Search PubMed⌕ Search

Biomedical subjects

B Schwilk

Publications and source records attributed to B Schwilk.

28 records · Page 2Linked to original sources
PubMed1994

Lessons learned while building an integrated ICU workstation.

The project LUCY (Linked Ulm Care sYstem) is described. The goal of this project was to build a research workstation in an Intensive Care Unit which enables evaluation of data/information processing and presentation concepts. Also evaluation of new devices and functions considering not only one device but the workplace as an entirety was an aim of the project. We describe the complete process of building from the stage of design until its testing in clinical routine. LUCY includes a patient monitor, a ventilator, 4 infusion pumps and 8 syringe pumps. All devices are connected to a preprocessing computer via serial interfaces. A high performance graphic workstation is used for central display of physiological and therapeutic variables. A versatile user interface provides touch screen, keyboard and mouse interaction. For fluid administration a bar code based control and documentation facility was included. While our scheduled development efforts were below 4 man-years, the overall man-power needed until the first routine test amounts to 8 man-years. Costs of devices and software sum up to 160,000 US$. First experiences in clinical routine show good general acceptance of the workplace concept. Analysing the recorded data we found 90% of the items to be redundant: individual filtering algorithms are necessary for each of nowaday's devices. The flexibility of the system concerning the implementation of new features is far from our expectations. Technical maintenance of the system during clinical operation requires continuous effort which we cannot afford in the current situation.

Algorithms↗
PubMed1994

Data quality of bedside monitoring in an intensive care unit.

Computerized record keeping promises complete, accurate and legible documentation. Reliable measurements are a prerequisite to fulfill these expectations. We analyzed the physiological variables provided by bedside monitoring devices in 657 bedside visits performed by an experienced Intensive Care nurse during 75 Intensive Care rounds. We registered which variables were displayed. If a variable was displayed, we assessed whether it could be used for documentation or should be rejected. If a value was rejected the reason was registered as: the measurement was not intended (superfluous display), the current clinical situation did not allow proper measurement, or other reasons. Basic variables (vital signs and respiration related variables) were displayed in more then 90%, specific variables (e.g. intracranial pressure) were displayed in less than 50% of the situations. Displayed variables were superfluous on an average of 11% because measurement was not intended. Variables like heart rate, temperature, airway pressure, minute volume of ventilation, arrhythmia, pulmonary arterial pressure, non-invasive blood pressure, and intracranial pressure provide high quality measured values (acceptance of more than 90%). Invasive arterial pressure, central venous pressure, respiration rate and oxygen saturation (via pulse oximetry) provided lower quality values with a rejection rate higher than 10%. Inappropriate sensor technology to match the clinical environment seems to be the root cause. In future the request for automatic documentation will increase. In order to avoid additional staff workload and to ensure reliable documentation, sensor technology especially related to respiration rate, blood pressure measurements, and pulse oximetry should be improved.

Data Collection↗
PubMed1994

System ergonomic analysis of the morning ward round in an intensive care unit.

OBJECTIVE: Our objective was to find out what is discussed during a bedside morning ward round (MWR), whether there are any weak points, and if a standard work process structure can be recommended. METHODS: An intensive care unit (ICU) consultant recorded in a predefined form the topics that were discussed in 225 bedside discussions. RESULTS: The median length of discussions was 5 min. In more than 60% of the discussions, items were considered related to the respiratory, neurological, and cardiovascular systems, as well as to surgical and nursing problems. Specific variables relating to organ system conditions were seldom used (e.g., inspired O2 concentration, 35%; temperature, 28%; ventilation mode, 25%). We recorded two interruptions per MWR; only 17% of them were related to urgent decisions. Information that could not be found in the patient's file usually concerned microbiology findings (10%) or surgical procedures (6%). CONCLUSIONS: We recommend the following structure: (1) Addressing the patient by saying "hello"; (2) presentation of information related to case history, acute status (findings and strategy) (including the function of the main organ systems), infection status, and nursing problems; (3) patient-related discussion; and (4) discussion of general treatment rules, triggered by individual patient condition.

Communication↗
PubMed1993

Ergonomics applied to anaesthesia record keeping.

For almost 100 years, the anaesthesia record has been the sole information tool trying to fulfill an ample catalogue of functions related to the anaesthesia information processes. Automated anaesthetic record systems have evolved around data being available online, as an imitation of the handwritten record. None has developed an information tool capable of an efficient utilization of the wide range of resources provided by modern technology to fulfill the information requirements of the anaesthetic environment. We used a system ergonomic analysis trying to find the best solutions. As a result of it we drafted an Anaesthesia Information Concept (AIC) in which the complexity of data & information (D&I) processes is broken down to modules called Clinical Information Process Units (CIPUs). A CIPU is mainly defined by the responsibility of a staff member and focuses on the basic system patient, staff and machine (all devices). The internal functions of a CIPU are treatment control and medicolegal documentation. The external functions are fulfilled by transferring required sets of D&I for subsequent treatment control (next CIPU), audit, quality control, cost calculation, etc. Using such an approach, an Anaesthesia Information Concept (AIC) can be realized by a wide range of modular and hybrid systems (combination of different tools such as paper records, computers, etc), as opposed to universal and single automated documentation systems, which up to now have failed to fulfill the information demands of the anaesthetic environment.

Anesthesia Department, Hospital↗
PubMed1993

[Preoperative risk factors and intraoperative and postoperative risk management in 11,890 anesthesias. Initial results of a prospective study].

OBJECTIVE: The relation of the frequency and severity of pitfalls, events and complications (PECs) was analysed in respect of preoperative risk factors. The epidemiological data were gathered as a contribution to a current project of the German Society for Anaesthesiology and Intensive Care. METHOD: Preoperative data (age, sex, preexisting diseases, pathological findings, grade of urgency and ASA-class) were integrated in a paper record, as well as the perioperative interventions and directly postoperative events, type of anaesthesia, and kind of operation. The automatically readable paper records were routinely in use for every patient. After control and correction the data were stored in a modern data base. MAIN RESULTS: From October 1, 91 to May 20, 92 11,890 anaesthesias were recorded. 2,959 of them with a total of 4,184 PECs. 2,397 PECs were cardiovascular, 875 respiratory. PECs of grade I (no impact on treatment in the recovery room [RR]) occurred in 14% of patients, grade II (impact on treatment in RR, but no impact on discharge to ward) 7.2%, grade III (prolonged stay in RR or special monitoring in the ward) 2.88%; grade IV (PEC leads to transfer to the ICU) 0.63%, and grade V (PEC leads to disabling damage or death) 0.13%. 13 of 15 patients suffering from PECs grade V were of ASA class 4 or 5. PECs had a certain relation to the ASA-classification of anaesthetic risk. But this relation is quite different in several surgical disciplines. CONCLUSIONS: Preoperatively known risk factors of the patient and the measures taken by specialists of various disciplines contribute to the incidence of PECs. Available data could be processed multicentrally and in standard form for producing prognostic data for risk prediction. Since PECs of grade II or higher are cost- relevant, requiring an interdisciplinary approach, it appears meaningful to base costing on such an interdisciplinary approach in accordance with the requirements of diagnosis and treatment.

Adolescent↗
PubMed1992

Patient-related data management.

Patient-related data management (PDM) has become an increasingly important and time-consuming task in intensive care medicine. Currently, all data are usually collected in a poorly structured patient chart consisting of forms and pictures, with about 400 manual entries a day. To handle this amount of data, we have designed a three-level patient system: level 1, summarizing the whole patient; level 2, summarizing one organ system or one isolated problem; and level 3, variables describing morphology and function of organ systems. PDM must be adapted to different clinical situations. We observed three different scenarios: (1) Exploratory PDM, where the clinician learns about the patient and builds up an individual patient model in his or her mind. (2) Operational PDM, where in routine care clinicians are part of a feedback control system, in which they use the patient-related model. (3) Summary PDM, where a clinician summarizes all the information gathered during a period when he or she was responsible for the patient. Computing tools based on clinical thinking and adapted to different situations can ensure accurate, clear, and concise patient care communication among the members of the intensive care staff.

Computer Systems↗
PubMed1992

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.

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)

Anesthesia Department, Hospital↗
PubMed1990

[Analysis of data management in anesthesia from an ergonomic viewpoint].

In the perioperative phase the anesthetist has to manage an increasing amount of knowledge, information and data. Using a system-ergonomic approach we can define three types of data management (DM): Exploratory DM, Operative DM, Concluding DM. The preliminary examination of the patient is Exploratory DM. Data are collected and recorded. Here, a well structured form prevents things being forgotten, provides forgetting anything. Help from electronic devices is not available. Control of anaesthesia is based on Operative DM. The anesthetist is part of an ongoing process. He investigates and records a situation based on his knowledge and experience and a prompt reaction to untoward circumstances may be necessary. Today's workplace provides insufficient support for this task. Data presentation is unstructured and distributed around the workplace which produces potentially dangerous overloading in critical situations. It is necessary to view the work layout as an integrated whole. The data being displayed must be hierarchically structured and appropriate to the situation. Concluding DM involves summarising data and information on completion of a process in ways appropriate to specific purposes. With this the anesthetist completes an anaesthesia and transfers the patient to the next unit, e.g. to the recovery room. He has to fill in several forms for clinical and statistical reasons. Electronic aids are available only for parts of some tasks. The goal should be a multifunctional summary satisfactory for clinical and statistical purposes, most aspects of which are created automatically by a computer system.

Anesthesiology↗