing, Baltimore, Maryland
| | - Ruth-Alma Turkson-Ocran
- Division of General Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Bunmi Ogungbe
- Johns Hopkins School of Nursing, Baltimore, Maryland
| | | | | | - Binu Koirala
- Johns Hopkins School of Nursing, Baltimore, Maryland
| | - Reiko Asano
- Catholic University of America, Washington, DC
| | | | | | - Nwakaego Nmezi
- MedStar National Rehabilitation Hospital, Washington, DC
| | - Yvonne Commodore-Mensah
- Johns Hopkins School of Nursing, Baltimore, Maryland
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Cheryl R. Dennison Himmelfarb
- Johns Hopkins School of Nursing, Baltimore, Maryland
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Johns Hopkins School of Medicine, Baltimore, Maryland
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Wallace H, Wang Q, Botha T, Hunter B, Lumsden N, Nelson C. Optimising diagnosis and management of kidney disease: an implementation trial of a clinical decision support system future health today. BMC Nephrol 2024; 25:57. [PMID: 38365641 PMCID: PMC10870544 DOI: 10.1186/s12882-024-03489-y] [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: 12/24/2023] [Accepted: 01/31/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Chronic kidney disease affects more than 10% of the world's population and is a non-communicable disease of global concern and priority. There is a significant implementation gap between best practice guideline recommendations and current kidney disease management. Previous research has shown the need to partner with primary care to improve education, collaboration, and kidney disease awareness. This implementation trial will explore use of an innovative clinical decision support software, Future Health Today, to improve screening, diagnosis, and management of kidney disease in primary care. The program will be supported by tertiary care outreach services. The primary aim is to test the hypothesis that the Future Health Today implementation program will improve screening, diagnosis, and management of kidney disease. Secondary aims are to evaluate primary care satisfaction and broader health service impacts. METHODS This pre-post implementation trial using an interrupted time series design will evaluate the clinical and service outcomes of Future Health Today, using a mixed methods study in twenty general practices with an estimated population size of 150,000. Deidentified patient data will be extracted from participating practices to examine the primary aims of the study. Surveys and semi-structured interviews with general practice will inform secondary hypotheses. Data linkage between primary care and tertiary care data will examine the broader health service impacts. DISCUSSION This investigator driven trial will assess the impact of Future Health Today software coupled with education and clinical outreach support. Investigators hypothesise that there will be improvement in appropriate screening, diagnosis, and management of kidney disease. This program has the potential to be scaled more broadly. TRIAL REGISTRATION Australian New Zealand Clinical Trial Registry: ACTRN12623001096640.
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Affiliation(s)
- Hannah Wallace
- Department of Medicine, Faulty of Medicine, Dentistry and Health Sciences, Western Clinical School, The University of Melbourne, Melbourne, VIC, Australia.
- Western Health Chronic Disease Alliance, Western Health, Footscray, VIC, Australia.
| | - Qiumian Wang
- Western Health Chronic Disease Alliance, Western Health, Footscray, VIC, Australia
| | - Tanita Botha
- Western Health Chronic Disease Alliance, Western Health, Footscray, VIC, Australia
- Biostatistics Units, Faculty of Health, Deakin University, Geelong, VIC, Australia
| | - Barbara Hunter
- Department of General Practice and Primary Care, Faulty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Natalie Lumsden
- Western Health Chronic Disease Alliance, Western Health, Footscray, VIC, Australia
- Department of General Practice and Primary Care, Faulty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Craig Nelson
- Department of Medicine, Faulty of Medicine, Dentistry and Health Sciences, Western Clinical School, The University of Melbourne, Melbourne, VIC, Australia
- Western Health Chronic Disease Alliance, Western Health, Footscray, VIC, Australia
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Bozyel S, Şimşek E, Koçyiğit Burunkaya D, Güler A, Korkmaz Y, Şeker M, Ertürk M, Keser N. Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases. Anatol J Cardiol 2024:74-86. [PMID: 38168009 PMCID: PMC10837676 DOI: 10.14744/anatoljcardiol.2023.3685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
Despite all the advancements in science, medical knowledge, healthcare, and the healthcare industry, cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The main reasons are the inadequacy of preventive health services and delays in diagnosis due to the increasing population, the failure of physicians to apply guide-based treatments, the lack of continuous patient follow-up, and the low compliance of patients with doctors' recommendations. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are systems that support complex decision-making processes by using AI techniques such as data analysis, foresight, and optimization. Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD. These are just some examples, and the use of AI for CVD decision support systems is rapidly evolving. However, for these systems to be fully reliable and effective, they need to be trained with accurate data and carefully evaluated by medical professionals.
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Affiliation(s)
- Serdar Bozyel
- Department of Cardiology, Health Sciences University, Kocaeli City Hospital, Kocaeli, Türkiye
| | - Evrim Şimşek
- Department of Cardiology, Ege University, Faculty of Medicine, İzmir, Türkiye
| | | | - Arda Güler
- Department of Cardiology, Health Sciences University, Mehmet Akif Ersoy Training and Research Hospital, İstanbul, Türkiye
| | - Yetkin Korkmaz
- Department of Cardiology, Health Sciences University, Sultan Abdulhamid Han Training and Research Hospital, İstanbul, Türkiye
| | - Mehmet Şeker
- Department of Cardiology, Health Sciences University, Sultan Abdulhamid Han Training and Research Hospital, İstanbul, Türkiye
| | - Mehmet Ertürk
- Department of Cardiology, Health Sciences University, Mehmet Akif Ersoy Training and Research Hospital, İstanbul, Türkiye
| | - Nurgül Keser
- Department of Cardiology, Health Sciences University, Sultan Abdulhamid Han Training and Research Hospital, İstanbul, Türkiye
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Santos FCD, Snigurska UA, Keenan GM, Lucero RJ, Modave F. Clinical Decision Support Systems for Palliative Care Management: A Scoping Review. J Pain Symptom Manage 2023; 66:e205-e218. [PMID: 36933748 PMCID: PMC11162595 DOI: 10.1016/j.jpainsymman.2023.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/18/2023]
Abstract
CONTEXT With the expansion of palliative care services in clinical settings, clinical decision support systems (CDSSs) have become increasingly crucial for assisting bedside nurses and other clinicians in improving the quality of care to patients with life-limiting health conditions. OBJECTIVES To characterize palliative care CDSSs and explore end-users' actions taken, adherence recommendations, and clinical decision time. METHODS The CINAHL, Embase, and PubMed databases were searched from inception to September 2022. The review was developed following the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews guidelines. Qualified studies were described in tables and assessed the level of evidence. RESULTS A total of 284 abstracts were screened, and 12 studies comprised the final sample. The CDSSs selected focused on identifying patients who could benefit from palliative care based on their health status, making referrals to palliative care services, and managing medications and symptom control. Despite the variability of palliative CDSSs, all studies reported that CDSSs assisted clinicians in becoming more informed about palliative care options leading to better decisions and improved patient outcomes. Seven studies explored the impact of CDSSs on end-user adherence. Three studies revealed high adherence to recommendations while four had low adherence. Lack of feature customization and trust in guideline-based in the initial stages of feasibility and usability testing were evident, limiting the usefulness for nurses and other clinicians. CONCLUSION This study demonstrated that implementing palliative care CDSSs can assist nurses and other clinicians in improving the quality of care for palliative patients. The studies' different methodological approaches and variations in palliative CDSSs made it challenging to compare and validate the applicability under which CDSSs are effective. Further research utilizing rigorous methods to evaluate the impact of clinical decision support features and guideline-based actions on clinicians' adherence and efficiency is recommended.
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Affiliation(s)
- Fabiana Cristina Dos Santos
- Department of Family, Community, and Health Systems Science (F.C.D.S, U.A.S., G.M.K.), College of Nursing, University of Florida, Gainesville, Florida, USA.
| | - Urszula A Snigurska
- Department of Family, Community, and Health Systems Science (F.C.D.S, U.A.S., G.M.K.), College of Nursing, University of Florida, Gainesville, Florida, USA
| | - Gail M Keenan
- Department of Family, Community, and Health Systems Science (F.C.D.S, U.A.S., G.M.K.), College of Nursing, University of Florida, Gainesville, Florida, USA
| | - Robert J Lucero
- School of Nursing (R.J.L.), University of California Los Angeles, Los Angeles, California, USA
| | - François Modave
- Department of MD-Anesthesiology (F.M), College of Medicine, University of Florida, Gainesville, Florida, USA
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Maxey J, Gupta A, Houchens N. Quality and safety in the literature: April 2023. BMJ Qual Saf 2023; 32:235-240. [PMID: 36931631 DOI: 10.1136/bmjqs-2023-015977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 03/19/2023]
Affiliation(s)
- Jordan Maxey
- Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Ashwin Gupta
- Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Nathan Houchens
- Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
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Jones JL, Simons K, Manski-Nankervis JA, Lumsden NG, Fernando S, de Courten MP, Cox N, Hamblin PS, Janus ED, Nelson CL. Chronic disease IMPACT (chronic disease early detection and improved management in primary care project): An Australian stepped wedge cluster randomised trial. Digit Health 2023; 9:20552076231194948. [PMID: 37588155 PMCID: PMC10426307 DOI: 10.1177/20552076231194948] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 07/28/2023] [Indexed: 08/18/2023] Open
Abstract
Background Interrelated chronic vascular diseases (chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease (CVD)) are common with high morbidity and mortality. This study aimed to assess if an electronic-technology-based quality improvement intervention in primary care could improve detection and management of people with and at risk of these diseases. Methods Stepped-wedge trial with practices randomised to commence intervention in one of five 16-week periods. Intervention included (1) electronic-technology tool extracting data from general practice electronic medical records and generating graphs and lists for audit; (2) education regarding chronic disease and the electronic-technology tool; (3) assistance with quality improvement audit plan development, benchmarking, monitoring and support. De-identified data analysis using R 3.5.1 conducted using Bayesian generalised linear mixed model with practice and time-specific random intercepts. Results At baseline, eight included practices had 37,946 active patients (attending practice ≥3 times within 2 years) aged ≥18 years. Intervention was associated with increased OR (95% CI) for: kidney health checks (estimated glomerular filtration rate, urine albumin:creatinine ratio (uACR) and blood pressure) in those at risk 1.34 (1.26-1.42); coded diagnosis of CKD 1.18 (1.09-1.27); T2D diagnostic testing (fasting glucose or HbA1c) in those at risk 1.15 (1.08-1.23); uACR in patients with T2D 1.78 (1.56-2.05). Documented eye checks within recommended frequency in patients with T2D decreased 0.85 (0.77-0.96). There were no significant changes in other assessed variables. Conclusions This electronic-technology-based intervention in primary care has potential to help translate guidelines into practice but requires further refining to achieve widespread improvements across the interrelated chronic vascular diseases.
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Affiliation(s)
- Julia L Jones
- Nephrology, Western Health, Melbourne, Australia
- Western Health Chronic Disease Alliance, Melbourne, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Australia
| | - Koen Simons
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Office for Research, Western Health, Melbourne, Australia
| | | | - Natalie G Lumsden
- Nephrology, Western Health, Melbourne, Australia
- Western Health Chronic Disease Alliance, Melbourne, Australia
- Department of General Practice, The University of Melbourne, Melbourne, Australia
| | | | - Maximilian P de Courten
- Mitchell Institute for Education and Health Policy, Melbourne, Australia
- Centre for Chronic Disease, Victoria University, Melbourne, Australia
| | - Nicholas Cox
- Western Health Chronic Disease Alliance, Melbourne, Australia
- Centre for Chronic Disease, Victoria University, Melbourne, Australia
- Cardiology, Western Health, Melbourne, Australia
| | - Peter Shane Hamblin
- Western Health Chronic Disease Alliance, Melbourne, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Australia
- Endocrinology and Diabetes, Western Health, Melbourne, Australia
| | - Edward D Janus
- Western Health Chronic Disease Alliance, Melbourne, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Australia
- Medicine, Western Health, Melbourne, Australia
| | - Craig L Nelson
- Nephrology, Western Health, Melbourne, Australia
- Western Health Chronic Disease Alliance, Melbourne, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Australia
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Shi X, He J, Lin M, Liu C, Yan B, Song H, Wang C, Xiao F, Huang P, Wang L, Li Z, Huang Y, Zhang M, Chen CS, Obst K, Shi L, Li W, Yang S, Yao G, Li X. Comparative Effectiveness of Team-Based Care With and Without a Clinical Decision Support System for Diabetes Management : A Cluster Randomized Trial. Ann Intern Med 2023; 176:49-58. [PMID: 36469915 DOI: 10.7326/m22-1950] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Uncontrolled hyperglycemia, hypercholesterolemia, and hypertension are common in persons with diabetes. OBJECTIVE To compare the effectiveness of team-based care with and without a clinical decision support system (CDSS) in controlling glycemia, lipids, and blood pressure (BP) among patients with type 2 diabetes. DESIGN Cluster randomized trial. (ClinicalTrials.gov: NCT02835287). SETTING 38 community health centers in Xiamen, China. PATIENTS 11 132 persons aged 50 years or older with uncontrolled diabetes and comorbid conditions, 5475 receiving team-based care with a CDSS and 5657 receiving team-based care alone. INTERVENTION Team-based care was delivered by primary care physicians, health coaches, and diabetes specialists in all centers. In addition, a computerized CDSS, which generated individualized treatment recommendations based on clinical guidelines, was implemented in 19 centers delivering team-based care with a CDSS. MEASUREMENTS Coprimary outcomes were mean reductions in hemoglobin A1c (HbA1c) level, low-density lipoprotein cholesterol (LDL-C) level, and systolic BP over 18 months and the proportion of participants with all 3 risk factors controlled at 18 months. RESULTS During the 18-month intervention, HbA1c levels, LDL-C levels, and systolic BP significantly decreased by -0.9 percentage point (95% CI, -0.9 to -0.8 percentage point), -0.49 mmol/L (CI, -0.53 to -0.45 mmol/L) (-19.0 mg/dL [CI, -20.4 to -17.5 mg/dL]), and -9.1 mm Hg (CI, -9.9 to -8.3 mm Hg), respectively, in team-based care with a CDSS and by -0.6 percentage point (CI, -0.7 to -0.5 percentage point), -0.32 mmol/L (CI, -0.35 to -0.29 mmol/L) (-12.5 mg/dL [CI, -13.6 to -11.3 mg/dL]), and -7.5 mm Hg (CI, -8.4 to -6.6 mm Hg), respectively, in team-based care alone. Net differences were -0.2 percentage point (CI, -0.3 to -0.1 percentage point) for HbA1c level, -0.17 mmol/L (CI, -0.21 to -0.12 mmol/L) (-6.5 mg/dL [CI, -8.3 to -4.6 mg/dL]) for LDL-C level, and -1.5 mm Hg (CI, -2.8 to -0.3 mm Hg) for systolic BP. The proportion of patients with controlled HbA1c, LDL-C, and systolic BP was 16.9% (CI, 15.7% to 18.2%) in team-based care with a CDSS and 13.0% (CI, 11.7% to 14.3%) in team-based care alone. LIMITATION There was no usual care control, and clinical outcome assessors were unblinded; the analysis did not account for multiple comparisons. CONCLUSION Compared with team-based care alone, team-based care with a CDSS significantly reduced cardiovascular risk factors in patients with diabetes, but the effect was modest. PRIMARY FUNDING SOURCE Xiamen Municipal Health Commission.
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Affiliation(s)
- Xiulin Shi
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China, Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China, and Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana (X.S.)
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, and Tulane University Translational Science Institute, New Orleans, Louisiana (J.H., C.S.C., K.O.)
| | - Mingzhu Lin
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Changqin Liu
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Bing Yan
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Haiqu Song
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Caihong Wang
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Fangsen Xiao
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Peiying Huang
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Liying Wang
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Zhibin Li
- Epidemiology Research Unit, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China (Z.L.)
| | - Yinxiang Huang
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Mulin Zhang
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Chung-Shiuan Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, and Tulane University Translational Science Institute, New Orleans, Louisiana (J.H., C.S.C., K.O.)
| | - Katherine Obst
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, and Tulane University Translational Science Institute, New Orleans, Louisiana (J.H., C.S.C., K.O.)
| | - Lizheng Shi
- Department of Health Policy and Management, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana (L.S.)
| | - Weihua Li
- Department of Cardiology, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China (W.L.)
| | - Shuyu Yang
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
| | - Guanhua Yao
- Xiamen Municipal Health Commission, Xiamen, China (G.Y.)
| | - Xuejun Li
- Department of Endocrinology and Diabetes, Xiamen Diabetes Institute, and Xiamen Clinical Medical Center for Endocrine and Metabolic Diseases, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, and Fujian Province Key Laboratory of Diabetes Translational Medicine, Xiamen, China (M.L., C.L., B.Y., H.S., C.W., F.X., P.H., L.W., Y.H., M.Z., S.Y., X.L.)
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Chen W, O’Bryan CM, Gorham G, Howard K, Balasubramanya B, Coffey P, Abeyaratne A, Cass A. Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation. Implement Sci Commun 2022; 3:81. [PMID: 35902894 PMCID: PMC9330991 DOI: 10.1186/s43058-022-00326-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 07/10/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS-exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems. METHODS A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach. RESULTS Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive. CONCLUSION This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research. TRIAL REGISTRATION PROSPERO CRD42020203716.
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Affiliation(s)
- Winnie Chen
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Claire Maree O’Bryan
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Kirsten Howard
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, NSW Australia
| | - Bhavya Balasubramanya
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Patrick Coffey
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Asanga Abeyaratne
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Alan Cass
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
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12
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Chen W, Howard K, Gorham G, O'Bryan CM, Coffey P, Balasubramanya B, Abeyaratne A, Cass A. Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. J Am Med Inform Assoc 2022; 29:1757-1772. [PMID: 35818299 PMCID: PMC9471723 DOI: 10.1093/jamia/ocac110] [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: 04/01/2022] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 01/10/2023] Open
Abstract
Objectives Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases. Material and Methods We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted. Results The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY. Conclusion We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies. Registration PROSPERO (CRD42020203716)
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Affiliation(s)
- Winnie Chen
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Kirsten Howard
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Claire Maree O'Bryan
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Patrick Coffey
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Bhavya Balasubramanya
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Asanga Abeyaratne
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Alan Cass
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
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13
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Karabukayeva A, Anderson JL, Hall AG, Feldman SS, Mehta T. Exploring a Need for a Cardiometabolic Disease Staging System as a Computerized Clinical Decision Support Tool: Qualitative Study. JMIR Form Res 2022; 6:e37456. [PMID: 35776499 PMCID: PMC9288101 DOI: 10.2196/37456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/02/2022] [Accepted: 06/16/2022] [Indexed: 12/05/2022] Open
Abstract
Background Although cardiometabolic diseases are leading causes of morbidity and mortality in the United States, computerized tools for risk assessment of cardiometabolic disease are rarely integral components of primary care practice. Embedding cardiometabolic disease staging systems (CMDS) into computerized clinical decision support systems (CDSS) may assist with identifying and treating patients at greatest risk for developing cardiometabolic disease. Objective This study aimed to explore the current approach to medical management of obesity and the need for CMDS designed to aid medical management of people living with obesity, at risk of being obese, or diabetic at the point of care. Methods Using a general inductive approach, this qualitative research study was guided by an interpretive epistemology. The method included semistructured, in-depth interviews with primary care providers (PCPs) from university-based community health clinics. The literature informed the interview protocol and included questions on PCPs’ experiences and the need for a tool to improve their ability to manage and prevent complications from overweight and obesity. Results PCPs (N=10) described their current approaches and emphasized behavioral treatments consisting of combined diet, physical activity, and behavior therapy as the first line of treatment for people who were overweight or obese. Results suggest that beneficial features of CDSS include (1) clinically relevant and customizable support, (2) provision of a comprehensive medical summary with trends, (3) availability of patient education materials and community resources, and (4) simplicity and ease of navigation. Conclusions Implementation of a CMDS via a CDSS could enable PCPs to conduct comprehensive cardiometabolic disease risk assessments, supporting clinical management of overweight, obesity, and diabetes. Results from this study provide unique insights to developers and researchers by identifying areas for design optimization, improved end user experience, and successful adoption of the CDSS.
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Affiliation(s)
- Aizhan Karabukayeva
- Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jami L Anderson
- Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Allyson G Hall
- Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sue S Feldman
- Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Graduate Medical Education, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Tapan Mehta
- Department of Family and Community Medicine, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
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14
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Xie CX, Chen Q, Hincapié CA, Hofstetter L, Maher CG, Machado GC. Effectiveness of clinical dashboards as audit and feedback or clinical decision support tools on medication use and test ordering: a systematic review of randomized controlled trials. J Am Med Inform Assoc 2022; 29:1773-1785. [PMID: 35689652 PMCID: PMC9471705 DOI: 10.1093/jamia/ocac094] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/04/2022] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Clinical dashboards used as audit and feedback (A&F) or clinical decision support systems (CDSS) are increasingly adopted in healthcare. However, their effectiveness in changing the behavior of clinicians or patients is still unclear. This systematic review aims to investigate the effectiveness of clinical dashboards used as CDSS or A&F tools (as a standalone intervention or part of a multifaceted intervention) in primary care or hospital settings on medication prescription/adherence and test ordering. METHODS Seven major databases were searched for relevant studies, from inception to August 2021. Two authors independently extracted data, assessed the risk of bias using the Cochrane RoB II scale, and evaluated the certainty of evidence using GRADE. Data on trial characteristics and intervention effect sizes were extracted. A narrative synthesis was performed to summarize the findings of the included trials. RESULTS Eleven randomized trials were included. Eight trials evaluated clinical dashboards as standalone interventions and provided conflicting evidence on changes in antibiotic prescribing and no effects on statin prescribing compared to usual care. Dashboards increased medication adherence in patients with inflammatory arthritis but not in kidney transplant recipients. Three trials investigated dashboards as part of multicomponent interventions revealing decreased use of opioids for low back pain, increased proportion of patients receiving cardiovascular risk screening, and reduced antibiotic prescribing for upper respiratory tract infections. CONCLUSION There is limited evidence that dashboards integrated into electronic medical record systems and used as feedback or decision support tools may be associated with improvements in medication use and test ordering.
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Affiliation(s)
- Charis Xuan Xie
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Qiuzhe Chen
- Institute for Musculoskeletal Health, Sydney, NSW, Australia.,Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Cesar A Hincapié
- Department of Chiropractic Medicine, Faculty of Medicine, University of Zurich and Balgrist University Hospital, Zurich, Switzerland.,Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Léonie Hofstetter
- Department of Chiropractic Medicine, Faculty of Medicine, University of Zurich and Balgrist University Hospital, Zurich, Switzerland
| | - Chris G Maher
- Institute for Musculoskeletal Health, Sydney, NSW, Australia.,Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Gustavo C Machado
- Institute for Musculoskeletal Health, Sydney, NSW, Australia.,Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
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15
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Song J, Wang X, Wang B, Gao Y, Liu J, Zhang H, Li X, Li J, Wang JG, Cai J, Herrin J, Armitage J, Krumholz HM, Zheng X. Effectiveness of a clinical decision support system for hypertension management in primary care: study protocol for a pragmatic cluster-randomized controlled trial. Trials 2022; 23:412. [PMID: 35578345 PMCID: PMC9109449 DOI: 10.1186/s13063-022-06374-x] [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: 06/27/2021] [Accepted: 04/29/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Clinical decision support systems (CDSS) are low-cost, scalable tools with the potential to improve guideline-based antihypertensive treatment in primary care, but their effectiveness needs to be tested, especially in low- and middle-income countries such as China. METHODS The Learning Implementation of Guideline-based decision support system for Hypertension Treatment (LIGHT) trial is a pragmatic, four-stage, cluster-randomized trial conducted in 94 primary care sites in China. For each city-based stage, sites are randomly assigned to either implementation of the CDSS for hypertension management (which guides doctors' treatment recommendations based on measured blood pressure and patient characteristics), or usual care. Patients are enrolled during the first 3 months after site randomization and followed for 9 months. The primary outcome is the proportion of hypertension management visits at which guideline-based treatment is provided. In a nested trial conducted within the CDSS, with the patient as the unit of randomization, the LIGHT-ACD trial, patients are randomized to receive different initial mono- or dual-antihypertensive therapy. The primary outcome of the LIGHT-ACD trial is the changes in blood pressure. DISCUSSION The LIGHT trial will provide evidence on the effectiveness of a CDSS for improving guideline adherence for hypertension management in primary care in China. The nested trial, the LIGHT-ACD trial, will provide data on the effect of different initial antihypertensive regimens for blood pressure management in this setting. TRIAL REGISTRATION ClinicalTrials.gov, identifier: LIGHT (NCT03636334) and LIGHT-ACD (NCT03587103). Registered on 3 July 2018.
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Affiliation(s)
- Jiali Song
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Xiuling Wang
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Bin Wang
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Yan Gao
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Jiamin Liu
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Haibo Zhang
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Xi Li
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Jing Li
- grid.415105.40000 0004 9430 5605National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037 China
| | - Ji-Guang Wang
- grid.16821.3c0000 0004 0368 8293The Shanghai Institute of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jun Cai
- grid.415105.40000 0004 9430 5605Hypertension Center, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, Beijing, China
| | - Jeph Herrin
- grid.47100.320000000419368710Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut USA
| | - Jane Armitage
- grid.4991.50000 0004 1936 8948Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF UK ,grid.4991.50000 0004 1936 8948MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Harlan M. Krumholz
- grid.16821.3c0000 0004 0368 8293The Shanghai Institute of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China ,grid.417307.6Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut USA ,grid.47100.320000000419368710Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut USA
| | - Xin Zheng
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037, China. .,National Clinical Research Center for Cardiovascular Diseases, Shenzhen, Coronary Artery Disease Center, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, China.
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Pratt R, Saman DM, Allen C, Crabtree B, Ohnsorg K, Sperl-Hillen JM, Harry M, Henzler-Buckingham H, O'Connor PJ, Desai J. Assessing the implementation of a clinical decision support tool in primary care for diabetes prevention: a qualitative interview study using the Consolidated Framework for Implementation Science. BMC Med Inform Decis Mak 2022; 22:15. [PMID: 35033029 PMCID: PMC8760770 DOI: 10.1186/s12911-021-01745-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 12/30/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In this paper we describe the use of the Consolidated Framework for Implementation Research (CFIR) to study implementation of a web-based, point-of-care, EHR-linked clinical decision support (CDS) tool designed to identify and provide care recommendations for adults with prediabetes (Pre-D CDS). METHODS As part of a large NIH-funded clinic-randomized trial, we identified a convenience sample of interview participants from 22 primary care clinics in Minnesota, North Dakota, and Wisconsin that were randomly allocated to receive or not receive a web-based EHR-integrated prediabetes CDS intervention. Participants included 11 clinicians, 6 rooming staff, and 7 nurse or clinic managers recruited by study staff to participate in telephone interviews conducted by an expert in qualitative methods. Interviews were recorded and transcribed, and data analysis was conducted using a constructivist version of grounded theory. RESULTS Implementing a prediabetes CDS tool into primary care clinics was useful and well received. The intervention was integrated with clinic workflows, supported primary care clinicians in clearly communicating prediabetes risk and management options with patients, and in identifying actionable care opportunities. The main barriers to CDS use were time and competing priorities. Finally, while the implementation process worked well, opportunities remain in engaging the care team more broadly in CDS use. CONCLUSIONS The use of CDS tools for engaging patients and providers in care improvement opportunities for prediabetes is a promising and potentially effective strategy in primary care settings. A workflow that incorporates the whole care team in the use of such tools may optimize the implementation of CDS tools like these in primary care settings. Trial registration Name of the registry: Clinicaltrial.gov. TRIAL REGISTRATION NUMBER NCT02759055. Date of registration: 05/03/2016. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT02759055 Prospectively registered.
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Affiliation(s)
- Rebekah Pratt
- Department of Family Medicine and Community Health, University of Minnesota, 717 Delaware Street, Minneapolis, MN, 55414, USA.
| | - Daniel M Saman
- Essentia Institute of Rural Health Research, 502 E 2nd St, Duluth, MN, 55805, USA
- Carle Foundation Hospital Clinical Business and Intelligence, 611 W Park Street, Urbana, IL, 61801, USA
| | - Clayton Allen
- Essentia Institute of Rural Health Research, 502 E 2nd St, Duluth, MN, 55805, USA
| | - Benjamin Crabtree
- Department of Family Medicine and Community Health, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Kris Ohnsorg
- HealthPartners Institute, 8170 33rd Avenue South, Bloomington, MN, 55425, USA
| | | | - Melissa Harry
- Essentia Institute of Rural Health Research, 502 E 2nd St, Duluth, MN, 55805, USA
| | | | - Patrick J O'Connor
- HealthPartners Institute, 8170 33rd Avenue South, Bloomington, MN, 55425, USA
| | - Jay Desai
- HealthPartners Institute, 8170 33rd Avenue South, Bloomington, MN, 55425, USA
- Minnesota Department of Health, 85 East 7th Place, PO Box 64882, St. Paul, MN, 55164-0882, USA
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17
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Desai J, Saman D, Sperl-Hillen JM, Pratt R, Dehmer SP, Allen C, Ohnsorg K, Wuorio A, Appana D, Hitz P, Land A, Sharma R, Wilkerson L, Crain AL, Crabtree BF, Bianco J, O'Connor PJ. Implementing a prediabetes clinical decision support system in a large primary care system: Design, methods, and pre-implementation results. Contemp Clin Trials 2022; 114:106686. [DOI: 10.1016/j.cct.2022.106686] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 11/30/2022]
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El Asmar ML, Dharmayat KI, Vallejo-Vaz AJ, Irwin R, Mastellos N. Effect of computerised, knowledge-based, clinical decision support systems on patient-reported and clinical outcomes of patients with chronic disease managed in primary care settings: a systematic review. BMJ Open 2021; 11:e054659. [PMID: 34937723 PMCID: PMC8705223 DOI: 10.1136/bmjopen-2021-054659] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES Chronic diseases are the leading cause of disability globally. Most chronic disease management occurs in primary care with outcomes varying across primary care providers. Computerised clinical decision support systems (CDSS) have been shown to positively affect clinician behaviour by improving adherence to clinical guidelines. This study provides a summary of the available evidence on the effect of CDSS embedded in electronic health records on patient-reported and clinical outcomes of adult patients with chronic disease managed in primary care. DESIGN AND ELIGIBILITY CRITERIA Systematic review, including randomised controlled trials (RCTs), cluster RCTs, quasi-RCTs, interrupted time series and controlled before-and-after studies, assessing the effect of CDSS (vs usual care) on patient-reported or clinical outcomes of adult patients with selected common chronic diseases (asthma, chronic obstructive pulmonary disease, heart failure, myocardial ischaemia, hypertension, diabetes mellitus, hyperlipidaemia, arthritis and osteoporosis) managed in primary care. DATA SOURCES Medline, Embase, CENTRAL, Scopus, Health Management Information Consortium and trial register clinicaltrials.gov were searched from inception to 24 June 2020. DATA EXTRACTION AND SYNTHESIS Screening, data extraction and quality assessment were performed by two reviewers independently. The Cochrane risk of bias tool was used for quality appraisal. RESULTS From 5430 articles, 8 studies met the inclusion criteria. Studies were heterogeneous in population characteristics, intervention components and outcome measurements and focused on diabetes, asthma, hyperlipidaemia and hypertension. Most outcomes were clinical with one study reporting on patient-reported outcomes. Quality of the evidence was impacted by methodological biases of studies. CONCLUSIONS There is inconclusive evidence in support of CDSS. A firm inference on the intervention effect was not possible due to methodological biases and study heterogeneity. Further research is needed to provide evidence on the intervention effect and the interplay between healthcare setting features, CDSS characteristics and implementation processes. PROSPERO REGISTRATION NUMBER CRD42020218184.
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Affiliation(s)
| | - Kanika I Dharmayat
- Department of Primary Care and Public Health, Imperial Centre for Cardiovascular Disease Prevention, Imperial College London, London, UK
| | - Antonio J Vallejo-Vaz
- Imperial Centre for Cardiovascular Disease Prevention (ICCP), Department of Primary Care and Public Health, School of Public Health, Imperial College London. London, United Kingdom, London, UK
- Department of Medicine, Faculty of Medicine, University of Seville, Seville, Spain
- Clinical Epidemiology and Vascular Risk, Instituto de Biomedicina de Sevilla, IBiS/Hospital Universitario Virgen del Rocío/Universidad de Sevilla/CSIC, Seville, Spain
| | - Ryan Irwin
- Department of Primary Care Clinical Sciences, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Nikolaos Mastellos
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK
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19
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Houwink EJF, Kasteleyn MJ, Alpay L, Pearce C, Butler-Henderson K, Meijer E, van Kampen S, Versluis A, Bonten TN, van Dalfsen JH, van Peet PG, Koster Y, Hierck BP, Jeeninga I, van Luenen S, van der Kleij RMJJ, Chavannes NH, Kramer AWM. SERIES: eHealth in primary care. Part 3: eHealth education in primary care. Eur J Gen Pract 2021; 26:108-118. [PMID: 32757859 PMCID: PMC7470053 DOI: 10.1080/13814788.2020.1797675] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Education is essential to the integration of eHealth into primary care, but eHealth is not yet embedded in medical education. OBJECTIVES In this opinion article, we aim to support organisers of Continuing Professional Development (CPD) and teachers delivering medical vocational training by providing recommendations for eHealth education. First, we describe what is required to help primary care professionals and trainees learn about eHealth. Second, we elaborate on how eHealth education might be provided. DISCUSSION We consider four essential topics. First, an understanding of existing evidence-based eHealth applications and conditions for successful development and implementation. Second, required digital competencies of providers and patients. Third, how eHealth changes patient-provider and provider-provider relationships and finally, understanding the handling of digital data. Educational activities to address these topics include eLearning, blended learning, courses, simulation exercises, real-life practice, supervision and reflection, role modelling and community of practice learning. More specifically, a CanMEDS framework aimed at defining curriculum learning goals can support eHealth education by describing roles and required competencies. Alternatively, Kern's conceptual model can be used to design eHealth training programmes that match the educational needs of the stakeholders using eHealth. CONCLUSION Vocational and CPD training in General Practice needs to build on eHealth capabilities now. We strongly advise the incorporation of eHealth education into vocational training and CPD activities, rather than providing it as a separate single module. How learning goals and activities take shape and how competencies are evaluated clearly requires further practice, evaluation and study.
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Affiliation(s)
- Elisa J F Houwink
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Marise J Kasteleyn
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Laurence Alpay
- Medical Technology Research Group, Inholland University of Applied Science, Haarlem, The Netherlands
| | - Christopher Pearce
- Centre for Transformation in Digital Health, University of Melbourne, Melbourne, Australia.,Department of General Practice, Monash University, Melbourne, Australia
| | | | - Eline Meijer
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Sanne van Kampen
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Anke Versluis
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Tobias N Bonten
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Jens H van Dalfsen
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Petra G van Peet
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands
| | - Ybranda Koster
- Medical Technology Research Group, Inholland University of Applied Science, Haarlem, The Netherlands
| | - Beerend P Hierck
- Center for Innovation in Medical Education, Leiden University Medical Center, Leiden, The Netherlands.,Department of Anatomy and Embryology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Teachers' Academy, Leiden University, Leiden, The Netherlands
| | - Ilke Jeeninga
- Center for Innovation in Medical Education, Leiden University Medical Center, Leiden, The Netherlands
| | - Sanne van Luenen
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Rianne M J J van der Kleij
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands.,Department of Obstetrics and Gynaecology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Niels H Chavannes
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands.,National eHealth Living Lab (NELL), Leiden, The Netherlands
| | - Anneke W M Kramer
- Department of Public Health and Primary Care (PHEG), Leiden University Medical Centre, Leiden, The Netherlands
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20
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Shi X, He J, Lin M, Liu C, Yan B, Song H, Wang C, Xiao F, Huang P, Wang L, Li Z, Huang Y, Zhang M, Chen CS, Obst K, Li W, Yang S, Yao G, Li X. Comparative effectiveness of team-based care with a clinical decision support system versus team-based care alone on cardiovascular risk reduction among patients with diabetes: Rationale and design of the D4C trial. Am Heart J 2021; 238:45-58. [PMID: 33957103 DOI: 10.1016/j.ahj.2021.04.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 04/25/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Diabetes has become a major public health challenge worldwide, especially in low- and middle-income countries (LMICs). Uncontrolled hyperglycemia, hypertension, and dyslipidemia major risk factors for all-cause mortality and cardiovascular disease (CVD) are common in patients with diabetes in China. We propose to compare the effectiveness of team-based care plus a clinical decision support system (CDSS) with team-based care alone on glycemic, blood pressure (BP), and lipid control, and clinical CVD reduction among patients with type-2 diabetes and at high risk for CVD. METHODS The Diabetes Complication Control in Community Clinics (D4C) study is a cluster-randomized trial conducted among 38 community health centers in Xiamen City, China. Nineteen clinics have been randomly assigned to team-based care plus CDSS and 19 to team-based care alone. Team-based care includes primary care providers, health coaches, and diabetes specialists working collaboratively with patients to achieve shared treatment goals for CVD risk factor reduction. The CDSS integrates guideline-based treatment algorithms for glycemic, BP, and lipid control, along with a patient's medical history and insurance policy, to recommend treatment and follow-up plans. In phase 1, the co-primary outcomes are mean reduction in glycated hemoglobin (HbA1c), systolic BP (SBP), and low-density lipoprotein (LDL)-cholesterol over 18 months, and the proportion of patients with controlled HbA1c, SBP, and LDL-cholesterol at 18 months' between the 2 comparison groups. In phase 2, the primary outcome is the difference in major CVD incidence (non-fatal stroke, non-fatal myocardial infarction, hospitalized heart failure, and CVD mortality) between the 2 comparison groups. Mean reduction in HbA1c, SBP, and LDL-cholesterol levels will be simultaneously modeled for a single overall treatment effect. CONCLUSION The D4C trial will generate evidence on whether a CDSS will further reduce the CVD burden among patients with diabetes beyond team-based care at community clinics. If proven effective, this implementation strategy could be scaled up within primary care settings in China and other LMICs to reduce CVD incidence and mortality among patients with diabetes.
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21
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E-healthonderwijs is een nieuwe uitdaging voor de huisartsopleiding. ACTA ACUST UNITED AC 2021; 64:19-22. [PMID: 34092805 PMCID: PMC8169429 DOI: 10.1007/s12445-021-1169-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Om goed voorbereid te zijn op de toekomst moeten aiossen zich ook in e-health verdiepen. Bij de huisartsopleiding ligt de nadruk echter meer op praktijkleren, waardoor nieuwe ontwikkelingen als e-health niet altijd voldoende aandacht krijgen. Aiossen moeten ook maar net een opleider treffen die zich met e-health bezighoudt. E-health verdient meer aandacht tijdens de opleiding, wat zowel de aios, de opleider als de docent ten goede zal komen.
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22
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Petkus H, Hoogewerf J, Wyatt JC. What do senior physicians think about AI and clinical decision support systems: Quantitative and qualitative analysis of data from specialty societies. Clin Med (Lond) 2021; 20:324-328. [PMID: 32414724 DOI: 10.7861/clinmed.2019-0317] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
AIMS The aim was to help physicians engage with NHS and other policymakers about the use, procurement and regulation of artificial intelligence, algorithms and clinical decision support systems (CDSS) in the NHS by identifying the professional benefits of and concerns about these systems. METHODS We piloted a three-page survey instrument with closed and open-ended questions on SurveyMonkey, then circulated it to specialty societies via email. Both quantitative and qualitative methods were used to analyse responses. RESULTS The results include the current usage of CDSS; identified benefits; concerns about quality; concerns about regulation, professional practice, ethics and liability, as well as actions being taken by the specialty societies to address these; and aspects of CDSS quality that need to be tested. CONCLUSION While results confirm many expected benefits and concerns about CDSS, they raise new professional concerns and suggest further actions to explore with partners on behalf of the physician community.
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23
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Nijman SWJ, Groenhof TKJ, Hoogland J, Bots ML, Brandjes M, Jacobs JJL, Asselbergs FW, Moons KGM, Debray TPA. Real-time imputation of missing predictor values improved the application of prediction models in daily practice. J Clin Epidemiol 2021; 134:22-34. [PMID: 33482294 DOI: 10.1016/j.jclinepi.2021.01.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 12/24/2020] [Accepted: 01/12/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVES In clinical practice, many prediction models cannot be used when predictor values are missing. We, therefore, propose and evaluate methods for real-time imputation. STUDY DESIGN AND SETTING We describe (i) mean imputation (where missing values are replaced by the sample mean), (ii) joint modeling imputation (JMI, where we use a multivariate normal approximation to generate patient-specific imputations), and (iii) conditional modeling imputation (CMI, where a multivariable imputation model is derived for each predictor from a population). We compared these methods in a case study evaluating the root mean squared error (RMSE) and coverage of the 95% confidence intervals (i.e., the proportion of confidence intervals that contain the true predictor value) of imputed predictor values. RESULTS -RMSE was lowest when adopting JMI or CMI, although imputation of individual predictors did not always lead to substantial improvements as compared to mean imputation. JMI and CMI appeared particularly useful when the values of multiple predictors of the model were missing. Coverage reached the nominal level (i.e., 95%) for both CMI and JMI. CONCLUSION Multiple imputations using either CMI or JMI is recommended when dealing with missing predictor values in real-time settings.
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Affiliation(s)
- Steven Willem Joost Nijman
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
| | - T Katrien J Groenhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jeroen Hoogland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Michiel L Bots
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | | | - Folkert W Asselbergs
- Division Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; Health Data Research UK, Institute of Health Informatics, University College London, London, UK
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
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24
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Nijman SWJ, Hoogland J, Groenhof TKJ, Brandjes M, Jacobs JJL, Bots ML, Asselbergs FW, Moons KGM, Debray TPA. Real-time imputation of missing predictor values in clinical practice. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2020; 2:154-164. [PMID: 36711167 PMCID: PMC9707891 DOI: 10.1093/ehjdh/ztaa016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/02/2020] [Accepted: 11/30/2020] [Indexed: 02/01/2023]
Abstract
Aims Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors, which is not always available in daily practice. We aim to describe two methods for real-time handling of missing predictor values when using prediction models in practice. Methods and results We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics. These characteristics may include both prediction model variables and other characteristics (auxiliary variables). The method was implemented using imputation from a joint multivariate normal model of the patient characteristics (joint modelling imputation; JMI). Data from two different cardiovascular cohorts with cardiovascular predictors and outcome were used to evaluate the real-time imputation methods. We quantified the prediction model's overall performance [mean squared error (MSE) of linear predictor], discrimination (c-index), calibration (intercept and slope), and net benefit (decision curve analysis). When compared with mean imputation, JMI substantially improved the MSE (0.10 vs. 0.13), c-index (0.70 vs. 0.68), and calibration (calibration-in-the-large: 0.04 vs. 0.06; calibration slope: 1.01 vs. 0.92), especially when incorporating auxiliary variables. When the imputation method was based on an external cohort, calibration deteriorated, but discrimination remained similar. Conclusions We recommend JMI with auxiliary variables for real-time imputation of missing values, and to update imputation models when implementing them in new settings or (sub)populations.
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Affiliation(s)
- Steven W J Nijman
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands,Corresponding author. Tel: +31 88 75 680 12,
| | - Jeroen Hoogland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - T Katrien J Groenhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Menno Brandjes
- Department of Health, Ortec B.V., Zoetermeer, Houtsingel 5, 2719 EA Zoetermeer, The Netherlands
| | - John J L Jacobs
- Department of Health, Ortec B.V., Zoetermeer, Houtsingel 5, 2719 EA Zoetermeer, The Netherlands
| | - Michiel L Bots
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, 62 Huntley St, Fitzrovia, London WC1E 6DD, UK,Health Data Research UK, Institute of Health Informatics, University College London, Gibbs Building, 215 Euston Rd, London NW1 2BE, UK
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands,Health Data Research UK, Institute of Health Informatics, University College London, Gibbs Building, 215 Euston Rd, London NW1 2BE, UK
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25
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Wright AK, Suarez-Ortegon MF, Read SH, Kontopantelis E, Buchan I, Emsley R, Sattar N, Ashcroft DM, Wild SH, Rutter MK. Risk Factor Control and Cardiovascular Event Risk in People With Type 2 Diabetes in Primary and Secondary Prevention Settings. Circulation 2020; 142:1925-1936. [PMID: 33196309 PMCID: PMC7664968 DOI: 10.1161/circulationaha.120.046783] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/10/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND To examine the association between the degree of risk factor control and cardiovascular disease (CVD) risk in type 2 diabetes and to assess if the presence of cardio-renal disease modifies these relationships. METHODS A retrospective cohort study using data from English practices from CPRD GOLD (Clinical Practice Research Datalink) and the SCI-Diabetes dataset (Scottish Care Information-Diabetes), with linkage to hospital and mortality data. We identified 101 749 with type 2 diabetes (T2D) in CPRD matched with 378 938 controls without diabetes and 330 892 with type 2 diabetes in SCI-Diabetes between 2006 and 2015. The main exposure was number of optimized risk factors: nonsmoker, total cholesterol ≤4 mmol/L, triglycerides ≤1.7 mmol/L, glycated haemoglobin (HbA1c) ≤53 mmol/mol (≤7.0%), systolic blood pressure <140mm Hg, or <130 mm Hg if high risk. Cox models were used to assess cardiovascular risk associated with levels of risk factor control. RESULTS In CPRD, the mean baseline age in T2D was 63 years and 28% had cardio-renal disease (SCI-Diabetes: 62 years; 35% cardio-renal disease). Over 3 years follow-up (SCI-Diabetes: 6 years), CVD events occurred among 27 900 (27%) CPRD-T2D, 101 362 (31%) SCI-Diabetes-T2D, and 75 520 (19%) CPRD-controls. In CPRD, compared with controls, T2D participants with optimal risk factor control (all risk factors controlled) had a higher risk of CVD events (adjusted hazard ratio, 1.21; 95% confidence interval, 1.12-1.29). In T2D participants from CPRD and SCI-Diabetes, pooled hazard ratios for CVD associated with 5 risk factors being elevated versus optimal risk factor control were 1.09 (95% confidence interval, 1.01-1.17) in people with cardio-renal disease but 1.96 (95% confidence interval, 1.82-2.12) in people without cardio-renal disease. People without cardio-renal disease were younger and more likely to have likely to have suboptimal risk factor control but had fewer prescriptions for risk factor modifying medications than those with cardio-renal disease. CONCLUSIONS Optimally managed people with T2D have a 21% higher CVD risk when compared with controls. People with T2D without cardio-renal disease would be predicted to benefit greatly from CVD risk factor intervention.
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Affiliation(s)
- Alison K. Wright
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, University of Manchester, United Kingdom (A.K.W., M.K.R.)
- Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, United Kingdom (A.K.W., D.M.A.)
| | - Milton Fabian Suarez-Ortegon
- Departamento de Alimentación y Nutrición, Facultad de Ciencias de la Salud, Pontificia Universidad Javeriana Seccional Cali, Colombia (M.F.S.-O.)
- Grupo de Investigación en Ciencias Básicas y Clínicas de la Salud, Facultad de Ciencias de la Salud, Pontificia Universidad Javeriana Seccional Cali, Colombia (M.F.S.-O.)
| | - Stephanie H. Read
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, United Kingdom (S.H.R., S.H.W.)
- Women’s College Research Institute, Women’s College Hospital, Toronto, Ontario, Canada (S.H.R.)
- Scottish Diabetes Research Network epidemiology group, Scotland, United Kingdom (S.H.W.)
| | - Evangelos Kontopantelis
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, United Kingdom (E.K.)
| | - Iain Buchan
- Department of Public Health and Policy, Institute of Population Health Sciences, University of Liverpool, United Kingdom (I.B.)
- Health eResearch Center, Farr Institute, Division of Informatics, Imaging & Data Sciences, School of Health Sciences, University of Manchester, United Kingdom (I.B.)
| | - Richard Emsley
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom (R.E.)
| | - Naveed Sattar
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, United Kingdom (N.S.)
| | - Darren M. Ashcroft
- Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, United Kingdom (A.K.W., D.M.A.)
| | - Sarah H. Wild
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, United Kingdom (S.H.R., S.H.W.)
| | - Martin K. Rutter
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, University of Manchester, United Kingdom (A.K.W., M.K.R.)
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, United Kingdom (M.K.R.)
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Cykert S, Keyserling TC, Pignone M, DeWalt D, Weiner BJ, Trogdon JG, Wroth T, Halladay J, Mackey M, Fine J, In Kim J, Cene C. A controlled trial of dissemination and implementation of a cardiovascular risk reduction strategy in small primary care practices. Health Serv Res 2020; 55:944-953. [PMID: 33047340 DOI: 10.1111/1475-6773.13571] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To assess the effect of dissemination and implementation of an intervention consisting of practice facilitation and a risk-stratified, population management dashboard on cardiovascular risk reduction for patients at high risk in small, primary care practices. STUDY SETTING A total of 219 small primary care practices (≤10 clinicians per site) across North Carolina with primary data collection from electronic health records (EHRs) from the fourth quarter of 2015 through the second quarter of 2018. STUDY DESIGN We performed a stepped-wedge, stratified, cluster randomized trial of a one-year intervention consisting of practice facilitation utilizing quality improvement techniques coupled with a cardiovascular dashboard that included lists of risk-stratified adults, aged 40-79 years and their unmet treatment opportunities. The primary outcome was change in 10-Year ASCVD Risk score among all patients with a baseline score ≥10 percent from baseline to 3 months postintervention. DATA COLLECTION/ EXTRACTION METHODS Data extracts were securely transferred from practices on a nightly basis from their EHR to the research team registry. PRINCIPLE FINDINGS ASCVD risk scores were assessed on 437 556 patients and 146 826 had a calculated 10-year risk ≥10 percent. The mean baseline risk was 23.4 percent (SD ± 12.6 percent). Postintervention, the absolute risk reduction was 6.3 percent (95% CI 6.3, 6.4). Models considering calendar time and stepped-wedge controls revealed most of the improvement (4.0 of 6.3 percent) was attributable to the intervention and not secular trends. In multivariate analysis, male gender, age >65 years, low-income (<$40 000), and Black race (P < .001 for all variables) were each associated with greater risk reductions. CONCLUSION A risk-stratified, population management dashboard combined with practice facilitation led to substantial reductions of 10-year ASCVD risk for patients at high risk. Similar approaches could lead to effective dissemination and implementation of other new evidence, especially in rural and other under-resourced practices. Registration: ClinicalTrials.Gov 15-0479.
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Affiliation(s)
- Samuel Cykert
- The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Thomas C Keyserling
- Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.,Center for Health Promotion and Disease Prevention, The Gillings School of Global Public Health, The University of North Carolina, Chapel Hill, North Carolina, USA
| | - Michael Pignone
- Department of Internal Medicine, The Dell Medical School, University of Texas, Austin, Texas, USA
| | - Darren DeWalt
- The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Bryan J Weiner
- Department of Global Public Health, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Justin G Trogdon
- Department of Health Policy and Management, The Gillings School of Global Public Health, The University of North Carolina, Chapel Hill, North Carolina, USA
| | - Thomas Wroth
- Community Care of North Carolina, Raleigh, North Carolina, USA
| | - Jacqueline Halladay
- The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Department of Family Medicine, The University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Monique Mackey
- The North Carolina Area Health Education Centers Program, Chapel Hill, North Carolina, USA
| | - Jason Fine
- Department of Biostatistics, The Gillings School of Global Public Health, The University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jung In Kim
- Department of Statistics, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, USA.,Department of Nutritional Sciences, College of Health and Human Development, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Crystal Cene
- The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
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Ledwoch J, Duncker D. [eHealth-smart devices revolutionizing cardiology]. Herzschrittmacherther Elektrophysiol 2020; 31:368-374. [PMID: 32661563 PMCID: PMC7355522 DOI: 10.1007/s00399-020-00700-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 06/21/2020] [Indexed: 12/25/2022]
Abstract
Hintergrund Der Nutzen von Smart Devices wie Handys und Smartwatches in der Kardiologie nimmt deutlich zu. Der gehäufte Einsatz wird vor allem auch von Patienten und der Industrie vorangetrieben. Fragestellung Welche Möglichkeiten bieten Smart Devices in der Kardiologie? Material und Methode Es wurde eine selektive Literaturrecherche durchgeführt. Naturwissenschaftliche und klinische Studien über die verschiedenen Einsatzmöglichkeiten der technischen Mittel wurden interpretiert. Ergebnisse Der Besitz und Gebrauch von Smartphones in Deutschland ist im weltweiten Vergleich sehr hoch. Dies ermöglicht einen sehr breiten Einsatz dieser Technologie auch im medizinischen Bereich. Die Anwendungsmöglichkeiten sind vielfältig und reichen von der Nutzung als Nachschlagewerk über einen „clinical decision support“ bis hin zur Erfassung von Biosignalen. Gerade die Kombination aus Biosignalmessung und Weiterverarbeitung der Information durch künstliche Intelligenz (KI) ermöglicht eine deutliche Verbesserung der bisherigen Diagnosemethoden und erlaubt extrem genaue Vorhersagen verschiedener kardiovaskulärer Krankheitsverläufe. Schlussfolgerung Smart Devices werden in der Kardiologie zunehmend im klinischen Alltag genutzt. Aufgrund der technischen Möglichkeiten wird der Einsatz sehr wahrscheinlich weiter steigen und einige Bereiche der Kardiologie deutlich verändern.
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Affiliation(s)
- Jakob Ledwoch
- Klinik für Kardiologie, Pneumologie und Internistische Intensivmedizin, München Klinik Neuperlach, Oskar-Maria-Graf-Ring 51, 81737, München, Deutschland.
| | - David Duncker
- Rhythmologie und Elektrophysiologie, Klinik für Kardiologie und Angiologie, Medizinische Hochschule Hannover, Hannover, Deutschland
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Groenhof TKJ, Kofink D, Bots ML, Nathoe HM, Hoefer IE, Van Solinge WW, Lely AT, Asselbergs FW, Haitjema S. Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data. JMIR Med Inform 2020; 8:e16400. [PMID: 32238333 PMCID: PMC7163416 DOI: 10.2196/16400] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/20/2019] [Accepted: 12/31/2019] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Direct feedback on quality of care is one of the key features of a learning health care system (LHS), enabling health care professionals to improve upon the routine clinical care of their patients during practice. OBJECTIVE This study aimed to evaluate the potential of routine care data extracted from electronic health records (EHRs) in order to obtain reliable information on low-density lipoprotein cholesterol (LDL-c) management in cardiovascular disease (CVD) patients referred to a tertiary care center. METHODS We extracted all LDL-c measurements from the EHRs of patients with a history of CVD referred to the University Medical Center Utrecht. We assessed LDL-c target attainment at the time of referral and per year. In patients with multiple measurements, we analyzed LDL-c trajectories, truncated at 6 follow-up measurements. Lastly, we performed a logistic regression analysis to investigate factors associated with improvement of LDL-c at the next measurement. RESULTS Between February 2003 and December 2017, 250,749 LDL-c measurements were taken from 95,795 patients, of whom 23,932 had a history of CVD. At the time of referral, 51% of patients had not reached their LDL-c target. A large proportion of patients (55%) had no follow-up LDL-c measurements. Most of the patients with repeated measurements showed no change in LDL-c levels over time: the transition probability to remain in the same category was up to 0.84. Sequence clustering analysis showed more women (odds ratio 1.18, 95% CI 1.07-1.10) in the cluster with both most measurements off target and the most LDL-c measurements furthest from the target. Timing of drug prescription was difficult to determine from our data, limiting the interpretation of results regarding medication management. CONCLUSIONS Routine care data can be used to provide feedback on quality of care, such as LDL-c target attainment. These routine care data show high off-target prevalence and little change in LDL-c over time. Registrations of diagnosis; follow-up trajectory, including primary and secondary care; and medication use need to be improved in order to enhance usability of the EHR system for adequate feedback.
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Affiliation(s)
- T Katrien J Groenhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Daniel Kofink
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michiel L Bots
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Hendrik M Nathoe
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Imo E Hoefer
- Laboratory of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Wouter W Van Solinge
- Laboratory of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - A Titia Lely
- Department of Obstetrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Folkert W Asselbergs
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom.,Health Data Research UK, Institute of Health Informatics, University College London, London, United Kingdom
| | - Saskia Haitjema
- Laboratory of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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29
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Minian N, Ivanova A, Voci S, Veldhuizen S, Zawertailo L, Baliunas D, Noormohamed A, Giesbrecht N, Selby P. Computerized Clinical Decision Support System for Prompting Brief Alcohol Interventions with Treatment Seeking Smokers: A Sex-Based Secondary Analysis of a Cluster Randomized Trial. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E1024. [PMID: 32041190 PMCID: PMC7037372 DOI: 10.3390/ijerph17031024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 01/29/2020] [Accepted: 02/04/2020] [Indexed: 12/11/2022]
Abstract
Although brief alcohol intervention can reduce alcohol use for both men and women, health care providers (HCPs) are less likely to discuss alcohol use or deliver brief intervention to women compared to men. This secondary analysis examined whether previously reported outcomes from a cluster randomized trial of a clinical decision support system (CDSS)-prompting delivery of a brief alcohol intervention (an educational alcohol resource) for patients drinking above cancer guidelines-were moderated by patients' sex. Patients (n = 5702) enrolled in a smoking cessation program at primary care sites across Ontario, Canada, were randomized to either the intervention (CDSS) or control arm (no CDSS). Logistic generalized estimating equations models were fit for the primary and secondary outcome (HCP offer of resource and patient acceptance of resource, respectively). Previously reported results showed no difference between treatment arms in HCP offers of an educational alcohol resource to eligible patients, but there was increased acceptance of the alcohol resource among patients in the intervention arm. The results of this study showed that these CDSS intervention effects were not moderated by sex, and this can help inform the development of a scalable strategy to overcome gender disparities in alcohol intervention seen in other studies.
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Affiliation(s)
- Nadia Minian
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
- Department of Family and Community Medicine, University of Toronto, 500 University Ave, Toronto, ON M5G 1V7, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 60 White Squirrel Way, Toronto, ON M6J 1H4, Canada
| | - Anna Ivanova
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
| | - Sabrina Voci
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
| | - Scott Veldhuizen
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
| | - Laurie Zawertailo
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
- Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Cir, Toronto, ON M5S 1A8, Canada
| | - Dolly Baliunas
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
- Dalla Lana School of Public Health, University of Toronto, 155 College, Toronto, ON M5T 3M7, Canada
| | - Aliya Noormohamed
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
| | - Norman Giesbrecht
- Dalla Lana School of Public Health, University of Toronto, 155 College, Toronto, ON M5T 3M7, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, 33 Russell St, Toronto, ON M5S 2S1, Canada
| | - Peter Selby
- Nicotine Dependence Service, Centre for Addiction and Mental Health, 175 College St, Toronto, ON M5T 1P7, Canada; (N.M.); (A.I.); (S.V.); (S.V.); (L.Z.); (D.B.); (A.N.)
- Department of Family and Community Medicine, University of Toronto, 500 University Ave, Toronto, ON M5G 1V7, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 60 White Squirrel Way, Toronto, ON M6J 1H4, Canada
- Dalla Lana School of Public Health, University of Toronto, 155 College, Toronto, ON M5T 3M7, Canada
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
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Tao L, Zhang C, Zeng L, Zhu S, Li N, Li W, Zhang H, Zhao Y, Zhan S, Ji H. Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice-Aided Diagnosis: Interrupted Time Series Study. JMIR Med Inform 2020; 8:e16912. [PMID: 31958069 PMCID: PMC6997922 DOI: 10.2196/16912] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 12/02/2019] [Accepted: 12/15/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. OBJECTIVE The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice-aided diagnosis in real-world research. METHODS This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. RESULTS The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all P<.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, P=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, P=.01). Similar results were obtained in the subgroup analysis. CONCLUSIONS The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians' diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.
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Affiliation(s)
- Liyuan Tao
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Chen Zhang
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Lin Zeng
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Shengrong Zhu
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Nan Li
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Wei Li
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Hua Zhang
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Yiming Zhao
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Siyan Zhan
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Hong Ji
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
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Heselmans A, Delvaux N, Laenen A, Van de Velde S, Ramaekers D, Kunnamo I, Aertgeerts B. Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial. Implement Sci 2020; 15:5. [PMID: 31910877 PMCID: PMC6947861 DOI: 10.1186/s13012-019-0955-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 11/27/2019] [Indexed: 12/23/2022] Open
Abstract
Background The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care. Methods A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate. Results Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system. Conclusions The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system. Trial registration ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.
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Affiliation(s)
- Annemie Heselmans
- Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok j, 3000, Leuven, Belgium.
| | - Nicolas Delvaux
- Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok j, 3000, Leuven, Belgium
| | - Annouschka Laenen
- Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok j, 3000, Leuven, Belgium
| | - Stijn Van de Velde
- Centre for Informed Health Choices, Division for Health Services, Norwegian Institute of Public Health, PO Box 222, Skøyen, 0213, Oslo, Norway
| | - Dirk Ramaekers
- Leuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 blok d, 3000, Leuven, Belgium
| | - Ilkka Kunnamo
- Duodecim, Scientific Society of Finnish Physicians, PO Box 874, Kaivokatu 10, 00101, Helsinki, Finland
| | - Bert Aertgeerts
- Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok j, 3000, Leuven, Belgium
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A computerised decision support system for cardiovascular risk management 'live' in the electronic health record environment: development, validation and implementation-the Utrecht Cardiovascular Cohort Initiative. Neth Heart J 2019; 27:435-442. [PMID: 31372838 PMCID: PMC6712110 DOI: 10.1007/s12471-019-01308-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Purpose We set out to develop a real-time computerised decision support system (CDSS) embedded in the electronic health record (EHR) with information on risk factors, estimated risk, and guideline-based advice on treatment strategy in order to improve adherence to cardiovascular risk management (CVRM) guidelines with the ultimate aim of improving patient healthcare. Methods We defined a project plan including the scope and requirements, infrastructure and interface, data quality and study population, validation and evaluation of the CDSS. Results In collaboration with clinicians, data scientists, epidemiologists, ICT architects, and user experience and interface designers we developed a CDSS that provides ‘live’ information on CVRM within the environment of the EHR. The CDSS provides information on cardiovascular risk factors (age, sex, medical and family history, smoking, blood pressure, lipids, kidney function, and glucose intolerance measurements), estimated 10-year cardiovascular risk, guideline-compliant suggestions for both pharmacological and non-pharmacological treatment to optimise risk factors, and an estimate on the change in 10-year risk of cardiovascular disease if treatment goals are adhered to. Our pilot study identified a number of issues that needed to be addressed, such as missing data, rules and regulations, privacy, and patient participation. Conclusion Development of a CDSS is complex and requires a multidisciplinary approach. We identified opportunities and challenges in our project developing a CDSS aimed at improving adherence to CVRM guidelines. The regulatory environment, including guidance on scientific evaluation, legislation, and privacy issues needs to evolve within this emerging field of eHealth.
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