Identification of Outcome Relevant Indicators in Routine Data

Study Overview
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Brief Summary
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The availability of electronic documentation systems in patient care means that large amounts of clinical routine data are available from which conclusions can be drawn for improving patient care. Compared to conventional research approaches, a data science-oriented approach offers the possibility of identifying patterns in routine data (pattern recognition) that are relevant for patient-centered outcomes. Numerous projects and sub-projects can be evaluated from this data set.

Detailed Description
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The patterns that are relevant for patient-centered outcomes can be combinations of different parameters (e.g. vital signs, laboratory values, previous illnesses), which in themselves do not necessarily have a pathogenic effect, but in a specific combination may have a high relevance for the patient-centered outcome. This project pursues as research goal the anesthesiological and intensive care risk reduction. To this end, the existing data sets of routine care are to be used to identify outcome-relevant patterns in order to derive recommendations for improving treatment in line with the patient's wishes. Standard Operating Procedures (SOPs) and Quality Indicators (QIs) in combination with the data of routine clinical care will be used as a basis. The approach outlined is closely linked to the development of quality-based treatment structures. In order to be able to offer medical treatment at a high level, associated processes must be known and operationalized, i.e. measurable. QIs (quality indicators) are an established instrument for measuring individual dimensions of treatment quality, and our clinic is a leading participant in this process at both national and international level (see Spies et al. Guidelines for Delirium, Analgesia and Sedation). The mapping of quality-based treatment structures as SOPs (Standard Operating Procedures) is also essential in this context (see Spies et al. SOPs in Anesthesiology and Pain Therapy, Thieme Verlag). By applying data science-based methods, this study pursues the overall goal of supporting the transfer of evidence-based findings in the form of QIs and SOPs into patient care. Numerous projects and sub-projects can be evaluated from this data set.

Official Title
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Retrospective Analysis for the Identification of Outcome Relevant Indicators (Patterns) in Routine Data and Investigation of the Influence on Patient-centered Outcome for a Data-driven Improvement of Quality-based Treatment of Perioperative and Intensive Care Patients

Conditions
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Anesthesiological Risk Reduction, Intensive Care Risk Reduction

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age: 0 to 120 years Gender: female, male, diverse Electronically documented anesthesiological or intensive care treatment in the HIS (Hospital Information System) and PDMS (Patient Data Management System) of the Charité (Dep. for Anesthesiology with focus on operative intensive care, CCM/CVK/CBF) since 2006 Exclusion Criteria: -none

Ages Eligible for Study
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Maximum Age: 120 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Functional status | The functional status of the patient is measured by routine score data. The scores, which measure physical, role, and social functioning, and mobility reflect worse/better outcome depending on the score construction. | 01.01.2016-31.12.2024 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Morbidity | Morbidity is evaluated by International Classification of Diseases (ICD) (10th version). /Operation codes (OPS) | 01.01.2016-31.12.2024 | 
| Mortality I | Mortality is measured by inhouse mortality | 01.01.2016-31.12.2024 | 
| Mortality II | Mortality is measured by long-term mortality (1 year) | 01.01.2016-31.12.2025 | 
| Accounting data | Accounting data are providid by the controlling department | 01.01.2016-31.12.2024 |