Challenges in Developing Effective Clinical Decision Support Systems
Bibliographic record
Abstract
Introduction to CDSSDecision making is one of the most important and frequent aspects of our daily activities.Personal decisions display our characteristics, behavior, successes, failures, and the nature of our personalities.These decisions affect us in different ways, such as reasoning style, relationships, education, purchasing, careers, investments, health, and entertainment.The effectiveness of such decisions is affected by our age, knowledge, environment, economic status, and regulations.Our business related decisions are influenced by our knowledge, experience, and the availability of supporting systems in terms of employed processes, standards and techniques.Due to the importance of decision making, different technologies have been developed to help humans make effective decisions in the shortest time.Current advances in Information and Communication Technology (ICT) have revolutionized the way people communicate, share information, and make effective decisions.Decision making is a complex intellectual task that uses assistance from different resources.In the past, such resources were restricted to personal knowledge, experience, logic, and human mentors.However, the norms of current society and existing technologies have enhanced critical decision making.Educational systems are not restricted to physical classrooms any more; on-line education is gradually taking over.Knowledge about a technical domain can be obtained easily using Internet search engines and free on-line scientific articles.Mentorship has expanded from colleagues and friends to a large community of domain experts through subject-specific social networking facilities.Moreover, due to ubiquitous wireless communication technologies, such facilities are also accessible from small and remote communities.As a result, technology and different web-based tools (browse and search, document sharing, data mining, maps, data bases, web services) can be utilized as computing support for people, to help them make more knowledgeable and effective decisions.The healthcare domain has recently embraced new information and communication technologies to improve the quality of healthcare delivery and medical services.This long overdue opportunity is expected to reduce high costs and medical errors in patient diagnosis and treatment; enhance the way healthcare providers interact; increase personal health knowledge of the public; improve the availability and quality of health services; and promote collaborative and patient-centric healthcare services.To meet demands arising from these improved services, new tools, methods, and business models must emerge.Clinical Decision Support Systems (CDSS) are defined as computer applications that assist practitioners and healthcare providers in decision making, through timely access to 1 www.intechopen.
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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 | 0.039 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".