The Association Between Organizational Characteristics and Strategic Information Systems Planning: A Study of U.S. Hospitals
Bibliographic record
Abstract
Despite the potential of Strategic Information System Planning (SISP) to reduce cost and improve quality, hospitals have been slow to have strategic plans on Information Systems. Our objective was to explore which organizational characteristics influence SISP in healthcare. Data on Information Systems plans from the HIMSS analytics database was combined with organizational characteristics data from the American Hospital Association. Logistic regression analyses on a sample of 2,495 hospitals revealed that hospitals with system membership and for profit status had a greater likelihood of selecting ‘computerized medical records’ (OR=1.88, OR=6.60 respectively, p<0.05), ‘decreasing medical errors’ (OR=7.02, p<0.05), ‘resolving integration issues’ (OR=1.36, OR=0.15 respectively, p<0.05), ‘migrating towards a paperless environment (OR=1.66, OR=8.28 respectively, p<0.05), and ‘reducing the number of software vendors’ (OR=1.78, OR=0.23 respectively, p<0.05) as their Information System plans. System membership and ownership status are associated with SISP. An understanding of the hospital characteristics that may impact Strategic Information Systems Planning, managers would assist managers in making informed decisions about planning and implementing Information Systems at their hospitals.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".