PubMed HealthSearch

PubMed · 2913586

Problem-knowledge coupling: a tool for physical therapy clinical practice.

Abstract

The purposes of this article are 1) to summarize the development, function, and philosophy of the Problem-Knowledge Coupler (PKC), a computerized tool that is designed to aid the clinical decision-making process in medical practice, and 2) to speculate on the value this system has in meeting clinical practice, needs and addressing professional practice issues in physical therapy. The evolution of the PKC system from its origins in the problem-oriented system of medical care is described, and the philosophy of problem-knowledge coupling is contrasted to the basic philosophy of traditional clinical medical practice and never computerized expert decision-support systems. We suggest that problem-knowledge coupling could be a useful tool in physical therapy because it improves the collection and synthesis of patient information and uses the unique nature of individual patient problems coupled with the clinical literature as the bases for clinical practice decisions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

N J Zimny, C J Tandy. 1989. Problem-knowledge coupling: a tool for physical therapy clinical practice.. https://doi.org/10.1093/ptj%2F69.2.155

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Radiotherapy treatment optimization is done by generating a set of tentative treatment plans, evaluating them, and selecting the plan closest to achieving a set of conflicting treatment objectives. The evaluation of potential plans involves making trade-offs among competing possible outcomes. Multiattribute decision theory provides a framework for specifying such trade-offs and using them to select optimal actions. Using these concepts, we have developed a plan-ranking model which ranks a set of tentative treatment plans from best to worst. Heuristics are used to refine this model so that it reflects the clinical condition of the patient being treated and the practice preference of the physician prescribing the treatment. A figure of merit is computed for each tentative plan and is used to rank the plans. The approach described is very general and can be used for other medical domains having similar characteristics. The figure of merit can also be used as an objective function by computer programs that attempt to automatically generate an optimal treatment plan.

Decision Making, Computer-Assisted