Using Business Intelligence to Analyze and Share Health System Infrastructure Data in a Rural Health Authority
Bibliographic record
Abstract
BACKGROUND: Health care organizations gather large volumes of data, which has been traditionally stored in legacy formats making it difficult to analyze or use effectively. Though recent government-funded initiatives have improved the situation, the quality of most existing data is poor, suffers from inconsistencies, and lacks integrity. Generating reports from such data is generally not considered feasible due to extensive labor, lack of reliability, and time constraints. Advanced data analytics is one way of extracting useful information from such data. OBJECTIVE: The intent of this study was to propose how Business Intelligence (BI) techniques can be applied to health system infrastructure data in order to make this information more accessible and comprehensible for a broader group of people. METHODS: An integration process was developed to cleanse and integrate data from disparate sources into a data warehouse. An Online Analytical Processing (OLAP) cube was then built to allow slicing along multiple dimensions determined by various key performance indicators (KPIs), representing population and patient profiles, case mix groups, and healthy community indicators. The use of mapping tools, customized shape files, and embedded objects further augment the navigation. Finally, Web forms provide a mechanism for remote uploading of data and transparent processing of the cube. For privileged information, access controls were implemented. RESULTS: Data visualization has eliminated tedious analysis through legacy reports and provided a mechanism for optimally aligning resources with needs. Stakeholders are able to visualize KPIs on a main dashboard, slice-and-dice data, generate ad hoc reports, and quickly find the desired information. In addition, comparison, availability, and service level reports can also be generated on demand. All reports can be drilled down for navigation at a finer granularity. CONCLUSIONS: We have demonstrated how BI techniques and tools can be used in the health care environment to make informed decisions with reference to resource allocation and enhancement of the quality of patient care. The data can be uploaded immediately upon collection, thus keeping reports current. The modular design can be expanded to add new datasets such as for smoking rates, teen pregnancies, human immunodeficiency virus (HIV) rates, immunization coverage, and vital statistical summaries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".