An Informatics Blueprint for Healthcare Quality Information Systems

Structural Data: Characteristics of the organizations and individuals providing care

Process Data: Encounters taking place between healthcare givers and the patient

Outcomes Data: Administrative, economic, and clinical outputs of the care given

Moderators: Factors influencing for whom and in what circumstances outcomes differ

Quantitative Data: Objective information, preferred over more subjective qualitative measures

Institutional Leaders: Specify the goals and objectives in creating the HQIS

Domain Experts: Specify and define the required data dictionary elements to meet the goals of the HQIS

System Analyst: Evaluates user requirements, analyzes workflow, and documents sources of information

Application Programmer: Creates facile user data interfaces to the HQIS

Caregivers: Generate the information amalgamated as data within the HQIS

Health Information Manager: Abstracts and codifies data from clinic notes, coordinates patient surveys, and follow-up

Core Dataset Creation: Single group of data elements that meets internal management and external reporting needs

Data Dictionary Development: Include clear precise definition of only those elements essential to meet HQIS goals, avoiding data dictionary ‘explosion'

External Standards: Consider pre-existing definitions and accepted ontologies whenever available

Data Amalgamation: Physical collection of data elements, through a manual “codification strategy, or electronically

Systems Analysis: Establish the data locations and flow, documented via the Unified Modeling Language

Patient-centric Data Structure: Specific data on each patient integrated into a single comprehensive view of the patient

Central Data Repository: Data merged from various sources and across time-points for permanent storage

System Analyst: Models and documents sources and flow of information

Database Architect: Creates information model for central repository database

Record Key Structure: Allows linkage between data elements and records across multiple platforms and over time

Retrievable Storage Format: Data stored separately from data entry and analysis software programs, available to many users

Technical Metadata: Source system, field name type, format, transformation rules, data integrity/consistency rules, missing value codes

Business Metadata: Definitions, directives, synonyms, classification codes, value codes, usage within case reports forms and output reports

Accuracy Metrics: Cross-field logic checks, longitudinal data integrity assessments, source documentation audits, visual data review

Completeness Metrics: Actual vs. expected accrual, proportion enrolled, actual vs. expected forms and visits, lag time to data entry

Electronic Audit Log: Records to document the nature and reason for each change to the data, allowing reconstruction of the data history

Metadata Analyst: Coordinates and documents data element definition process and related metadata

Data Quality Manager: Provides review, auditing, and training functions

Application Programmer: Automates logic checks, QA reports, and “suspicion checks” to be run by users routinely

Biostatistician: Programs cross-field and cross-record QA reports for rectification by the Data Quality Manager

Institutional Leaders: Determines appropriate conditions and procedures for data access and sharing

Privacy Officer: Carries out procedures according to the security policy

Database Administrator: Automates security and confidentially procedures within the HQIS

Metadata Synchronization: Ensure continual alignment between data fields and the correct definitions and directives

Data Completeness Alert: HQIS is “self-aware” of the nature and timing of expected data elements to be completed

Point-of-Care Data Capture: Data at the time and place of data creation

Automated Error Checking: User interface traps errors via logic and suspicion checks

Manual Data Verification: Review of electronic data against source documents

Change Control: For all edits, record nature, reason, date and time, & person

Access Control: Protect data through authorization form, ID/passwords, digital authentication

Confidentiality Procedures: Restrict information to those with reason to have access, at the necessary level of data

Data Encryption: Encode data so that it can only be de-coded by an appropriate “key”

Counting: Generation of data on the impact of clinical guidelines on practice and quality of care

Descriptive Statistics: Averages and frequencies to assess on performance variances

Normative Benchmark: Compares distance between healthcare system's performance and a selected group mean

Criterion Benchmark: Compares health-care systems performance to top performer on desired measures

Time Series: Graphical data trends to compare the performance of a single system/subsystem to an historical pattern

Institutional Leaders: Define analyses to identify trends or deficiencies

Guideline Committee: Expert panel to derive practical guidelines against which data are benchmarked

Clinical Data Specialist: Provides training in system usage, and assists in preparation of analytic data files

Decision Support Analyst: Analyzes HQIS data to support management decision-making and outcomes research

Biostatistician: Provides scientifically valid design, analysis of results, data mining for new associations, and interpretation of findings

End User Training: Provide all system users and stakeholders with adequate orientation to the HQIS

Data Query Methods: Retrieval and exchange of data for administrative, clinical care, and outcomes research purposes, including data marts and ad hoc data queries by users

Dashboard Report: Aggregated data reflecting the input, throughput, and output variables to diagnose problem areas and derive interventions

Patient Summary Report: Listing of patterns of care for individual patients, to allow practitioner to evaluate the care given to each patient on a case by case basis

Institutional Leaders: Design reports of data from centralized repository to inform administrators and physicians of patterns of care and trends in patient outcomes

Caregivers: Utilize benchmarking and patient reports to compare practice patterns to guidelines and other physicians, and to influence behavior when warranted

Administrators: Utilize feedback from benchmarking reports to analyze how healthcare system is performing and mandate any required changes

Guideline Committee: Utilize HQIS reports to assess the currency and validity of the practice guidelines

Report Construction: Numeric and graphical data displays generated at specified times, with formats varied by user type and background

Feedback Loops: Two-way communication to practitioners and guideline committee regarding results of HQIS analysis and reporting, to improve guidelines and healthcare quality