Figure 2 (below) shows the process of building health science knowledgebases used by evidence-based decision support tools. This gives an expanded view of the processes corresponding to the quality metrics, practice guidelines, knowledge services and tools, and CQI feedback loops in Figure 1 (see #’s 4, 5, 10, 13-16, 20, 23, 24 in Figure 1).

Note that this figure starts at the bottom and works it way up.

1) Data about the patient, the patient’s problem, and treatments rendered are collected using the HIT tools (e.g., Diagnostic Aid, EHR, CPOE, Clinical pathways). The data are stripped of patient identifiers and sent to databases, which researchers and other knowledge workers can access and study. These data include clinical and financial outcomes, variance information, as well as patient and provider data.

2) The stored data and knowledge people have in their heads are disseminated in loosely-coupled networks of collaborating knowledge workers, e.g., in consensus groups.

3) These collaborators share and discuss their ideas, examine interventions recommended for treating specific types of people with specific types of problems (i.e., personalized care models) that have not yet been validated, and propose research studies.

4) The collaborators then:
  • Use analytic tools to find patterns in the data
  • Discuss and challenge one another’s interpretations of the data, and the assumptions and predictions they make
  • Build clinical models reflecting diagnostic and associated treatment processes
  • Share and evolve these models.

5) These analyses and discussions result in the validation or the invalidation of intervention-recommendation models. The validated models are supported by the scientific evidence showing that particular interventions are safe, effective, and efficient when used to treat particular types of patients with particular health problems in particular situations. The invalidated models have scientific evidence that shows when particular interventions are not safe, effective, and efficient when used to treat particular types of patients with particular health problems in particular situations; so they are useful for determining when not to use a certain intervention. Note that intervention-recommendation models lacking the scientific evidence to be either validated or invalidated remain under evaluation.

6) The validated and invalidated intervention-recommendation models become evidence-based practice guidelines.

7) The evidence-based practice guidelines are stored in health science knowledgebases.

8) Each evidence-based practice guideline is associated with reference and instructional materials, which are also stored in the knowledgebase.

9) The evidence-based practice guidelines and related materials in the science knowledgebases are disseminated to authorized stakeholders, where they are stored locally and accessed for use in the decision support tools, including Computerized Practice Guidelines and Clinical Pathways (see Figure 1, #s 4 & 5 and 15 & 16).

10) The decision tools send data about the care process and outcome to the research databases, as per step #1, which is used to create new and modify existing practice guidelines. This is an ongoing feedback loop leading to continually improving guidelines and outcomes.


Next: Tactic - Utilize Knowledge Services and e-Learning

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