Studies in Health Technology and Informatics最新文献

Electronic patient data use and handling are critical issues in terms of privacy, confidentiality, security, and the Health Insurance Portability and Accountability Act (HIPAA) regulations. The risks associated with electronic patient data are not limited to identity theft but rather include a person's social, economic, and psychological well-being. However, there have not been many studies that have focused on the associated risk factors that could lead to these situations. This paper identifies those risks related to electronic patient data breaches by means of a grounded theory approach and develops a systemic risk management plan that enables engineering managers and risk managers to more effectively and efficiently overcome risks associated with electronic patient data. Purpose: The purpose of this paper is to identify the risks associated with electronic patient data breach using a grounded theory approach and also to recommend a set of guidelines to support a better, effective, and efficient system and thereby overcome these risks. Patients and methods: No patients were involved either to participate in this study or any of their opinions are reflected with this research.

Purpose: To investigate the correlative factors of vision regression after Implantable Collamer Lens (ICL) surgery from the perspective of binocular vision and to develop a predictive model for the refractive change over the long term.

Methods: This was a prospective study of 30 subjects (60 implants), who underwent ICL surgery at Guangdong General Hospital. Corrected distance visual acuity, uncorrected distance visual acuity, spherical equivalent (SE), axial length and binocular examination using virtual reality were analyzed before, 1 month and 1 year after the surgery.

Results: Mean SE was (-12.01 ± 0.86) D preoperatively, (-0.44 ± 0.13)D 1 month postoperatively and (-0.97 ± 0.15)D 1 year postoperatively. The average regression 1 year postoperatively (-0.53 ± 0.25D of SE) was significant (P = 0.01). Mean axial length at the base line and 1 year after the surgery was (28.35 ± 0.39) mm and (28.91 ± 0.57) mm. The regression was correlated with the binocular equilibrium difference (r = 0.553, P = 0.002), and the degree of perceptual eye position shift, especially the horizontal shift (r = -0.620, P = 0.000). The important variables relevant to myopic regression were preoperative horizontal perceptual eye position shift (B = -0.007, P = 0.001) and binocular equilibrium difference (B = 0.146, P = 0.012).

Conclusion: Perceptual eye position deviation and the binocular equilibrium difference could cause binocular gazing instability, and the unstable retinal image created a 'micro retinal-defocus phenomena', leading to an elongation of the axial length and resulting in myopic regression after ICL surgery.

Clinical decision support system (CDSS) plays an essential role nowadays and CDSS for treatment provides clinicians with the clinical evidence of candidate prescriptions to assist them in making patient-specific decisions. Therefore, it is essential to find a partition of patients such that patients with similar clinical conditions are grouped together and the preferred prescriptions for different groups are diverged. A comprehensive clinical guideline often provides information of patient partition. However, for most diseases, the guideline is not so detailed that only limited circumstances are covered. This makes it challenging to group patients properly. Here we proposed an approach that combines clinical guidelines with medical data to construct a nested decision tree for patient partitioning and treatment recommendation. Compared with pure data-driven decision tree, the recommendations generated by our model have better guideline adherence and interpretability. The approach was successfully applied in a real-world case study of patients with hyperthyroidism.

Patient portals can have positive consequences in the empowerment of people with depression by raising awareness about their condition. Patient portals are important yet challenging technologies in the field of mental health care. We conducted a scoping review aiming to investigate some important characteristics and features of the mental health websites and related patient portal services for the target audience including people with depression. For this purpose, two reviewers independently entered the keywords in the popular search engines including Google, Yahoo, and Bing in April 2019, in order to find mental health websites that provide patient portal service targeting depression. Examination of the inclusion and exclusion criteria led finally to the selection of 31 websites. We found out that some features of patient portals including the online questionnaires, messaging between the patient and the healthcare provider, and medication refill were more consistent with the areas on which the mental healthcare providers focus, and thus can be effective in improving the progression of these areas. It is essential for patient portal providers to put more focus on expressing the patient portal's features and objectives on their websites. Besides, it is also necessary to conduct further research to investigate the obstacles and facilitators of the interactive features of the patient portals in the field of mental health care, particularly depression.

To provide clinical data in distributed research architectures, a fundamental challenge involves defining and distributing suitable metadata within Metadata Repositories. Especially for structured data, data elements need to be bound against suitable terminologies; otherwise, other systems will only be able to interpret the data with complex and error-prone manual involvement. As current Metadata Repository implementations lack support for querying externally defined terminologies in FHIR terminology servers, we propose an intermediate solution that uses appropriate annotations on metadata elements to allow run-time Terminology Services mediated queries of that metadata. This allows a very clear separation of concerns between the two related systems, greatly simplifying terminological maintenance. The system performed well in a prototypical deployment.

For observational studies, which are relevant especially for chronic conditions like chronic wounds, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) offers a standardized database schema. In this study an ETL process for the transition of wound related data was developed. After understanding the data in general and mapping the relevant codes to concepts available in OMOP, the ETL process was implemented. In a first step, a generic algorithm to convert data to a csv format was implemented in Java. The resulting csv file was then processed within KNIME to be loaded into an OMOP CDM conformant database. During the whole ETL process, HL7 FHIR CodeSystem and ConceptMap resources were used for coding and mapping. First clinical test cases to retrieve data were successfully processed as an example to demonstrate the feasibility and usefulness. They concerned wound size at the first visit and the main issues of patients in the wound quality of life questionnaire (n = 24). In general, the ETL process worked well, yet some challenges arose, like post coordinated SNOMED codes or conditions, which might occur more than once.

Introduction: One possibility to support veterinarians in times of a vet shortage is by providing animal owners with a technical decision support for deciding whether their animal needs to be seen by a vet. As the first step in the user-centered development of such an mHealth application for equestrians, an analysis of the context of use was done.

Methods: The analysis was carried out by reviewing existing literature and conducting an online survey with 100 participants.

Results: Characteristics of the user group and the usage context are presented using an adaptation of the four layers of diversity. Many equestrians are lacking health-related knowledge and competencies as well as social networks supporting them in decision making and gaining further information. This may apply to owners of other animal species in broad ranges as well.

Conclusion: The results of the analysis provide information to software developers and researchers on mHealth applications for pet owners in general and equestrians in particular to focus their work on the users' needs and therefore provide efficient results/software.

Today, many menstruating individuals track their cycles with mobile apps. These cycle apps use a lot of highly sensitive personal data. The goal of this study is to evaluate current cycle apps based on data privacy and medical criteria. First, a market analysis of currently available apps was conducted. Second, a scoring system was developed based on Digital Health application (Digitale Gesundheitsanwendungen, DiGA in German) guidelines, Mobile App Rating Scale (MARS), and other resources. A total of 18 apps were evaluated. The final scores (range from 0 to 1) ranged from 0.12 (worst result) to 0.64 (best result). The average "data privacy score" was 0.4, and the average "medical score" was 0.11. Only six apps received any points in the medical part of the scoring. A clear weakness of many tested apps was the issue of data minimization. 89% of the apps had permissions that were not necessary for this type of health app.

Introduction: In industrialised countries, one in ten patients suffers harm during hospitalization. Critical Incident Reporting Systems (CIRS) aim to minimize this by learning from errors and identifying potential risks. However, a lack of interoperability among the 16 CIRS in Germany hampers their effectiveness.

Methods: This study investigates reports' syntactic and semantic interoperability across seven different reporting systems. Syntactic interoperability was examined using WHO's Minimal Information Models (MIM), while semantic interoperability was evaluated with SNOMED concepts.

Results: The findings reveal a low structural overlap, with only two terms correctly represented in the SNOMED CT terminology. In addition, most systems showed no syntactic interoperability.

Conclusion: Improving interoperability is essential for increasing the effectiveness and usability of CIRS. The study suggests a unified data model such as MIM or using Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) resources and expanding SNOMED CT with patient safety-relevant terms for semantic interoperability. Given the current lack of both syntactic and semantic interoperability in CIRS, developing a patient safety ontology is recommended for efficient critical incident analysis too.

Representing knowledge in a comprehensible and maintainable way and transparently providing inferences thereof are important issues, especially in the context of applications related to artificial intelligence in medicine. This becomes even more obvious if the knowledge is dynamically growing and changing and when machine learning techniques are being involved. In this paper, we present an approach for representing knowledge about cancer therapies collected over two decades at St.-Johannes-Hospital in Dortmund, Germany. The presented approach makes use of InteKRator, a toolbox that combines knowledge representation and machine learning techniques, including the possibility of explaining inferences. An extended use of InteKRator's reasoning system will be introduced for being able to provide the required inferences. The presented approach is general enough to be transferred to other data, as well as to other domains. The approach will be evaluated, e. g., regarding comprehensibility, accuracy and reasoning efficiency.