title: Can a clinician predict the technical equipment a patient will need during intensive care unit treatment? An approach to standardize and redesign the intensive care unit workstation.
abstract: The technical equipment of today's intensive care unit (ICU) workstation has been characterized by a gradual, incremental accumulation of individual devices, whose presence is dictated by patient needs. These devices usually present differently designed controls, operate under different alarm philosophies, and cannot communicate with each other. By contrast, ICU workstations could be equipped permanently and in a standardized manner with electronically linked modules if the attending physicians could reliably predict, at the time of admission, the patient's equipment needs. Over a period of 3 1/2 months, the doctors working in our 20-bed surgical ICU made 1,000 predictions concerning outcome, equipment need, duration of artificial ventilation, and duration of hospitalization for 300 recently admitted patients. The interviews were made within the first 24 hours after admission. The doctors being interviewed were usually (i.e., in over 90% of cases) unfamiliar with the patient. Information concerning the patient's general state of health, special pre-ICU events, and complications was offered to the interviewed clinician because this information represents standard admission data. It was found that the equipment need (represented by two different setups, "high tech" and "low tech") could be predicted most reliably (96.4% correct predictions) compared with a prediction on outcome of ICU treatment (94.5%), on duration of artificial ventilation (75.4%), and on duration of stay (43.4%). There was no significant (p greater than 0.05) difference in the reliability of predictions between residents and consultants. Factors influencing the postoperative equipment need varied with surgical specialty.(ABSTRACT TRUNCATED AT 250 WORDS)
authors.affiliation: Department of Anaesthesia, University of Ulm, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Hähnel
authors.forename: W
authors.initials: W
authors.lastname: Friesdorf
authors.forename: B
authors.initials: B
authors.lastname: Schwilk
authors.forename: T
authors.initials: T
authors.lastname: Marx
authors.forename: S
authors.initials: S
authors.lastname: Blessing
mesh_terms: D000761:Anesthesia Department, Hospital; D006348:Cardiac Surgical Procedures / Q000706:statistics & numerical data; D001026:Coronary Artery Bypass / Q000706:statistics & numerical data; D003422:Critical Care / Q000706:statistics & numerical data; D004867:Equipment Design; D005260:Female; D006351:Heart Valves / Q000601:surgery; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D007902:Length of Stay; D008297:Male; D013677:Medical Laboratory Science / Q000295:instrumentation*; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D017063:Outcome Assessment, Health Care; D011336:Probability; D012121:Respiration, Artificial; D018805:Sepsis; D015996:Survival Rate; D013997:Time Factors