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Naamneh R, Bodas M. The effect of electronic medical records on medication errors, workload, and medical information availability among qualified nurses in Israel- a cross sectional study. BMC Nurs 2024; 23:270. [PMID: 38658976 PMCID: PMC11044371 DOI: 10.1186/s12912-024-01936-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Errors in medication administration by qualified nursing staff in hospitals are a significant risk factor for patient safety. In recent decades, electronic medical records (EMR) systems have been implemented in hospitals, and it has been claimed that they contribute to reducing such errors. However, systematic research on the subject in Israel is scarce. This study examines the position of the qualified nursing staff regarding the impact of electronic medical records systems on factors related to patient safety, including errors in medication administration, workload, and availability of medical information. METHODS This cross-sectional study examines three main variables: Medication errors, workload, and medical information availability, comparing two periods- before and after EMR implementation based on self-reports. A final sample of 591 Israeli nurses was recruited using online private social media groups to complete an online structured questionnaire. The questionnaires included items assessing workload (using the Expanding Nursing Stress Scale), medical information availability (the Carrington-Gephart Unintended Consequences of Electronic Health Record Questionnaire), and medical errors (the Medical Error Checklists). Items were assessed twice, once for the period before the introduction of electronic records and once after. In addition, participants answered open-ended questions that were qualitatively analyzed. RESULTS Nurses perceive the EMR as reducing the extent of errors in drug administration (mean difference = -0.92 ± 0.90SD, p < 0.001), as well as the workload (mean difference = -0.83 ± 1.03SD, p < 0.001) by ∼ 30% on average, each. Concurrently, the systems are perceived to require a longer documentation time at the expense of patients' treatment time, and they may impair the availability of medical information by about 10% on average. CONCLUSION The results point to nurses' perceived importance of EMR systems in reducing medication errors and relieving the workload. Despite the overall positive attitudes toward EMR systems, nurses also report that they reduce information availability compared to the previous pen-and-paper approach. A need arises to improve the systems in terms of planning and adaptation to the field and provide appropriate technical and educational support to nurses using them.
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Affiliation(s)
- Raneen Naamneh
- Department of Emergency & Disaster Management, School of Public Health, Faculty of Medical and Health Sciences, Tel-Aviv University, 39040, Tel-Aviv-Yafo, Israel
| | - Moran Bodas
- Department of Emergency & Disaster Management, School of Public Health, Faculty of Medical and Health Sciences, Tel-Aviv University, 39040, Tel-Aviv-Yafo, Israel.
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De Mesa RYH, Galingana CLT, Tan-Lim CSC, Javelosa MAU, Panganiban JMS, Fabian NMC, Calderon Y, Rey MP, Bernal-Sundiang N, Sanchez JT, Dans LF, Casile RU, Dans AL. Facing the digital frontier: exploring user acceptance of electronic health records in an urban, rural and remote setting in the Philippines. BMJ Open Qual 2024; 13:e002621. [PMID: 38637020 PMCID: PMC11029422 DOI: 10.1136/bmjoq-2023-002621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVES A thorough understanding of user needs and behavioural intent-to-use underpins the development of a responsive health information system. This study aimed to examine health workers' intent-to-use an electronic health record (EHR) system in an urban, rural and remote setting in the Philippines. METHODS Following the Unified Theory of Acceptance and Use of Technology framework, user acceptance and the factors influencing intent-to-use the EHR were examined through a self-administered questionnaire. A total of 128 EHR users, comprising physicians, nurses, midwives, barangay health workers and administrative staff, were surveyed. Median scores for each domain were compared across the sites using the Kruskal-Wallis test. Ridge regression analysis was used to identify factors associated with behavioural intent-to-use. RESULTS Over 94% of users across all sites reported their intent-to-use the EHR in the near future. Of the seven predictor variables examined, only self-efficacy was found to be significantly associated with behavioural intent-to-use. Intent-to-use the EHR increased by 31% (p=0.007) for each unit increase in self-efficacy score among participants. DISCUSSION Acceptance was high across the three sites, with self-efficacy being a predictor of intent-to-use the technology. This suggests that users are more likely to adopt an EHR if they believe they have the capacity to successfully navigate the technology and perform their designated tasks with it. CONCLUSION Co-producing interventions with primary care providers is crucial in sustaining EHR systems. Rather than developing a technology based on the surveillance needs of policymakers, an EHR developed from the grassroots was shown to be well-received by end-users.
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Affiliation(s)
- Regine Ynez H De Mesa
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
| | - Cara Lois T Galingana
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
| | - Carol Stephanie C Tan-Lim
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
- Department of Clinical Epidemiology, University of the Philippines Manila, Manila, Philippines
| | - Mark Anthony U Javelosa
- Department of Clinical Epidemiology, University of the Philippines Manila, Manila, Philippines
| | | | - Noleen Marie C Fabian
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
- University of the East Ramon Magsaysay Memorial Medical Center Inc, Quezon City, Philippines
| | - Ysabela Calderon
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
| | - Mia P Rey
- Department of Accounting and Finance, Cesar E.A. Virata School of Business, University of the Philippines Diliman, Quezon City, Philippines
| | - Nannette Bernal-Sundiang
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
| | - Josephine T Sanchez
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
| | - Leonila F Dans
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
- Department of Clinical Epidemiology, University of the Philippines Manila, Manila, Philippines
| | - Ray U Casile
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
| | - Antonio L Dans
- Center for Integrative and Development Studies, University of the Philippines Diliman, Quezon City, Philippines
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Kumari R, Chander S. Improving healthcare quality by unifying the American electronic medical report system: time for change. Egypt Heart J 2024; 76:32. [PMID: 38489094 PMCID: PMC10942963 DOI: 10.1186/s43044-024-00463-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 03/03/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND In recent years, innovation in healthcare technology has significantly improved the efficiency of the healthcare system. Advancements have led to better patient care and more cost-effective services. The electronic medical record (EMR) system, in particular, has enhanced interoperability and collaboration across healthcare departments by facilitating the exchange and utilization of patient data. The COVID-19 pandemic further accelerated this trend, leading to a surge in telemedicine services, which rely on electronic communication to deliver healthcare remotely. MAIN BODY Integrating artificial intelligence (AI) and machine learning (ML) in healthcare have been instrumental in analyzing vast data sets, allowing for identifying patterns and trends that can improve care delivery and pinpoint potential issues. The proposal of a unified EMR system in the USA aims to capitalize on these technological advancements. Such a system would streamline the sharing of patient information among healthcare providers, improve the quality and efficiency of care, and minimize the likelihood of errors in patient treatment. CONCLUSION A unified electronic medical record system represents a promising avenue for enhancing interoperability within the US healthcare sector. By creating a more connected and accessible network of patient information, it sets the stage for a transformation in healthcare delivery. This change is imperative for maintaining the momentum of progress in healthcare technology and realizing the full potential of recent advancements in patient care and system efficiency.
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Affiliation(s)
- Roopa Kumari
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy PI, New York, NY, 10029, USA
| | - Subhash Chander
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy PI, New York, NY, 10029, USA.
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Madbouly A, Bolon YT. Race, ethnicity, ancestry, and aspects that impact HLA data and matching for transplant. Front Genet 2024; 15:1375352. [PMID: 38560292 PMCID: PMC10978785 DOI: 10.3389/fgene.2024.1375352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Race, ethnicity, and ancestry are terms that are often misinterpreted and/or used interchangeably. There is lack of consensus in the scientific literature on the definition of these terms and insufficient guidelines on the proper classification, collection, and application of this data in the scientific community. However, defining groups for human populations is crucial for multiple healthcare applications and clinical research. Some examples impacted by population classification include HLA matching for stem-cell or solid organ transplant, identifying disease associations and/or adverse drug reactions, defining social determinants of health, understanding diverse representation in research studies, and identifying potential biases. This article describes aspects of race, ethnicity and ancestry information that impact the stem-cell or solid organ transplantation field with particular focus on HLA data collected from donors and recipients by donor registries or transplant centers.
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Affiliation(s)
- Abeer Madbouly
- Center for International Blood and Marrow Transplant Research (CIBMTR), Minneapolis, MN, United States
| | - Yung-Tsi Bolon
- Center for International Blood and Marrow Transplant Research (CIBMTR), Minneapolis, MN, United States
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Fernandes M, Westover MB, Singhal AB, Zafar SF. Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.08.24304011. [PMID: 38559062 PMCID: PMC10980121 DOI: 10.1101/2024.03.08.24304011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Multi-center electronic health records (EHR) can support quality improvement initiatives and comparative effectiveness research in stroke care. However, limitations of EHR-based research include challenges in abstracting key clinical variables from non-structured data at scale. This is further compounded by missing data. Here we develop a natural language processing (NLP) model that automatically reads EHR notes to determine the NIH stroke scale (NIHSS) score of patients with acute stroke. METHODS The study included notes from acute stroke patients (>= 18 years) admitted to the Massachusetts General Hospital (MGH) (2015-2022). The MGH data were divided into training (70%) and hold-out test (30%) sets. A two-stage model was developed to predict the admission NIHSS. A linear model with the least absolute shrinkage and selection operator (LASSO) was trained within the training set. For notes in the test set where the NIHSS was documented, the scores were extracted using regular expressions (stage 1), for notes where NIHSS was not documented, LASSO was used for prediction (stage 2). The reference standard for NIHSS was obtained from Get With The Guidelines Stroke Registry. The two-stage model was tested on the hold-out test set and validated in the MIMIC-III dataset (Medical Information Mart for Intensive Care-MIMIC III 2001-2012) v1.4, using root mean squared error (RMSE) and Spearman correlation (SC). RESULTS We included 4,163 patients (MGH = 3,876; MIMIC = 287); average age of 69 [SD 15] years; 53% male, and 72% white. 90% patients had ischemic stroke and 10% hemorrhagic stroke. The two-stage model achieved a RMSE [95% CI] of 3.13 [2.86-3.41] (SC = 0.90 [0.88-0. 91]) in the MGH hold-out test set and 2.01 [1.58-2.38] (SC = 0.96 [0.94-0.97]) in the MIMIC validation set. CONCLUSIONS The automatic NLP-based model can enable large-scale stroke severity phenotyping from EHR and therefore support real-world quality improvement and comparative effectiveness studies in stroke.
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Affiliation(s)
- Marta Fernandes
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, Massachusetts, United States
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, United States
| | - Aneesh B. Singhal
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, Massachusetts, United States
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, Massachusetts, United States
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Janarthanan V, Kumaran M S, Nagrale NV, Singh OG, Raj KV. Legal and Ethical Issues Associated With Challenges in the Implementation of the Electronic Medical Record System and Its Current Laws in India. Cureus 2024; 16:e56518. [PMID: 38646271 PMCID: PMC11026987 DOI: 10.7759/cureus.56518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2024] [Indexed: 04/23/2024] Open
Abstract
Electronic health records (EHR) have revolutionized healthcare by providing efficient access to patient information, but their implementation poses various challenges. This paper examines the ethical and legal issues surrounding EHR adoption, particularly focusing on the healthcare landscape in India. Ethical considerations, including patient autonomy, confidentiality, beneficence, and justice, must guide EHR implementation to protect patient rights and privacy. Legal issues such as medical errors, malpractice, data breaches, and billing inaccuracies underscore the importance of robust policies and security measures. Threats to EHRs, such as phishing attacks, malware, encryption vulnerabilities, and insider threats, emphasize the need for comprehensive cybersecurity strategies. Overcoming challenges in EHR implementation requires meticulous planning, financial investment, staff training, and stakeholder support. Despite the complexities involved, the benefits of EHR adoption in improving patient care and operational efficiency justify the efforts required to address legal, ethical, and technical concerns. Embracing EHRs while mitigating associated risks is essential for delivering high-quality healthcare in the digital age.
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Affiliation(s)
| | - Senthil Kumaran M
- Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Madurai, Madurai, IND
| | - Ninad V Nagrale
- Forensic Medicine, All India Institute of Medical Sciences, Kalyani, Kolkata, IND
| | - O Gambhir Singh
- Forensic Medicine, All India Institute of Medical Sciences, Kalyani, Kolkata, IND
| | - Karthi Vignesh Raj
- Forensic Medicine, All India Institute of Medical Sciences, Guwahati, Guwahati, IND
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Hügle T. Advancing Rheumatology Care Through Machine Learning. Pharmaceut Med 2024; 38:87-96. [PMID: 38421585 PMCID: PMC10948517 DOI: 10.1007/s40290-024-00515-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.
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Affiliation(s)
- Thomas Hügle
- Department of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Avenue Pierre-Decker 4, 1001, Lausanne, Switzerland.
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Payton KSE, Bennett MV, Schulman J, Benitz WE, Stellwagen L, Darmstadt GL, Quinn J, Kristensen-Cabrera AI, Breault CC, Bolaris M, Lefrak L, Merrill J, Sharek PJ. 28 NICUs participating in a quality improvement collaborative targeting early-onset sepsis antibiotic use. J Perinatol 2024:10.1038/s41372-024-01885-8. [PMID: 38378826 DOI: 10.1038/s41372-024-01885-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/11/2023] [Accepted: 01/15/2024] [Indexed: 02/22/2024]
Abstract
OBJECTIVE There is widespread overuse of antibiotics in neonatal intensive care units (NICUs). The objective of this study was to safely reduce antibiotic use in participating NICUs by targeting early-onset sepsis (EOS) management. STUDY DESIGN Twenty-eight NICUs participated in this statewide multicenter antibiotic stewardship quality improvement collaborative. The primary aim was to reduce the total monthly mean antibiotic utilization rate (AUR) by 25% in participant NICUs. RESULT Aggregate AUR was reduced by 15.3% (p < 0.001). There was a wide range in improvement among participant NICUs. There were no increases in EOS rates or nosocomial infection rates related to the intervention. CONCLUSION Participation in this multicenter NICU antibiotic stewardship collaborative targeting EOS was associated with an aggregate reduction in antibiotic use. This study informs efforts aimed at sustaining improvements in NICU AURs.
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Affiliation(s)
- Kurlen S E Payton
- Department of Pediatrics, Division of Neonatology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- California Perinatal Quality Care Collaborative (CPQCC), Stanford, CA, USA.
| | - Mihoko V Bennett
- California Perinatal Quality Care Collaborative (CPQCC), Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph Schulman
- CA Department of Health Care Services, California Children's Services, Sacramento, CA, USA
| | - William E Benitz
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa Stellwagen
- Department of Pediatrics, Division of Academic General Pediatrics, University of California San Diego School of Medicine, San Diego, CA, USA
| | - Gary L Darmstadt
- Prematurity Research Center, Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jenny Quinn
- California Perinatal Quality Care Collaborative (CPQCC), Stanford, CA, USA
| | | | - Courtney C Breault
- California Perinatal Quality Care Collaborative (CPQCC), Stanford, CA, USA
| | - Michael Bolaris
- Department of Pediatrics, Division of Infectious Disease, Harbor-University of California Los Angeles Medical Center, Los Angeles, CA, USA
| | - Linda Lefrak
- California Department of Public Health, Center for Health Care Quality, Health Care Associated Infections Program, Richmond, CA, USA
| | - Jeff Merrill
- Sutter Health Summit Medical Center, Oakland, CA, USA
| | - Paul J Sharek
- California Perinatal Quality Care Collaborative (CPQCC), Stanford, CA, USA
- Department of Pediatrics, Division of Hospitalist Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Hospitalist Medicine, University of Washington School of Medicine, Seattle, WA, USA
- Center for Quality and Patient Safety, Seattle Children's Hospital, Seattle, WA, USA
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Kim J, Villarreal M, Arya S, Hernandez A, Moreira A. Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics. J Clin Med 2024; 13:1077. [PMID: 38398389 PMCID: PMC10889493 DOI: 10.3390/jcm13041077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Bronchopulmonary dysplasia (BPD), a chronic lung disease predominantly affecting premature infants, poses substantial clinical challenges. This review delves into the promise of biomedical informatics (BMI) in reshaping BPD research and care. We commence by highlighting the escalating prevalence and healthcare impact of BPD, emphasizing the necessity for innovative strategies to comprehend its intricate nature. To this end, we introduce BMI as a potent toolset adept at managing and analyzing extensive, diverse biomedical data. The challenges intrinsic to BPD research are addressed, underscoring the inadequacies of conventional approaches and the compelling need for data-driven solutions. We subsequently explore how BMI can revolutionize BPD research, encompassing genomics and personalized medicine to reveal potential biomarkers and individualized treatment strategies. Predictive analytics emerges as a pivotal facet of BMI, enabling early diagnosis and risk assessment for timely interventions. Moreover, we examine how mobile health technologies facilitate real-time monitoring and enhance patient engagement, ultimately refining BPD management. Ethical and legal considerations surrounding BMI implementation in BPD research are discussed, accentuating issues of privacy, data security, and informed consent. In summation, this review highlights BMI's transformative potential in advancing BPD research, addressing challenges, and opening avenues for personalized medicine and predictive analytics.
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Affiliation(s)
- Jennifer Kim
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Mariela Villarreal
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Shreyas Arya
- Division of Neonatal-Perinatal Medicine, Dayton Children’s Hospital, Dayton, OH 45404, USA
| | - Antonio Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Alvaro Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
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Kong X, Tao X, Li L, Zhao X, Ren J, Yang S, Chen X, Xiang H, Wu G, Li Y, Dong D. Global trends and partial forecast of adverse effects of medical treatment from 1990 to 2019: an epidemiological analysis based on the global burden of disease study 2019. BMC Public Health 2024; 24:295. [PMID: 38273270 PMCID: PMC10809510 DOI: 10.1186/s12889-023-17560-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The possibility of adverse effects of medical treatment (AEMT) is increasing worldwide, but little is known about AEMT in China. This study analyzed the health burden of AEMT in China in recent years through the Global Burden of Disease Study (GBD) 2019 and compared it with the worldwide average level and those in different sociodemographic index (SDI) regions. METHODS We calculated the age-standardized rate (ASR) of deaths, disability-adjusted life years (DALYs), years of life lost (YLLs), years lived with disability (YLDs), incidence and prevalence attributed to AEMT in China, worldwide and countries with different sociodemographic indices during 1990-2019 using the latest data and methods from the GBD 2019. RESULTS From 1990 to 2019, the global age-standardized death rate (ASDR), DALYs, and YLLs for AEMT showed a significant downward trend and were negatively associated with the SDI. By 2040, the ASDR is expected to reach approximately 1.58 (95% UI: 1.33-1.80). From 1990 to 2019, there was no significant change in the global incidence of AEMT. The occurrence of AEMT was related to sex, and the incidence of AEMT was greater among females. In addition, the incidence of AEMT-related injuries and burdens, such as ASR of DALYs, ASR of YLLs and ASR of YLDs, was greater among women than among men. Very old and very young people were more likely to be exposed to AEMT. CONCLUSIONS From 1990 to 2019, progress was made worldwide in reducing the harm caused by AEMT. However, the incidence and prevalence of AEMT did not change significantly overall during this period. Therefore, the health sector should pay more attention to AEMT and take effective measures to reduce AEMT.
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Affiliation(s)
- Xin Kong
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
- School of pharmacy, Dalian Medical University, Dalian, 116044, China
| | - Xufeng Tao
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Lu Li
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Xinya Zhao
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
- School of pharmacy, Dalian Medical University, Dalian, 116044, China
| | - Jiaqi Ren
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
- School of pharmacy, Dalian Medical University, Dalian, 116044, China
| | - Shilei Yang
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Xuyang Chen
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Hong Xiang
- Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Guoyu Wu
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
| | - Yunming Li
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
| | - Deshi Dong
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
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Tariq S, Tariq S, Shoukat AA. Centralized healthcare database for ensuring better healthcare: Are we lagging behind? Pak J Med Sci 2024; 40:257-258. [PMID: 38356836 PMCID: PMC10862436 DOI: 10.12669/pjms.40.3.9084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/05/2023] [Indexed: 02/16/2024] Open
Abstract
doi: https://doi.org/10.12669/pjms.40.3.9084
How to cite this: Tariq S, Tariq S, Shoukat AA. Centralized healthcare database for ensuring better healthcare: Are we lagging behind? Pak J Med Sci. 2024;40(3):---------. doi: https://doi.org/10.12669/pjms.40.3.9084
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Sundus Tariq
- Sundus Tariq, Department of Physiology, International School of Medicine, Istanbul Medipol University, Research Institute for Health Sciences and Technologies (SABITA), Istanbul, Turkey
| | - Saba Tariq
- Saba Tariq, Department of Pharmacology and Therapeutics, University Medical & Dental College, The University of Faisalabad, Faisalabad, Pakistan, University of Birmingham, Birmingham, United Kingdom
| | - Ahmad Adnan Shoukat
- Ahmad Adnan Shoukat, Department of Biomedical Engineering and Bioinformatics, School of Engineering and Natural Sciences, Istanbul Medipol University, Istanbul, Turkey
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Bednorz A, Mak JKL, Jylhävä J, Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clin Interv Aging 2023; 18:2171-2183. [PMID: 38152074 PMCID: PMC10752027 DOI: 10.2147/cia.s400887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/05/2023] [Indexed: 12/29/2023] Open
Abstract
Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.
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Affiliation(s)
- Adam Bednorz
- John Paul II Geriatric Hospital, Katowice, Poland
- Institute of Psychology, Humanitas Academy, Sosnowiec, Poland
| | - Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland
| | - Dorota Religa
- Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden
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13
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Stadhouder A, van Rossenberg LX, Kik C, Muijs SPJ, Öner FC, Houwert RM. Natural Experiments as a Study Method in Spinal Trauma Surgery: A Systematic Review. Global Spine J 2023:21925682231220889. [PMID: 38073538 DOI: 10.1177/21925682231220889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2023] Open
Abstract
STUDY DESIGN Systematic review. OBJECTIVES To determine if the natural experiment design is a useful research methodology concept in spinal trauma care, and to determine if this methodology can be a viable alternative when randomized controlled trials are either infeasible or unethical. METHODS A Medline, Embase and Cochrane database search was performed between 2004 and 2023 for studies comparing different treatment modalities of spinal trauma. All observational studies with a natural experiment design comparing different treatment modalities of spinal trauma were included. Data extraction and quality assessment with the MINORS criteria was performed. RESULTS Four studies with a natural experiment design regarding patients with traumatic spinal fractures were included. All studies were retrospective, one study collected follow-up data prospectively. Three studies compared different operative treatment modalities, whereas one study compared different antibiotic treatment strategies. Two studies compared preferred treatment modalities between expertise centers, one study between departments (neuro- and orthopedic surgery) and one amongst surgeons. For the included retrospective studies, MINORS scores (maximum score 18) were high ranging from 12-17 and with a mean (SD) of 14.6 (1.63). CONCLUSIONS Since 2004 only four studies using a natural experiment design have been conducted in spinal trauma. In the included studies, comparability of patient groups was high emphasizing the potential of natural experiments in spinal trauma research. Natural experiments design should be considered more frequently in future research in spinal trauma as they may help to address difficult clinical problems when RCT's are infeasible or unethical.
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Affiliation(s)
- Agnita Stadhouder
- Department of Orthopaedics and Sports Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Luke Xander van Rossenberg
- Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
- Department of Trauma Surgery, Diakonessenhuis, Utrecht, Netherlands
| | - Charlotte Kik
- Department of Neurosurgery, Erasmus MC, Rotterdam, Netherlands
| | - S P J Muijs
- Department of Orthopaedics, University Medical Center Utrecht, Utrecht, Netherlands
| | - F C Öner
- Department of Orthopaedics, University Medical Center Utrecht, Utrecht, Netherlands
| | - R Marijn Houwert
- Department of Trauma Surgery, University Medical Center Utrecht, Utrecht, Netherlands
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14
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Trivedi S, Hall S, Inglis F, Chaudhry A. Proof-of-concept solution to create an interoperable timeline of healthcare data. BMJ Health Care Inform 2023; 30:e100754. [PMID: 37940189 PMCID: PMC10693683 DOI: 10.1136/bmjhci-2023-100754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 10/11/2023] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVES To overcome the barriers of interoperability by sharing simulated patient data from different electronic health records systems and presenting them in an intuitive timeline of events. METHODS The 'Patient Story' software comprising database and blockchain, PS Timeline Windows interface, PS Timeline Web interface and network relays on Azure cloud was customised for Epic and Lorenzo electonic patient record (EPR) systems used at different hospitals, using site-specific adapters. RESULTS Each site could view their own clinical documents and view each other's site specific, fully coded test sets of (Care Connect) medications, conditions and allergies, in an aggregated single view. DISCUSSION This work has shown that clinical data from different EPR systems can be successfully integrated and visualised on a single timeline, accessible by clinicians and patients. CONCLUSION The Patient Story system combined the timeline visualisation with successful interoperability across healthcare settings, as well giving patients the ability to directly interact with their timeline.
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Affiliation(s)
- Sapna Trivedi
- Clinical Informatics (eHospital), Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Stephen Hall
- Clinical Informatics (eHospital), Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Fiona Inglis
- Clinical Informatics (eHospital), Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Afzal Chaudhry
- Epic Systems Corporation, (previously eHospital), Epic Systems, The Core, St Thomas St, Bristol, UK
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15
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Fernando M, Abell B, Tyack Z, Donovan T, McPhail SM, Naicker S. Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review. J Med Internet Res 2023; 25:e45163. [PMID: 37851492 PMCID: PMC10620641 DOI: 10.2196/45163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 08/18/2023] [Accepted: 09/14/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals. OBJECTIVE This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described. METHODS A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies. RESULTS A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework. CONCLUSIONS Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs.
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Affiliation(s)
- Manasha Fernando
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Bridget Abell
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Zephanie Tyack
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Thomasina Donovan
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
- Digital Health and Informatics Directorate, Metro South Health, Brisbane, Australia
| | - Sundresan Naicker
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
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16
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Schwarz M, Ward EC, Coccetti A, Simmons J, Burrett S, Juffs P, Perkins K. Exploring maturity of electronic medical record use among allied health professionals. HEALTH INF MANAG J 2023:18333583231198100. [PMID: 37702314 DOI: 10.1177/18333583231198100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
BACKGROUND Electronic medical records (EMRs) have the potential to improve and streamline the quality and safety of patient care. Harnessing the full benefits of EMR implementation depends on the utilisation of advanced features, defined as "mature usage." At present, little is known about the maturity of EMR usage by allied health professionals (AHPs). OBJECTIVE To examine current maturity of EMR use by AHPs and explore perceived barriers to mature EMR utilisation and optimisation. METHOD AHPs were recruited from three health services. Participants completed a 27-question electronic questionnaire based on the EMR Adoption Framework, which measures clinician EMR utilisation (0 = paper chart, 5 = theoretical maximum) across 10 EMR feature categories. Interviews were conducted with both clinicians and managers to explore the nature of current EMR utilisation and perceived facilitators and barriers to mature usage. RESULTS Questionnaire responses were obtained from 193 participants AHPs. The majority of questions (74%) showed a mean score of <3, indicating a lack of mature EMR use. Pockets of mature usage were identified in the categories of health information, referrals and administration processes. Interviews with 21 clinicians and managers revealed barriers to optimisation across three themes: (1) limited understanding of EMR opportunities; (2) complexity of the EMR change process and (3) end-user and environmental factors. CONCLUSION Mature usage across EMR feature categories of the EMR Adoption Framework was low. However, questionnaire and qualitative interview data suggested pockets of mature utilisation. IMPLICATIONS Achieving mature allied health EMR use will require strategies implemented at the clinician, EMR support, and service levels.
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Affiliation(s)
| | - Elizabeth C Ward
- Queensland Health, Australia
- The University of Queensland, Brisbane Australia
| | | | | | - Sara Burrett
- Gold Coast Hospital and Health Service, Australia
| | - Philip Juffs
- West Moreton Hospital and Health Service, Australia
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17
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Yin Y. Prediction and analysis of time series data based on granular computing. Front Comput Neurosci 2023; 17:1192876. [PMID: 37576071 PMCID: PMC10413556 DOI: 10.3389/fncom.2023.1192876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
The advent of the Big Data era and the rapid development of the Internet of Things have led to a dramatic increase in the amount of data from various time series. How to classify, correlation rule mining and prediction of these large-sample time series data has a crucial role. However, due to the characteristics of high dimensionality, large data volume and transmission lag of sensor data, large sample time series data are affected by multiple factors and have complex characteristics such as multi-scale, non-linearity and burstiness. Traditional time series prediction methods are no longer applicable to the study of large sample time series data. Granular computing has unique advantages in dealing with continuous and complex data, and can compensate for the limitations of traditional support vector machines in dealing with large sample data. Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed, and the basic principles of traditional time series forecasting methods and granular computing are investigated. Secondly, in terms of predicting the trend of data changes, it is proposed to apply the fuzzy granulation algorithm to first convert the sample data into coarser granules. Then, it is combined with a support vector machine to predict the range of change of continuous time series data over a period of time. The results of the simulation experiments show that the proposed model is able to make accurate predictions of the range of data changes in future time periods. Compared with other prediction models, the proposed model reduces the complexity of the samples and improves the prediction accuracy.
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Affiliation(s)
- Yushan Yin
- School of Electro-Mechanical Engineering, Xidian University, Xi’an, China
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18
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Ting DSJ, Deshmukh R, Ting DSW, Ang M. Big data in corneal diseases and cataract: Current applications and future directions. Front Big Data 2023; 6:1017420. [PMID: 36818823 PMCID: PMC9929069 DOI: 10.3389/fdata.2023.1017420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.
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Affiliation(s)
- Darren S. J. Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom,Birmingham and Midland Eye Centre, Birmingham, United Kingdom,Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom,*Correspondence: Darren S. J. Ting ✉
| | - Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Daniel S. W. Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
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19
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Jin H, Wang Z, Guo A, Zhang H, Liu W, Zhu Y, Hua M, Shi J, Shi J, Yu D. Patterns of multimorbidity in community health centres in Shanghai, China: a retrospective, cross-sectional study based on outpatient data from 2014 to 2018. BMJ Open 2022; 12:e048727. [PMID: 36198446 PMCID: PMC9535180 DOI: 10.1136/bmjopen-2021-048727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVE Caring for patients with multimorbidity is an important part of primary care. It has become increasingly relevant that understanding the spectrum of multimorbidity will help general practitioners (GPs) acquire working knowledge and improve management skills. However, there was little research on characteristics of multimorbidity in primary care in China. This study aimed to identify the spectrum of frequency, proportion and ranking of multimorbidity patterns in adult patients seen at community health centres (CHCs) in Shanghai, China. DESIGN AND SETTING This was an observational, retrospective, cross-sectional study analysis of outpatient data of 244 CHCs in Shanghai, China. PARTICIPANTS Adult patients with chronic disease who visited Shanghai CHCs during 2014-2018 were selected from Shanghai CHC electronic medical records database using the International Classification of Diseases 10th Revision codes matched to the Second Version of International Classification of Primary Care codes. PRIMARY AND SECONDARY OUTCOME MEASURES A number of adult patients with chronic disease were counted. Then frequency, proportion and rank of disease patterns of multimorbidity were analysed. RESULTS Analysis of 301 651 158 electronic health records of 5 909 280 adult patients (54.2% females) found the multimorbidity proportion to be 81.2%. The prevalence of multimorbidity increased with age, which climbed from 43.7% among those aged 19-34 to 94.9% among those more than 80 years of age. The proportion of multimorbidity was higher in females (83.2%) than males (79.7%). Vascular and metabolic diseases were the most frequent diseases for patients over 45 years old. CONCLUSIONS Multimorbidity has brought huge challenges to primary care practice in Shanghai. The Shanghai government should strengthen its support for the multitargeted prevention of chronic diseases and the improvement of GPs' management capabilities.
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Affiliation(s)
- Hua Jin
- Department of General Practice,Clinical Research Center for General Practice, Yangpu Hospital,School of Medicine,Tongji University, Shanghai, China
- Shanghai General Practice and Community Health Development Research Center, Shanghai, China
| | - Zhaoxin Wang
- Shanghai General Practice and Community Health Development Research Center, Shanghai, China
- Department of Social Medicine and Health Management, School of Public Health,Shanghai Jiaotong University School of Medicine, Shanghai, China
- School of Management, Hainan Medical University, Haikou, China
| | - Aizhen Guo
- Department of General Practice,Clinical Research Center for General Practice, Yangpu Hospital,School of Medicine,Tongji University, Shanghai, China
- Shanghai General Practice and Community Health Development Research Center, Shanghai, China
| | - Hanzhi Zhang
- Department of General Practice,Clinical Research Center for General Practice, Yangpu Hospital,School of Medicine,Tongji University, Shanghai, China
- Shanghai General Practice and Community Health Development Research Center, Shanghai, China
| | - Wei Liu
- Huangpu District Dapuqiao Community Health Center, Shanghai, China
| | - Yuqin Zhu
- Department of Emergency, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ming Hua
- Jing'an District Daning Community Health Center, Shanghai, China
| | - Jianjun Shi
- Department of General Practice,Clinical Research Center for General Practice, Yangpu Hospital,School of Medicine,Tongji University, Shanghai, China
- Shanghai General Practice and Community Health Development Research Center, Shanghai, China
| | - Jianwei Shi
- Department of Social Medicine and Health Management, School of Public Health,Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Dehua Yu
- Department of General Practice,Clinical Research Center for General Practice, Yangpu Hospital,School of Medicine,Tongji University, Shanghai, China
- Shanghai General Practice and Community Health Development Research Center, Shanghai, China
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20
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An Interoperable Electronic Health Record System for Clinical Cardiology. INFORMATICS 2022. [DOI: 10.3390/informatics9020047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.
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21
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Arabi YM, Al Ghamdi AA, Al-Moamary M, Al Mutrafy A, AlHazme RH, Al Knawy BA. Electronic medical record implementation in a large healthcare system from a leadership perspective. BMC Med Inform Decis Mak 2022; 22:66. [PMID: 35292008 PMCID: PMC8922058 DOI: 10.1186/s12911-022-01801-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/03/2022] [Indexed: 11/17/2022] Open
Abstract
Background Information on the use of change management models to guide electronic medical records (EMR) implementation is limited. This case study describes the leadership aspects of a large-scale EMR implementation using Kotter’s change management model.
Methods This case study presents the experience in implementing a new EMR system from the leadership perspective at King Abdulaziz Medical City, a large tertiary care hospital in Riyadh, Kingdom of Saudi Arabia. We described the process of implementation and outlined the challenges and opportunities, throughout the journey from the pre-implementation to the post-implementation phases.
Results We described the corresponding actions to the eight domains of Kotter’s change management model: creating a sense of urgency, building the guiding team, developing a change vision and strategy, understanding and buy-in, removing obstacles, creating short-term wins, building on the change and anchoring the changes in corporate culture. Conclusions The case study highlights that EMR implementation is not a pure information technology project but rather is a technical-based complex social adaptive project that requires a specific set of leadership competencies that are central to its success. It demonstrates that change management models might be useful for large-scale EMR implementation.
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Affiliation(s)
- Yaseen M Arabi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. .,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia. .,Intensive Care Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia.
| | - Abdullah Ali Al Ghamdi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Clinical Affairs, Family Medicine and Primary Healthcare, King Abdulaziz Medical City, Riyadh, Saudi Arabia
| | - Mohamed Al-Moamary
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Development and Quality Management, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdulaziz Medical City, Riyadh, Saudi Arabia
| | - Abdullah Al Mutrafy
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,King Abdullah Specialized Children's Hospital, Riyadh, Saudi Arabia.,Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Raed H AlHazme
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.,College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,Information Technology Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia.,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Bandar Abdulmohsen Al Knawy
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.,King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
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22
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Stausberg J, Uslu A. Authors' Reply to: Interpretation bias towards positive impacts of digital interventions in healthcare. Comment on “Value of the Electronic Medical Record for Hospital Care: Update From the Literature” (Preprint). J Med Internet Res 2022; 24:e37419. [PMID: 35254272 PMCID: PMC8933797 DOI: 10.2196/37419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 02/21/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Jürgen Stausberg
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Aykut Uslu
- USLU Medizininformatik, Düsseldorf, Germany
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23
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Shakibaei Bonakdeh E. Interpretation bias towards positive impacts of digital interventions in healthcare. Comment on “Value of the Electronic Medical Record for Hospital Care: Update
From the Literature” (Preprint). J Med Internet Res 2022; 24:e37208. [PMID: 35254276 PMCID: PMC8933794 DOI: 10.2196/37208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022] Open
Affiliation(s)
- Erfan Shakibaei Bonakdeh
- Department of Management, Monash Business School, Monash University, Melbourne, VIC, Australia
- Pharmacy Department, Alfred Health, Melbourne, VIC, Australia
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