The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes. The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis; and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability. The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based "Four-headed" model incorporating four distinct classification heads. This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller.
- Klíčová slova
- Coding, Diagnosis coding, ICD, Medical, Text classification,
- MeSH
- elektronické zdravotní záznamy MeSH
- klinické kódování * MeSH
- lidé MeSH
- mezinárodní klasifikace nemocí * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Česká republika MeSH
With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector. The Raft algorithm is a cornerstone that improves the diagnostic decision-making process, increases confidence in patients, and sets a new standard for robust healthcare systems. In the proposed consensus algorithm, a weighted sum of two influencing factors including the physician acceptability and inter-physicians' reliability is used for selecting the participating physicians. An investigation is conducted to see how well the Raft algorithm performs in overcoming prescription-related roadblocks that support a compelling argument for improved patient care. Apart from its technological benefits, the proposed approach seeks to revolutionize the healthcare system by fostering trust between patients and providers. Raft's ability to communicate presents the proposed solution as an effective way to deal with healthcare issues and ensure security.
- Klíčová slova
- Blockchain, Consensus algorithm, Electronic healthcare system, Security, Transparency,
- MeSH
- algoritmy * MeSH
- blockchain * MeSH
- elektronické zdravotní záznamy MeSH
- konsensus MeSH
- lékaři MeSH
- lidé MeSH
- poskytování zdravotní péče MeSH
- zabezpečení počítačových systémů MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
People living with diabetes have many medical devices available to assist with disease management. A critical aspect that must be considered is how systems for continuous glucose monitoring and insulin pumps communicate with each other and how the data generated by these devices can be downloaded, integrated, presented and used. Not only is interoperability associated with practical challenges, but also devices must adhere to all aspects of regulatory and legal frameworks. Key issues around interoperability in terms of data ownership, privacy and the limitations of interoperability include where the responsibility/liability for device and data interoperability lies and the need for standard data-sharing protocols to allow the seamless integration of data from different sources. There is a need for standardised protocols for the open and transparent handling of data and secure integration of data into electronic health records. Here, we discuss the current status of interoperability in medical devices and data used in diabetes therapy, as well as regulatory and legal issues surrounding both device and data interoperability, focusing on Europe (including the UK) and the USA. We also discuss a potential future landscape in which a clear and transparent framework for interoperability and data handling also fulfils the needs of people living with diabetes and healthcare professionals.
- Klíčová slova
- Big data, Diabetes therapy, Glucose monitoring, Insulin delivery systems, Interoperability, Medical devices, Review,
- MeSH
- diabetes mellitus * farmakoterapie MeSH
- elektronické zdravotní záznamy MeSH
- krevní glukóza MeSH
- lidé MeSH
- selfmonitoring glykemie * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- Geografické názvy
- Spojené království MeSH
- Názvy látek
- krevní glukóza MeSH
OBJECTIVE: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful. MATERIALS AND METHODS: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research. RESULTS: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study. DISCUSSION: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time. CONCLUSION: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence.
- Klíčová slova
- OMOP common data model, data standardization, observational data,
- MeSH
- celosvětové zdraví * MeSH
- databáze faktografické MeSH
- elektronické zdravotní záznamy MeSH
- lékařství * MeSH
- lidé MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
- Geografické názvy
- Evropa MeSH
This paper deals with a developed information system called a Personal Genetic Card (PGC). The system aims to integrate the known clinical knowledge (interpretations and recommendations) linked to genetic information with the analysis results of a patient. Genetic information has an increasing influence on the clinical decision of physicians as well as other medical and health services. All these services need to connect the genetic profile with the phenotypes such as drug metabolization, drug toxicity, drug dosing, or intolerance of some substances. It still applies that the best way to represent data of medical records is a structured form of record. Many approaches can be used to define the structure (syntax) of the record and the content (semantics) of the record and to exchange data in forms of various standards and terminologies. Moreover, the genetic analysis field has its terminology databases for representing genetic information (e.g. HGNC, NCBI). The next step is to connect the genetic analysis results with c clinical knowledge (interpretation, recommendation). This step is crucial because the genetic analysis results have clinical benefits if we can assign them to some valid clinical knowledge. And the best final result is when we can make a better recommendation based on the genetic results and clinical knowledge. Genetic knowledge databases (e.g. PharmGKB, SNPedia, ClinVar) contain many interpretations and even recommendations for genetic analysis results based on different purposes. This situation is appropriate for developing the PGC system that takes inspiration from case-based reasoning in purpose to allow integration of the assumptions and knowledge about phenotypes and the real genetic analysis results in the structured form.
- Klíčová slova
- Information system, eHealth, genetic analysis, standards, terminology,
- MeSH
- chorobopisy - počítačové systémy * MeSH
- fenotyp MeSH
- genetické testování * MeSH
- sémantika MeSH
- Publikační typ
- časopisecké články MeSH
Electronic Health Record (EHR) systems currently in use are not designed for widely interoperable longitudinal health data. Therefore, EHR data cannot be properly shared, managed and analyzed. In this article, we propose two approaches to making EHR data more comprehensive and FAIR (Findable, Accessible, Interoperable, and Reusable) and thus more useful for diagnosis and clinical research. Firstly, the data modeling based on the LinkML framework makes the data interoperability more realistic in diverse environments with various experts involved. We show the first results of how diverse health data can be integrated based on an easy-to-understand data model and without loss of available clinical knowledge. Secondly, decentralizing EHRs contributes to the higher availability of comprehensive and consistent EHR data. We propose a technology stack for decentralized EHRs and the reasons behind this proposal. Moreover, the two proposed approaches empower patients because their EHR data can become more available, understandable, and usable for them, and they can share their data according to their needs and preferences. Finally, we explore how the users of the proposed solution could be involved in the process of its validation and adoption.
- Klíčová slova
- Distributed electronic health records, FAIR principles, HL7 FHIR, bio-data management, ontology,
- MeSH
- data management MeSH
- elektronické zdravotní záznamy * MeSH
- lidé MeSH
- sémantický web * MeSH
- software MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
pHealth is a data (personal health information) driven approach that use communication networks and platforms as technical base. Often it' services take place in distributed multi-stakeholder environment. Typical pHealth services for the user are personalized information and recommendations how to manage specific health problems and how to behave healthy (prevention). The rapid development of micro- and nano-sensor technology and signal processing makes it possible for pHealth service provider to collect wide spectrum of personal health related information from vital signs to emotions and health behaviors. This development raises big privacy and trust challenges especially because in pHealth similarly to eCommerce and Internet shopping it is commonly expected that the user automatically trust in service provider and used information systems. Unfortunately, this is a wrong assumption because in pHealth's digital environment it almost impossible for the service user to know to whom to trust, and what the actual level of information privacy is. Therefore, the service user needs tools to evaluate privacy and trust of the service provider and information system used. In this paper, the authors propose a solution for privacy and trust as results of their antecedents, and for the use of computational privacy and trust. To answer the question, which antecedents to use, two literature reviews are performed and 27 privacy and 58 trust attributes suitable for pHealth are found. A proposal how to select a subset of antecedents for real life use is also provided.
- Klíčová slova
- antecedents, eCommerce, pHealth, privacy, trust,
- MeSH
- chorobopisy - počítačové systémy MeSH
- důvěra MeSH
- soukromí * MeSH
- zdravotní záznamy osobní * MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: The electronic patient record (EPR) has been introduced into nursing homes in order to facilitate documentation practices such as assessment and care planning, which play an integral role in the provision of dementia care. However, little is known about how the EPR facilitates or hinders these practices from the end-user's perspective. Therefore, the objective of this qualitative study was to explore the usability issues associated with the EPR for assessment and care planning for people with dementia in nursing homes from a staff perspective. METHODS: An exploratory, qualitative research design with a multiple case study approach was used. Contextual Inquiry was carried out with a variety of staff members (n = 21) who used the EPR in three nursing homes situated in Belgium, Czech Republic and Spain. Thematic analysis was used to code interview data, with codes then sorted into a priori components of the Health Information Technology Evaluation Framework: device, software functionality, organisational support. Two additional themes, structure and content, were also added. RESULTS: Staff provided numerous examples of the ways in which EPR systems are facilitating and hindering assessment and care planning under each component, particularly for people with dementia, who may have more complex needs in comparison to other residents. The way in which EPR systems were not customisable was a common theme across all three homes. A comparison of organisational policies and practices revealed the importance of training, system support, and access, which may be linked with the successful adoption of the EPR system in nursing homes. CONCLUSIONS: EPR systems introduced into the nursing home environment should be customisable and reflect best practice guidelines for dementia care, which may lead to improved outcomes and quality of life for people with dementia living in nursing homes. All levels of nursing home staff should be consulted during the development, implementation and evaluation of EPR systems as part of an iterative, user-centred design process.
- Klíčová slova
- Assessment, Care plan, Dementia, Electronic health records, Electronic patient records, Nursing home,
- MeSH
- demence * MeSH
- elektronické zdravotní záznamy * MeSH
- kvalita života MeSH
- lidé MeSH
- pečovatelské domovy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Objective: The electronic patient record (EPR) has been introduced into nursing homes with the aim of reducing time spent on documentation, improving documentation quality and increasing transferability of information, all of which should facilitate care provision. However, previous research has shown that EPR may be creating new burdens for staff. The purpose of this literature review is to explore how EPR is facilitating or hindering care provision in nursing homes. Methods: An integrative literature review was carried out using four electronic databases to search for relevant articles. After screening, 22 articles were included for thematic synthesis. Results: Thematic synthesis resulted in six analytical themes linked to care provision: time for direct care; accountability; assessment and care planning; exchange of information; risk awareness; and person-centered care. Conclusion: For EPR to facilitate care provision in nursing homes, consideration should be given to the type of device used for documentation, as well as the types of applications, the functionality, content, and structure of EPR. Further research exploring the experiences of end users is required to identify the optimal characteristics of an EPR system specifically for use in nursing homes.
- Klíčová slova
- Dementia care, electronic health record, electronic patient record, long-term care, nursing home,
- MeSH
- elektronické zdravotní záznamy * MeSH
- kvalita zdravotní péče * MeSH
- lidé MeSH
- péče orientovaná na pacienta MeSH
- pečovatelské domovy MeSH
- postoj zdravotnického personálu * MeSH
- zdravotnický personál psychologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
BACKGROUND: Protected health information burned in pixel data is not indicated for various reasons in DICOM. It complicates the secondary use of such data. In recent years, there have been several attempts to anonymize or de-identify DICOM files. Existing approaches have different constraints. No completely reliable solution exists. Especially for large datasets, it is necessary to quickly analyse and identify files potentially violating privacy. METHODS: Classification is based on adaptive-iterative algorithm designed to identify one of three classes. There are several image transformations, optical character recognition, and filters; then a local decision is made. A confirmed local decision is the final one. The classifier was trained on a dataset composed of 15,334 images of various modalities. RESULTS: The false positive rates are in all cases below 4.00%, and 1.81% in the mission-critical problem of detecting protected health information. The classifier's weighted average recall was 94.85%, the weighted average inverse recall was 97.42% and Cohen's Kappa coefficient was 0.920. CONCLUSION: The proposed novel approach for classification of burned-in text is highly configurable and able to analyse images from different modalities with a noisy background. The solution was validated and is intended to identify DICOM files that need to have restricted access or be thoroughly de-identified due to privacy issues. Unlike with existing tools, the recognised text, including its coordinates, can be further used for de-identification.
- Klíčová slova
- Burned-in protected health information, Classification, DICOM, De-identification, HIPAA, Text detection,
- MeSH
- algoritmy MeSH
- datové soubory jako téma MeSH
- důvěrnost informací MeSH
- elektronické zdravotní záznamy MeSH
- lidé MeSH
- soukromí * MeSH
- zabezpečení počítačových systémů * MeSH
- zákon o převoditelnosti a povinném vyúčtování zdravotního pojištění (USA) MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Spojené státy americké MeSH
