Clinical Data Processing System for Automated Patient Assessment

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Solution Overview

Problem

Existing clinical data systems overwhelm healthcare workers with vast amounts of patient data, requiring manual review and selection of relevant information, leading to potential errors and increased time delays.

Innovation Solution

A clinical data processing system that organizes and analyzes clinically significant information using result flags and predetermined rules to automatically infer data associations, customize displays, and provide relevant observations, facilitating timely and accurate patient assessments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If clinicians manually review and select relevant information from patient data, then they can assess individual information items, but it increases time delay and may lead to missed results

Engineering Contradiction:

Improvepatient assessment reliabilityVSAvoidtime delay in reviewing results

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically flagging abnormal or critical results before they are needed for assessment. The clinical data processor proactively identifies and highlights relevant information, preparing it for immediate review rather than requiring clinicians to search through unorganized data manually.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback to clinicians by automatically generating summaries and highlighting abnormal results based on predefined criteria. This feedback mechanism ensures that critical information is immediately visible and cannot be missed, while also reducing the time needed to identify relevant data points.

Inventive Principle:

Principle #23Feedback

2Reliability

If clinicians manually search and select each associated result, then they can review comprehensive information, but it increases the risk of human error

Engineering Contradiction:

Improveaccuracy of patient assessmentVSAvoidease of data review process

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs self-service by automatically organizing, filtering, and presenting relevant clinical data without requiring manual intervention. The clinical data processor autonomously identifies abnormal results, retrieves associated information, and formats it for review, eliminating the need for clinicians to manually search through comprehensive datasets.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system segments the comprehensive clinical data into manageable components by separating normal from abnormal results, and by organizing associated information into distinct categories. This segmentation makes the data easier to review while maintaining comprehensiveness, reducing both time and error risks.

Inventive Principle:

Principle #1Segmentation

3Ease of operation

If existing systems display individual information items, then they provide simple viewing, but they fail to provide contextual organization of data

Engineering Contradiction:

Improvesimplicity of data viewingVSAvoidloss of contextual relationships

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system merges individual information items into organized contextual groups. The clinical data processor combines related data elements, abnormal results, and associated information into unified presentations that maintain their individual simplicity while providing comprehensive contextual organization for easier understanding.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS8099304B2System and user interface for processing patient medical data

Publication Date: 2012.01.17 CERNER INNOVATION INC

AI summary

A clinical data processing system systematically organizes and analyzes clinically significant information of a patient using a result flag indicating a critical or abnormal result to automate display of a view of clinical data in various contexts including diagnosis, insurance, medical complaint assessment and others to improve patient care. A system for use in processing patient clinical data for access by a user includes a repository associating an observation with a clinical significance indicator and with data indicating observations relevant to evaluation of the observation having the associated clinical significance indicator. A clinical data processor uses the repository fork automatically providing data for display in response to receiving data representing an input observation and an associated clinical significance indicator. The data for display supports a user in making a patient assessment and includes the input observation and associated relevant clinical data items. A display processor initiates generation of data representing an image including the data for display.