Investing in Information Technology: Where Do Canadian Hospitals Stand?
Bibliographic record
Abstract
Investments in information technology (IT) can have a significant impact on an organization’s performance. Well-managed IT investments that are carefully selected and focused on meeting defined needs can propel an organization forward, improving performance while reducing costs. Likewise, poor investments, those that are inadequately justified or whose costs, risks and benefits are poorly managed, can hinder and even restrict an organization’s performance. Investing in information technology (the hardware, software and human capital to mobilize it) in hospitals is a complicated issue. It competes with other pressing matters facing hospital management, such as capital spending on diagnostic and therapeutic equipment, the need for more nursing to address more complex patient needs, the difficulty in managing operating costs in a fundingstrained environment, and achieving efficient, effective and high-quality care processes. The visionary CIO or Director of Information Services will be able to envisage a myriad of IT or Information Management applications that may be able to alleviate or minimize some of these pressures. However, the vision may not reach the decision-makers, or in some cases the proposed solution may carry a heavy cost burden that the organization is just unwilling or unable to incur at the time. It is a situation that many hospitals are facing across Canada. Those who are ready to make some type of investment are left with the question: How much? A recent study by the Governance Institute has indicated that about one-third of hospital boards are becoming more involved in IT purchases.1 Boards need information to make decisions on large investments, such as clinical information systems, and in doing so, they try to identify an industry standard or at least an understanding of what their peers are spending. One of the proxy measures that can be employed to gauge the investment made in IT and its importance for the organization is spending on IT relative to total hospital operating costs. This measure can then be used for comparison among hospitals and even across different industries. The recently released 2001 CIHI/HayGroup Benchmarking Comparison of Canadian Hospitals can provide a better understanding of the investment of IT in Canadian hospitals. The Benchmarking Comparison is an annual study that provides participating hospitals with comparisons of the clinical efficiency, operational efficiency and quality of care of Canadian teaching and community hospitals. A number of hospital financial and productivity indicators, including the derivation of costs with respect to information systems, can be extracted from the study’s benchmarking databases.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".