Bibliographic record
Abstract
Background and context: Given the constant evolving developments in information technology (IT) in healthcare in Canada and Ontario, and the relatively nonexistent body of literature on IT management issues from the perspectives of top IT managers (i.e. Chief Information Officers, IT directors, IT top managers) in hospitals, a follow up study of IT management issues to the study done by Jaana et al. is conducted. Purpose: To develop an authoritative list of IT management issues in Ontario hospitals and compare the results to the earlier study and the literature. Methods: Using the Ranking Type Delphi technique, the responses from IT top managers in three main panels of Ontario hospitals were solicited through a controlled iterative feedback process. The hospitals were divided into the academic panel (n = 6), community panel (n = 12), and the rural panel (n = 8) for a total of 26 out of 33 participants who completed the study. Results: 26 issues were raised and a total of 24 issues were ranked in the study. Among the 14 common issues between the three panels, the top five issues were limited funding, keeping infrastructure current, external security threats, increasing cost, and managing demands for IT projects. Comparing with the study by Jaana et al. (2011), a total of 7 new issues emerged which are concerned with technology, regulatory challenges, and human issues. A total of 10 issues were dropped from the earlier study spanning areas of strategic, technological, organizational, and human issues. The participants in the study did not significantly differ individually based on their background characteristics, where the only significant difference observed between the hospital panels was due to hospital characteristics. During the brainstorming phase a total of 195 issues were provided which were consolidated by two researchers to form a list of 26 IT management issues, with an inter coder reliability of 88%. The issues with a 4.5 out of 7 rating and higher on a Likert scale were retained to narrow down the list. This resulted in 19 issues for the rural and community panels, and 21 issues for the academic panel, with 14 of the 26 issues being common to all three panels. The ranking phase was conducted with two rounds of ranking due to the low consensus levels during the first round. The consensus level after two rounds was; W academic = 0.235, W community = 0.254, and W rural = 0.381. Contributions: This study presents a significant contribution to the management of medical informatics field by providing an approach to categorize IT management issues to observe trends overtime as well as present the application of a seminal framework to explain the changes in these issues as organizations change and grow overtime. At the management and practical levels, the list of prioritized issues provides an evidence base for top IT managers to make IT related decisions at the organizational level. The list also acts as a second benchmarking tool to evaluate hospital performance overtime with the various issues. At the policy development level, provincial governments can use the list to devise comprehensive IT management strategies to address the various regulatory issues reported. Future research can focus on exploring the resonating behind the rankings provided and replicating this study over time and across various geographies so that a large survey can be developed to follow the evolutions of IT management issues in healthcare over time.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".