Data hospital workflow makes research data quality easier to review
The Data Hospital: A Workflow-Based Concept for Explainable Research Data Quality Assistance
Human-Computer Interaction
Summary
Data quality in research depends on many factors like the data itself, how it's used, and the decisions made about it. The authors propose a system called the Data Hospital, which treats datasets like patients going through different stages of review and treatment to improve quality. This approach keeps track of every change and decision, making it easier to assess and explain the data quality. The authors show a prototype illustrating some parts of this workflow and suggest steps for future evaluation.
What this means in practice
- •For hospital data teams: Implement structured data quality workflows that track and validate research datasets through specified intervention stages.
- •For data governance officers: Create transparent processes for data quality assurance that document decisions and enable replay of interventions.
A position paper. It proposes an approach and reports no results.
Authors
Lennard Scheurer, Robert Porzel, Vinicius Carrillo Beber, Rainer Malaka
Abstract
Research data quality is multidimensional and purpose-dependent: it emerges from the interplay of data, intended use, contextual knowledge, documentation, intervention decisions, and traceability. This concept paper presents the Data Hospital, a human-in-the-loop control and interaction model for research data quality. Using a hospital metaphor, datasets are admitted, contextualized, assessed, reviewed in specialized stations, modified only through approved interventions, validated, documented, and made replayable where interventions are sufficiently specified. The concept combines deterministic profiling and inspectable evidence with optional evidence-bound explanation by Dr. Data and explicit user decisions. Preserved Raw Data and controlled working states separate observation from intervention. The contribution is not a new cleaning or imputation algorithm, but a ten-stage workflow that makes assessability, uncertainty, intervention authority, provenance, and process reproducibility visible. The prototype is an implementation-backed demonstrator rather than a released research artifact and illustrates selected parts of the concept through representational standardization, imputation, Patient File documentation, and replay. The paper concludes with a staged agenda for subsequent technical and user-centered evaluation.