| I. INPUT |
• 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 |
| II. STORAGE |
• 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 |
|
• 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 |
| III. CONTROL |
• 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” |
| IV. PROCESSING |
• 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 |
| V. OUTPUT |
• 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 |
