The Advice Infrastructure For Generating And Delivering Evidence-Informed Clinical Decision Support Services: A Knowledge Management Approach
Bibliographic record
Abstract
Next generation computerized healthcare services are destined to be knowledge-centric—i.e. leveraging best-evidence and best clinical practices to provide evidence-informed, patient-centered, safe, timely, and cost-effective care services. In this paper, we present an health informatics based clinical decision support infrastructure—termed as ADVICE (Agile Decisional Validation and Individualized Care Environment)—that offers evidence-informed decision support services for both physicians and patients. The ADVICE infrastructure (Fig 1) purports an integrated healthcare environment that syngerizes both healthcare knowledge and healthcare data to derive decision support services. The key functional aspects of ADVICE are: (a) transformation and translation of clinical practice guidelines (CPG) to evidence-informed decision-support for physicians; (b) engagement and empowerment of patients in their care process; and (c) health data analytics to derive health operational intelligence. We take a knowledge management approach to develop the key modules of the ADVICE infrastructure, which are as follows: (A) Physician Oriented Clinical Decision Support: This module offers the functionality to develop CPG based Clinical Decision Support Systems (CDSS) that can be deployed within an health institution to provide evidence-informed patient care at the point-of-care. We have developed a semantic web based CPG operationalization framework that comprises three main modules: (a) CPG computerization, whereby we use our CPG ontology (Fig 2) to semantically model and computerize disease-specific CPG; (b) CPG institutionalization, whereby a generic computerized CPG is institutionalized with respect to an institution's constraints, such as policies, resources, quality indicators. This is achieved by employing workflow modeling methods to transform the computerized CPG to an institution-specific clinical workflow; and (c) CPG operationalization, whereby a computerized clinical workflow is executed using patient data to deliver evidence-informed care recommendations to physicians. We have developed specialized CPG execution engines that operationalize a computerized CPG to provide recommendations for both single disease and comorbid diseases. The CDSS can be integrated with EMR and delivered through web-based interfaces and mobile devices. (B) Patient-Oriented Care Services: ADVICE offers a continuum of personalized, proactive and persistent home-based care services designed to assist patients throughout their care journey. The key patient-oriented care services offered are: (i) Personalized CarePlan that depicts the longitudinal discourse of the patient's care journey (fig 3); (ii) Personalized Self-management Programs to educate patients to self-manage their conditions. Educational and motivations interventions are tailored based on the patient's health and behavior profiles, and are delivered to patients through mobile devices (smart phones); (iii) Patient Surveillance to monitor the health state of a patient and then provide rapid response in need of care. This is achieved by continuously processing patient data from a range of home-based health monitoring devices and then generating alerts based on patient-specific alert rules. ADVICE infrastructure offers an innovative suite of technology-enabled healthcare services delivered through mobile devices and secure websites. ADVICE offers a new approach to the determination of care interventions—i.e. integrating clinical factors with patient-specific health, psychosocial and behavioral aspects to contextualize the care interventions. We will present demonstrator CDSS (fig 4) and patient self-management applications for cancer care, comorbid congestive heart failure and atrial fibrillation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".