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Healthcare Data Organization
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
The overall clinical information system consists of: 1) Data-acquisition system, including device and clinical interfaces; 2) local EMR databases; 3) knowledge-bases of medical-terms and software, including adapters and transformers and 4) information-exchange system to handle heterogeneity and to exchange information with other datamarts. The patient-specific information, including laboratory results, radiology reports, radiology images, operative notes, clinical information flow, prescriptions, medication administration information, patient history, biosignal analysis and patients' progress notes forms the medical context system, which facilitates the diagnosis of the patients' disease-states.
Introduction
Published in Tim Scott, Thomas G Rundall, Thomas M Vogt, John Hsu, Jos Aarts, Implementing an Electronic Medical Record System, 2018
Tim Scott, Thomas G Rundall, Thomas M Vogt, John Hsu, Jos Aarts
The rise and fall of the Clinical Information System (CIS) in the Hawaii region needs to be seen in context of the Kaiser Permanente medical care program. That national program in turn has to be set in context of the history of American medicine.76 In briefest terms, there is no comprehensive national health service in the USA. Most working Americans and their dependants are insured against ill health as an employment benefit. Others may insure their health privately with insurance companies or health plans, or even pay healthcare providers directly for their services. Persons aged 65 and older are covered by the federal Medicare health insurance program, and very low income individuals and families are eligible for health insurance benefits under Medicaid, a joint federal-state funded program. A patchwork of many other specialty health insurance programs, funded at the federal, state, and local levels – especially for children – has emerged over the past 50 years, but still many Americans fall through the cracks of these programs. It has been estimated that about 45 million people in the USA were uninsured and 16 million people aged 19–64 underinsured in 2003 (35% or 61 million in total).66 While it is possible for an uninsured person to receive care from a private clinic or hospital as a charity patient, most uninsured people receive their medical care from county or city funded public hospitals and clinics.
Module 4: Reliable and accurate data
Published in Raj Rattan, Ruth Chambers, Gill Wakley, Clinical Governance in General Dental Practice, 2017
Raj Rattan, Ruth Chambers, Gill Wakley
A clinical information system can be defined ‘as one which will contain all the administrative, demographic and person-based information relating to an individual’s healthcare which the clinician needs, when and where needed, to provide relevant, evidence-based care to that patient’.2
Treatment regimens in patients over 64 years with acute myeloid leukaemia: a retrospective single-institution, multi-site analysis
Published in Hematology, 2023
Tabea Sutter, Marcus Schittenhelm, Thomas Volken, Thomas Lehmann
We retrospectively analysed n = 54 older patients with AML treated at the healthcare network of the cantonal hospital St. Gallen in Switzerland from January, 1 2017 to October, 1 2020 to assess for survival outcome and quality of life parameters according to the treatment chosen. All patients were >64 years and had a confirmed diagnosis of AML according to World Health Organization 2016 criteria [18]. Patients were identified using the institutional registry. Patient- and disease-specific variables, date of diagnosis, treatment and date of death were collected from the clinical information system covering the entire local network. The day of bone marrow biopsy was used as the date of diagnosis, alternatively, if bone marrow examination was not performed, the date of peripheral blood blast count reaching or exceeding 20%, respectively the date of flow cytometry analysis, was used. Prognostic groups were categorized according to the European Leukemia Net (ELN) 2017 guidelines [19]. This retrospective data analysis was approved by the local ethics committee (Ethikkomission Ostschweiz).
Red blood cell distribution width at admission predicts outcome in critically ill patients with kidney failure: a retrospective cohort study based on the MIMIC-IV database
Published in Renal Failure, 2022
Rongqian Hua, Xuefang Liu, Enwu Yuan
All data were extracted from the Medical Information Mart for Intensive Care IV database (MIMIC-IV version 1.0), a publicly accessible database updated in March 2021, and maintained by the Department of Medicine at Beth Israel Deaconess Medical Center and the Laboratory for Computational Physiology at Massachusetts Institute of Technology. MIMIC-IV contains comprehensive information for patients admitted to the Beth Israel Deaconess Medical Center from 2008 to 2019 [18]. Any credentialed user of the PhysioNet can freely access the database. A modular approach is adopted when organizing data to highlight the data source. The data were rooted in two in-hospital database systems: a custom hospital-wide electronic health record (EHR) and an ICU-specific clinical information system. The database is comprised of details of over 500,000 hospital admissions and 70,000 ICU admissions. The identities of all patients in this database were eliminated to protect their privacy. One of our coauthors, Xuefang Liu, participated in an online training course to obtain access to the database. She passed two exams termed (‘Conflicts of Interest’ and ‘Data or Specimens Only Research’) and obtained access to this database (certification number: 43645869).
Nurses’ perceptions of the sustainability of a standardised assessment for preventing complications in a ICU: a qualitative study
Published in Contemporary Nurse, 2019
Kim Lam Soh, Patricia M. Davidson, Gavin Leslie, Michelle DiGiacomo, Kim Geok Soh
During the post-intervention phase, the hospital information system was started (in January 2011), and many nurses had difficulty in learning the system and placed less emphasis on the Waterlow PI assessment. Limited processes of paper documentation were still in used, such as for patient medication, daily treatment chart and nursing care plan. It would be expected that when the information technology system was implemented nurses spent less time for charting and more time for patients care. A study on the implementation of a clinical information system in an ICU found that patient care took 81.1% of the total nurses’ working time in 2000 and 86.6% in 2002 (Saarinen & Aho, 2005). The time used to document nursing care increased by 3.6% (P > 0.05), or 15 min after implementation of the clinical information system. Perhaps in this study nurses need more time learning the system because introducing a hospital information system in this setting has represented a major change in practice which is distracting, time consuming and might displace other nursing care activities.