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Biomedical subjects

M LoBue

Publications and source records attributed to M LoBue.

10 recordsLinked to original sources
PubMed2000

Wireless clinical alerts for physiologic, laboratory and medication data.

A fully interfaced clinical information system (CIS) contains physiologic, laboratory, blood gas, medication and other data that can be used as the information base for a comprehensive alerting system. Coupled with an event driven rules engine, a CIS can generate clinical alerts which may both prevent medical errors and assist caregivers in responding to critical events in a timely way. The authors have developed a clinical alerting system which delivers alerts and reminders to clinicians in real time via a alphanumeric display pagers. This paper will describe the system, the type and number of alerts generated, and the impact on clinical practice. A major issue remains in measuring the impact of wireless alerts on patient outcomes.

Algorithms↗
PubMed1997

The effect of surgical ICU triage patterns on differing severity adjusted outcomes in France and the United States.

INTRODUCTION: Surgical patients treated in French intensive care units (ICU's) appear to have higher mortality rates than patients in the United States. We hypothesized that this may be due to the French practice of not transferring dying patients from the ICU. We wished to determine if the different mortality rates could be explained by transfer practices for dying patients or other factors such as severity of illness. METHODS: Flowsheet data for 6,787 consecutive surgical ICU (SICU) patients from our institution over a 31 month period was entered into an ICU Clinical Information System which calculated the Day 1 Simplified Acute Physiology Score (SAPS) for each patient upon admission to the SICU. SICU and overall hospital mortality data were matched with severity data and the complete data set was analyzed against results for 2,604 surgical patients in French ICU's. Since terminally ill patients in France are not transferred to floor care, we also compared the French ICU mortality rate with both our SICU mortality rate and combined SICU and surgical floor mortality rates. RESULTS: Our overall SICU mortality was 1.7% and our combined SICU and hospital mortality was 4.2%, while the French ICU mortality was 14.1%. The French ICU's had more patients with higher severity of illness as measured by SAPS. When the effects of ICU transfer practices and severity of illness were considered, there were no mortality differences seen among patients admitted to the different units after elective surgery. Significant differences in mortality were seen when patients admitted emergently were studied. CONCLUSIONS: The differences in severity adjusted ICU mortality between French ICU's and our SICU are explained by different triage practices for terminally ill patients following elective ICU admission. These triage differences do not fully explain the mortality differences seen among patients emergently admitted to the ICU. Other factors such as the presence of trauma, ICU staffing practices, patient mix or other unidentified factors may be responsible for the severity adjusted differences in mortality among emergency surgical ICU patients.

Emergency Service, Hospital↗
PubMed1995

Real-time wireless decision support alerts on a Palmtop PDA.

The authors devised a system which continuously analyzes data exported from a Clinical Information System for the occurrence of exceptional or life-threatening clinical events. A configurable rule-based system was created to detect and act on such events. When detected, the system formats an alerting message, dials a modem and transmits the message to a commercial satellite relay system. Ennunciated by an alerting beep and blinking LED on a PCMCIA receiver card, the alert message appears on the screen of a Palmtop Personal Digital Assistants (PDA) carried by designated clinicians.

Algorithms↗
PubMed1991

Quality assurance and utilization assessment: the major by-products of an ICU clinical information system.

In 1985 we developed a method of automatically extracting indices of severity of illness and intensity of interventions from CIS charts daily. These indices, when combined with outcome measures such as length of stay and mortality, provide a powerful new tool for quality management in the ICU. In this paper we describe our ICU's severity adjusted survival rates as compared to internationally publish norms. In addition we provide a detailed analysis of glucose levels in our ICU, which suggests that glucose control in surgical ICU patients is more closely related to measured severity of illness than administration of intravenous alimentation per se. CIS extracted indices provide a new basis for continuous quality measurement and improvement in the ICU.

Blood Glucose↗
PubMed1990

Integrated computerized databases for medical data management beyond the bedside.

Computers are beginning to be utilized extensively for direct patient care, assisting nursing and medical staff with data collection and review at the bedside. However, most clinical data management systems are optimized for bedside patient care and offer limited resources for multi-patient data analysis. At Cedars-Sinai Medical Center, a network of computer systems has been developed to provide linkages between clinical, administrative and outcome data for Surgical Intensive Care Unit (SICU) patients. Increasingly, such data is needed to evaluate the relationship between severity of illness and patient outcome and the utilization of expensive critical care resources. Over the parts 3 years, comprehensive data on 6,755 consecutive SICU patients receiving 18,394 days of care have been accumulated by our PDMS. Using linkages constructed to other hospital systems and databases, trends for severity of illness, severity adjusted survival, census, bed utilization, nursing utilization and many other parameters have been constructed. These linkages are valuable in documenting cost-effective and medically-effective patient care practices.

Computer Communication Networks↗
PubMed1990

Decision support alerts for clinical laboratory and blood gas data.

We have designed and implemented a computerized Intensive Care Unit (ICU) decision support alerting system which analyzes all incoming laboratory and blood gas data for critically abnormal values and trends. A computerized patient data management system (HP 78709A PDMS, Hewlett-Packard Co., Waltham, MA) serving 20 Surgical ICU beds is networked to a Clinical Laboratory Information System and a blood gas computer system. The ALERTS subsystem operates on the PDMS as an automatic program triggered by the receipt of fresh laboratory data. Three types of ALERTS are detected: (1) high and low critical values, (2) calculation-adjusted critical values, and (3) critical trends. Once detected, a specific ALERT message is displayed at the bottom of the patient's bedside PDMS terminal and at the central station. Over an eight month period a total of 1,515 ALERTS were detected from amongst approximately 115,000 laboratory data results transmitted to the Surgical Intensive Care Unit (SICU). Slightly over half of all ALERTS were caused by critical blood gas values. ALERTS were found to be a sensitive indicator of severity of illness: patients with one or more ALERTS suffered an ICU mortality of 9.52%, compared to 0% mortality in patients with no ALERTS. We conclude that automated laboratory data ALERTS represent a valuable decision support tool for the management of high risk ICU patients.

Blood Gas Analysis↗
PubMed1988

An automatic PDMS interface for the Urotrack Plus 220 urimeter.

Periodic measurements of urine volume and temperature in critically ill patients are time consuming, unclean procedures. These measurements may be automated with an electronic urimeter (Urotrack Plus 220, C.R. Bard Company, Murray Hill, NJ). This device contains an RS-232 output port which transmits a complete device status report once per second. We interfaced 20 Urotrack Plus urimeters to a single I/O port of a computerized patient data management system (Hewlett-Packard 78709A PDMS, Hewlett-Packard Company, Waltham, MA). This interface required daisy chained controllers for port switching and a communications adapter for flow control. The urimeters have proven to be cost-effective, labor-saving devices. The PDMS interface provides a continuous display of measured variables and completely automates data entry for flowsheet documentation. Automatic urimetry data acquisition is estimated to save approximately 27 nursing minutes per patient per day.

Attitude of Health Personnel↗
PubMed1988

The PDMS as a focal point for distributed patient data.

Distributed data links are essential requirements for a successful patient data management system (PDMS). These links funnel clinically needed data into bedside ICU workstations. We have constructed four data links which acquire most of the objective data required for direct patient care. Data from bedside monitors is acquired via a standard HP Signal Distribution Network. Urine volumes and core bladder temperatures are acquired over a link to 20 electronic urimeters. Clinical laboratory data is obtained over an HP General Purpose Data Link (GPDL) to a VAX 11/785 laboratory system. Blood gas data is obtained over a second GPDL link to a DEC 11/23 computer. The ICU staff is notified of incoming lab results with bedside video messages. Combined with automated calculations, these data links eliminate thousands of data entry keystrokes daily and allow the PDMS to serve as the focal point for real-time patient care.

Computer Systems↗
PubMed1987

Automatic extraction of intensity-intervention scores from a computerized surgical intensive care unit flowsheet.

Systems that objectively score severity of illness and intensity of patient care interventions have been used to guide the appropriate use of intensive care facilities, provide information on nurse staffing ratios, validate subjective classifications of patient illness, and normalize scientific and financial studies for severity of illness. Existing scoring systems require a well-trained observer to perform a thorough chart review to complete manual scoring forms. We have designed a new system in which computerized intensity-intervention scores are automatically extracted from electronic intensive care unit flowsheets, eliminating both manual labor and potential observer variation. In prospective studies, these computerized scores correlated well with manual TISS scores, intensive care unit mortality, intensive care unit length of stay, hospital length of stay, and a subjective classification of patients to graded levels of hospital care. Such automated scores may be used for real-time allocation of health care resources and normalization of prospective studies for severity of illness.

California↗