Electronic Health record Dataset

Hello everyone, kindly find below sample dataset containing Patient Id, Patient Demographic (Male, Female, Unknown)

Feel free to analyze the data using various tools.

This dataset contains below columns:

patientunitstayid, patienthealthsystemstayid: Unique Patient Id

Patient Demographics: gender: Male, Female, Unknown age ethnicity

Hospital Details: hospitalid: Each hospital was given unique id wardid: Ward Id is given in which patient was treated apacheadmissiondx: Disease diagnosed admissionheight: Height of the patients hospitaladmittime24: Admission time to the hospital hospitaladmitsource: Department Source of the admission hospitaldischargeyear: Discharge year from the hospital hospitaldischargetime24: Discharge time from the hospital hospitaldischargelocation: Patient Discharge to which location (Home, Death, Other hospital. etc) hospitaldischargestatus (Alive, Expired)

Hospital Unit Details: unittype: Unit in which admitted unitadmittime24: Time of admision to the Unit unitadmitsource: Department source for the unit unitvisitnumber: No. of times visited unitstaytype: Admit, readmit, etc admissionweight: Weight during the admission dischargeweight: Weight during the Discharge unitdischargetime24: Discharge time from the Unit unitdischargelocation: Patient Discharge to which location (Home, Death, Other hospital. etc) unitdischargestatus: (Alive, Expired)

Date of admission and discharge is not given in the dataset, you can assume it to be 24 hours data.

I have worked on a dashboard assessing no. of patients admitted, avg. duration of hospital stay, disease condition for which they are admitted etc.

You can also do your analysis. Do share your findings with me. Thanks!

a Healthcare-associated infections were defined as (i) index positive blood culture collected ≥48hrs after hospital admission, and no signs or symptoms of the infection noted at time of admission; OR (ii) index positive blood culture collected <48hrs after hospital admission if any of the following criteria are met: received intravenous therapy in an ambulatory setting in the 30 days before onset of BSI, attended a hospital clinic or haemodialysis in the 30 days before onset of BSI, hospitalised in an acute care hospital for ≥ 2 days in the 90 days prior to onset of BSI, resident of nursing home or long-term care facility.bStaphylococcus aureus bacteraemia was defined as uncomplicated if all of the following criteria were met: exclusion of endocarditis; no evidence of metastatic infection; absence of implanted prostheses; follow-up blood cultures at 2–4 days culture-negative for S. aureus; defervescence within 72 h of initiating effective therapy. Percentages shown are of entire S. aureus BSI population.† Three patients had chronic diabetic foot ulcers as a source of their S. aureus BSI, and in all cases the contiguous underlying bone was also found to be infected.MRSA = methicillin-resistant Staphylococcus aureus. NA = not applicable. BSI = bloodstream infection.Data are displayed as median (interquartile range) and number (percentage). P values are calculated by Mann-Whitney and Fisher’s exact test respectively.

The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Summary statistics for gender, age, source of admission, discharge status, primary payer and local health district of patient residence of patients who had a reportable ambulatory surgical procedure. For state-level ambulatory reportable procedures, please see, https://opendata.utah.gov/Health/2014-Utah-State-Level-Ambulatory-Surgery-Procedure/xdgg-9vk2

For facility-level information, please visit: http://health.utah.gov/hda/report/outpatient.php

Improving outcomes for people with multiple long term conditions is a priority as set out in the NHS long term plan. ADMISSION is a Research Collaborative funded by UK Research and Innovation and the National Institute for Health Research and Care Research that brings together scientists, clinicians and patients from five UK universities and hospitals (Newcastle University and Newcastle Hospitals NHS Foundation Trust, University of Birmingham (PIONEER – the Health Data Research UK Acute Care Hub),  Manchester Metropolitan University, University of Dundeeand University College London) to transform understanding of multiple long-term conditions in hospital patients.

As part of this, PIONEER has curated a highly granular dataset of 119,815 unique hospitalised patients focusing on the impact of multiple long term conditions. The data includes admission details, demography, initial presentation, presenting symptoms, diagnoses, treatments, therapy, medications, imaging, wards, investigations, procedures, operations and outcomes. The current dataset includes admissions from 01-01-2000 to 07-02-2024 but can be expanded to assess other timelines of interest.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

This dataset provides simulated hospital patient admission records with detailed acuity scores and categorical labels, supporting advanced triage modeling and resource planning. It includes patient demographics, diagnoses, admission details, and outcome data, making it ideal for healthcare analytics, operational optimization, and predictive modeling of patient care needs.

This dataset provides comprehensive hospital admission data from Riyadh, Saudi Arabia, for the period 2022 to 2024. The data was collected from public and private hospitals, including Riyadh General Hospital, King Saud Hospital, and Riyadh National Hospital. It aims to support research and policy development related to public health and healthcare system optimization. https://www.moh.gov.sa/en/Ministry/Pages/default.aspx

The dataset contains detailed information on cardiorespiratory hospital admissions, capturing hourly records to allow for in-depth temporal analyses. It integrates various features related to patient demographics, medical conditions, and hospital performance, providing a holistic view of healthcare demand and trends in Riyadh.

Dataset Overview Owner: General Directorate of Health Affairs, Ministry of Health, Saudi Arabia Location: Riyadh, Saudi Arabia Time Period: January 2022 – September 2024 Format: CSV Number of Records: ~23,000 hourly observations Periodicity: Hourly Features: admission_date: The precise date and time of hospital admission (YYYY-MM-DD HH:mm:ss). hospital_name: Name of the hospital where the admission occurred (e.g., Riyadh General Hospital). admission_count: The number of admissions during the specified hour. condition_type: Type of cardiorespiratory condition (e.g., Asthma, COPD, Heart Attack, Other Respiratory Issues). patient_age_group: The age group of admitted patients (e.g., 0–17, 18–45, 46–65, 66+). patient_gender: Gender of the patients (Male/Female). readmission_count: Count of patients readmitted within 30 days. severity_level: Severity level of the condition upon admission (Mild, Moderate, Severe). length_of_stay_avg: Average length of stay (in days) for admitted patients. seasonal_indicator: Seasonal classification for the date of admission (Winter, Spring, Summer, Fall). comorbid_conditions_count: Number of additional health conditions affecting admitted patients. primary_diagnosis_code: Diagnostic code for the primary condition (e.g., J45, J44, I21). daily_medication_dosage: Total daily dosage of medications prescribed for cardiorespiratory conditions (mg). emergency_visit_count: The number of emergency visits for cardiorespiratory issues during the hour. Key Applications: Healthcare Demand Analysis: Study patterns of hospital admissions and understand peak demand periods. Public Health Research: Investigate correlations between environmental factors and hospitalizations for respiratory and cardiovascular conditions. Policy and Decision-Making: Develop data-driven policies to optimize healthcare resource allocation and readiness. Epidemiological Studies: Analyze the impact of comorbidities and demographic factors on hospital admissions. Data Insights: The dataset highlights temporal trends in hospital admissions, enabling the identification of peak periods of healthcare demand. Features such as condition_type, severity_level, and seasonal_indicator offer valuable insights into the interplay between environmental factors and public health. It provides granular patient demographic data, supporting targeted healthcare strategies and policy development. Rich diagnostic and readmission data support advanced predictive modeling for patient outcomes. Licensing: Please refer to the terms and conditions of the General Directorate of Health Affairs and the Ministry of Health, Saudi Arabia, for the usage and redistribution of this dataset.

Keywords: Healthcare, Public Health, Hospital Admissions, Riyadh, Cardiorespiratory Illness, Asthma, COPD, Emergency Visits, Saudi Arabia, Epidemiology, 2022–2024, Hourly Data

This publication reports on Admitted Patient Care activity in England for the financial year 2023-24. This report includes but is not limited to analysis of hospital episodes by patient demographics, diagnoses, external causes/injuries, operations, bed days, admission method, time waited, specialty, provider level analysis and Adult Critical Care (ACC). It describes NHS Admitted Patient Care Activity, Adult Critical Care activity and performance in hospitals in England. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care and may also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. The data source for this publication is Hospital Episode Statistics (HES). It contains final data and replaces the provisional data that are released each month. HES contains records of all admissions, appointments and attendances at NHS-commissioned hospital services in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore, this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Please send queries or feedback via email to enquiries@nhsdigital.nhs.uk. Author: Secondary Care Open Data and Publications, NHS England. Lead Analyst: Karl Eichler

Characteristics and demographics at admission (n = 934).

The Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency department visits that do not result in an admission. The SEDD include all patients, regardless of the expected payer including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., gender, age, race), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers.

Restricted access data files are available with a data use agreement and brief online security training.

Clinical-demographic characteristics of critically ill patients at admission.

The Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency department visits that do not result in an admission. The SEDD include all patients, regardless of the expected payer including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age, race), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers.

Restricted access data files are available with a data use agreement and brief online security training.

IntroductionPeople living with dementia (PwD) admitted in emergency to an acute hospital may be at higher risk of inappropriate care and poorer outcomes including longer hospitalisations and higher risk of emergency re-admission or death. Since 2009 numerous national and local initiatives in England have sought to improve hospital care for PwD. We compared outcomes of emergency admissions for cohorts of patients aged 65+ with and without dementia at three points in time.MethodsWe analysed emergency admissions (EAs) from the Hospital Episodes Statistics datasets for England 2010/11, 2012/13 and 2016/17. Dementia upon admission was based on a diagnosis in the patient’s hospital records within the last five years. Outcomes were length of hospital stays (LoS), long stays (> = 15 days), emergency re-admissions (ERAs) and death in hospital or within 30 days post-discharge. A wide range of covariates were taken into account, including patient demographics, pre-existing health and reasons for admission. Hierarchical multivariable regression analysis, applied separately for males and females, estimated group differences adjusted for covariates.ResultsWe included 178 acute hospitals and 5,580,106 EAs, of which 356,992 (13.9%) were male PwD and 561,349 (18.6%) female PwD. Uncontrolled differences in outcomes between the patient groups were substantial but were considerably reduced after control for covariates. Covariate-adjusted differences in LoS were similar at all time-points and in 2016/17 were 17% (95%CI 15%-18%) and 12% (10%-14%) longer for male and female PwD respectively compared to patients without dementia. Adjusted excess risk of an ERA for PwD reduced over time to 17% (15%-18%) for males and 17% (16%-19%) for females, but principally due to increased ERA rates amongst patients without dementia. Adjusted overall mortality was 30% to 40% higher for PwD of both sexes throughout the time-period; however, adjusted in-hospital rates of mortality differed only slightly between the patient groups, whereas PwD had around double the risk of dying within 30 days of being discharged.ConclusionOver the six-year period, covariate-adjusted hospital LoS, ERA rates and in-hospital mortality rates for PwD were only slightly elevated compared to similar patients without dementia and remaining differences potentially reflect uncontrolled confounding. PwD however, were around twice as likely to die shortly after discharge, the reasons for which require further investigation. Despite being widely used for service evaluation, LoS, ERA and mortality may lack sensitivity to changes in hospital care and support to PwD.

Patients stratified by positive or negative 3GCREB status at admission, 3GCREB prevalence study, Berlin, Germany, 2014/anal 2015.

This dataset forms part of ADMISSION, the NIHR and UKRI funded programme of work to better understand and to design healthcare services which are better able to care for patients with multiple long term conditions.

This dataset includes >70,000 adult patients who were acutely admitted to hospital. It includes longitudinal and detailed data for these people over 10-years, designed to trace the accrual, progression and impact of multiple co-morbidities as well as acute health care use and outcomes.

The dataset contains highly granular data regarding patient demographics and the specific co-morbidities associated with each patient, categorised according to ICD-10 codes. It provides a structured sequence of data related to the acute care and inpatient process, capturing critical timings, acuity, medications, laboratory results, observations, readmissions, and mortality rates.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. Data access must reference ADMISSION and the papers which describe this resource. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

This dataset provides county-level demographic data (sex, adjudication, age, race/ethnicity, and service setting) for youth admitted to and discharged from the care and custody of the Office of Children and Family Services (OCFS) each year. Data are counted using a youth’s first admission or discharge in a calendar year. Admissions data are aggregate based on the responsible (court) county. Discharges data are aggregate based on the county of residence.

1Estimated from first positive western blot.2Participant had negative ELISA 1 year prior to entry.

This is a report on admitted patient care activity in English NHS hospitals and English NHS-commissioned activity in the independent sector. This annual publication covers the financial year ending March 2022. It contains final data and replaces the provisional data that are released each month. The data are taken from the Hospital Episodes Statistics (HES) data warehouse. HES contains records of all admissions, appointments and attendances for patients at NHS hospitals in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with a number of breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Hospital Adult Critical Care (ACC) data are now included within this report, following the discontinuation of the 'Hospital Adult Critical Care Activity' publication. The ACC data tables are not a designated National Statistic and they remain separate from the APC data tables. The ACC data used in this publication draws on records submitted by providers as an attachment to the admitted patient care record. These data show the number of adult critical care records during the period, with a number of breakdowns including admission details, discharge details, patient demographics and clinical information. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This document will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Supplementary analysis has been produced, by NHS Digital, containing experimental statistics using the Paediatric Critical Care Minimum Data Set (PCCMDS) data, collected by NHS Digital, against activity published in NHS Reference Costs. This analysis seeks to assist users of the data in understanding the data quality of reported paediatric critical care data. Also included within this release, is supplementary analysis that has been produced in addition to the Retrospective Review of Surgery for Urogynaecological Prolapse and Stress Urinary Incontinence using Tape or Mesh: Hospital Episode Statistics (HES), Experimental Statistics, April 2008 - March 2017. It contains a count of Finished Consultant Episodes (FCEs) where a procedure for urogynaecological prolapse or stress urinary incontinence using tape or mesh has been recorded during the April 2021 to March 2022 period. Please Note: A summary of information relating to procedures for the treatment of Stress Urinary Incontinence is published here for transparency and scrutiny. Follow up is taking place with individual Trusts to confirm that specific treatment is as described for activity occurring since April 2021. This will lead to more accurate information on these procedures that occurred since April 2021 being being available in the future. In collating this information, it has already become clear that some Trusts mis-coded these procedures in Commissioning Data Set return used to produce these statistics. Alongside this the clinical coding guidance has been refined to enable more accurate identification of specific treatments. The data published here has been published for transparency purposes. However, for these reasons small numbers reported on treatments for this condition should be used as a starting point for further investigation rather than a definitive view.

Demographics, clinical characteristics, and laboratory parameters at admission of patients hospitalized for COVID-19.

Background

Community acquired pneumonia (CAP) is a leading cause of hospital admission, and in older adults has high rates of mortality and complications. CAP is associated with increased long-term mortality and loss of independence for older adults. CAP typically affects older adults with co-morbidities. Complications such as sepsis, and empyema (infected fluid around the lung) prolong hospital admission, result in additional interventions in hospital and have higher mortality than CAP alone. The causative agents for CAP are often poorly identified in real world clinical practice.

The treatment of patients with CAP is complex. Key decisions relate to the antibiotics used, the way antibiotics are given (in a tablet or by a drip) and the place of care (home, hospital and in hospital, a normal ward or intensive care). These data will allow analyses on differing antimicrobial treatments and outcomes, as well as differing pathways of care. This data has been constructed to support machine learning including algorithm generation and testing models.

PIONEER geography The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.

EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”.

Scope: All patients admitted to hospital from 2000 because of Community Acquired Pneumonia. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaints, therapy, ventilation route, assessments components (AMT10, falls, MMS, thrombosis and waterlow), physiology readings (temperature, blood pressure, respiratory rate, NEWS2 score, oxygen saturations, AVPU scale and others), Sample analysis results (bilirubin, urea, albumin, platelets, white blood cells and others) drug administered and all outcomes. Linked images available (radiographs, CT scans, MRI).

Available supplementary data: CAP admission data from 2000 onwards. Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.