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Quality management, certification and rating of health information on the Net with MedCERTAIN: using a medPICS/RDF/XML metadata structure for implementing eHealth ethics and creating trust globally.

MedCERTAIN (MedPICS Certification and Rating of Trustworthy Health Information on the Net, http://www.medcertain.org/) is a recently launched international project funded under the European Union's (EU) "Action Plan for safer use of the Internet". It provides a technical infrastructure and a conceptual basis for an international system of "quality seals", ratings and self-labelling of Internet health information, with the final aim to establish a global "trustmark" for networked health information. Digital "quality seals" are evaluative metadata (using standards such as PICS=Platform for Internet Content Selection, now being replaced by RDF/XML) assigned by trusted third-party raters. The project also enables and encourages self-labelling with descriptive metainformation by web authors. Together these measures will help consumers as well as professionals to identify high-quality information on the Internet. MedCERTAIN establishes a fully functional demonstrator for a self- and third-party rating system enabling consumers and professionals to filter harmful health information and to positively identify and select high quality information. We aim to provide a trustmark system which allows citizens to place greater confidence in networked information, to encourage health information providers to follow best practices guidelines such as the Washington eHealth Code of Ethics, to provide effective feedback and law enforcement channels to handle user complaints, and to stimulate medical societies to develop standard for patient information. The project further proposes and identifies standards for interoperability of rating and description services (such as libraries or national health portals) and fosters a worldwide collaboration to guide consumers to high-quality information on the web.

Certification↗

EU-project medCERTAIN: Certification and Rating of Trustworthy and Assessed Health Information on the Net.

Med-CERTAIN (MedPICS Certification and Rating of Trustworthy Health Information on the Net, http://www.medpics.org/medcertain/) is a recently launched project funded under the EU Action Plan for safer use of the Internet. It will provide the technical infrastructure for an international system of "quality seals" for Internet health information. Digital "quality seals" may be evaluative metainformation (using standards such as PICS = Platform for Internet Content Selection, now being replaced by RDF/XML) assigned by trusted third-party raters. The project will also enable and encourage self-labelling with descriptive metainformation by web authors. Together these measures will help consumers to identify high-quality information on the Internet. Med-CERTAIN will establish a fully functional demonstrator for a self- and third-party rating system enabling patients and consumers to filter harmful health information and to positively identify and select high quality information. We aim to provide a system allow European citizens to place greater trust in networked information, exemplified in the domain of health information, whilst also making a significant contribution for similar projects with different target domains. The project will demonstrate how PICS-based content rating and filtering technologies can automate and exploit value-adding resource description services. The proposed technology strategy combines a pragmatic use of simple existing technologies for data acquisition with a future-oriented standards policy intended to lead rather than follow the evolution of definitions for information-mediation services.

Europe↗

MedCERTAIN: quality management, certification and rating of health information on the Net.

MedCERTAIN (MedPICS Certification and Rating of Trustworthy Health Information on the Net, http://www.medcertain.org/) is a recently launched international project funded under the European Union's (EU) "Action Plan for safer use of the Internet. It provides a technical infrastructure and a conceptual basis for an international system of "quality seals", ratings and self-labelling of Internet health information, with the final aim to establish a "trustmark" for networked health information. Digital "quality seals" are evaluative metadata (using standards such as PICS = Platform for Internet Content Selection, now being replaced by RDF/XML) assigned by trusted third-party raters. The project also enables and encourages self-labelling with descriptive meta-information by web authors. Together these measures will help consumers as well as professionals to identify high-quality information on the Internet. MedCERTAIN establishes a fully functional demonstrator for a self- and third-party rating system enabling consumers and professionals to filter harmful health information and to positively identify and select high quality information. We aim to provide a system which allows citizens to place greater trust in networked information, exemplified in the domain of health information, whilst also making a significant contribution for similar projects with different target domains. The project will demonstrate how PICS-based content rating and filtering technologies can automate and exploit value-adding resource description services. It further proposes standards for interoperability of rating services.

Databases as Topic↗

Beyond data and technology: the need for new thinking to enable the era of precision prevention.

BACKGROUND: Global flagship initiatives increasingly advocate for proactive health maintenance to alleviate the growing burden on reactive, disease-focused healthcare systems. Precision prevention is conceived as the targeted modulation of causal pathways across the disease continuum, from latent risk and pre-disease states to clinical manifestation, surpassing conventional public health prevention strategies that prioritise managing population-level risk factors. Traditional discovery and implementation models, however, remain poorly aligned with the pace and breadth of scientific and technological advances. This review outlines key barriers to scaling precision prevention and argues for the integration of conceptual, methodological, and policy perspectives into a single implementation‑oriented framework. MAIN: Individualised risk stratification lies at the core of precision prevention. Genomics serves as a stable substrate for lifetime susceptibility assessment, while meaningful prediction in multifactorial chronic disease requires additional risk monitoring using dynamic intermediate molecular markers and high-resolution exposomic data. Machine learning and other artificial intelligence (AI) methods are increasingly helpful tools for integrating large, heterogeneous and temporally structured real-world data to generate personalised predictions of health trajectories. Trustworthy AI-enabled risk prediction or decision-support systems are expected to provide transparency about model logic, assumptions and performance. In discovery, existing diagnostic classifications and conventional case-control designs can obscure mechanistic heterogeneity. Shifting toward precision phenotyping and biologically grounded disease redefinition could reveal a new layer of molecular understanding. Evidence generation strategies that reflect the temporal change of disease, including high‑risk enrichment, surrogate endpoints, and adaptive, trajectory-based monitoring, are particularly important for common conditions with prolonged latency periods (e.g., cancer, cardiovascular disease). Features often dismissed as "noise", such as stochastic molecular variation and minimal exposures, may in fact encode meaningful individual-level signals and thus merit investigation. CONCLUSION: To shift healthcare from reactive treatment toward proactive health maintenance requires coordinated action from stakeholders to reshape the pillars of discovery, reform outcome assessments and modernise implementation strategies.

Humans↗

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗

Nurse-Led Home-Based Mobile Health Cardiac Rehabilitation Program for Patients With Chronic Heart Failure: A Randomized Controlled Trial.

This 12-week randomized controlled trial evaluated a nurse-led mHealth intervention for patients with chronic heart failure, conceptually informed by Riegel's middle-range theory of self-care of chronic illness. The program integrated wearable activity tracking with weekly nurse-led behavioral coaching, reflecting the core self-care processes of monitoring, maintenance, and management. Compared with usual care, the intervention significantly improved daily step count, 6-minute walk distance, metabolic equivalents, and left ventricular ejection fraction. Findings highlight the effectiveness of theory-informed, nurse-delivered mHealth strategies in enhancing physical activity and cardiopulmonary function, while underscoring the critical role of advanced practice nurses in home-based chronic disease management.

Aged↗

Mobile health apps improve Health-Related Quality of Life in Type 2 Diabetes Mellitus by enhancing medication adherence: A multicentre randomised controlled trial with mediation analysis.

AIMS: This study evaluated whether a gamified mHealth application (CareAide&#xae;) improves Health-Related Quality of Life (HRQoL) in Type 2 Diabetes Mellitus (T2DM) and whether this effect is mediated by medication adherence. METHODS: Prespecified secondary analysis of the T2DM cohort from a 6-month multicentre RCT (NCT06068309; N&#x202f;=&#x202f;663; three Malaysian hospitals). Participants were randomised 1:1 to standard care or CareAide&#xae;. Adherence (MMAS-8), EQ-5D-5L utility (Malaysian value set), and AQoL-6D were assessed at baseline and 6 months. Simple mediation analysis (PROCESS Model 4; 5000 bootstraps) adjusted for baseline HRQoL. RESULTS: CareAide&#xae; significantly predicted higher MMAS-8 scores (mean difference +1.756; d = 1.638; p&#x202f;<&#x202f;0.001). Higher MMAS-8 scores significantly predicted improved AQoL-6D utility (b = 0.024; p&#x202f;<&#x202f;0.001). The direct effect on AQoL-6D was non-significant (p&#x202f;=&#x202f;0.248). Bootstrapped indirect effect confirmed full mediation via AQoL-6D (0.042; 95% CI [0.024, 0.060]). A sensitivity analysis adjusting for baseline HbA1c confirmed full mediation (indirect = 0.034; 95% CI [0.015, 0.052]; n&#x202f;=&#x202f;563). EQ-5D-5L utility showed a significant direct between-group difference at 6 months (p&#x202f;=&#x202f;0.012) but did not operate as a mediation outcome. CONCLUSIONS: Medication adherence fully mediates the AQoL-6D HRQoL benefit of a gamified mHealth intervention in T2DM, as confirmed by both the primary and HbA1c-adjusted sensitivity analyses. These findings support integration of behaviourally informed digital adjuncts into routine primary diabetes care.

Humans↗

Effect of a pharmacist-led mHealth app on adherence, quality of life, and glycaemic control in diabetes: A multicentre RCT.

AIMS: To evaluate whether CareAide&#xae;, a pharmacist-driven mHealth application, improves medication adherence, health-related quality of life (HRQoL), and glycaemic control in diabetes mellitus using structural equation modelling. METHODS: Pre-specified secondary analysis of the type 2 diabetes mellitus cohort from a 6-month multicentre open-label randomised controlled trial (N&#xa0;=&#xa0;663) across three Malaysian hospitals. Adherence was assessed by MMAS-8 (subjective) and Proportion of Days Covered (PDC; pharmacy-verified). HRQoL was measured by AQoL-6D and EQ-5D-5&#xa0;L. Structural equation modelling (SEM), Necessary Condition Analysis, and Importance-Performance Map Analysis (cIPMA) were applied. RESULTS: CareAide&#xae; produced large adherence gains (MMAS-8: 7.31 vs 5.55, d&#xa0;=&#xa0;1.64; PDC&#xa0;&#x2265;&#xa0;80%: 81.6% vs 33.0%; both p&#xa0;<&#xa0;0.001). Early 3-month adherence was the strongest predictor of sustained 6-month adherence in both models (&#x3b2; std&#xa0;=&#xa0;0.567 and 0.688; p&#xa0;<&#xa0;0.001). AQoL-6D utility improved significantly (0.669 vs 0.618; d&#xa0;=&#xa0;0.353, p&#xa0;<&#xa0;0.001), driven by coping (d&#xa0;=&#xa0;0.447) and relationships (d&#xa0;=&#xa0;0.254) domains. HRQoL did not mediate adherence; gains were a direct independent benefit. The intervention effect on HbA1c was not statistically significant in the PDC-based SEM model (&#x3b2;&#xa0;=&#xa0;&#xa0;-&#xa0;0.333, p&#xa0;=&#xa0;0.065); a group difference was, however, supported by baseline-adjusted ANCOVA (&#x3b2;&#xa0;=&#xa0;&#xa0;-&#xa0;0.41%, p&#xa0;=&#xa0;0.002), and the complete-case comparison was non-significant (p&#xa0;=&#xa0;0.153), so glycaemic findings warrant cautious interpretation. cIPMA identified the intervention as the primary optimisation target. CONCLUSIONS: CareAide&#xae; significantly improves medication adherence and psychosocial quality of life. Evidence for glycaemic benefit came from baseline-adjusted analysis (ANCOVA), though findings should be interpreted with caution given incomplete HbA1c data at one site. The first three months are the most critical period for pharmacist support. In this dataset, PDC appeared more sensitive than MMAS-8 to the HbA1c signal within 6&#xa0;months, but this finding requires confirmation in longer studies with more complete HbA1c data. TRIAL REGISTRATION: ClinicalTrials.gov NCT06068309.

Aged↗

Multidisciplinary mHealth Rehabilitation for Patients With Abdominal Cancer Who Are Receiving Chemoradiotherapy: Randomized Phase II Trial.

BACKGROUND: Concurrent chemoradiotherapy (CCRT) for abdominal cancer frequently induces muscle loss, weight loss, and malnutrition. OBJECTIVE: This exploratory randomized phase II trial evaluated whether a multidisciplinary, mobile health (mHealth)-based multimodal rehabilitation program could preserve handgrip strength and muscle mass in patients with abdominal cancer undergoing CCRT. METHODS: In this prospective, multicenter, randomized, open-label phase II trial (NCT05325554), 111 eligible patients with abdominal malignancies scheduled for CCRT were randomly assigned (1:1) to receive either multidisciplinary mHealth rehabilitation care (MRC; n=57) or standard care (SC; n=54). The MRC program was delivered by a dedicated multidisciplinary team using the AiNST mHealth platform and wearable heart rate monitors. The primary end point was handgrip strength at the end of CCRT (analyzed with analysis of covariance adjusting for baseline). Secondary end points were exploratory and analyzed without multiplicity adjustment; sensitivity analysis using false discovery rate (FDR) correction was performed. RESULTS: Between February 2022 and April 2023, 111 patients were enrolled. Adherence was high (n=93, 83.9% achieved exercise targets). After adjusting for baseline handgrip strength, the MRC group had significantly higher handgrip strength at the end of CCRT than the SC group (adjusted mean difference 4.87 kg, 95% CI 3.36-6.38; P<.001). Exploratory analyses of secondary end points (without multiplicity adjustment) showed that the MRC group also had better preservation of body weight (P=.005), skeletal muscle mass (P<.001), serum albumin (P=.009), prealbumin (P=.02), and lower rates of hematological toxicity (P<.05), as well as improved psychological status (distress thermometer [DT] and Hospital Anxiety and Depression Scale [HADS]) and nutritional scores (Nutritional Risk Screening 2002 [NRS-2002] and Patient-Generated Subjective Global Assessment [PG-SGA]) at the end of CCRT (all P<.05). All nominally significant secondary end points remained significant after FDR correction (q<.05). These findings are preliminary and should be interpreted with caution due to the open-label design, population heterogeneity, and exploratory secondary analyses. CONCLUSIONS: In this exploratory phase II trial, a multidisciplinary, mHealth-based multimodal rehabilitation program was associated with better preservation of handgrip strength, muscle mass, and nutritional status, as well as lower rates of certain treatment toxicities, compared with SC. However, definitive conclusions are limited by the open-label design, heterogeneity of tumor types, and short follow-up. Larger, blinded phase III trials are needed to confirm these findings.

Humans↗

Smartphone apps for obesity management: A systematic review using self-determination theory.

BACKGROUND: While bariatric surgery and pharmacotherapy are effective treatments for obesity, ongoing supportive care remains a challenge. Smartphone applications (apps) may assist with symptom management, but their effectiveness and practical use in obesity treatment is unclear. This review evaluated the effectiveness, acceptability, and feasibility of these apps in supporting individuals following obesity treatment. To better understand how these apps may promote sustained engagement and behaviour change, their design was analysed using Self-Determination Theory (SDT). METHODS: A systematic search was conducted across MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, SCOPUS, and CENTRAL databases. Eligible studies included randomised and non-randomised interventions involving adults (&#x2265;18&#xa0;years) with obesity (BMI&#xa0;&#x2265;&#xa0;30&#xa0;kg/m2) who had undergone bariatric surgery or pharmacotherapy. Interventions had to include an app designed to support post-treatment symptom management. Findings were synthesised narratively, and app features were mapped to SDT constructs of autonomy, competence, and relatedness. RESULTS: Five studies (three RCTs, two cohort studies) involving 1,133 participants were included (female: 78&#xa0;%; median age: 47.63&#xa0;years). Most apps targeted post-bariatric surgery care; only one focused on pharmacotherapy. Common features included tracking, reminders, and education, supporting autonomy and competence. Relatedness features such as communication and peer support were least represented. Two studies reported improvements in weight-related outcomes and one in medication adherence. Effects on quality of life, self-efficacy, and healthcare utilisation were not significant. Patient satisfaction was reported in one study, with 95&#xa0;% expressing positive feedback, though formal assessments of feasibility and acceptability were limited. CONCLUSION: Smartphone apps show potential to support obesity management, particularly after bariatric surgery. While some evidence suggests benefits for weight loss and adherence outcomes, the limited studies and variability of reporting prevent conclusive observations in other outcomes. Future app development should integrate behavioural theory to address psychological needs, nutritional risks and promote holistic self-management beyond weight control.

Female↗

Treatment of urinary incontinence in cystic fibrosis.

BACKGROUND: Urinary incontinence (UI) is a newly recognised problem in cystic fibrosis. Whilst prevalence is well documented, there are no reports of assessment and treatment of the problem. METHODS: A previous study reports the prevalence of UI in women with CF to be 51/75 (68%). Nineteen women subsequently requested help for the problem and were referred to a physiotherapist specialising in women's health. A digital assessment was performed to measure pelvic floor muscle strength and endurance. An individualised programme of pelvic floor muscle exercises (PFME) was taught based on the assessment. A questionnaire addressed issues of assessment and treatment. RESULTS: 12 women were assessed. The median (range) age, FEV1% predicted and BMI were 20.9 (19.3-46.1) years, 45.9 (14.8-82.7) and 20.5 (16.1-26.0), respectively. The median strength of the pelvic floor muscle was moderate (Oxford Scale grade 3) with a hold time (endurance) of 5 s. At reassessment (median time 13.1 weeks), there was an improvement in endurance (P = 0.04), with no change in strength. This was supported by a subjective improvement in symptoms. Patients found the exercises were difficult to perform, difficult to fit into their treatment programme and adherence was poor. CONCLUSIONS: PFME are effective at improving endurance and reducing leakage over the short-term. Women are reluctant to be assessed and the CF team should provide support and encouragement with treatment. Long-term outcome and the mechanisms of UI in this group of patients need further evaluation.

Adolescent↗

Use of Wearable Sensors in Angelman Syndrome: A Systematic Review.

BACKGROUND: Wearable sensors are a promising method for collecting clinical trial outcome data for people with Angelman syndrome (AS). However, there has yet to be a systematic probe into the ways in which wearable sensors have been successfully used in AS. The current study aims to provide a quantitative summary of wearable sensors used in AS, including contexts of use and psychometric properties, and to present key narrative highlights. METHOD: Literature searches were performed in three electronic databases: APA PsycInfo, PubMed and Web of Science Core Collection. Data items were categorized into four categories: sample characteristics, study methodological details, wearable sensor characteristics and psychometric properties assessed. Sample characteristics included sample size, age, biological sex, race/ethnicity and cognitive/developmental functioning. Study methodological details were subdivided into study design and setting. Wearable sensor characteristics included sensor type, placement site, means of attachment, assessed construct and sensor-related data loss. Psychometric properties assessed included reliability and validity of sensor-derived data. RESULTS: We identified 16 articles through our systematic review. Wearable sensors were used to study sleep (n&#x2009;=&#x2009;10, 62.5%), language (n&#x2009;=&#x2009;2, 12.5%), gait (n&#x2009;=&#x2009;2, 12.5%), caregiver proximity (n&#x2009;=&#x2009;1, 6.3%), EEG power (n = 1, 6.3%),&#xa0;and arousal (n&#x2009;=&#x2009;1, 6.3%) in AS through actigraphs, vocalization recorders, inertial sensors, radio-frequency identification watches, wireless EEG caps,&#xa0;and functional near-infrared spectroscopy caps, respectively. Findings from these studies broadly indicate that wearable sensors are feasible, reliable and valid for assessing a range of behaviours relevant to AS. CONCLUSIONS: Wearable sensors are a promising solution to enhance assessments in AS. However, with the small extant literature characterized by small sample sizes and restricted focus on a few relevant features in AS, there remains ample opportunities to explore the use of wearable sensors in people with AS. Additional studies will better inform clinical decision-making and ultimately improve the lives of people with AS and their families.

Humans↗

Effectiveness of a digi-physical tool and working method for paediatric obesity treatment in Abu Dhabi: a non-inferiority intervention study using an external historical comparator.

BACKGROUND: Effective paediatric obesity treatment requires high intensity, scalable interventions. A digi-physical tool for paediatric obesity treatment has shown positive results in Stockholm, Sweden. This study evaluates whether the same treatment method is effective in a different cultural setting. METHODS: This non-inferiority intervention study, using an external historical comparator, included 60 consecutively recruited children aged 6-15.9 years with obesity who initiated treatment at Sheikh Shakhbout Medical City in Abu Dhabi between June and December 2023. Patients were treated with Evira, a digi-physical tool and working method enabling high intensity individualized care, real-time monitoring, and interactive patient-clinician communication. The primary outcome was BMI z-score change at 26 weeks. Non-inferiority was assessed using a predefined margin of 0.10 BMI z-score, with outcomes compared to a prior published trial in Stockholm (n&#x2009;=&#x2009;107). RESULTS: A total of 112 children were included in the analysis (Abu Dhabi cohort, n&#x2009;=&#x2009;35; Stockholm cohort, n&#x2009;=&#x2009;77). The adjusted mean change in BMI z-score was -&#x2009;0.20 (95% CI: -&#x2009;0.28, -&#x2009;0.12) in the Abu Dhabi cohort and -&#x2009;0.20 (- 0.26, -&#x2009;0.14) in the Stockholm cohort (p&#x2009;=&#x2009;0.88). Non-inferiority was confirmed, (predefined margin 0.10 was not exceeded). A clinically significant BMI z-score reduction (&#x2265;&#x2009;0.20 units) was achieved by 45.7% of participants in Abu Dhabi and 36.4% in Stockholm (p&#x2009;=&#x2009;0.35). Non-retention rates at 26 weeks were 41.7% vs. 28.0%, respectively (p&#x2009;=&#x2009;0.07). CONCLUSIONS: The findings provide promising evidence that treatment outcomes achieved with the digi-physical treatment tool were comparable in the Abu Dhabi and Stockholm cohorts, supporting its feasibility in a second cultural and healthcare setting.

Humans↗

Access control based on attribute certificates for medical intranet applications.

BACKGROUND: Clinical information systems frequently use intranet and Internet technologies. However these technologies have emphasized sharing and not security, despite the sensitive and private nature of much health information. Digital certificates (electronic documents which recognize an entity or its attributes) can be used to control access in clinical intranet applications. OBJECTIVES: To outline the need for access control in distributed clinical database systems, to describe the use of digital certificates and security policies, and to propose the architecture for a system using digital certificates, cryptography and security policy to control access to clinical intranet applications. METHODS: We have previously developed a security policy, DIMEDAC (Distributed Medical Database Access Control), which is compatible with emerging public key and privilege management infrastructure. In our implementation approach we propose the use of digital certificates, to be used in conjunction with DIMEDAC. RESULTS: Our proposed access control system consists of two phases: the ways users gain their security credentials; and how these credentials are used to access medical data. Three types of digital certificates are used: identity certificates for authentication; attribute certificates for authorization; and access-rule certificates for propagation of access control policy. Once a user is identified and authenticated, subsequent access decisions are based on a combination of identity and attribute certificates, with access-rule certificates providing the policy framework. CONCLUSIONS: Access control in clinical intranet applications can be successfully and securely managed through the use of digital certificates and the DIMEDAC security policy.

Certification↗

[New approaches in first aid for cardiovascular patients].

Sudden cardiac arrest is a major problem of our society. Ventricular fibrillation strikes without any warning. It leads to unconsciousness and death occurs within minutes. Every year at least 100,000 people die from sudden cardiac arrest in Germany alone. The following article is concerned with how todays rescue system can be improved in order to increase survival rates. The time which passes between occurrence of the ventricular fibrillation and the therapeutic defibrillation plays a major role. It is being observed that decentralizing the rescue system is of great advantage. The idea of the "first responder", involving laymen, family members, and company paramedics, reduces the rescue time extraordinarily. The introduction of a digital emergency health record which provides the doctor with all the important data on the patient briefly seems to be of equal importance. Hereby, delays based on false information can be reduced to a minimum. Optimizing the equipment by means of implementing automatic procedures which enable the application by laymen is just as important. But the key to medical success appears to be educating and repeatedly training as many people as possible.

Computer Systems↗