Using the PharmCAT tool for Pharmacogenetic clinical decision support.

Here, we will provide our insights into the usage of PharmCAT as part of a pharmacogenetic clinical decision support pipeline, which addresses the challenges in mapping clinical dosing guidelines to variants to be extracted from genetic datasets. After a general outline of pharmacogenetics, we describe some features of PharmCAT and how we integrated it into a pharmacogenetic clinical decision support system within a clinical information system. We conclude with promising developments regarding future PharmCAT releases.

Clinical Profiles of Japanese Patients with Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis Collected by a Nationwide System from 2006 to 2023.

Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are potentially life-threatening severe cutaneous adverse drug reactions. These diseases are rare, and their onset is difficult to predict because of their idiosyncratic reactivity. The Japan Severe Adverse Reactions Research Group, led by the National Institute of Health Sciences, has operated a nationwide to collect clinical information and genomic samples from patients with SJS/TEN since 2006. This study evaluated the associations of clinical symptoms with sequelae and specific causative drugs/drug groups in Japanese patients with SJS/TEN to identify clinical clues for SJS/TEN treatment and prognosis. Acetaminophen, antibiotics, and carbocisteine were linked to high frequencies of severe ocular symptoms and ocular sequelae (p < 0.05). For erythema and erosion areas, antipyretic analgesics had higher rates of skin symptom affecting <10% of the skin than the other drugs, suggesting narrower lesions (p < 0.004). Hepatic dysfunction, was common in both SJS and TEN, and antiepileptic drugs carried higher risks of hepatic dysfunction than the other drug groups (p = 0.0032). This study revealed that the clinical manifestations of SJS/TEN vary according to the causative drugs.

The Paradoxes of Digital Tools in Hospitals: Qualitative Interview Study.

BACKGROUND: Digital tools are progressively reshaping the daily work of health care professionals (HCPs) in hospitals. While this transformation holds substantial promise, it leads to frustrating experiences, raising concerns about negative impacts on clinicians' well-being. OBJECTIVE: The goal of this study was to comprehensively explore the lived experiences of HCPs navigating digital tools throughout their daily routines. METHODS: Qualitative in-depth interviews with 52 HCPs representing 24 medical specialties across 14 hospitals in Switzerland were performed. RESULTS: Inductive thematic analysis revealed 4 main themes: digital tool use, workflow and processes, HCPs' experience of care delivery, and digital transformation and management of change. Within these themes, 6 intriguing paradoxes emerged, and we hypothesized that these paradoxes might partly explain the persistence of the challenges facing hospital digitalization: the promise of efficiency and the reality of inefficiency, the shift from face to face to interface, juggling frustration and dedication, the illusion of information access and trust, the complexity and intersection of workflows and care paths, and the opportunities and challenges of shadow IT. CONCLUSIONS: Our study highlights the central importance of acknowledging and considering the experiences of HCPs to support the transformation of health care technology and to avoid or mitigate any potential negative experiences that might arise from digitalization. The viewpoints of HCPs add relevant insights into long-standing informatics problems in health care and may suggest new strategies to follow when tackling future challenges.

Developing an integrated clinical decision support system for the early identification and management of kidney disease-building cross-sectoral partnerships.

BACKGROUND: The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care. AIM: This paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care. METHODS: Building on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system's design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation. RESULTS: We established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events. CONCLUSION: Disconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. In this first phase, we report how robust partnerships and effective governance processes can overcome these barriers to support clinical decision making and contribute to holistic care.

Healthcare-Associated-Infections: preliminary results from a real-time reporting system of an Italian neurologic research hospital.

Background: Healthcare-Associated-Infections are a critical concern in healthcare settings, posing serious threats to patient safety and causing significant morbidity, mortality, and financial strain. This study aims to calculate healthcare-associated-infections trends in the hospital setting through an automatic reporting system. Study design: The study is a descriptive analysis of automatically generated trends of an innovative digital tool based on existing hospital information flows. Methods: An algorithm was developed within a Clinical Information System to create a suite of quality indicators for monitoring healthcare-associated-infections trends. The algorithm used criteria related to admission, laboratory tests and antimicrobial administrations. A descriptive analysis was conducted for patients aged 18 or older, admitted to a neurological or to a neuro-rehabilitation department of a neurologic hospital from 2019 to 2022. Results: The results showed fluctuations in healthcare-associated-infections prevalence from 2.9% to 5.6% and hospital infec-tions prevalence from 4.5% to 10.9%, with notable increases in 2020 and 2021. The majority (70.3%) of healthcare associated infections identified by the tool were confirmed to be potentially hospital-acquired, according to the European Centre of Disease Prevention and Control's definition. Discussion and Conclusions: The study posits the algorithm as a vital tool for automatically monitoring hospital infections, providing valuable preliminary results for improving care quality and guiding the infections' prevention and control strategies, with plans to benchmark the algorithm against a gold standard in the future.

End-users' perspectives on factors affecting implementation and utilization of the Iranian electronic health record system: a qualitative study in a developing country.

BACKGROUND: As one of the most important information technologies for storing, managing, and exchanging health information, the electronic health record (EHR) plays a major role in the health system. However, these systems in developing countries have been associated with multidimensional issues. The purpose of the present study was the assessment of nonclinical end-users' points of view on the implementation and utilization of the Iranian electronic health record system. METHODS: This was a large qualitative study conducted in 2021 for 7 months from February to August. In this study, data were collected through in-depth semi-structured interviews with 70 non-clinical end-users in 22 public and six private hospitals of West Azerbaijan province in Iran. To analyze the data, the thematic analysis method was used. RESULTS: The study results indicated that technical, human, cultural, managerial, and financial readiness are the most important factors affecting the implementation of EHRs in Iran. Among the mentioned factors, technical and human readiness were emphasized more by the users. Also, technical, organizational, human, and managerial factors were identified as factors influencing EHRs utilization, and technical and organizational factors had a stronger role in the system utilization. CONCLUSIONS: According to the results, several factors influence EHR implementation and adequate utilization in Iran. To achieve the predetermined goals of this system, implementation issues and problems of using the system should be considered and solved.

Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study.

BACKGROUND: The accuracy of electrocardiogram (ECG) interpretation by doctors are affected by the available clinical information. However, having a complete set of clinical details before making a diagnosis is very difficult in the clinical setting especially in the early stages of the admission process. Therefore, we developed an artificial intelligence-assisted ECG diagnostic system (AI-ECG) using natural language processing to provide screened key clinical information during ECG interpretation. METHODS: Doctors with varying levels of training were asked to make diagnoses from 50 ECGs using a common ECG diagnosis system that does not contain clinical information. After a two-week-blanking period, the same set of ECGs was reinterpreted by the same doctors with AI-ECG containing clinical information. Two cardiologists independently provided diagnostic criteria for 50 ECGs, and discrepancies were resolved by consensus or, if necessary, by a third cardiologist. The accuracy of ECG interpretation was assessed, with each response scored as correct/partially correct = 1 or incorrect = 0. RESULTS: The mean accuracy of ECG interpretation was 30.2% and 36.2% with the common ECG system and AI-ECG system, respectively. Compared to the unaided ECG system, the accuracy of interpretation was significantly improved with the AI-ECG system (P for paired t-test = 0.002). For senior doctors, no improvement was found in ECG interpretation accuracy, while an AI-ECG system was associated with 27% higher mean scores (24.3 ± 9.4% vs. 30.9 ± 10.6%, P = 0.005) for junior doctors. CONCLUSION: Intelligently screened key clinical information could improve the accuracy of ECG interpretation by doctors, especially for junior doctors.

Primary Site Identification of Soft-Tissue Mass: Things to Know in MRI Assessment.

The spectrum of soft-tissue mass is varied, including neoplastic and nonneoplastic/inflammatory lesions. However, soft-tissue tumors have similar imaging findings and, therefore, the diagnosis of soft-tissue mass is challenging. Although careful assessment of the internal characteristics on imaging can often narrow the differential diagnoses, the differential diagnosis may be out of the question if identification of the soft-tissue mass origin is missed. The purpose of this article is to review the imaging findings and the essential anatomy to identify the primary site of the soft-tissue mass, and discuss the associated potential pitfalls. In order not to fall into a pitfall, recognition of characteristic imaging findings indicating the origin of the soft-tissue mass and anatomical knowledge of the normal tissue distribution are necessary. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.

OBJECTIVE: Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient's physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. METHODS: The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. RESULTS: Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. CONCLUSIONS: In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

Development and implementation of a clinical information system-based protocol to improve nurse satisfaction of end-of-life care in a single intensive care unit.

BACKGROUND: Patients treated in Australian intensive care units (ICUs) have an overall mortality rate of 5.05%. This is due to the critical nature of their disease, the increasing proportion of patients with multiple comorbidities, and advanced age. This has made treating patients during the end of life an integral part of intensive care practice and requires a high quality of care. With the increased use of electronic clinical information systems, a standardised protocol encompassing end-of-life care may provide an efficient method for documentation, communication, and timely delivery of comfort care. OBJECTIVE: The aim of the study was to determine if an electronic clinical information system-based end-of-life care protocol improved nurses' satisfaction with the practice of end-of-life care for patients in the ICU. DESIGN: This is a prospective single-centre observational study. SETTING: The study was carried out at a 20-bed cardiothoracic and general ICU between 2015 and 2017. PARTICIPANTS: The study participants were ICU nurses. INTERVENTION: Electronic clinical information-based end-of-life care protocol was used in the study. OUTCOME: The primary outcome was nurse satisfaction obtained by a survey. RESULTS: The number of respondents for the before survey and after survey was 58 (29%) and 64 (32%), respectively. There was a significant difference between the before survey and the after survey with regard to feeling comfortable in transitioning from curative treatment (median = 2 [interquartile range {IQR} = 2, 3] vs 3 [IQR = 2, 3], p = 0.03), feeling involved in the decision to move from curative treatment to end-of-life care (median = 2 [IQR = 2, 2] vs 2 [IQR 2, 3], p = 0.049), and feeling religious beliefs/rituals should be respected during the end-of-life process (median = 4 [IQR = 3, 4] vs. 4 [IQR = 4, 4], p = 0.02). There were some practices that had a low satisfaction rate on both the before survey and after survey. However, a high proportion of nurses were satisfied with many of the end-of-life care practices. CONCLUSION: The nurses were highly satisfied with many aspects of end-of-life care practices in this unit. The use of an electronic clinical information system-based protocol improved nurse satisfaction and perception of quality of end-of-life care practices for three survey questions.

Patient Empowerment During the COVID-19 Pandemic by Ensuring Safe and Fast Communication of Test Results: Implementation and Performance of a Tracking System.

BACKGROUND: Overcoming the COVID-19 crisis requires new ideas and strategies for online communication of personal medical information and patient empowerment. Rapid testing of a large number of subjects is essential for monitoring and delaying the spread of SARS-CoV-2 in order to mitigate the pandemic's consequences. People who do not know that they are infected may not stay in quarantine and, thus, risk infecting others. Unfortunately, the massive number of COVID-19 tests performed is challenging for both laboratories and the units that conduct throat swabs and communicate the results. OBJECTIVE: The goal of this study was to reduce the communication burden for health care professionals. We developed a secure and easy-to-use tracking system to report COVID-19 test results online that is simple to understand for the tested subjects as soon as these results become available. Instead of personal calls, the system updates the status and the results of the tests automatically. This aims to reduce the delay when informing testees about their results and, consequently, to slow down the virus spread. METHODS: The application in this study draws on an existing tracking tool. With this open-source and browser-based online tracking system, we aim to minimize the time required to inform the tested person and the testing units (eg, hospitals or the public health care system). The system can be integrated into the clinical workflow with very modest effort and avoids excessive load to telephone hotlines. RESULTS: The test statuses and results are published on a secured webpage, enabling regular status checks by patients; status checks are performed without the use of smartphones, which has some importance, as smartphone usage diminishes with age. Stress tests and statistics show the performance of our software. CTest is currently running at two university hospitals in Germany-University Hospital Ulm and University Hospital Tübingen-with thousands of tests being performed each week. Results show a mean number of 10 (SD 2.8) views per testee. CONCLUSIONS: CTest runs independently of existing infrastructures, aims at straightforward integration, and aims for the safe transmission of information. The system is easy to use for testees. QR (Quick Response) code links allow for quick access to the test results. The mean number of views per entry indicates a reduced amount of time for both health care professionals and testees. The system is quite generic and can be extended and adapted to other communication tasks.

The impact of incomplete clinical information and initial biopsy technique on the histopathological diagnosis of cutaneous melanoma.

BACKGROUND/OBJECTIVES: Increased rates of histopathological misdiagnosis of melanoma have been associated with incisional punch more so than shave biopsy when compared with complete excisional biopsy. It is unknown how the increasing utilisation of shave biopsy may impact melanoma diagnosis. The extent to which the provision of clinical information to the pathologist may improve diagnostic accuracy remains unclear. This study assessed the impact of both initial biopsy technique and provision of adequate clinical information to pathologists on the accuracy of histopathological diagnosis of melanoma and disease progression. METHODS: We conducted a retrospective cohort with nested case-control study of all histopathological false-negative and false-positive melanoma diagnoses from January 2014 to May 2019 from the Victorian Melanoma Service electronic database. Cases were assessed for the initial biopsy type, provision of clinical information on pathology request forms and disease progression associated with false-negative diagnosis. RESULTS: Partial shave biopsy had higher odds of false-negative (OR 5.19, 95% CI 2.89-9.32; P < 0.001) and false-positive diagnoses (OR 1.95, 95% CI 1.45-2.63; P < 0.001) of melanoma when compared with elliptical excisional biopsy. These odds ratios were comparable with those found with incisional punch biopsy. Providing the suspected clinical diagnosis to pathologists also reduced the odds of false-negative diagnosis with melanoma progression by 3.8-fold (P = 0.02). CONCLUSION: The choice of initial biopsy technique and providing the suspected clinical diagnosis to pathologists are important for correct histopathological diagnosis of cutaneous melanoma and prevention of further disease progression.

Attitudes of nurses towards clinical information systems: a systematic review and meta-analysis.

AIM: This systematic review and meta-analysis evaluates the attitudes of Iranian nurses towards clinical information systems in nursing practice. BACKGROUND: Nurses are essential in the successful adoption and implementation of clinical information systems. METHODS: A systematic search was performed in Medline, Embase, Cochrane, CINAHL, Scopus, and Web of Science and Farsi databases, to retrieve relevant studies. The methodological quality of the studies is assessed via the Joanna Briggs Institute's critical appraisal checklist for analytical cross-sectional studies. The random effect model was utilized to analyse the data due to the high heterogeneity in the included studies (n = 17). RESULTS: Results indicate that clinical information systems impact on at least seven aspects of nursing practice, including documentation, patient safety, quality of treatment, communication, treatment management, nursing tasks and hospital resource management. Results also indicated that one aspect of using clinical information systems in nursing practice is satisfaction with the 'quality and design of clinical information systems', such as ease of use and learning, flexibility and software speed. CONCLUSION: Clinical information systems can contribute to different aspects of nursing practice. However, their design should improve significantly in order to help nurses perform their professional activities in an efficient and satisfactory manner. IMPLICATIONS FOR NURSING POLICY: Before the full deployment of clinical information systems, their usability should be tested. In pilot testing, nurses should provide necessary feedback about how well the systems work and improvements needed to meet their professional goals.

[Design and Implementation of Multifunctional Interactive Electronic Bedside Card System for Inpatients Based on Internet of Things Technology].

This research is based on data from clinical information systems such as HIS, EMR, LIS, etc, based on the functions of the traditional paper bedside card, relying on wired network technology, using the Internet of Things technology to design and develop a multi-functional intelligent interactive electronic bedside card system for inpatients. The functional framework of the system is introduced and discussed in detail, and the design is carried out from several aspects of system architecture, network architecture, software architecture, database and software system. The results show that the system has stable performance and can ensure the real-time and accuracy of medical information. The implementation of the system can fully meet the needs of clinical first-line ward management, reduce the workload of nursing staff, improve work efficiency, and reduce the possibility of medical accidents. At the same time, it can facilitate patients to see their own health information, improve patient experience. It has important guiding significance for the development of ward management and hospital information construction.

[Design and Implementation of Multifunctional Interactive Electronic Bedside Card System for Inpatients Based on Internet of Things Technology].

This research is based on data from clinical information systems such as HIS, EMR, LIS, etc, based on the functions of the traditional paper bedside card, relying on wired network technology, using the Internet of Things technology to design and develop a multi-functional intelligent interactive electronic bedside card system for inpatients. The functional framework of the system is introduced and discussed in detail, and the design is carried out from several aspects of system architecture, network architecture, software architecture, database and software system. The results show that the system has stable performance and can ensure the real-time and accuracy of medical information. The implementation of the system can fully meet the needs of clinical first-line ward management, reduce the workload of nursing staff, improve work efficiency, and reduce the possibility of medical accidents. At the same time, it can facilitate patients to see their own health information, improve patient experience. It has important guiding significance for the development of ward management and hospital information construction.

Clinical information extraction for preterm birth risk prediction.

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records.

Comprehensive assessment of deceased donor kidneys with clinical characteristics, pre-implant biopsy histopathology and hypothermic mechanical perfusion parameters is highly predictive of delayed graft function.

Background: Due to the current high demand for transplant tissue, an increasing proportion of kidney donors are considered extended criteria donors, which results in a higher incidence of delayed graft function (DGF) in organ recipients. Therefore, it is important to fully investigate the risk factors of DGF, and establish a prediction system to assess donor kidney quality before transplantation.Methods: A total of 333 donation after cardiac death kidney transplant recipients were included in this retrospective study. Both univariate and multivariate analyses were used to analyze the risk factors of DGF occurrence. Receiver operating characteristic (ROC) curves were used to analyze the predictive value of variables on DGF posttransplant.Results: The donor clinical scores, kidney histopathologic Remuzzi scores and hypothermic mechanical perfusion (HMP) parameters (flow and resistance index) were all correlated. 46 recipients developed DGF postoperatively, with an incidence of 13.8% (46/333). Multivariate logistic regression analysis of the kidney transplants revealed that the independent risk factors of DGF occurrence post-transplantation included donor score (OR = 1.12, 95% CI 1.06-1.19, p < 0.001), Remuzzi score (OR = 1.21, 95% CI 1.02-1.43, p = 0.029) and acute tubular injury (ATI) score (OR = 4.72, 95% CI 2.32-9.60, p < 0.001). Prediction of DGF with ROC curve showed that the area under the curve was increased to 0.89 when all variables (donor score, Remuzzi score, ATI score and HMP resistance index) were considered together.Conclusions: Combination of donor clinical information, kidney pre-implant histopathology and HMP parameters provide a more accurate prediction of DGF occurrence post-transplantation than any of the measures alone.

[Development of Clinical Information Navigation System Based on 3D Human Model].

A clinical information navigation system based on 3D human body model is designed. The system extracts the key information of diagnosis and treatment of patients by searching the historical medical records, and stores the focus information in a predefined structured patient instance. In addition, the rule mapping is established between the patient instance and the three-dimensional human body model, the focus information is visualized on the three-dimensional human body model, and the trend curve can be drawn according to the change of the focus, meanwhile, the key diagnosis and treatment information and the original report reference function are provided. The system can support the analysis, storage and visualization of various types of reports, improve the efficiency of doctors' retrieval of patient information, and reduce the treatment time.

Miyagi Medical and Welfare Information Network: A Backup System for Patient Clinical Information after the Great East Japan Earthquake and Tsunami.

On March 11, 2011, the Great East Japan Earthquake and ensuing tsunami that hit the northeastern coastal region of Japan caused about 18,000 casualties and destroyed numerous buildings. Additionally, many medical facilities were damaged and patient medical records lost. In order to maintain patient clinical information, a prefectural medical network system, the Miyagi Medical and Welfare Information Network (MMWIN), began providing backup data storage services in 2013 for hospitals, clinics, pharmacies, and other care facilities as a precaution for upcoming disasters. This system also facilitates the sharing of clinical information trans-institutionally as long as patients provide consent for this. In the present study, we examined the development of the MMWIN and its efficiency during the 5 years from its launch, and identified general problems to maintain such a backup system. At the end of 2018, the system contained backup data from more than 11 million patients with more than 420 million data items; more than 900 facilities were MMWIN users, and the number of patients consenting to sharing their clinical information reached 90,000. The use of the system has become widespread and the accumulating data should be utilized for research in the future. Maintaining a balance between income and cost is critical to make this project independent from local government subsidies.

Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR.

BACKGROUND: Clinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important. OBJECTIVE: This study aimed to develop a retrieval method to identify CIMs online to represent EHR data. METHODS: We proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics. RESULTS: We downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering "diagnostic reports," "problem list," "patients background," "clinical decision," etc, as well as models that other retrieval methods could not find, such as "problems and diagnoses." CONCLUSIONS: The graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable.