IT STRATEGIC PLANNING IN HOSPITALS: FROM THEORY TO PRACTICE
Bibliographic record
Abstract
OBJECTIVES: To date, IT strategic planning has been mostly theory-based with limited information on "best practices" in this area. This study presents the process and outcomes of IT strategic planning undertaken at a pediatric hospital (PH) in Canada. METHODS: A five-stage sequential and incremental process was adopted. Various tools / approaches were used including review of existing documentation, internal survey (n = 111), fifteen interviews, and twelve workshops. RESULTS: IT strategic planning was informed by 230 individuals (12 percent of hospital community) and revealed consistency in the themes and concerns raised by participants (e.g., slow IT projects delivery rate, lack of understanding of IT priorities, strained communication with IT staff). Mobile and remote access to patients' information, and an integrated EMR were identified as top priorities. The methodology and used approach revealed effective, improved internal relationships, and ensured commitment to the final IT strategic plan. Several lessons were learned including: maintaining a dynamic approach capable of adapting to the fast technology evolution; involving stakeholders and ensuring continuous communication; using effective research tools to support strategic planning; and grounding the process and final product in existing models. CONCLUSIONS: This study contributes to the development of "best practices" in IT strategic planning, and illustrates "how" to apply the theoretical principles in this area. This is especially important as IT leaders are encouraged to integrate evidence-based management into their decision making and practices. The methodology and lessons learned may inform practitioners in other hospitals planning to engage in IT strategic planning in the future.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".