Ways your organization uses technology to gather patient and health care information

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Why is Technology Important in Healthcare?

Discuss ways your organization uses technology to gather patient and health care information, and how this information and data are used to direct patient care and outcomes.

 

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Patient care technology has become increasingly complex, transforming the way nursing care is conceptualized and delivered. Before extensive application of technology, nurses relied heavily on their senses of sight, touch, smell, and hearing to monitor patient status and to detect changes (Powell-Cope, Nelson and Patterson 2018). Over time, the nurses’ unaided senses were replaced with technology designed to detect physical changes in patient conditions. Consider the case of pulse oxymetry. Before its widespread use, nurses relied on subtle changes in mental status and skin color to detect early changes in oxygen saturation, and they used arterial blood gasses to confirm their suspicions. Now pulse oxymetry allows nurses to identify decreased oxygenation before clinical symptoms appear, and thus more promptly diagnose and treat underlying causes.

Traveling Salesman Problem – Genetic Algorithm
The Traveling Salesman Problem is a famous NP-complete problem involving the generation of the shortest route connecting nodes within a graph, with the condition of starting and stopping at the same node. Given the problem’s classification as NP-complete, there is no polynomial algorithm which can perfectly solve this problem. However, randomized optimization can be utilized to calculate approximate solutions. We will apply our collection of optimization algorithms – Randomized Hill Climbing, Simulated Annealing, Genetic Algorithms, and a newcomer, MIMIC – in order to determine the best-performing optimization algorithm for this specific problem.
In order to determine the optimal algorithm, we designed two experiments; one to see how each algorithm’s accuracy scales to increased problem complexity, and another to observe each algorithm’s optimization efficiency (by determining how accuracy converges over a fixed amount of iterations). We used the default testing hyperparameters provided by our ABAGAIL implementation, which are listed in Table 4.

SA Starting Temp Cooling Factor
1E12 0.95
GA Pop. Size # to Mate # to Mutate
200 150 20
MIMIC Sample Count # to Keep
200 100
Table 4. Optimization algorithm hyperparameters, pulled from ABAGAIL’s Traveling Salesman testing implementation. Randomized Hill Climbing not listed, as no hyperparameters are applicable.

We ran our complexity experiment over Traveling Salesman problems with size from 50 to 250 (with steps of 50), where the size N represents the number of nodes in the graph (Figure 5). It’s evident that for all algorithms, increased graph nodes result in poorer fitness. This isn’t indicative of the algorithm failing to scale, however; the fitness function evaluates the inverse of the calculated path’s distan

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