Studies in health technology and informatics
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The realisation of semantic interoperability, in which any EHR data may be communicated between heterogeneous systems and fully understood by computers as well as people on receipt, is a challenging goal. Despite the use of standardised generic models for the EHR and standard terminology systems, too much optionality and variability exists in how particular clinical entries may be represented. Clinical archetypes provide a means of defining how generic models should be shaped and bound to terminology for specific kinds of clinical data. ⋯ Drawing on several years of work within communities of practice developing archetypes and implementing systems from them, this paper presents quality requirements for the development of archetypes. Clinical engagement on a wide scale is also needed to help grow libraries of good quality archetypes that can be certified. Vendor and eHealth programme engagement is needed to validate such archetypes and achieve safe, meaningful exchange of EHR data between systems.
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Stud Health Technol Inform · Jan 2012
Clinical TrialIntraoperative Neurophysiologic Monitoring (INM) in scoliosis surgery.
Even among skilled spinal deformity surgeons, neurologic deficits are inherent potential complications of spine surgery. The aim was to assess the meaning of changes and to evaluate the critical rates of Somatosensory Evoked Potentials (SEP) and Motor Evoked Potentials (MEP) for Neurologic Deficit (ND) occurrence associated with scoliosis surgery. A Group of 30 patients with idiopathic scoliosis treated surgically by posterior correction and stabilisation were included. ⋯ There was no correlation between flexibility and correction of the curve and SEP and MEP decrease. The safe level for MEP was not determined but its meaning for the outcome was more important than SEP value. The need of (INM) during scoliosis surgery to avoid (ND) was confirmed.
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Stud Health Technol Inform · Jan 2012
Personalised mobile health and fitness apps: lessons learned from myFitnessCompanion®.
Smartphones and tablets are slowly but steadily changing the way we look after our health and fitness. Today, many high quality mobile apps are available for users and health professionals and cover the whole health care chain, i.e. information collection, prevention, diagnosis, treatment and monitoring. Our team has developed a mobile health and fitness app called myFitnessCompanion® which has been available via Android market since February 2011. ⋯ We discuss the acceptance of health apps by end-users and healthcare industry. We discuss how mobile health apps will be distributed in the near future, the use of Personal Health Record (PHR) systems such as Microsoft HealthVault and the impact of regulations (FDA) on the future of mobile health apps. The paper is based on seven years of experience by the authors as mobile health and fitness application developers and we discuss the challenges and opportunities for app developers in the health industry.
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Stud Health Technol Inform · Jan 2012
Virtual medical record implementation for enhancing clinical decision support.
Development of clinical decision support systems (CDS) is a process which highly depends on the local databases, this resulting in low interoperability. To increase the interoperability of CDS a standard representation of clinical information is needed. ⋯ Beside the implementation of the vMR, the architecture integrates: a Data Manager, an interface, a decision making system (based on Egadss), a retrieving data module. Conclusions are issued.
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Stud Health Technol Inform · Jan 2012
Identifying types and causes of errors in mortality data in a clinical registry using multiple information systems.
Errors may occur in the registration of in-hospital mortality, making it less reliable as a quality indicator. We assessed the types of errors made in in-hospital mortality registration in the clinical quality registry National Intensive Care Evaluation (NICE) by comparing its mortality data to data from a national insurance claims database. Subsequently, we performed site visits at eleven Intensive Care Units (ICUs) to investigate the number, types and causes of errors made in in-hospital mortality registration. ⋯ The remaining 20% were five types of manual transcription errors and human failures to record outcome data. Clinical registries should be aware of the possible existence of errors in recorded outcome data and understand their causes. In order to prevent errors, we recommend to thoroughly verify the software that is used in the registration process.