Hospital Information System Data Research Articles

Hematological malignancies: Prevalence and hematological characteristics in a single center in southern Saudi Arabia.

To determine the prevalence of leukemia in the Aseer region of Saudi Arabia and the importance of hematological, biochemical and coagulation profiles for leukemic patients in the context of disease management. This retrospective study comprised 210 patients between 2012 and 2022 who had been diagnosed with leukemia at different ages. The multiple unpaired t-test was used to compare leukemic patients with control samples, which consisted of healthy individuals, and p<0.05 was taken as significant. The data was compiled from Aseer Central Hospital in the Aseer region and collected through peripheral blood smear and bone marrow biopsy (2012-2017) or by flow cytometry (2018-2022), according to the hospital information system and registry data. Of the total 210 leukemic patients (61.4% males and 38.6% females), 104 cases (2012-2017) were diagnosed based on peripheral blood smear and bone marrow biopsy, and 106 cases (2018-2022) based on flow cytometry. Fifteen subtypes of leukemia were identified, with chronic myeloid leukemia being the most common (34.2%), followed by acute myeloid leukemia (17.6%), chronic lymphoblastic leukemia (11.9%), and B-cell acute lymphoid leukemia (9.5%). Other rare cases were also found. Of the 210 leukemia cases diagnosed in the Aseer region between 2012-2022, the most common subtype was chronic myeloid leukemia, followed by acute myeloid leukemia. In all leukemia subtypes, distinctive significant changes were observed in hematological parameters, biochemical parameters, and coagulation profiles.

A case-control study on the clinical characteristics of granisetron-related arrhythmias and the development of a predictive nomogram.

Automatic monitoring and assessment are increasingly employed in drug safety evaluations using hospital information system data. The increasing concern about granisetron-related arrhythmias requires real-world studies to improve our understanding of its safety. This study aimed to analyze the incidence, clinical characteristics, and risk factors of granisetron-related arrhythmias in hospitalized patients using real-world data obtained from the Adverse Drug Event Active Surveillance and Assessment System-II (ADE-ASAS-II) and concurrently aimed to develop and validate a nomogram to predict the occurrence of arrhythmias. Retrospective automatic monitoring of inpatients using granisetron was conducted in a Chinese hospital from January 1, 2017, to December 31, 2021, to determine the incidence of arrhythmias using ADE-ASAS- II. Propensity score matching was used to balance confounders and analyze clinical characteristics. Based on risk factors identified through logistic regression analysis, a prediction nomogram was established and internally validated using the Bootstrap method. Arrhythmias occurred in 178 of 72,508 cases taking granisetron with an incidence of 0.3%. Independent risk factors for granisetron-related arrhythmias included medication duration, comorbid cardiovascular disease, concomitant use of other 5-hydroxytryptamine 3 receptor antagonists, alanine aminotransferase > 40 U/L, and blood urea nitrogen > 7.5mmol/L. The nomogram demonstrated good differentiation and calibration, with enhanced clinical benefit observed when the risk threshold ranged from 0.10 to 0.82. The nomogram, based on the five identified independent risk factors, may be valuable in predicting the risk of granisetron-related arrhythmias in the administered population, offering significant clinical applications.

Racial disparities in breast cancer survival after treatment initiation in Brazil: a nationwide cohort study

Breast cancer is a public health priority in Brazil and ensuring equity in health care is one of the cancer control plan goals. Our aim was to present the first assessment on the influence of race or skin colour on breast cancer survival at the national level. In this nationwide cohort study, data on women who initiated treatment for breast cancer in the public health-care system (Sistema Unico de Saúde), Brazil, were assembled through record linkage of administrative and mortality information systems. The administrative information systems were the Outpatient Information System (data from high complexity procedure authorisations) and the Hospital Information System (data from hospitalisation authorisations). We included women aged 19 years or older who started treatment between Jan 1, 2008, and Nov 30, 2010; self-identified as having White, Black, or Brown race or skin colour; had tumour stage I-IV; and were treated with chemotherapy or radiotherapy, or both. Patients were followed up until Dec 31, 2015. Patients with only hormone therapy records or who underwent only surgery were excluded. The Kaplan-Meier method was used to estimate crude overall survival for race or skin colour by time since treatment initiation, and Cox regression to estimate all-cause mortality hazard ratios (HRs) before and after adjustment for other covariates. We identified 59 811 women treated for stage I-IV breast cancer. 37 318 (62·4%) women identified themselves as White, 18 779 (31·4%) as Brown, and 3714 (6·2%) as Black. 5-year overall survival probability was higher for White women (74% [95% CI 73-74]) than Black women (64% [62-65]; p<0·0001). In adjusted regression models stratified by the absence of hormone therapy, Black women had a 24% (HR 1·24 [95% CI 1·16-1·34]; p<0·0001) higher risk of all-cause death than White women, and in the presence of hormone therapy Black women had a 25% (1·25 [1·14-1·38]; p<0·0001) higher risk of all-cause death than White women. Black skin colour was identified as a statistically significant risk marker for lower 5-year survival probability and higher risk of all-cause death among women treated for breast cancer by the Sistema Unico de Saúde. Actions to understand and mitigate this unfair difference in health results are urgently needed. Conselho Nacional de Desenvolvimento Científico e Tecnológico and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior and Pró-Reitoria de Pesquisa da Universidade Federal de Minas Gerais.

A real-world data analysis-based study of Chinese medicine treatment patterns after breast cancer surgery.

Based on the real clinical data of Hospital Information System to explore the common clinical syndromes of traditional Chinese medicine after breast cancer surgery, analysis of traditional Chinese medicine in the treatment of breast cancer after the compatibility law. The real medical records of breast cancer patients after surgery in a tertiary hospital in Sichuan Province were collected and screened to build a medical record database. Python language was used for data preprocessing to remove outliers and fill in missing values. Using International Business Machines Corporation (IBM) Statistical Product and Service Solutions (SPSS) Modeler software, Apriori association rules algorithm for data analysis, mining Chinese medicine treatment of breast cancer after common syndromes and the corresponding medication rules. A total of 472 cases of clinical real medical record data were included. Data analysis showed that there were 42 TCM syndromes after breast cancer surgery, of which the highest frequency was liver depression and spleen deficiency, qi deficiency and blood stasis, qi stagnation and blood stasis, qi and blood deficiency, qi and yin deficiency, phlegm and blood stasis. A total of 416 kinds of traditional Chinese medicine were involved. High-frequency drugs included angelica sinensis, coix seed, bupleurum, ginger magnolia bark, keel, oyster, astragalus, platycodon grandiflorum, antler frost, vinegar tortoise shell, poria cocos, lily, Jianqu, Ophiopogon japonicus (Maidong), Shancigu, etc. A total of 18 pairs of commonly used drug combinations were excavated, such as Fushen-Gancao-Chaihu-Angelica, Huangqi-Baishao-Jianghoupu, Chaihu-Huanhua-Maidong-Lily, Baizhu-Huangqi-Maidong, Fuling-Baishao, etc. The clinical syndrome type of traditional Chinese medicine after breast cancer surgery is mainly liver depression and spleen deficiency syndrome. The clinical treatment is mainly soothing liver and relieving depression, and harmonizing liver and spleen. Analyze the syndrome type and the corresponding drug compatibility law, and provide decision support for the clinical dialectical prescription of traditional Chinese medicine after breast cancer surgery.

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.

A Mobile App (Concerto) to Empower Hospitalized Patients in a Swiss University Hospital: Development, Design, and Implementation Report.

Patient empowerment can be associated with better health outcomes, especially in the management of chronic diseases. Digital health has the potential to promote patient empowerment. Concerto is a mobile app designed to promote patient empowerment in an in-patient setting. This implementation report focuses on the lessons learned during its implementation. The app was conceptualized and prototyped during a hackathon. Concerto uses hospital information system (HIS) data to offer the following key functionalities: a care schedule, targeted medical information, practical information, information about the on-duty care team, and a medical round preparation module. Funding was obtained following a feasibility study, and the app was developed and implemented in four pilot divisions of a Swiss University Hospital using institution-owned tablets. The project lasted for 2 years with effective implementation in the four pilot divisions and was maintained within budget. The induced workload on caregivers impaired project sustainability and warranted a change in our implementation strategy. The presence of a killer function would have facilitated the deployment. Furthermore, our experience is in line with the well-accepted need for both high-quality user training and a suitable selection of superusers. Finally, by presenting HIS data directly to the patient, Concerto highlighted the data that are not fit for purpose and triggered data curation and standardization initiatives. This implementation report presents a real-world example of designing, developing, and implementing a patient-empowering mobile app in a university hospital in-patient setting with a particular focus on the lessons learned. One limitation of the study is the lack of definition of a "key success" indicator.

Hematology profile analysis in coronavirus disease 2019 (COVID-19) patients.

Some hematological parameters were reported as markers to assess severity of COVID-19 patients. Comorbidities were risk factors for severe COVID-19. Differences in hematology profile based on severity and comorbidity, and correlation between hematology profile and Ct value were never studied at Makassar, Indonesia. The aim of this study were to know the differences of hematology profile based onseverity and comorbidity, and the correlation between hematology profile and Ct value in COVID-19 patients. This study was retrospective, cross-sectional of confirmed COVID-19 patients who had been hospitalized at Dr. Wahidin Sudirohusodo hospital, Makassar, since June to August 2020. Hematology profile, Ct value, comorbidity, and severity of COVID-19 patients were obtained from Hospital Information System Data. From 217 patients, subjects were 102 (47%) male dan 115 (53%) female, 127 mild-moderate patients (58.5%) and 90 severe patients (41.5%), 143 patients (65%) without comorbidity, 74 patients (35%) with comorbidity. White blood cells (WBC), red cell distribution width (RDW), neutrophil and monocyte count, and neutrophil lymphocyte ratio (NLR) were significantly higher in severe patients than mild-moderate patients (p<0.05), besides RBC, hemoglobin, hematocrit, lymphocyte and thrombocyte count were significantly lower in severe patients than mild-moderate patients (p<0.05). Hematology profile was not different significantly based on comorbidity and was not correlated significantly with Ct value, except eosinophil count (r=0.161; p=0.018). We suggest that hematology profile could predict the severity of COVID-19 patients. Moreover, eosinophil count could be considered to predict the infectivity of patient with COVID-19.

Evaluation of the Costs and Outcomes of COVID-19 Therapeutic Regimens in Hospitalized Patients in Shiraz.

COVID-19 patients in critical conditions are hospitalized and treated with various protocols including antiviral drugs, which have been updated repeatedly. This study was aimed to analyze the demographics, costs, and outcomes of drug regimens in COVID-19 patients hospitalized in “Ali Asghar” hospital, affiliated with Shiraz University of Medical Sciences, from March 2019 to December 2020 as a retrospective study, approved by the ethics committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.1003) on Dec. 28, 2020. Using hospital information system (HIS) data, 2174 patients receiving favipiravir, remdesivir, interferon-β, and Kaletra® were analyzed. Descriptive, univariate, and regression analyses were used. The costs and consequences of different drug regimens were significantly different (P value < 0.05); the highest and lowest costs belonged to remdesivir and Kaletra®, respectively. The highest and lowest mean length of stay and mortality were related to remdesivir and favipiravir, respectively. Mortality did not differ significantly with various regimens. Length of stay was significantly shorter with favipiravir and Kaletra® than interferon-β. Remdesivir had significantly the highest cost. Age presented a significantly positive relationship with mortality and length of stay. Besides, ICU admission significantly increased mortality, length of stay, and costs. Underlying diseases and low blood oxygen saturation contributed to mortality. COVID-19 correlation with age and underlying diseases is accordant with the published data. Given the highest costs and broad usage of remdesivir, besides controversies regarding its outcomes and side effects, a stricter evaluation of remdesivir benefits seems essential. Totally, COVID-19 therapeutic protocols should be selected carefully to optimize costs and outcomes.

Risk factors for development of hematotoxicity in elderly patients treated with Linezolid

Objective: To analyze the clinical data, especially the occurrence of hematotoxicity, of hospitalized elderly patients who took Linezolid (LZD), and to further explore the related risk factors. Methods: Our study enrolled the elderly inpatients treated with linezolid at the Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from January 2018 to September 2021. The hospital information system data were retrieved to retrospectively analyze the clinical characteristics of patients, particularly the clinical characteristics and related risk factors of patients who experienced hematotoxic reactions to LZD. Results: Of the 233 eligible cases included, 103 patients received empirical use of LZD (44.21%). The total effective rate was 76.39% (178/233). Among the 57 effective cases who received blood drug concentration monitoring, the trough concentration of LZD was high in 36.84 % (21/57) of elderly patients. Moreover, there were 15 patients with thrombocytopenia alone, 3 patients with decreased hemoglobin (HB) alone, and 3 patients with both thrombocytopenia and HB decrease. The patients who experienced hematotoxicity (developed a certain degree of hemoglobin decrease and/or thrombocytopenia) were more likely to be complicated with renal impairment (χ²=6.642, P=0.036), concomitantly using proton pump inhibitor (PPI) (χ²=4.566, P=0.033), and had a longer course of LZD treatment (P=0.041). There was no linear correlation between the trough concentration of LZD and glomerular filtration rate evaluated by Modification of Diet in Renal Disease Formula (eGFRMDRD) (R=0.226, P=0.136). Conclusions: The elderly patients, especially those with renal impairment, concomitant treatment with PPI, and a longer course of LZD treatment, exhibited a higher risk of hematotoxicity during LZD treatment. Hence, we should strengthen the protection of renal function, reduce drug interaction, and dynamically monitor the blood drug concentration of LZD to adjust its dose, thus implementing safer and more effective treatments.

Development and validation of a nomogram based on the hospital information system for quantitative assessment of the risk of cardiocerebrovascular complications of diabetes.

BackgroundAlthough the prevention and treatment of the cardiocerebrovascular complications (CCVCs) of diabetes have been clarified, their incidence is still high. This is largely due to the lack of predictive models to objectively assess the risk of CCVC in patients with type 2 diabetes mellitus (T2DM), reducing their treatment adherence. Despite the fact that the risk factors of CCVC in T2DM patients have been identified, no prediction model for identifying T2DM patients with the risk of CCVC is available. Therefore, the aim of this study is to establish a nomogram based on hospital information system data to quantitatively assess the risk of CCVCs in T2DM patients. This model is contributed to individualized therapeutic treatments and motivating T2DM patients to adhere to lifestyle interventions.MethodsThe medical records of 1,556 T2DM patients, comprising 1,145 cases in the training cohort and 411 in the validation cohort were retrospectively analyzed. CCVCs of diabetes, including coronary heart disease, cerebral ischemia, and intracerebral hemorrhage, were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to screen the independent correlates of CCVCs from the demographic information and laboratory test data, which were utilized to establish a nomogram for predicting the risk of CCVCs in these patients. We used internal and external validation based on the training and validation cohorts to evaluate the model performance.ResultsThe incidence of CCVCs in the training cohort (26.99%) was similar to the validation cohort (25.79%). Disease duration, body mass index (BMI), systolic blood pressure (SBP), glycosylated hemoglobin (HbA1c), and uric acid (UA) levels were finally included in the established nomogram. In both the internal and external validation, the nomogram showed good discrimination [area under the curve (AUC) =0.850 and 0.825, respectively] and calibration (P=0.127 and P=0.096, respectively). Decision curve analysis showed that the nomogram produced a net benefit in both the training and validation cohorts.ConclusionsThe nomogram developed for predicting the risk of CCVC in T2DM patients may help improve treatment adherence. Further multi-center prospective investigations are required to predict the timing of CVCC in T2DM patients.

Design and Optimization of Urinary Real-Time Nursing Model Based on Medical Internet of Things.

The medical Internet of Things (IoTs) can bind intelligent sensing devices with urinary nursing recipients and integrate information into various hospital information systems through network communication, so as to realize the intelligent perception, data collection, remote monitoring, information sharing, and other functions of urinary real-time nursing recipients. The urinary real-time nursing model can complete the expansion of hospital information system data to the bedside and the instant exchange of terminal data with the system through the medical IoTs and wireless local area network. Based on the summary and analysis of previous research results, this paper expounds on the research status and significance of the urinary real-time nursing model, elaborates on the development background, current status, and future challenges of medical IoTs, introduces the methods and principles of medical IoTs level evaluation system and transformation rules-based optimization algorithm, conducts urinary real-time nursing model design based on medical IoTs, analyzes model hardware design based on medical IoTs, performs model software design based on medical IoTs, proposes the urinary real-time nursing model optimization based on medical IoTs, explores the front-end function optimization of the urinary real-time nursing, implements the system program optimization of the urinary real-time nursing, discusses the hierarchical architecture of the urinary real-time nursing model, and finally carries out the role function analysis of the medical IoTs in the urinary real-time nursing model. The study results show that the urinary real-time nursing model based on medical IoTs can accurately and efficiently identify, manage, and integrate clinical nursing procedures and data such as patients, diagnoses, drugs, and can optimize nursing workflow, strengthen quality control, and improve nursing efficiency and provide patients with more convenient nursing services. The research results of this paper provide a reference for further research on the design and optimization of urinary real-time nursing model based on medical IoTs.

Hospital admissions through the National Health System (SUS) in the east of south expanded health region of Minas Gerais, Brazil, between 2014 and 2018

Hospitalizations occur electively in cases with no imminent risk to life or in cases of urgency and emergency, which demand immediate medical assistance. In a Health Region, these hospitalizations are conducted in reference hospitals. This study aimed to analyse hospital admissions in the Eastern South Region of Minas Gerais, by comparing the number of urgency and emergency admissions with elective procedures admissions and with the number of urgency admissions with sensitive to primary care conditions (CSAP). This longitudinal-retrospective-descriptive study was conducted with Hospital Information System (SIH) data relative to the 2014-2018 interval. Altogether, there were registered 129,524 hospitalizations, with 17,546 (13.55%) being elective admissions and 111,978 (86.45%) urgency procedures. Of the urgency hospitalizations total, 20,108 (17.95%) were CASP hospitalizations. The frequency of urgency hospitalization was six times higher than the elective hospitalization one, and even SPCC emergency admissions exceeded the elective hospitalizations offer.

Survival of Patients on Maintenance Hemodialysis Base on Comorbidity of Cardiovascular Disease in Persahabatan Central General Hospital 2015-2019

Chronic kidney disease is the progressive loss of kidney function over months or years. The significant increase in new cases of chronic kidney disease is in line with the increasing number of patients undergoing hemodialysis as kidney replacement therapy in an effort to survive. Comorbid cardiovascular disease is a major risk factor for morbidity and mortality with chronic kidney disease. The study was conducted to determine the survival of hemodialysis patients in the group with comorbid cardiovascular disease and the group without comorbid cardiovascular disease. This study used a retrospective cohort design. The location of this study was conducted at Persahabatan Central Public Hospital, DKI Jakarta, and used secondary data from the hospital information system data from 2015 to 2019. The variables significantly related to the survival of patients undergoing hemodialysis with comorbid cardiovascular disease were age, complications of anemia, diabetes mellitus, and hypertension. The age variable has a p-value of 0.029 with an HR of 1.54 (95% CI OR 1.043-2.262). The anemia variable has a p-value of 0.013 with an HR of 1.60 (95% CI 1.117-2.515). The diabetes mellitus variable has a p-value of 0.000 with HR2.71 (95% CI 1.780-4.11). The hypertension variable has a p-value of 0.004 with HR1.79 (95% CI 1.208-2.646). In conclusion, patients undergoing hemodialysis with comorbid cardiovascular disease have a risk of death of 0.76 times compared to patients undergoing hemodialysis with the comorbid non-cardiovascular disease. This study's internal validity was not good due to selection bias and non-differential misclassification information bias. Thus, the results of this study cannot be generalized.

Hyperuricemia prevalence and its association with metabolic disorders: a multicenter retrospective real-world study in China.

BackgroundThe prevalence of hyperuricemia (HUA) and gout continues to increase in China. Research suggests that HUA may be related to many diseases other than gout. However, further population research is required to investigate the association between HUA and metabolic syndromes. This study sought to investigate the prevalence of HUA in an average population in China, and the association between serum uric acid (UA) levels and related metabolic disorders.MethodsThis multicenter retrospective real-world study examined the hospital information system data of 4 tertiary hospitals in 3 provinces in China. The data of patients aged between 18 and 80 years, who had attended at least 1 medical appointment at which their UA level was recorded, were analyzed to evaluate associations between UA levels and metabolic disorders.ResultsAmong the 374,506 enrolled subjects (49.7% male; mean age 51.5 years old), the overall prevalence of HUA and gout were 14.8% and 0.5%, respectively. The prevalence was higher among males than females (17.6% vs. 12.0%, 0.8% vs. 0.1%; both P<0.001). Groups exhibiting higher UA levels had increased adjusted odds ratios for dyslipidemia and chronic kidney disease (CKD) in both sexes. Changes in UA levels from the baseline were negatively correlated with changes in the estimated glomerular filtration rate and hemoglobin A1c among both sexes (all P<0.001), and were positively correlated with changes in total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) (all P<0.05) among males, and changes in TC, TG, LDL-C and glucose (all P<0.001) among females.ConclusionsHUA is associated with dyslipidemia and CKD both cross-sectionally and longitudinally. Similar phenomena were observed in both sexes.

Evaluating the Effective Factors of Hospital Rooms on Patients' Recovery Using the Data Mining Method.

This study aims to investigate the effective environmental factors of hospital rooms in patients' recovery through data mining techniques. Previous studies have shown the positive effect of the interior environment of the hospitals on patients' recovery. The methods of these studies were mainly based on the evidence and patients' perception while hospital environments are associated with a large amount of data that make them an appropriate case for data mining studies. But data mining studies in hospitals mainly focused on medical and management purposes rather than evaluating the interior environment condition. We analyzed the hospital information system data of a hospital using Python programming language and some of its libraries. Preprocessing and eliminating the outliers, labeling and clustering of diseases, data visualization and analysis, final evaluation, and concluding were done using the knowledge discovery in databases process. Pearson coefficient value for rooms' area was .5 and, respectively, for the distance from the ward entrance and nursing station were .75 and .70. The χ2 values for the variables of room types, location, and occupation were 24.62, 18.98, and 21.53, respectively, and for the beds' location was 0.12. The results confirmed the correlation of the length of stay with the room types, location, and occupation, distance from the nursing station and ward entrance and also showed a moderate correlation with the rooms' area. However, no evidence was found about the relationship between the beds' location in rooms and patients' length of hospital stay.

Analyzing Acute Care Surgery Patient Flow in the Emergency Department During COVID-19 Pandemic.

During the COVID-19 pandemic, the number of visits in emergency departments (ED) worldwide decreased significantly based on several studies. This study aims to compare the patient flow in the emergency surgery department during the COVID-19 pandemic and a control period in the emergency department of a public tertiary care hospital in Greece. The overall patient flow reduction regarding the ED visits between the two examined periods was 49.07%. The emergency surgery department's corresponding visits were 235 and 552, respectively, which indicated an overall patient flow decrease of 57.43%. Chi-square analysis showed that age groups and ambulance use had statistically significant associations with the periods examined. An independent samples t-test was applied and deduced that the average patient's age was statistically significantly higher in the COVID-19 pandemic than in the non-pandemic period. By analyzing hospital information system data, useful conclusions can be drawn to prepare a surgical emergency unit better and optimize resource allocation in a healthcare facility in similar critical situations.

OpenEHR Mapper - A Tool to Fuse Clinical and Genomic Data Using the openEHR Standard.

Precision medicine is an emerging and important field for health care. Molecular tumor boards use a combination of clinical and molecular data, such as somatic tumor mutations to decide on personalized therapies for patients who have run out of standard treatment options. Personalized treatment decisions require clinical data from the hospital information system and mutation data to be accessible in a structured way. Here we introduce an open data platform to meet these requirements. We use the openEHR standard to create an expert-curated data model that is stored in a vendor-neutral format. Clinical and molecular patient data is integrated into cBioPortal, a warehousing solution for cancer genomic studies that is extended for use in clinical routine for molecular tumor boards. For data integration, we developed openEHR Mapper, a tool that allows to (i) process input data, (ii) communicate with the openEHR repository, and (iii) export the data to cBioPortal. We benchmarked the mapper performance using XML and JSON as serialization format and added caching capabilities as well as multi-threading to the openEHR Mapper.

Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment.

No-show appointments waste resources and decrease the sustainability of care. This study is an attempt to evaluate patient no-shows based on modes of appointment-making and types of appointments. We collected hospital information system data and appointment data including characteristics of patients, service providers, and clinical visits over a three-month period (1 September 2018 to 30 November 2018), at a large tertiary hospital in Seoul, Korea. We used multivariate logistic regression analyses to identify the factors associated with no-shows (Model 1). We further assessed no-shows by including the interaction term (“modes of appointment-making” X “type of appointment”) (Model 2). Among 1,252,127 appointments, the no-show rate was 6.12%. Among the modes of appointment-making, follow-up and online/telephone appointment were associated with higher odds of no-show compared to walk-in. Appointments for treatment and surgery had higher odds ratios of no-show compared to consultations. Tests for the interaction between the modes of appointment-making and type of appointment showed that follow-up for examination and online/telephone appointments for treatment and surgery had much higher odds ratios of no-shows. Other significant factors of no-shows include age, type of insurance, time of visit, lead time (time between scheduling and the appointment), type of visits, doctor’s position, and major diagnosis. Our results suggest that future approaches for predicting and addressing no-show should also consider and analyze the impact of modes of appointment-making and type of appointment on the model of prediction.

Correlation between bone turnover and metabolic markers with age and gender: a cross-sectional study of hospital information system data

BackgroundBone turnover and metabolic indicators are related to age and gender. Age and gender should be matched in subjects in disease control research of bone turnover and metabolism, but strict matching of gender and age increases the difficulty and cost of the research. Therefore, the aim of this study was to solve it is necessary to strictly match age and gender in clinical research in bone metabolism.MethodsA cross-sectional study was conducted from the data were extracted from the HIS of ZhuJiang Hospital. Data relating to seven bone turnover and metabolic indicators from 1036 patients between January 2018 and October 2019 were analyzed.ResultsP1NP, β-CTx and 25(OH)D were significant different in individuals younger than 20 years of age. ALP was significantly higher in those under 20 years of age and lower at age 20–39 compared with other age groups. The concentrations of Ca and P were different among the groups aged 0–19, 20–39, and 40–59 years of age groups but exhibited no difference above 60 years of age. PTH expression was not dependent on age. P1NP, β-CTx and PTH concentrations were not significantly different between the genders within the same age group. ALP was significantly different between genders within the age range 20–59 years. Ca and 25(OH)D were significantly different between the genders for those older than 60. Serum P was significantly different in the two genders for those aged 40–79. Patients received both alfacalcidol and calcium treatment differently from the others in P1NP, β-CTx, Serum Ca, P and ALP.ConclusionP1NP and β-CTx were highly correlated with age. If these two indictors require analysis in a case control study, the patients and controls should be strictly matched by age under 20 years. The demarcation point for ALP was 40 years of age. Ca and P were strongly recommended strict matching according to age in disease research. The difference in P1NP, β-CTx, 25(OH)D and ALP between genders depends on age differences. Medication history should be considered in bone turnover and metabolic clinical research.

PCV29 Iqvia Hospital Information System DATA Secondary Use for Outcome Identification to Assess the Validity of Definitions

PCV29 Iqvia Hospital Information System DATA Secondary Use for Outcome Identification to Assess the Validity of Definitions