Incomplete recording of race/colour in health information systems in Brazil: time trend, 2009-2018. / Tendência temporal da incompletude do registro da raça/cor nos sistemas de informação em saúde do Brasil, 2009-2018.

This ecological study of time trends and multiple groups evaluated incompleteness in the race/colour field of Brazilian health information system records and the related time trend, 2009-2018, for the diseases and disorders most prevalent in the black population. The Romero and Cunha (2006) classification was applied in order to examine incompleteness using secondary data from Brazil's National Notifiable Diseases System, Hospital Information System and Mortality Information System, by administrative regions of Brazil, while percentage underreporting and time trend were calculated using simple linear regression models with Prais-Winsten correction (p-value<0.05). All records scored poorly except those for mortality from external causes (excellent), tuberculosis (good) and infant mortality (fair). An overall downward trend was observed in percentage incompleteness. Analysis by region found highest mean incompleteness in the North (30.5%), Northeast (33.3%) and Midwest (33.0%) regions. The Southeast and Northeast regions showed the strongest downward trends. The findings intended to increase visibility on the implications of the race/color field for health equity.

Propõe-se avaliar a incompletude e a tendência temporal do preenchimento do campo raça/cor das doenças e agravos mais prevalentes na população negra nos Sistemas de Informação em Saúde do Brasil, 2009-2018. Trata-se de estudo ecológico de tendência temporal e múltiplos grupos. Foi adotada a classificação de Romero e Cunha (2006) para análise da incompletude e utilizados dados secundários do Sistema Nacional de Agravos de Notificação, Sistema de Informações Hospitalares e Sistema de Informações sobre Mortalidade do Brasil e regiões brasileiras, calculada a proporção de subnotificação e a tendência temporal, utilizando o modelo de regressão linear simples, com correção Prais-Winsten (p-valor<0,05). Excetuando-se os registros de mortalidade por causas externas (excelente), tuberculose (bom) e mortalidade infantil (regular), todos os registros apresentaram escore ruim. Observou-se tendência decrescente da proporção de incompletude. A análise por região mostrou que as maiores médias de incompletude foram registradas na região Norte (30,5%), Nordeste (33,3%) e Centro-Oeste (33,0%). As regiões Sudeste e Nordeste foram as que mais apresentaram tendência decrescente. Os resultados visam ampliar a visibilidade acerca das implicações do preenchimento do campo raça/cor para a equidade em saúde.

Probabilistic Linkage Creates a Novel Database to Study Bronchiolitis Care in the PICU.

OBJECTIVES: Lack of a comprehensive database containing diagnosis, patient and clinical characteristics, diagnostics, treatments, and outcomes limits needed comparative effectiveness research (CER) to improve care in the PICU. Combined, the Pediatric Hospital Information System (PHIS) and Virtual Pediatric Systems (VPS) databases contain the needed data for CER, but limits on the use of patient identifiers have thus far prevented linkage of these databases with traditional linkage methods. Focusing on the subgroup of patients with bronchiolitis, we aim to show that probabilistic linkage methods accurately link data from PHIS and VPS without the need for patient identifiers to create the database needed for CER. METHODS: We used probabilistic linkage to link PHIS and VPS records for patients admitted to a tertiary children's hospital between July 1, 2017 to June 30, 2019. We calculated the percentage of matched records, rate of false-positive matches, and compared demographics between matched and unmatched subjects with bronchiolitis. RESULTS: We linked 839 of 920 (91%) records with 4 (0.5%) false-positive matches. We found no differences in age (P = .76), presence of comorbidities (P = .16), admission illness severity (P = .44), intubation rate (P = .41), or PICU stay length (P = .36) between linked and unlinked subjects. CONCLUSIONS: Probabilistic linkage creates an accurate and representative combined VPS-PHIS database of patients with bronchiolitis. Our methods are scalable to join data from the 38 hospitals that jointly contribute to PHIS and VPS, creating a national database of diagnostics, treatment, outcome, and patient and clinical data to enable CER for bronchiolitis and other conditions cared for in the PICU.

Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack.

Elderly hypertensive patients diagnosed with transient ischemic attack (TIA) are at a heightened risk for developing acute ischemic stroke (AIS). This underscores the critical need for effective risk prediction and identification of predictive factors. In our study, we utilized patient data from peripheral blood tests and clinical profiles within hospital information systems. These patients were followed for a three-year period to document incident AIS. Our cohort of 11,056 individuals was randomly divided into training, validation, and testing sets in a 5:2:3 ratio. We developed an XGBoost model, developed using selected indicators, provides an effective and non-invasive method for predicting the risk of AIS in elderly hypertensive patients diagnosed with TIA. Impressively, this model achieved a balanced accuracy of 0.9022, a recall of 0.8688, and a PR-AUC of 0.9315. Notably, our model effectively encapsulates essential data variations involving mixed nonlinear interactions, providing competitive performance against more complex models that incorporate a wider range of variables. Further, we conducted an in-depth analysis of the importance and sensitivity of each selected indicator and their interactions. This research equips clinicians with the necessary tools for more precise identification of high-risk individuals, thereby paving the way for more effective stroke prevention and management strategies.

Structured Data Retrieval and Analysis of HL7 v2 Messages with Elasticsearch.

The era of the electronic health record (EHR) requires lots of semantic interoperability for data sharing and reusability. We select HL7 v2 messages as the most common structured data type in hospital information systems, to investigate the plausibility of using Elasticsearch (ES) as a healthcare search engine and data analytics tool. Due to the facts, Elasticsearch can be integrated as a powerful searchable database for practical healthcare applications, to analyze structured healthcare data from various locations. It allows easy and efficient searching for complex query tasks.

Reviewing Challenges in Specifying Interoperability Requirement in Procurement of Health Information Systems.

Procurement of health information systems (HIS) is a complex and critical task that requires early identification of interoperability requirements. However, specifying adequate requirements is often associated with several challenges. We examined relevant peer-reviewed literature and public documents (policy documents, annual reports, and newspapers) to summarize existing challenges in specifying interoperability requirement during procurement of HISs. In this study, 32 public documents and 2343 peer-reviewed articles were found using Google search engine, Springer, PubMed and ScienceDirect. Collected data were analyzed using a thematic coding schema. Our result shows that challenges related to describing the needs properly, conflicting needs and knowledge gaps are shared between most articles. Further research in the direction of developing a model that can bridge knowledge gaps, facilitate interdisciplinary collaboration, and help to avoid fuzzy requirements is needed.

Implementation of a Multilingual Electronic Medical Questionnaire: For Use Inside or Outside the Hospital.

We implemented a multilingual medical questionnaire system, which allows patients to answer questionnaires both in and out of the hospital. The response data are sent to and stored as structured data on the server in hospital information system, and could be converted to Japanese and quoted as part of progress notes in the electronic medical record.

Development of Clinical Decision Support System for Patient Blood Management in Hospital Information System.

A data pipeline was developed to send and receive patient blood management (PBM) data from all medical institutions in Korea. By incorporating the collected data with national big data, the system will be able to generate key performance index for each medical institution. The central PBM system also provides feedback to each individual medical institution.

Designing a comprehensive minimum dataset for patients with COVID-19 in Iranian hospital information systems.

BACKGROUND: The Minimum Data Set (MDS) plays a vital role in data exchange, collection and quality improvement. In the context of the COVID-19 pandemic, there is a need for a tailored MDS that aligns with the specific information needs of the Iranian community and integrates seamlessly into the country's Hospital Information Systems (HIS). OBJECTIVE: The study aimed to develop a comprehensive MDS for COVID-19 patients in Iran, with objectives to identify essential data elements and integrate the MDS into HIS, enhancing data exchange and supporting decision-making. METHOD: This study employed a comparative-descriptive approach to design COVID-19 patient data elements based on World Health Organisation and Centers for Disease Control and Prevention guidelines. The Delphi technique involved 35 experts in two rounds for checklist refinement. The finalised MDS consisted of 9 main terms and 80 sub-terms, analysed using descriptive statistics and IBM SPSS software. RESULTS: Of 35 experts involved with the study, 69% were male and 31% female, and Health Information Management experts were the majority (34%). The refined MDS for COVID-19 in Iran comprises 50 data elements, while 30 elements were excluded. The MDS includes 8 main terms and 80 sub-terms, with unanimous approval for identity, underlying disease, and treatment sections. CONCLUSION: The customised MDS for COVID-19 patients in Iran addresses data collection challenges and supports effective disease prevention and management. By providing comprehensive and reliable information, the MDS enhances healthcare quality, facilitates timely access to medical records, and fosters integrated health services.

Re-ordered fuzzy conformance checking for uncertain clinical records.

Modern hospitals implement clinical pathways to standardize patients' treatments. Conformance checking techniques provide an automated tool to assess whether the actual executions of clinical processes comply with the corresponding clinical pathways. However, clinical processes are typically characterized by a high degree of uncertainty, both in their execution and recording. This paper focuses on uncertainty related to logging clinical processes. The logging of the activities executed during a clinical process in the hospital information system is often performed manually by the involved actors (e.g., the nurses). However, such logging can occur at a different time than the actual execution time, which hampers the reliability of the diagnostics provided by conformance checking techniques. To address this issue, we propose a novel conformance checking algorithm that leverages principles of fuzzy set theory to incorporate experts' knowledge when generating conformance diagnostics. We exploit this knowledge to define a fuzzy tolerance in a time window, which is then used to assess the magnitude of timestamp violations of the recorded activities when evaluating the overall process execution compliance. Experiments conducted on a real-life case study in a Dutch hospital show that the proposed method obtains more accurate diagnostics than the state-of-the-art approaches. We also consider how our diagnostics can be used to stimulate discussion with domain experts on possible strategies to mitigate logging uncertainty in the clinical practice.

Internações por condições sensíveis à atenção primária à saúde: associação com a cobertura da atenção primária, 2015 ­ 2021 / Hospitalization for sensitive conditions in primary care: association with primary health care support, 2015-2021 / Hospitalizaciones por afecciones sensibles a la atención primaria: asociación con la cobertura de atención primaria, 2015 ­ 2021

Objetivo: Avaliar as tendências e associações relacionadas as coberturas e internações por condições sensíveis à atenção primária à saúde no município de Fortaleza/Ceará/Brasil, no período de 2015 a 2021. Métodos: Estudo transversal com dados secundários (Sistema de Informações Hospitalares do Sistema Único de Saúde, E- gestor atenção básica e o Instituto Brasileiro de Geografia e Estatística). Utilizou-se o coeficiente de correlação de Pearson para as associações. Resultados: Foram registrados 176.330 internações por condições sensíveis, totalizando 8 principais, correspondendo a 78.5% do total. Obteve-se correlação inversa significativa entre a cobertura de atenção primária e internações por condições sensíveis: r=-0.86, (IC95%: -0.91/-0.61); p<0.001, bem como uma correlação moderada com cobertura de agente comunitário e internações (r=-0.59 (IC95%: -0.68/-0.54); p<0.001) Conclusão: O aumento das internações por condições sensíveis está diretamente relacionado com a cobertura da atenção primária. Além disso, enfrenta-se uma dupla carga de doenças, coexistindo as doenças infecciosas/parasitárias em concomitância com as crônicas.

Objective: To assess trends and associations related to coverage and hospitalizations for conditions sensitive to primary health care in the city of Fortaleza/Ceará/Brazil, from 2015 to 2021. Methods: Cross-sectional study with secondary data (Hospital Information System of the National Unified Health System, E- manager for primary care and the Brazilian Institute of Geography and Statistics). Pearson's correlation coefficient was used to measure associations. Results: 176,330 hospitalizations for sensitive conditions were recorded, totaling 8 main ones, corresponding to 78.5% of the total. A significant inverse correlation was obtained between primary care coverage and hospitalizations for sensitive conditions: r=-0.86, (95%CI: -0.91/-0.61); p<0.001, as well as a moderate correlation with community agent coverage and hospitalizations (r=-0.59 (95%CI: -0.68/-0.54); p<0.001) Conclusion: The increase in hospitalizations for sensitive conditions is directly associated to the primary care coverage. In addition, there is a double burden of disease, with infectious/parasitic diseases coexisting with chronic ones.

Evaluar las tendencias y asociaciones relacionadas con la cobertura y hospitalizaciones por condiciones sensibles a la atención primaria de salud en la ciudad de Fortaleza/Ceará/Brasil de 2015 a 2021. Métodos: Estudio transversal con datos secundarios (Sistema de Informações Hospitalares do Sistema Único de Saúde, E-gestor atenção básica e Instituto Brasileiro de Geografia e Estatística). Se utilizó el coeficiente de correlación de Pearson para las asociaciones. Resultados: Hubo 176.330 hospitalizaciones por condiciones sensibles, totalizando 8 condiciones principales, correspondiendo a 78,5% del total. Se obtuvo una correlación inversa significativa entre la cobertura de atención primaria y las hospitalizaciones por afecciones sensibles: r=- 0,86, (IC 95%: -0,91/-0,61); p<0,001, así como una correlación moderada con la cobertura de agentes comunitarios y las hospitalizaciones (r=-0,59 (IC 95%: -0,68/-0,54); p<0,001) Conclusión: El aumento de las hospitalizaciones por afecciones sensibles está directamente relacionado con la cobertura de atención primaria. Además, se enfrenta a una doble carga de enfermedad, coexistiendo enfermedades infecciosas/parasitarias en concomitancia con enfermedades crónicas.

The Large-Scale Implementation of a Health Information System in Brazilian University Hospitals: Process and Outcomes.

Governments around the globe are paving the way for healthcare services that can have a profound impact on the overall well-being and development of their nations. However, government programs to implement health information technologies on a large-scale are challenging, especially in developing countries. In this article, the process and outcomes of the large-scale implementation of a hospital information system for the management of Brazilian university hospitals are analyzed. Based on a qualitative approach, this research involved 21 hospitals and comprised a documentary search, interviews with 24 hospital managers and two system user focus groups, and a questionnaire of 736 respondents. Generally, we observed that aspects relating to the wider context of system implementation (macro level), the managerial structure, cultural nuances, and political dynamics within each hospital (meso level), as well as the technology, work activities, and individuals themselves (micro level) acted as facilitators and/or obstacles to the implementation process. The dynamics and complex interactions established between these aspects had repercussions on the process, including the extended time necessary to implement the national program and the somewhat mixed outcomes obtained by hospitals in the national network. Mostly positive, these outcomes were linked to the eight emerging dimensions of practices and work processes; planning, control, and decision making; transparency and accountability; optimization in the use of resources; productivity of professionals; patient information security; safety and quality of care; and improvement in teaching and research. We argued here that to maximize the potential of information technology in healthcare on a large-scale, an integrative and cooperative vision is required, along with a high capacity for change management, considering the different regional, local, and institutional contexts.

Cloud-based digital healthcare development for precision medical hospital information system.

Aim: This study aims to develop a cloud-based digital healthcare system for precision medical hospital information systems (P-HIS). Methods: In 2020, international standardization of P-HIS clinical terms and codes was performed. In 2021, South Korea's first tertiary hospital cloud was established and implemented successfully. Results: P-HIS was applied at Korea's first tertiary general hospital. Common data model-compatible precision medicine/medical service solutions were developed for medical support. Ultrahigh-quality medical data for precision medicine were acquired and built using big data. Joint global commercialization and dissemination/spreading were achieved using the P-HIS consortium and global common data model-based observational medical outcome partnership network. Conclusion: To provide personalized precision medical services in the future, establishing and using big medical data is essential.

Personalizable AI platform for universal access to research and diagnosis in digital pathology.

BACKGROUND AND MOTIVATION: Digital pathology has been evolving over the last years, proposing significant workflow advantages that have fostered its adoption in professional environments. Patient clinical and image data are readily available in remote data banks that can be consumed efficiently over standard communication technologies. The appearance of new imaging techniques and advanced artificial intelligence algorithms has significantly reduced the burden on medical professionals by speeding up the screening process. Despite these advancements, the usage of digital pathology in professional environments has been slowed down by poor interoperability between services resulting from a lack of standard interfaces and integrative solutions. This work addresses this issue by proposing a cloud-based digital pathology platform built on standard and open interfaces. METHODS: The work proposes and describes a vendor-neutral platform that provides interfaces for managing digital slides, and medical reports, and integrating digital image analysis services compatible with existing standards. The solution integrates the open-source plugin-based Dicoogle PACS for interoperability and extensibility, which grants the proposed solution great feature customization. RESULTS: The solution was developed in collaboration with iPATH research project partners, including the validation by medical pathologists. The result is a pure Web collaborative framework that supports both research and production environments. A total of 566 digital slides from different pathologies were successfully uploaded to the platform. Using the integration interfaces, a mitosis detection algorithm was successfully installed into the platform, and it was trained with 2400 annotations collected from breast carcinoma images. CONCLUSION: Interoperability is a key factor when discussing digital pathology solutions, as it facilitates their integration into existing institutions' information systems. Moreover, it improves data sharing and integration of third-party services such as image analysis services, which have become relevant in today's digital pathology workflow. The proposed solution fully embraces the DICOM standard for digital pathology, presenting an interoperable cloud-based solution that provides great feature customization thanks to its extensible architecture.

[Spatial analysis of maternal morbidity and mortality in users of the Brazilian Unified National Health System in the city of Rio de Janeiro, Brazil, 2014-2016]. / Análise espacial da morbimortalidade materna em usuárias do Sistema Único de Saúde no Município do Rio de Janeiro, Brasil, 2014-2016.

The objective of this study is to analyze the maternal morbidity and mortality of women treated in hospitals of the Brazilian Unified National Health System (SUS) in the city of Rio de Janeiro in the period 2014-2016. An ecological study was conducted using data from the Brazilian Information System on Live Birth (SINASC), the Brazilian Mortality Information System (SIM), and the Brazilian Hospital Information System (SIH/SUS). For the analysis of the maternal mortality ratio (MMR), data from the SIM were used. For the analysis of maternal morbidity, World Health Organization criteria were used to estimate the ratios of maternal near miss and potentially life-threatening conditions. SINASC was used to retrieve data on the number of live births, for demographic characterization, social aspects, and access to prenatal care. To evaluate the spatial association between the indicators MMR, ratios of maternal near miss, and potentially life-threatening conditions and the demographic, social, obstetric, and access indicators, obtained from SINASC, the bivariate Moran Index was estimated with a significance level of 0.05, using the GeoDa program. In the period analyzed, the MMR in the Rio de Janeiro was 94.16/100,000 live births, the ratio of maternal near miss was 28.21/1,000 live births, and the potentially life-threatening conditions was 34.31/1,000 live births. Cases of potentially life-threatening conditions were used for the first time in this study and presented diagnoses and procedures during hospitalization more consistent with the maternal mortality profile in the city of Rio de Janeiro. There was a significant association between MMR and percentage of live births in SUS, potentially life-threatening conditions and percentage of live births in SUS, and potentially life-threatening conditions and being single.

O objetivo deste estudo é analisar a morbimortalidade materna de mulheres atendidas em hospitais do Sistema Único de Saúde (SUS) no Município do Rio de Janeiro, Brasil, no período de 2014 a 2016. Foi realizado estudo ecológico, por meio da coleta de dados do Sistema de Informações sobre Nascidos Vivos (SINASC), Sistema de Informação sobre Mortalidade (SIM) e Sistema de Informações Hospitalares (SIH/SUS). Para analisar a razão de mortalidade materna (RMM), foram utilizados dados do SIM. Para investigar a morbidade materna, adotaram-se critérios da Organização Mundial da Saúde para estimar as razões de near miss materno e de condições potencialmente ameaçadoras à vida. Dados do SINASC foram usados para número de nascidos vivos e caracterização demográfica, social e de acesso a serviço de pré-natal. Para avaliar a associação espacial entre os indicadores RMM, razões de near miss materno e condições potencialmente ameaçadoras à vida e os indicadores demográficos, sociais, obstétricos e de acesso obtidos no SINASC, foi calculado o índice de Moran bivariado com nível de 0,05 de significância, por meio do programa GeoDa. No período analisado, a RMM no Município do Rio de Janeiro foi de 94,16/100 mil nascidos vivos, a razão de near miss materno de 28,21/1.000 nascidos vivos e a razão de condições potencialmente ameaçadoras à vida de 34,31/1.000 nascidos vivos. Casos de condições potencialmente ameaçadoras à vida foram utilizados pela primeira vez neste estudo e apresentaram diagnósticos de internação e procedimentos realizados mais condizentes com o perfil de mortalidade materna no Município do Rio de Janeiro. Houve associação significativa entre RMM e percentual de nascidos vivos no SUS, razão de condições potencialmente ameaçadoras à vida e percentual de nascidos vivos no SUS e razão de condições potencialmente ameaçadoras à vida e ser solteira.

El objetivo de este estudio es analizar la morbimortalidad materna de las mujeres atendidas en hospitales del Sistema Único de Salud (SUS) del municipio de Rio de Janeiro, Brasil, en el período 2014-2016. Fue realizado un estudio ecológico, por medio del uso de datos del Sistema de Información sobre Nacidos Vivos (SINASC), Sistema de Información sobre Mortalidad (SIM) y del Sistema de Información Hospitalaria (SIH/SUS). Para el análisis de la razón de mortalidad materna (RMM) se utilizaron los datos SIM. Para el análisis de la morbilidad materna se utilizaron los criterios de la Organización Mundial de la Salud para estimar las razón de near miss materno y de condiciones potencialmente amenazantes a la vida. Para el número de nacidos vivos y la información demográfica, social y de acceso al servicio de atención prenatal fueron utilizados datos del SINASC. Para evaluar la asociación espacial entre los indicadores RMM, razon de near miss materno y razon de condiciones potencialmente amenazantes a la vida y los indicadores demográficos, sociales, obstétricos y de accesos obtenidos en el SINASC fue calculado el Índice de Moran bivariado con nivel de 0,05 de significación, usando el programa GeoDa. En el período analizado, la RMM en el municipio de Rio de Janeiro fue de 94,16/100.000 nascidos vivos, la razón de near miss materno de 28,21/1.000 nascidos vivos y la razón de condiciones potencialmente amenazantes a la vida de 34,31/1.000 nascidos vivos. Los casos de condiciones potencialmente amenazantes a la vida se utilizaron por primera vez en este estudio y presentaron diagnósticos de hospitalización y procedimientos realizados más acordes con el perfil de mortalidad materna en el Municipio de Rio de Janeiro Hubo una asociación significativa entre RMM y el porcentaje de nacidos vivos en el SUS, razón de condiciones potencialmente amenazantes a la vida y el porcentaje de nacidos vivos en el SUS y razón de condiciones potencialmente amenazantes a la vida y ser soltera.

Ranking the Critical Success Factors for Hospital Information Systems Using a Fuzzy Analytical Hierarchy Process.

The design, development, and maintenance of hospital information systems face major challenges, which ultimately cause failures of these information systems. This study aimed to identify and rank critical success factors for hospital information systems by applying a fuzzy analytical hierarchy process. Potential critical success factors that could contribute to the success of hospital information systems were identified and extracted through a systematic review of the relevant studies. A questionnaire containing the critical success factors was designed and distributed to 250 hospital information system professionals. The hierarchical structure of the critical success factors was defined by using an exploratory factor analysis, and pairwise comparison matrices of the fuzzy analytical hierarchy process model were designed based on the identified factor structure. As a result, 50 potential critical success factors were extracted from 21 articles, and their content validity and face validity were assessed by the experts. Based on the exploratory factor analysis results, 36 critical success factors were classified into seven dimensions: organizational fitness, user-friendliness, maintainability, portability, productivity, reliability, and organizational and external support. The fuzzy analytical hierarchy process results indicated that reliability, user-friendliness, and organizational fitness (with 20.3, 19.9, and 18 points, respectively) had the greatest impact on the success of hospital information systems. The findings revealed that managers and policymakers should consider these critical success factors in designing and developing hospital information systems.

Factors Influencing Clinicians' Use of Hospital Information Systems for Infection Prevention and Control: Cross-Sectional Study Based on the Extended DeLone and McLean Model.

BACKGROUND: Healthcare-associated infections have become a serious public health problem. Various types of information systems have begun to be applied in hospital infection prevention and control (IPC) practice. Clinicians are the key users of these systems, but few studies have assessed the use of infection prevention and control information systems (IPCISs) from their perspective. OBJECTIVE: This study aimed to (1) apply the extended DeLone and McLean Information Systems Success model (D&M model) that incorporates IPC culture to examine how technical factors like information quality, system quality, and service quality, as well as organizational culture factors affect clinicians' use intention, satisfaction, and perceived net benefits, and (2) identify which factors are the most important for clinicians' use intention. METHODS: A total of 12,317 clinicians from secondary and tertiary hospitals were surveyed online. Data were analyzed using partial least squares-structural equation modeling and the importance-performance matrix analysis. RESULTS: Among the technical factors, system quality (ß=.089-.252; P<.001), information quality (ß=.294-.102; P<.001), and service quality (ß=.126-.411; P<.001) were significantly related to user satisfaction (R2=0.833), use intention (R2=0.821), and perceived net benefits (communication benefits [R2=0.676], decision-making benefits [R2=0.624], and organizational benefits [R2=0.656]). IPC culture had an effect on use intention (ß=.059; P<.001), and it also indirectly affected perceived net benefits (ß=.461-.474; P<.001). In the importance-performance matrix analysis, the attributes of service quality (providing user training) and information quality (readability) were present in the fourth quadrant, indicating their high importance and low performance. CONCLUSIONS: This study provides valuable insights into IPCIS usage among clinicians from the perspectives of technology and organization culture factors. It found that technical factors (system quality, information quality, and service quality) and hospital IPC culture have an impact on the successful use of IPCISs after evaluating the application of IPCISs based on the extended D&M model. Furthermore, service quality and information quality showed higher importance and lower performance for use intention. These findings provide empirical evidence and specific practical directions for further improving the construction of IPCISs.

[Development of mortality prediction model for critically ill patients based on multidimensional and dynamic clinical characteristics].

OBJECTIVE: To develop a mortality prediction model for critically ill patients based on multidimensional and dynamic clinical data collected by the hospital information system (HIS) using random forest algorithm, and to compare the prediction efficiency of the model with acute physiology and chronic health evaluation II (APACHE II) model. METHODS: The clinical data of 10 925 critically ill patients aged over 14 years old admitted to the Third Xiangya Hospital of Central South University from January 2014 to June 2020 were extracted from the HIS system, and APACHE II scores of the critically ill patients were extracted. Expected mortality of patients was calculated according to the death risk calculation formula of APACHE II scoring system. A total of 689 samples with APACHE II score records were used as the test set, and the other 10 236 samples were used to establish the random forest model, of which 10% (n = 1 024) were randomly selected as the validation set and 90% (n = 9 212) were selected as the training set. According to the time series of 3 days before the end of critical illness, the clinical characteristics of patients such as general information, vital signs data, biochemical test results and intravenous drug doses were selected to develope a random forest model for predicting the mortality of critically ill patients. Using the APACHE II model as a reference, receiver operator characteristic curve (ROC curve) was drawn, and the discrimination performance of the model was evaluated through the area under the ROC curve (AUROC). According to the precision and recall, Precision-Recall curve (PR curve) was drawn, and the calibration performance of the model was evaluated through the area under the PR curve (AUPRC). Calibration curve was drawn, and the consistency between the predicted event occurrence probability of the model and the actual occurrence probability was evaluated through the calibration index Brier score. RESULTS: Among the 10 925 patients, there were 7 797 males (71.4%) and 3 128 females (28.6%). The average age was (58.9±16.3) years old. The median length of hospital stay was 12 (7, 20) days. Most patients (n = 8 538, 78.2%) were admitted to intensive care unit (ICU), and the median length of ICU stay was 66 (13, 151) hours. The hospitalized mortality was 19.0% (2 077/10 925). Compared with the survival group (n = 8 848), the patients in the death group (n = 2 077) were older (years old: 60.1±16.5 vs. 58.5±16.4, P < 0.01), the ratio of ICU admission was higher [82.8% (1 719/2 077) vs. 77.1% (6 819/8 848), P < 0.01], and the proportion of patients with hypertension, diabetes and stroke history was also higher [44.7% (928/2 077) vs. 36.3% (3 212/8 848), 20.0% (415/2 077) vs. 16.9% (1 495/8 848), 15.5% (322/2 077) vs. 10.0% (885/8 848), all P < 0.01]. In the test set data, the prediction value of random forest model for the risk of death during hospitalization of critically ill patients was greater than that of APACHE II model, which showed by that the AUROC and AUPRC of random forest model were higher than those of APACHE II model [AUROC: 0.856 (95% confidence interval was 0.812-0.896) vs. 0.783 (95% confidence interval was 0.737-0.826), AUPRC: 0.650 (95% confidence interval was 0.604-0.762) vs. 0.524 (95% confidence interval was 0.439-0.609)], and Brier score was lower than that of APACHE II model [0.104 (95% confidence interval was 0.085-0.113) vs. 0.124 (95% confidence interval was 0.107-0.141)]. CONCLUSIONS: The random forest model based on multidimensional dynamic characteristics has great application value in predicting hospital mortality risk for critically ill patients, and it is superior to the traditional APACHE II scoring system.

Multi-Perspective Process Mining Interfaces for HL7 AuditEvent Repositories: XES and OCEL.

BACKGROUND: Medical information systems frequently use event logging, but these logs are not suitable for process mining as they are not logged in a standardized format. OBJECTIVES: Our goal is to enrich medical event logs for use in process mining. METHOD: We present an approach to convert events from standards- based repositories into the XES and OCEL formats commonly used in process mining. RESULTS: We tested this approach using simulated data from the Austrian breast cancer screening program. CONCLUSION: We aim to apply it to analyze care guidelines and improve hospital processes in the future.

Making a Virtue of Necessity - A Highly Structured Clinical Data Warehouse as the Source of Assured Truth in a Hospital.

Data-driven decision-making in health care is becoming increasingly important in daily clinical use. A data warehouse, storing all the clinically relevant information in a highly structured way, is a primary basis for achieving this goal. We are developing a clinical data warehouse where more than 20 years of clinical data can be persisted, and newly generated data from different sources can be integrated. A back room was created to store all hospital information system data in a PostgreSQL database. Due to the enormous number of diverse forms in the hospital information system, a broker service was developed that integrates the individual data sources into the data warehouse as soon as they are released for storage. The front room represents the interface from the infrastructure to the targeted analysis. Database query and visualization tools or business intelligence tools can display and analyze processed and interleaved data. In all areas of business and medicine, structured and quality-adjusted data is of major importance. With the help of a clinical data warehouse system, it is possible to perform patient-centered analyses and thus realize optimal therapy. Furthermore, it is possible to provide staff and management with dashboards for control purposes.

Why Are Data Missing in Clinical Data Warehouses? A Simulation Study of How Data Are Processed (and Can Be Lost).

In recent years, the development of clinical data warehouses (CDW) has put Electronic Health Records (EHR) data in the spotlight. More and more innovative technologies for healthcare are based on these EHR data. However, quality assessments on EHR data are fundamental to gain confidence in the performances of new technologies. The infrastructure developed to access EHR data - CDW - can affect EHR data quality but its impact is difficult to measure. We conducted a simulation on the Assistance Publique - Hôpitaux de Paris (AP-HP) infrastructure to assess how a study on breast cancer care pathways could be affected by the complexity of the data flows between the AP-HP Hospital Information System, the CDW, and the analysis platform. A model of the data flows was developed. We retraced the flows of specific data elements for a simulated cohort of 1,000 patients. We estimated that 756 [743;770] and 423 [367;483] patients had all the data elements necessary to reconstruct the care pathway in the analysis platform in the "best case" scenarios (losses affect the same patients) and in a random distribution scenario (losses affect patients at random), respectively.