A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS
Abstract
1. Introduction
2. Data Entry Management and Control
2.1. Data Range Control
2.2. Data Entry Obligations Controlling
2.3. Leveraging Automated Text Generation for Accurate Data Entry Management
3. Data Standardization and Normalization
- Improved user interface: Standardized data formats and consistent terminology make user interfaces more straightforward to learn and navigate [32];
- Enhanced decision-making: Consistent data representation across different platforms and systems allows for more accurate and reliable clinical decision-making [24];
- Increased system interoperability: Standardized data formats facilitate seamless integration with other healthcare systems, improving data sharing and collaboration [30].
Data Transformation Pipeline
- ➢
- Data collection:
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- Sources: Data are gathered from multiple sources, such as electronic medical records (EMRs), laboratory systems, radiology databases, wearable health devices, and patient-reported outcomes;
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- Challenge: Data often arrives in different formats, with missing fields or irregular entries.
- ➢
- Data cleaning:
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- Operations: Handles erroneous, incomplete, or irrelevant data through imputation (filling in missing values), outlier removal, and error correction;
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- Focus on gaps: Missing data due to human error, system inefficiencies, or disconnected subsystems are resolved here. Techniques like predictive imputation, default values, or physician-driven corrections are applied.
- ➢
- Data standardization:
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- Purpose: Converts diverse terminologies, units, and coding schemas into a unified framework—for example, ICD-10 codes and SNOMED CT terminology;
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- Impact on gaps: Standardization ensures that gaps caused by mismatched terminologies are bridged, making datasets interoperable.
- ➢
- Data normalization:
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- Process: Scales data into consistent ranges (e.g., blood pressure in mmHg) or formats (e.g., dates in “YYYY-MM-DD”);
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- Addressing gaps: Normalization identifies outliers and irregularities that may hint at gaps in understanding or reporting.
- ➢
- Data integration:
- ▪
- Objective: Combines data from multiple sources into a single, unified repository;
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- Role in gaps: Integration resolves gaps caused by data fragmentation across subsystems, creating a comprehensive view for decision support.
- ➢
- Data analysis and enrichment:
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- Techniques: Applies advanced analytics, predictive modeling, and enrichment using external datasets (e.g., population health statistics);
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- Gap insights: Identifies systemic gaps by analyzing patterns and generating recommendations for future improvements.
- ➢
- Data presentation:
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- Focus: Presents clean, consistent, and actionable data to users via dashboards or alerts in the CDSS;
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- HCI element: Assists in making the output comprehensible and prevents user fatigue and system override.
- ➢
- Incomplete data: Missing entries in key fields, such as medication history or lab results, can severely degrade decision-making processes. Research shows that healthcare systems often face challenges with data incompleteness, impacting clinical outcomes [43];
- ➢
- Inconsistent data: Variations in data representation, like “hypertension” vs. “HTN”, introduce inconsistencies that hinder practical analysis. Some methods can handle inconsistencies in prediction models [44];
- ➢
- Fragmented data: Data spread across isolated systems without integration leads to care fragmentation, which is associated with adverse outcomes and higher costs [45];
- ➢
- Outdated data: Relying on outdated information affects decision-making accuracy and relevance [46].
- ❖
- Clinical risk: Missing or erroneous data in a CDSS can directly impact patient outcomes by compromising the quality of diagnoses and treatment plans [47]. For instance, incomplete lab results, missing imaging studies, or gaps in medication history can prevent healthcare providers from identifying critical clinical patterns that may delay lifesaving interventions. Moreover, inaccurate or incomplete records can lead to physicians’ inability to identify contraindications, allergies, and comorbidities and provide poor treatment options. These risks underlie the importance of having a robust integrated data pipeline that will ensure the CDSS systems present the clinician with a complete and accurate patient profile for decision-making [24];
- ❖
- User dissatisfaction: Users’ trust in a CDSS is considerably breached if incomplete or unreliable data come up with questionable recommendations [48]. Generally, physicians depend on timely, accurate, and complete data for precise medical decisions. Suppose either data gaps or inconsistencies provide a CDSS output. In that case, healthcare providers may avoid investing more time in manually verifying information or searching for missing details across systems. This can also raise the cognitive load, break workflows, lower efficiency, and lead to frustration; this diminishes the system’s perceived value. These experiences, over time, decrease user confidence in CDSS usage, limiting its adoption and effectiveness within clinical environments [49];
- ❖
- Algorithm bias: Machine learning algorithms in CDSSs require complete and representative data for reliable predictions. When datasets contain the mentioned data gaps (due to incomplete records, underrepresented populations, or inconsistent data integration), bias can be introduced to the model. In some cases, if some demographic groups or clinical variables are underrepresented, the system may generate predictions that advantage one subset of patients at the expense of others, thereby undermining fairness and equity in care [50]. Additionally, missing data degrade model stability and decrease its predictive accuracy, leading to unreliable recommendations that can hinder clinical decision-making. These biases can only be resolved by robust data integration approaches and efficient handling of incomplete records for CDSS tools to provide fair and consistent outcomes [51,52].
4. Data Integration Within Clinical Decision Support Systems
4.1. Bridging Data Integration with Human–Computer Interaction
- ▪
- Improved data presentation: Integrated and standardized data allow for precise, user-friendly interfaces that present medical information efficiently. Physicians can access patient records, test results, and diagnostic recommendations without navigating fragmented data sources [60];
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- Enhanced trust and engagement: Since physicians receive reliable, comprehensive, and accurate data during interaction with the system, they can trust CDSSs. When clinicians acquire consistently desired results based on integrated data, they are more likely to engage with and rely on the system for decision-making [61].
4.2. Categorizing Data Integration to Enhance Usability and Functionality of Clinical Decision Support Systems
4.3. Empirical Case Studies About Data Integration Impact on Decision Accuracy and Human–Computer Interaction in Clinical Decision Support Systems
5. Discussion
5.1. Real-World Standardization Challenges
5.2. Implementation Challenges
5.3. Human-Centered Design Considerations
5.4. Implementation Constraints
5.5. Practical Implementation Guidelines
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Integration Type | Description | References |
---|---|---|
Electronic health record integration | Incorporating patient data from EHRs into CDSSs | [63] |
Genomic data integration | Incorporating genomic information into CDSSs | [64] |
Medical imaging integration | Incorporating imaging data (e.g., X-rays, MRIs) into CDSSs | [65] |
Laboratory data integration | Incorporating lab results into CDSSs | [66] |
Pharmacy data integration | Incorporating medication data into CDSSs | [67] |
Wearable device data integration | Incorporating data from patient wearables (e.g., heart rate monitors) into CDSSs | [68] |
Social determinants of health (SDOH) data integration | Incorporating socioeconomic and environmental data into CDSSs | [69] |
Research data integration | Incorporating the latest clinical research findings into CDSSs | [70] |
Patient-reported data integration | Incorporating data directly reported by patients (e.g., symptoms, outcomes) into CDSSs | [71] |
Administrative data integration | Incorporating administrative information (e.g., billing, scheduling) into CDSSs | [72] |
Data Management Aspects | Impacted HCI Elements | Improvements in CDSS Functionality | References |
---|---|---|---|
Data range control (validity) | Error feedback, system reliability | Minimizes invalid data entries, enhancing algorithmic precision | [22,23,29] |
Data entry obligations (completeness) | Structured interaction flow, task efficiency, usability | Ensures complete dataset availability, reducing query failures | [26,29,82] |
Data consistency (terminology) | Lower interpretive ambiguity, interface clarity | Enhanced reasoning algorithms, improving natural language processing | [32,81,83,84] |
Data standardization (interoperability) | Predictable interface behavior, workflow integration | System interoperability enables cross-system data synchronization | [30,31,85,86,87] |
Data normalization (units/ranges) | Visual consistency, reliable data interpretation, minimized user confusion | Optimizing automated anomaly detection, accurate alerts | [88,89,90] |
Data integration | Data accessibility, seamless multi-source data flow | Consolidates data streams, accelerating real-time analytics | [53,54,55,91] |
Data presentation | Information visualization, decision clarity | Enhances graphical rendering, faster decision-making, improving predictive accuracy | [92,93,94] |
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Azadi, A.; García-Peñalvo, F.J. A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS. Data 2025, 10, 60. https://doi.org/10.3390/data10050060
Azadi A, García-Peñalvo FJ. A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS. Data. 2025; 10(5):60. https://doi.org/10.3390/data10050060
Chicago/Turabian StyleAzadi, Ali, and Francisco José García-Peñalvo. 2025. "A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS" Data 10, no. 5: 60. https://doi.org/10.3390/data10050060
APA StyleAzadi, A., & García-Peñalvo, F. J. (2025). A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS. Data, 10(5), 60. https://doi.org/10.3390/data10050060