Researchon updating computer systems in hospitals
A thematic analysis using was employed to understand the complex relations among themes and sub-themes to discover the patterns in the data. Users found the new system increased the efficiency of workflows and saved time.
They reported less redundancy of work and improved communication among medical team members.
The size of a room and weighing in at 27 tons, ENIAC was developed to calculate missile trajectories for the U. Because of their exorbitant cost, use of early computers was limited to large corporations to help manage accounting.
In the 1960s, academic institutions followed suit and began developing computer systems to streamline their growing business operations.
Critical care involves highly complex decision making. Despite the growth of critical care, however, the basic approach of data collection and management has remained largely unchanged over the past 40 years.
Large volumes of data are collected from disparate sources and reviewed usually retrospectively; and even that is difficult.
There is limited medical device interoperability and integration with the electronic medical record (EMR) remains incomplete at best and cumbersome.
In addition (and partly as a result of these limitations), standard analytical approaches provide little insight into a patient’s actual pathophysiologic state.
These systems have evolved along several parallel lines beginning, not surprisingly, in 1946 with the introduction of the Electronic Numerical Integrator and Computer (ENIAC), the first general-purpose computer (see Table 1). Five years later, IBM introduced the first commercially available computer, the Engineering Research Associates (ERA) 1103.No area is more data intensive than the intensive care unit.While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced.Understanding the dynamics of critical illness requires precisely time-stamped physiologic data (sampled frequently enough to accurately recreate the detail of physiologic waveforms) integrated with clinical context and processed with a wide array of linear and nonlinear analytical tools.This is well beyond the capability of typical commercial monitoring systems.
Such an understanding derived from advanced data analytics can aid physicians in making timely and informed decisions and improving patient outcomes.